基于工程地震风险评估的巨灾债券定价模型
|
摘要
随着我国经济的快速发展和对外开放程度的日益加深,特别是加入WTO之后,地震风险管理从计划体制转向市场体制,地震保险和巨灾保险衍生品等金融手段在防震减灾工作中的重要性越来越显现出来。本文从防震减灾金融手段面临的问题切入,在系统总结我国巨灾保险现状和保险资金运用情况、金融衍生品和巨灾衍生品发展和定价模型理论研究的基础上,以地震损失比超越概率曲线为结合点,分别向目前广泛应用的工程地震风险评估方法的改进和巨灾债券定价模型的研究两方面展开。前者包括与巨灾债券等金融手段结合过程中亟待解决的问题;后者是在地震风险管理发展过程中面临的,在地震保险、再保险展业中需考虑的问题。最终,发展了一个与工程地震风险评估密切结合的巨灾债券定价模型。主要的创新成果包括以下四个方面。
(1)从与地震保险、巨灾债券等金融手段结合的角度,指出了目前我国普遍使用的工程地震风险评估方法中存在的两个问题,并提出了相应的改进方法。
通过对地震危险性分析过程的详细推导,揭示了现有方法对低烈度发生概率低估的根本原因。通过地震不很活跃的我国东北某场址和地震活动水平较高的华北某场址的两个实例,提出了改进方法。先将危险性分析给出的年超越概率缩短到较短时间段内,摒除在长时间段内对结果产生影响的概率。借助各时段独立不相关的泊松假定,用较低烈度的超越概率减去较高烈度的,得到短时间内各烈度的发生概率,再推回到长时间段内得到各烈度的发生概率。进一步,考察改进方法对地震保险费率的影响,用两种方法得到的结果计算了期望损失率,说明传统方法的结果偏低,厘定的费率也会偏低,对保险公司不利。
针对现有方法对高烈度损失高估的问题,提出了设定地震的解决方案,借助基于GIS防震减灾信息和辅助决策系统的空间分析功能,形成一套实施方法和步骤,结合工程实例详细论证了合理性和适用性。针对由高频敏感的峰值加速度得到的设定地震是否对长周期幅值起控制作用的质疑,说明了设定地震确定的原则。
借助某大型企业防震减灾信息及辅助决策系统,评估设定的三个地震高烈度区范围及相应损失,构造出地震损失比的超越概率曲线,与传统方法构造的曲线比较,说明本文方法可以有效地遏制高估,能够用于巨灾债券定价的精细计算。
(2)提出了在巨灾债券定价模型中充分体现工程系统易损性重要影响的新思路,将债券定价与工程地震风险评估密切结合。
在分析巨灾债券定价理论发展的基础上,指出现有巨灾债券定价模型是由金融衍生品的定价原理发展而来的,将原本模拟股票等标的证券价格变化的随机模型用于模拟巨灾损失指数、巨灾赔款损失率、巨灾造成的损失或保险公司损失等,没有反映出灾害的危险性和承灾环境的变化等特点。本文发展的模型中,保险、再保险的赔偿、巨灾发生概率等均依据工程地震风险评估结果,充分体现了区域地震环境、当地工程结构抗震能力和经济总规模对债券价格的影响。采用的巨灾定义与保险公司的偿付情况不直接相关,一定程度上规避了模型中的道德风险。通过工程地震风险评估和投保者的空间分布情况,可以评估发行公司在巨灾中的损失,减少了基差风险。
(3)充分体现了巨灾债券作为地震保险有效补充手段的特点,强调地震巨灾债券与地震保险之间的协调,在两者的现金流间建立联系,保障发行公司在地震风险上的资金的平衡。
假设发行巨灾债券的是保险公司,在发行公司的地震风险现金流间构造了局部均衡环境,令保险公司的期望支出等于投资者的期望收入,使交易双方的期望效用均达到最大化,提出一个新的巨灾债券定价模型。在两类市场假设下,分别用几何布朗运动和跳跃-扩散过程描述保险公司收取的地震险保费和缴纳的再保费,将当地发生地震巨灾的可能性、债券有效期、风险再投资收益、返还本金的比例、债券的发行费用以及有效期内收取的地震险保费、缴纳的再保费和相应赔偿等作为定价模型的影响因素。
(4)设计了一个产品算例,对本文提出的方法进行了可行性验证。以某大型企业作为算例,得到了两类市场假设下的地震巨灾债券年利率、期末收益和债券初始价格,分析了年利率、期末收益和价格对有效期、发行额、再投资收益率和再投资比例的敏感性。通过与作者原来建议的债券定价雏型比较,说明了当收益率较小时,本文模型克服了巨灾债券利率与无风险利率差别不大的缺点,改进了利率的低估及可能会导致债券竞争力缺乏等现象。将本文模型与国内外现有的巨灾债券定价模型进行比较,说明了本文模型的主要优点。
最后,提出有待进一步研究的问题。
With the rapid economic development, population, prosperous business, density of buildings, transportation systems, indispensable infrastructures and perfect facilities in a metropolitian area are significantly increased. Many destructive earthquakes in recent years show that the world we live is more vulnerable to an earthquake disaster. If such disaster is intensive enough, it will become a catastrophe. What we can do is to reduce the vulnerability of our society, which is what managing and reducing catastrophic risk are all about.
Earthquake risk management is a very complex process involved with engineering mitigation measures and non-engineering countermeasures. The former, prescribed in seismic design codes and prevention counter plans, played a great role in earthquake disaster reduction of China in decades. The latter must be emphasized as the rapid economic development and more opening of China towards the world, especially after joining in the WTO, while the management system is moving from the planned model to the market model. In order to diversify the salvation for earthquake disasters, some instruments, such as insurance, reinsurance and catastrophe insurance derivatives experienced in developed countries and regions may be used as references.
Hurricane Andrew and Northridge Earthquake caused the insurance industry to realize the impact that a catastrophe could have on the industry’s solvency, and have caused the industry to reevaluate its exposure to loss from a single event or multiple major events in a short period of time, and to look for additional sources of capital to finance or spread the risk. Traditionally, insurers transfer insurance risk through the purchase of reinsurance. However, the reinsurance industry has a limited capacity available to deal with catastrophes. Major carriers, if they were to choose to purchase, could not purchase enough reinsurance to cover a catastrophe, and the surplus of these carriers is exposed in the event of a catastrophe or a series of moderate-sized disasters within a short period. Even if reinsurance could be purchased to cover the catastrophe, the aggregation of exposure to reinsurers could be so large that several reinsurers could become insolvent.
Catastrophes that might seriously impair the capital base of the worldwide (re)insurance industry may cause only a ripple if spread through the global capital markets. The capital market can provide (re)insurers with far more financing capacity than what has been available previously, and it can provide insurers with a vehicle for dispersing risk widely. The evolution of catastrophe insurance derivatives is reviewed in this dissertation, and some popular catastrophe insurance derivatives, including CAT Future, CAT Options, CAT Swaps, Insurance Loss Warranties, Credit Line, Contingent Surplus Notes, Catastrophe Equity Puts and CAT bonds, are introduced. It is important to determine a trigger event in pricing for a CAT bond. There are three general types of trigger events, including issuer’s loss, industry index and pure parametric trigger, which will be introduced in detail. And there are also other trigger events, like modeled losses and parametric index.
The result from a research group in Beijing University in 2005 showed that the accumulative potential loss in China was about $1.3 trillion among effective domestic policies of flood and storm according to a preliminary statistic analysis, in which about 80%-90% of the loss was contributed from the home damage. At present, the claim payments for natural disasters in China are mainly from the government and public endowment, and insurance and reinsurance are only the supplements. So only a fraction of above-mentioned potential losses is insured. Since the insurance industry in China is still at the developing stage, the premium increases each year but the claim rate is low, the profit of insurers is getting higher, which causes insurers, to some extent, weakened the sense of reinsurance. Actually, risk will be accumulated quickly as the expansion of business, and the stabilization of insurers will be threatened. Consequently, the demand of reinsurance must be large. On the other hand, according to the WTO commitments, the proportion of mandatory reinsurance should be reduced by 5% each year, so there was no mandatory reinsurance in 2006. Meanwhile some transnational (re)insurers, like Swiss Re and Munich Re, have entered into the market. In order to retain the market competitive, Chinese insurers have to expand the coverage. Catastrophe insurance already issued by these transnational (re)insurers is in insistent demand. It is also a necessary instrument on the view of the evolution of catastrophic risk management.
As we can see, in the evolution, the catastrophic risk management is turning from passive defense to active management and the effective financial instruments became more and more important in some developed countries. However in China, it is still at the groping stage. A key factor in dealing with risk from catastrophic losses is the critical role of the government. The Chinese government emphasizes the natural disaster mitigation all the time, and some financial instruments have been studied especially in the reformation of the management system. And the research of earthquake insurance is currently in its preliminary stage. However, the insurance industry cannot bear the catastrophic risk by itself. It is necessary to diversify the financing channels through the catastrophe insurance derivatives and make use of the capacity of the capital market to disperse risk. From the evolution of earthquake insurance and the utilization of insurance funds in China, it can be concluded: (1) the catastrophe insurance is an indispensable instrument not only for catastrophic losses mitigation but also for the social stabilization; (2) the insurance funds must be reinvested to improve the capacity of insurers; (3) the catastrophe insurance derivatives, especially CAT bonds, are expected to be powerful instruments to supple earthquake insurance on both time and spatial domains. In this process, the key point is to develop a pricing model combined with the Chinese market practice and to build a bridge between insurance and capital markets. It is also important to provide an institutional guarantee from the aspects of legal and policy for the effective operation of these derivatives. The purpose of this dissertation is to build a pricing model for a CAT bond combined with seismic risk assessment.
In some pricing models, the distribution of losses, claims or loss indices etc are simulated based on the loss experience and the trading market from the view of financial economics. However, for earthquake disasters, losses including property and life are mainly derived from the damage of engineering structures, so the amount of losses cannot represent the component of losses. The proposed pricing model will be combined with the engineering seismic risk assessment closely. An exceeding probability curve of loss rate, the rate of earthquake losses to Industry Value Added (IVA) in last year, is the joint of two parts in this dissertation: the engineering seismic risk assessment, and the financial instruments for earthquake disaster reduction. The former involves with problems in the current seismic risk assessment methodology combining with financial instruments, such as CAT bonds; the latter, based on the former, involves with pricing a CAT bond from the view of engineering in the development of earthquake risk management instruments. In other words, the occurrence probability of the defined catastrophe from the former is a parameter in the pricing model for the CAT bond.
Engineering seismic risk assessment, consisting of seismic hazard analysis and vulnerability evaluation, is really a very comprehensive assessment as the first and fundamental step in earthquake disaster prevention process. In China, seismic hazards and vulnerabilities of many cities were assessed in the past few decades. Seismic hazard analysis is a fundamental aspect of seismic risk assessment. The widely adopted probabilistic approach of seismic hazard analysis involves with local seismicity, potential earthquake sources and attenuations of ground motion. The vulnerability evaluation is to infer a conditional probability of the potential damage of engineering structure given various levels of earthquake intensity. It describes a relationship between damages and ground motion for a specific type of structure. Typically, some vulnerability matrixes for structures in many cities and/or regions are established based on the earthquake damage experience in China and founded on macro intensity. In general, the assessments are completed separately and then they are combined together for the loss estimation. These two parts are completed by seismological team and engineering team separately. Seismic hazard expression must be improved to combine these together, especially for the application in financial instruments such as insurance and CAT bonds. Some suggestions are presented in the first part.
(1) In the seismic risk assessment procedure adopted in many cities of China, the expected total loss of buildings in a given period of time can be calculated as the product of the losses, including indoor property loss, of Sth type building being in kth damage state and the probability of Sth type building being in kth damage state PS(Dk). PS(Dk) is referred to as the engineering seismic risk and can be described as the product of the conditional probability of Sth type building being in kth damage state given intensity I, or so-called vulnerability of Sth type building PS(Dk|I) and the possibility of intensity I occurred P(I). P(I) called seismic hazard depends on the regional seismic environment and attenuation relationship of ground motion. It is generally referred to as seismic hazard curve and it can be derived from P(I≥i). In nature, earthquake intensity is a sequential classified variable, so P(I≥i) is not really a continuous curve. Of course, the intensity I can be substituted with other ground motion parameter Y, so that P(Y>y) is a continuous curve.
In general, P(I) is the product of P(I≥i) minus P(I≥i+1). However, the result of the subtraction will not be reasonable, if I is low. For example, the result of seismic hazard analysis in a region with low seismicity shows P(I=i) is less than P(I=i+1), and one can see there must be something wrong. In nature, P(I) must be a monotone decreasing function, i.e. P(I=i) must be greater than P(I=i+1), even if intensity is low. To deal with the procedure of seismic hazard, one can understand that P(I≥i) is contributed by earthquakes in many potential sources with various magnitudes and frequencies. It means that P(I≥i) consists of not only P(I=i) and P(I≥i+1), but also P(I=i and I>i). The later cannot be ignored in seismic zone with strong activity for intensity less thanⅦand long evaluate period, like 50 years or 100 years. The general seismic hazard analysis assumes that the occurrence of earthquake is independent each other. Therefore a solution is that the exceeding probability in a short time period t can be firstly calculated from the hazard in long period T. Obviously, P(I=i and I>i) can be ignored when the period is short. In general, one month is short enough for a region with generic seismic activity; it should be shorten to days for region with high activity. The occurrence probability in short period can be obtained, and that in long period can be obtained. Hazard curves from the traditional method and the modified one are compared. Another example is given to show that the similar underestimation may happen for higher intensity in a region with high seismicity. Exceeding probability curve of loss ratio is drawn and compared with that from the traditional way to show the big difference.
(2) The result of seismic hazard analysis is the occurring or exceeding probability of an earthquake action in a city or region for a specified exposure time. However, high intensity areas are general small and cannot cover the whole metropolis because of the fast attenuation in epicenter area. And it never happens that several earthquakes occur in a given period to cause the same intensity and cover the whole metropolis one by one. In general, high intensity areas are always limited and the whole city will not be an intensity 8 or greater area, so there will be an overestimation if the exceeding probabilities of high intensity events are applied directly in loss estimation. Scenario earthquake approach can be adopted as the solution to this overestimation. One or more scenario earthquakes can characterize the hazard with the same exceeding probability. It must be consistent with the regional seismic environment and be derived from the regional attenuation relationship of ground motion.
To make it clear, a case study is demonstrated in this dissertation. High intensities and their probabilities of occurrence are converted into three scenario earthquakes by probabilistic analysis based scenario earthquake method to represent the probabilistic meaning in the deterministic way. This method was presented in the mid of 1990s and has been adopted in many microzonations, seismic hazard analysis of critical project sites and some codes, like NRC Regulatory Guide.165. By this way, the isoseismals and loss from the earthquake can be estimated explicitly, and design spectra can also be determined very well. Since then, many processes to determine scenario earthquakes have been worked out. For example, Ishikawa and Kameda recommended the mean magnitude and distance should be determined separately for each natural frequency of interest and for each seismic zone that contributes to the hazard on the site. The dominant earthquake was recommended to be the mean magnitude and distance of the seismic event that caused a ground-motion exceedence at the chosen return period by McGuire. Luo emphasized that the ground motion on a site should be equal to the ground motion with the corresponding exceeding probability. Chen suggested choosing a scenario earthquake from several ones satisfying conditions with the principle of maximum probability.
Concerning with the principle that the peak ground acceleration (PGA) from the scenario earthquake must be kept the same as the corresponding design parameter, the supervisor of the author developed a process, although there is strict principles, the steps depend on the conditions and the process never be published. So in this dissertation, the steps are summarized and discussed in details. In the process, the most dominant potential source area (MDPSA) is selected from PHSA result firstly. According to the design PGA corresponding to three design levels (exceeding probability 63%, 10% and 2-3% in 50 years, respectively), and attenuation relationship, the distances of major and minor axes given magnitudes no bigger than the upper bound magnitude of the MDPSA, can be calculated. The scenario earthquakes are determined finally from the constrain condition of that the distance matches the spatial area of the MDPSA.
It is not prescribed clearly in the Chinese code of“Evaluation of seismic safety for engineering sites (GB17741-2005)”if the scenario earthquake method is applicable to construct design spectra. In fact, there are different opinions for improving the code. Some compilers worried about whether the scenario earthquake determined from attenuation relationship of peak ground acceleration (PGA) can contribute properly to the amplitudes at long period. In order to show the applicability of scenario earthquake method to construct design spectra, scenario earthquake determined from attenuation relationship of peak ground acceleration is examined to see if it can contribute properly to the amplitudes at long period. In some cases, the spectra from the scenario earthquakes are quite close to the corresponding uniform spectra, else the spectral amplitudes are not very different from the amplitudes of uniform spectra at short period range, and those determined by attenuation relationship of spectral amplitude at period 1.0 second are closer to uniform one at long period range. The key point in the process is to determine the MDPSA. The contribution of each PSA on the motion at the site depends on the seismicity parameter, area and location of each PSA, the location of the site, and so on. As a rule, the ground motion at short period range attenuates with distance quickly, the contribution of near PSA is obvious; and the ground motion at long period range attenuates relatively slowly, the contribution of near PSA fades and that of far one increases. Therefore, the hazard curves for two natural periods (0.0 and 1.0 second) must be examined carefully to see if one source dominates the hazard at both periods, to make sure whether it is reasonable to represent the hazard with one or more scenario earthquake(s). In a word, spectra from scenario earthquakes can be adopted as design spectra directly if the most dominant areas are the same for spectral amplitudes at short and long period ranges, and more than one spectrum or enveloped spectra of those from scenario earthquakes are suggested to be adopted for design, if the areas are quite different.
In order to produce the exceeding probability curves of loss rate, it is necessary to analyze the damage from the scenario earthquakes. Many spatial operations must be involved in the calculation, and the amount of data available for each event is in tens of thousands of items. A GIS based decision-making support system for earthquake disaster reduction can be adopted for its spatial operating capacity.
GIS based systems for damage evaluation are developed for many cities and regions worldwide. This kind of systems is a powerful tool to perform spatial analysis and mapping of losses due to a scenario earthquake. The damaged areas of buildings, damaged length of highways, bridges or pipelines, damaged number of electric power or communicate facilities in 5 damage states from scenario earthquake can be assessed very fast. So spatial distributions of casualties, the caused shaking, damage and loss of engineering structures from these three scenario earthquakes are estimated by a GIS based system. By this way, the overestimation of losses in metropolis is avoided. Finally, the exceeding probability curves of loss rate for the traditional and improved ways are drawn and compared.
In the second part, it is emphasized how to price a catastrophe insurance derivative, CAT bond, as a supplement of earthquake insurance, that is, how to build a pricing model based on the improved engineering seismic risk assessment method.
Although“derivatives”is a new instrument in insurance industry, it has a long history in financial market, including forward contracts, futures, swaps and options etc. As derivatives become more and more active and provide lower-cost solutions to investor and entity, option-pricing theories have been adopted in a wide variety of financial instruments and contracts. All of these pricing models can be distributed into two classes, complete market models and incomplete market ones.
The vast majority of these models are assumed in complete market, where all sources of risks can be hedged perfectly, because of the possibility of replication of all assets. In other words, all derivatives can be perfectly replicated using a finite number of specified bonds. In incomplete market, such replication by dynamic hedging is not possible, which means that there are sources of risks that cannot be perfectly hedged. Assets’prices are supposed to be ruled by a dynamic process based on a constant drift, jumps and stochastic volatility. Incomplete market is due to the fact that jumps and stochastic volatility are not traded in market. The incomplete market modeling leads to some drawbacks, so we cannot set a portfolio perfectly hedged with derivatives written on these assets. Some pricing models of financial derivatives are reviewed emphatically as the foundation of next section, including Louis Bachelier (1900), Markowitz (1952), Sprenkle (1961), Boness (1964), William Sharpe (1964) and J. Lintner (1965), Samanelson (1965), Kassouf (1969), Fischer Black and Myron Scholes(1973), Merton (1976), Madan D. B. and E. Seneta (1990) etc. Some pricing models of CAT bonds in two types of markets, evolved from the models of financial derivatives, are reviewed, including Cummins and Geman (1995), Litzenberger et al (1996), Briys (1997), Henri Loubergéet al (1999), Morton N. Lane et al (1998-2004), Wang (2002,2004), Christofides (2004), Angelika Sch?chlin (2001), Yu. Baryshnikov et al (2001), Lee and Yu (2002), Cummins and Phillips (1999), Samuel H. Cox et al (2000), Li Yong (2005). In these models, the stochastic processes described the prices of subject matters is adopted to describe catastrophe loss, loss index or issuer’s loss which are not traded in market.
However, for earthquake disaster, direct losses are from the damage of engineering structures mainly, the statistic data of historical loss cannot present the engineering characters, like the type, age, purpose, with or without seismic design, etc. So it is not reasonable to estimate the losses from future earthquakes and price CAT bonds through historical data only, the engineering characters should be represented in pricing models.
Obviously, both earthquake insurance and its derivatives widen the mitigation channel against earthquake disasters. There might be a complementary relation between the parameters of these instruments to balance the issuer’s seismic risk. To show the complementarity, our setup in the final chapter makes an insurer who provides with earthquake insurance policy also, the issuer of a CAT bond. If the insured amount is high, the circulation of the CAT bond needs to be increased to handle high claim payments; otherwise, the circulation to be low to reduce interest payment. Seismic hazard assessment is characterized by scenario earthquakes, and the probability of earthquake catastrophe occurrence estimated by the improved seismic risk assessment method mentioned above. A pricing model for the CAT bond, according to the equilibrium between the incomes of investors and the issuer of the CAT bond, is built. In this model, a stochastic process, Geometric Brownian Motion, is adopted to describe the earning of earthquake insurance premium and the payout of reinsurance premium with two different sets of parameters respectively. The probability of earthquake catastrophe occurrence is an input of the model, ratio and yield of reinvestment, principal protected ratio, issuance fee, circulation and maturity are designed as factors. A partial equilibrium circumstances among the cash flows of the issuer on earthquake risk is built to price the CAT bond in complete market firstly.
However, for earthquake risk, the market is incomplete because the insurance premium will jump after a destructive earthquake or effected by some social factors, not a catastrophe. The jump of premium is proved in some cases, like several earthquakes in Japan. In this dissertation, it is also proved from a certain angle by the influence of fire losses on property insurance premium in the next year, the correlation coefficient is 0.9083. It is supposed that the market is arbitrage-free, and Jump-Diffusion process with two different sets of parameters is adopted to describe the earning of insurance premium and the payout of reinsurance premium in incomplete market. The jump size is supposed as standard log-normal distribution, and the jump times follow an independent Poisson process.
For explanation, a region in Northeast of China is adopted as an example. Since earthquake insurance has not been launched in large-scale in China now, related data and information is not enough. We have to suggest that the insurance rate in this field is 5% and the policy-holders are distributed uniformly in space. Then, the probable maximum loss of buildings from a probable maximum earthquake (M=6), assessed by the improved method, is RMB 1.556 billion (2004 value), which is discounted from 998.2 million (1994), considering the influence of interest rate and inflation. So the probable maximum claim payment is 77.79 million.
In recent years, several insurers covered earthquake risk as extraneous risk of enterprise property insurance, and the premium rate is 10% of main risk, which is limited strictly. It is supposed that 50% policy-holders of enterprise property insurance purchase the additional earthquake insurance, then the data is converted to those in the region by the ratio of annual national GDP to the affiliated city’s, in which 90% are adopted as the earning of earthquake insurance premium in the region. Earthquake reinsurance premium is adopted as 20% of the insurance premium. The expected volatility of the earning of insurance premium is 0.0544, the standard deviation is 0.0084, and those of the payout of reinsurance premium are 0.0109 and 0.0017 respectively.
According to the improved engineering seismic risk assessment method and the definition of catastrophe in this dissertation, the loss from the maximum considered earthquake is not a catastrophe, and the occurring probability of a defined earthquake catastrophe in 3 years p is 0.0003114%. If a catastrophe occurs, the loss should be 15.436 billion at least, so the claim payment of earthquake insurance is 771.8 million, and that of reinsurance is 154.4 million. If it does not occur, the claim payment of earthquake insurance is 77.79 million, and that of reinsurance is 15.56 million. Let the period of validity T be 3 years, the proportion of reinvestment k 10%, the yield r1 5%, the circulation of the CAT bond M 100 million, the risk-free interest rate r0 3.96%, the principal protected ratio n 50% and the issuance fee B 1% of the circulation. Then, the earning of insurance premium and the payout of reinsurance premium in complete and incomplete market can be obtained. The jumps of insurance and reinsurance premium from destructive earthquakes are considered in incomplete market. The annual coupon rates of the CAT bond r are 5.52% and 6.67% in complete and incomplete markets respectively. An investor will be refunded 118.01 yuan (complete market) and 122.48 yuan (incomplete market) for a coupon bond at maturity if the defined catastrophe does not occur, and the prices of a zero coupon bond, at t=0, are 84.74 yuan (complete market) and 81.64 yuan (incomplete market). In other words, the investor purchases the CAT bond at price of 84.74 yuan or 81.64 yuan and will get 100 yuan back at maturity. If the catastrophe occurs, half of the principal will be lost, that is, the investor will get 50 yuan, 42.37 yuan or 40.82 yuan back. The sensitivity of the annual coupon rate, the interest at maturity and the price on four factors are analyzed, the order, from high to low, is the period of validity, circulation, the yield of reinvestment and the proportion of reinvestment.
By comparing with the pricing model in the author’s Master thesis, this model is improved in the following areas: (1) M, r1, n, B and p are the factors in the primary model. In this model, the character of CAT bonds as a catastrophe insurance derivative is emphasized, the cash flows of earthquake (re)insurance premium are considered. (2) There are two states in the primary one, the catastrophe occurs or not. The influence of devastating earthquakes, not catastrophes, on the cash flows is considered in this one. (3) All parameters in the primary one are constant. This model is time-depended, although the interest rate of the CAT bond is calculated at maturity. For pricing in T, this model can be referred. For example, if a catastrophe occurs early in the period, the premium is not accumulated enough, M needs to be increased with other conditions unchanged. (4) The proportion of reinvestment is improved. 10% of funds financing from the CAT bond and the initial premium is venture investment, 90% is risk-free investment. The stability of insurance funds is assured and proceeds are increased. Let r1 is 5%, 6% or 7%, r is calculated by these two model. The comparison shows the rate from the primary one is underestimated if the yield is low. For example, r is just higher than r0 0.04% if r1 is 5%. So the attraction of the CAT bond will be lost if the factors are considered simply.
By comparing with current models, this model is characterized as: (1) Combined with engineering seismic risk assessment. The expected losses from future earthquakes are estimated by seismic hazard analysis and vulnerability estimation. The exceeding probability curve of loss rate is constructed to obtain the occurring probability of a catastrophe. The expected claim payment of insurers can be estimated by the assessment, if the earthquake insurance information is sufficient. (2) Geometric Brownian Motion and Jump-Diffusion process are adopted to describe the cash flow of insurance premium in complete and incomplete markets respectively, since it is irrational to describe catastrophic losses, insurers’loss or loss index etc by stochastic processes in current ones. (3) The complementarity between CAT bond and earthquake insurance is built to balance the capital of the issuer on earthquake risk. (4) A partial equilibrium circumstances among the cash flows of the issuer on earthquake risk is built in complete market. In incomplete market, a jump process is added in the circumstances. (5) According to the definition, the loss or occurrence of a catastrophe is not correlated with the operation of insurers directly, the moral hazard of this model can be avoided to a certain extent. The basis risk can be reduced also, since the loss of the issue in a catastrophe can be estimated by seismic risk assessment and the distribution of the policyholders.
(1)从与地震保险、巨灾债券等金融手段结合的角度,指出了目前我国普遍使用的工程地震风险评估方法中存在的两个问题,并提出了相应的改进方法。
通过对地震危险性分析过程的详细推导,揭示了现有方法对低烈度发生概率低估的根本原因。通过地震不很活跃的我国东北某场址和地震活动水平较高的华北某场址的两个实例,提出了改进方法。先将危险性分析给出的年超越概率缩短到较短时间段内,摒除在长时间段内对结果产生影响的概率。借助各时段独立不相关的泊松假定,用较低烈度的超越概率减去较高烈度的,得到短时间内各烈度的发生概率,再推回到长时间段内得到各烈度的发生概率。进一步,考察改进方法对地震保险费率的影响,用两种方法得到的结果计算了期望损失率,说明传统方法的结果偏低,厘定的费率也会偏低,对保险公司不利。
针对现有方法对高烈度损失高估的问题,提出了设定地震的解决方案,借助基于GIS防震减灾信息和辅助决策系统的空间分析功能,形成一套实施方法和步骤,结合工程实例详细论证了合理性和适用性。针对由高频敏感的峰值加速度得到的设定地震是否对长周期幅值起控制作用的质疑,说明了设定地震确定的原则。
借助某大型企业防震减灾信息及辅助决策系统,评估设定的三个地震高烈度区范围及相应损失,构造出地震损失比的超越概率曲线,与传统方法构造的曲线比较,说明本文方法可以有效地遏制高估,能够用于巨灾债券定价的精细计算。
(2)提出了在巨灾债券定价模型中充分体现工程系统易损性重要影响的新思路,将债券定价与工程地震风险评估密切结合。
在分析巨灾债券定价理论发展的基础上,指出现有巨灾债券定价模型是由金融衍生品的定价原理发展而来的,将原本模拟股票等标的证券价格变化的随机模型用于模拟巨灾损失指数、巨灾赔款损失率、巨灾造成的损失或保险公司损失等,没有反映出灾害的危险性和承灾环境的变化等特点。本文发展的模型中,保险、再保险的赔偿、巨灾发生概率等均依据工程地震风险评估结果,充分体现了区域地震环境、当地工程结构抗震能力和经济总规模对债券价格的影响。采用的巨灾定义与保险公司的偿付情况不直接相关,一定程度上规避了模型中的道德风险。通过工程地震风险评估和投保者的空间分布情况,可以评估发行公司在巨灾中的损失,减少了基差风险。
(3)充分体现了巨灾债券作为地震保险有效补充手段的特点,强调地震巨灾债券与地震保险之间的协调,在两者的现金流间建立联系,保障发行公司在地震风险上的资金的平衡。
假设发行巨灾债券的是保险公司,在发行公司的地震风险现金流间构造了局部均衡环境,令保险公司的期望支出等于投资者的期望收入,使交易双方的期望效用均达到最大化,提出一个新的巨灾债券定价模型。在两类市场假设下,分别用几何布朗运动和跳跃-扩散过程描述保险公司收取的地震险保费和缴纳的再保费,将当地发生地震巨灾的可能性、债券有效期、风险再投资收益、返还本金的比例、债券的发行费用以及有效期内收取的地震险保费、缴纳的再保费和相应赔偿等作为定价模型的影响因素。
(4)设计了一个产品算例,对本文提出的方法进行了可行性验证。以某大型企业作为算例,得到了两类市场假设下的地震巨灾债券年利率、期末收益和债券初始价格,分析了年利率、期末收益和价格对有效期、发行额、再投资收益率和再投资比例的敏感性。通过与作者原来建议的债券定价雏型比较,说明了当收益率较小时,本文模型克服了巨灾债券利率与无风险利率差别不大的缺点,改进了利率的低估及可能会导致债券竞争力缺乏等现象。将本文模型与国内外现有的巨灾债券定价模型进行比较,说明了本文模型的主要优点。
最后,提出有待进一步研究的问题。
With the rapid economic development, population, prosperous business, density of buildings, transportation systems, indispensable infrastructures and perfect facilities in a metropolitian area are significantly increased. Many destructive earthquakes in recent years show that the world we live is more vulnerable to an earthquake disaster. If such disaster is intensive enough, it will become a catastrophe. What we can do is to reduce the vulnerability of our society, which is what managing and reducing catastrophic risk are all about.
Earthquake risk management is a very complex process involved with engineering mitigation measures and non-engineering countermeasures. The former, prescribed in seismic design codes and prevention counter plans, played a great role in earthquake disaster reduction of China in decades. The latter must be emphasized as the rapid economic development and more opening of China towards the world, especially after joining in the WTO, while the management system is moving from the planned model to the market model. In order to diversify the salvation for earthquake disasters, some instruments, such as insurance, reinsurance and catastrophe insurance derivatives experienced in developed countries and regions may be used as references.
Hurricane Andrew and Northridge Earthquake caused the insurance industry to realize the impact that a catastrophe could have on the industry’s solvency, and have caused the industry to reevaluate its exposure to loss from a single event or multiple major events in a short period of time, and to look for additional sources of capital to finance or spread the risk. Traditionally, insurers transfer insurance risk through the purchase of reinsurance. However, the reinsurance industry has a limited capacity available to deal with catastrophes. Major carriers, if they were to choose to purchase, could not purchase enough reinsurance to cover a catastrophe, and the surplus of these carriers is exposed in the event of a catastrophe or a series of moderate-sized disasters within a short period. Even if reinsurance could be purchased to cover the catastrophe, the aggregation of exposure to reinsurers could be so large that several reinsurers could become insolvent.
Catastrophes that might seriously impair the capital base of the worldwide (re)insurance industry may cause only a ripple if spread through the global capital markets. The capital market can provide (re)insurers with far more financing capacity than what has been available previously, and it can provide insurers with a vehicle for dispersing risk widely. The evolution of catastrophe insurance derivatives is reviewed in this dissertation, and some popular catastrophe insurance derivatives, including CAT Future, CAT Options, CAT Swaps, Insurance Loss Warranties, Credit Line, Contingent Surplus Notes, Catastrophe Equity Puts and CAT bonds, are introduced. It is important to determine a trigger event in pricing for a CAT bond. There are three general types of trigger events, including issuer’s loss, industry index and pure parametric trigger, which will be introduced in detail. And there are also other trigger events, like modeled losses and parametric index.
The result from a research group in Beijing University in 2005 showed that the accumulative potential loss in China was about $1.3 trillion among effective domestic policies of flood and storm according to a preliminary statistic analysis, in which about 80%-90% of the loss was contributed from the home damage. At present, the claim payments for natural disasters in China are mainly from the government and public endowment, and insurance and reinsurance are only the supplements. So only a fraction of above-mentioned potential losses is insured. Since the insurance industry in China is still at the developing stage, the premium increases each year but the claim rate is low, the profit of insurers is getting higher, which causes insurers, to some extent, weakened the sense of reinsurance. Actually, risk will be accumulated quickly as the expansion of business, and the stabilization of insurers will be threatened. Consequently, the demand of reinsurance must be large. On the other hand, according to the WTO commitments, the proportion of mandatory reinsurance should be reduced by 5% each year, so there was no mandatory reinsurance in 2006. Meanwhile some transnational (re)insurers, like Swiss Re and Munich Re, have entered into the market. In order to retain the market competitive, Chinese insurers have to expand the coverage. Catastrophe insurance already issued by these transnational (re)insurers is in insistent demand. It is also a necessary instrument on the view of the evolution of catastrophic risk management.
As we can see, in the evolution, the catastrophic risk management is turning from passive defense to active management and the effective financial instruments became more and more important in some developed countries. However in China, it is still at the groping stage. A key factor in dealing with risk from catastrophic losses is the critical role of the government. The Chinese government emphasizes the natural disaster mitigation all the time, and some financial instruments have been studied especially in the reformation of the management system. And the research of earthquake insurance is currently in its preliminary stage. However, the insurance industry cannot bear the catastrophic risk by itself. It is necessary to diversify the financing channels through the catastrophe insurance derivatives and make use of the capacity of the capital market to disperse risk. From the evolution of earthquake insurance and the utilization of insurance funds in China, it can be concluded: (1) the catastrophe insurance is an indispensable instrument not only for catastrophic losses mitigation but also for the social stabilization; (2) the insurance funds must be reinvested to improve the capacity of insurers; (3) the catastrophe insurance derivatives, especially CAT bonds, are expected to be powerful instruments to supple earthquake insurance on both time and spatial domains. In this process, the key point is to develop a pricing model combined with the Chinese market practice and to build a bridge between insurance and capital markets. It is also important to provide an institutional guarantee from the aspects of legal and policy for the effective operation of these derivatives. The purpose of this dissertation is to build a pricing model for a CAT bond combined with seismic risk assessment.
In some pricing models, the distribution of losses, claims or loss indices etc are simulated based on the loss experience and the trading market from the view of financial economics. However, for earthquake disasters, losses including property and life are mainly derived from the damage of engineering structures, so the amount of losses cannot represent the component of losses. The proposed pricing model will be combined with the engineering seismic risk assessment closely. An exceeding probability curve of loss rate, the rate of earthquake losses to Industry Value Added (IVA) in last year, is the joint of two parts in this dissertation: the engineering seismic risk assessment, and the financial instruments for earthquake disaster reduction. The former involves with problems in the current seismic risk assessment methodology combining with financial instruments, such as CAT bonds; the latter, based on the former, involves with pricing a CAT bond from the view of engineering in the development of earthquake risk management instruments. In other words, the occurrence probability of the defined catastrophe from the former is a parameter in the pricing model for the CAT bond.
Engineering seismic risk assessment, consisting of seismic hazard analysis and vulnerability evaluation, is really a very comprehensive assessment as the first and fundamental step in earthquake disaster prevention process. In China, seismic hazards and vulnerabilities of many cities were assessed in the past few decades. Seismic hazard analysis is a fundamental aspect of seismic risk assessment. The widely adopted probabilistic approach of seismic hazard analysis involves with local seismicity, potential earthquake sources and attenuations of ground motion. The vulnerability evaluation is to infer a conditional probability of the potential damage of engineering structure given various levels of earthquake intensity. It describes a relationship between damages and ground motion for a specific type of structure. Typically, some vulnerability matrixes for structures in many cities and/or regions are established based on the earthquake damage experience in China and founded on macro intensity. In general, the assessments are completed separately and then they are combined together for the loss estimation. These two parts are completed by seismological team and engineering team separately. Seismic hazard expression must be improved to combine these together, especially for the application in financial instruments such as insurance and CAT bonds. Some suggestions are presented in the first part.
(1) In the seismic risk assessment procedure adopted in many cities of China, the expected total loss of buildings in a given period of time can be calculated as the product of the losses, including indoor property loss, of Sth type building being in kth damage state and the probability of Sth type building being in kth damage state PS(Dk). PS(Dk) is referred to as the engineering seismic risk and can be described as the product of the conditional probability of Sth type building being in kth damage state given intensity I, or so-called vulnerability of Sth type building PS(Dk|I) and the possibility of intensity I occurred P(I). P(I) called seismic hazard depends on the regional seismic environment and attenuation relationship of ground motion. It is generally referred to as seismic hazard curve and it can be derived from P(I≥i). In nature, earthquake intensity is a sequential classified variable, so P(I≥i) is not really a continuous curve. Of course, the intensity I can be substituted with other ground motion parameter Y, so that P(Y>y) is a continuous curve.
In general, P(I) is the product of P(I≥i) minus P(I≥i+1). However, the result of the subtraction will not be reasonable, if I is low. For example, the result of seismic hazard analysis in a region with low seismicity shows P(I=i) is less than P(I=i+1), and one can see there must be something wrong. In nature, P(I) must be a monotone decreasing function, i.e. P(I=i) must be greater than P(I=i+1), even if intensity is low. To deal with the procedure of seismic hazard, one can understand that P(I≥i) is contributed by earthquakes in many potential sources with various magnitudes and frequencies. It means that P(I≥i) consists of not only P(I=i) and P(I≥i+1), but also P(I=i and I>i). The later cannot be ignored in seismic zone with strong activity for intensity less thanⅦand long evaluate period, like 50 years or 100 years. The general seismic hazard analysis assumes that the occurrence of earthquake is independent each other. Therefore a solution is that the exceeding probability in a short time period t can be firstly calculated from the hazard in long period T. Obviously, P(I=i and I>i) can be ignored when the period is short. In general, one month is short enough for a region with generic seismic activity; it should be shorten to days for region with high activity. The occurrence probability in short period can be obtained, and that in long period can be obtained. Hazard curves from the traditional method and the modified one are compared. Another example is given to show that the similar underestimation may happen for higher intensity in a region with high seismicity. Exceeding probability curve of loss ratio is drawn and compared with that from the traditional way to show the big difference.
(2) The result of seismic hazard analysis is the occurring or exceeding probability of an earthquake action in a city or region for a specified exposure time. However, high intensity areas are general small and cannot cover the whole metropolis because of the fast attenuation in epicenter area. And it never happens that several earthquakes occur in a given period to cause the same intensity and cover the whole metropolis one by one. In general, high intensity areas are always limited and the whole city will not be an intensity 8 or greater area, so there will be an overestimation if the exceeding probabilities of high intensity events are applied directly in loss estimation. Scenario earthquake approach can be adopted as the solution to this overestimation. One or more scenario earthquakes can characterize the hazard with the same exceeding probability. It must be consistent with the regional seismic environment and be derived from the regional attenuation relationship of ground motion.
To make it clear, a case study is demonstrated in this dissertation. High intensities and their probabilities of occurrence are converted into three scenario earthquakes by probabilistic analysis based scenario earthquake method to represent the probabilistic meaning in the deterministic way. This method was presented in the mid of 1990s and has been adopted in many microzonations, seismic hazard analysis of critical project sites and some codes, like NRC Regulatory Guide.165. By this way, the isoseismals and loss from the earthquake can be estimated explicitly, and design spectra can also be determined very well. Since then, many processes to determine scenario earthquakes have been worked out. For example, Ishikawa and Kameda recommended the mean magnitude and distance should be determined separately for each natural frequency of interest and for each seismic zone that contributes to the hazard on the site. The dominant earthquake was recommended to be the mean magnitude and distance of the seismic event that caused a ground-motion exceedence at the chosen return period by McGuire. Luo emphasized that the ground motion on a site should be equal to the ground motion with the corresponding exceeding probability. Chen suggested choosing a scenario earthquake from several ones satisfying conditions with the principle of maximum probability.
Concerning with the principle that the peak ground acceleration (PGA) from the scenario earthquake must be kept the same as the corresponding design parameter, the supervisor of the author developed a process, although there is strict principles, the steps depend on the conditions and the process never be published. So in this dissertation, the steps are summarized and discussed in details. In the process, the most dominant potential source area (MDPSA) is selected from PHSA result firstly. According to the design PGA corresponding to three design levels (exceeding probability 63%, 10% and 2-3% in 50 years, respectively), and attenuation relationship, the distances of major and minor axes given magnitudes no bigger than the upper bound magnitude of the MDPSA, can be calculated. The scenario earthquakes are determined finally from the constrain condition of that the distance matches the spatial area of the MDPSA.
It is not prescribed clearly in the Chinese code of“Evaluation of seismic safety for engineering sites (GB17741-2005)”if the scenario earthquake method is applicable to construct design spectra. In fact, there are different opinions for improving the code. Some compilers worried about whether the scenario earthquake determined from attenuation relationship of peak ground acceleration (PGA) can contribute properly to the amplitudes at long period. In order to show the applicability of scenario earthquake method to construct design spectra, scenario earthquake determined from attenuation relationship of peak ground acceleration is examined to see if it can contribute properly to the amplitudes at long period. In some cases, the spectra from the scenario earthquakes are quite close to the corresponding uniform spectra, else the spectral amplitudes are not very different from the amplitudes of uniform spectra at short period range, and those determined by attenuation relationship of spectral amplitude at period 1.0 second are closer to uniform one at long period range. The key point in the process is to determine the MDPSA. The contribution of each PSA on the motion at the site depends on the seismicity parameter, area and location of each PSA, the location of the site, and so on. As a rule, the ground motion at short period range attenuates with distance quickly, the contribution of near PSA is obvious; and the ground motion at long period range attenuates relatively slowly, the contribution of near PSA fades and that of far one increases. Therefore, the hazard curves for two natural periods (0.0 and 1.0 second) must be examined carefully to see if one source dominates the hazard at both periods, to make sure whether it is reasonable to represent the hazard with one or more scenario earthquake(s). In a word, spectra from scenario earthquakes can be adopted as design spectra directly if the most dominant areas are the same for spectral amplitudes at short and long period ranges, and more than one spectrum or enveloped spectra of those from scenario earthquakes are suggested to be adopted for design, if the areas are quite different.
In order to produce the exceeding probability curves of loss rate, it is necessary to analyze the damage from the scenario earthquakes. Many spatial operations must be involved in the calculation, and the amount of data available for each event is in tens of thousands of items. A GIS based decision-making support system for earthquake disaster reduction can be adopted for its spatial operating capacity.
GIS based systems for damage evaluation are developed for many cities and regions worldwide. This kind of systems is a powerful tool to perform spatial analysis and mapping of losses due to a scenario earthquake. The damaged areas of buildings, damaged length of highways, bridges or pipelines, damaged number of electric power or communicate facilities in 5 damage states from scenario earthquake can be assessed very fast. So spatial distributions of casualties, the caused shaking, damage and loss of engineering structures from these three scenario earthquakes are estimated by a GIS based system. By this way, the overestimation of losses in metropolis is avoided. Finally, the exceeding probability curves of loss rate for the traditional and improved ways are drawn and compared.
In the second part, it is emphasized how to price a catastrophe insurance derivative, CAT bond, as a supplement of earthquake insurance, that is, how to build a pricing model based on the improved engineering seismic risk assessment method.
Although“derivatives”is a new instrument in insurance industry, it has a long history in financial market, including forward contracts, futures, swaps and options etc. As derivatives become more and more active and provide lower-cost solutions to investor and entity, option-pricing theories have been adopted in a wide variety of financial instruments and contracts. All of these pricing models can be distributed into two classes, complete market models and incomplete market ones.
The vast majority of these models are assumed in complete market, where all sources of risks can be hedged perfectly, because of the possibility of replication of all assets. In other words, all derivatives can be perfectly replicated using a finite number of specified bonds. In incomplete market, such replication by dynamic hedging is not possible, which means that there are sources of risks that cannot be perfectly hedged. Assets’prices are supposed to be ruled by a dynamic process based on a constant drift, jumps and stochastic volatility. Incomplete market is due to the fact that jumps and stochastic volatility are not traded in market. The incomplete market modeling leads to some drawbacks, so we cannot set a portfolio perfectly hedged with derivatives written on these assets. Some pricing models of financial derivatives are reviewed emphatically as the foundation of next section, including Louis Bachelier (1900), Markowitz (1952), Sprenkle (1961), Boness (1964), William Sharpe (1964) and J. Lintner (1965), Samanelson (1965), Kassouf (1969), Fischer Black and Myron Scholes(1973), Merton (1976), Madan D. B. and E. Seneta (1990) etc. Some pricing models of CAT bonds in two types of markets, evolved from the models of financial derivatives, are reviewed, including Cummins and Geman (1995), Litzenberger et al (1996), Briys (1997), Henri Loubergéet al (1999), Morton N. Lane et al (1998-2004), Wang (2002,2004), Christofides (2004), Angelika Sch?chlin (2001), Yu. Baryshnikov et al (2001), Lee and Yu (2002), Cummins and Phillips (1999), Samuel H. Cox et al (2000), Li Yong (2005). In these models, the stochastic processes described the prices of subject matters is adopted to describe catastrophe loss, loss index or issuer’s loss which are not traded in market.
However, for earthquake disaster, direct losses are from the damage of engineering structures mainly, the statistic data of historical loss cannot present the engineering characters, like the type, age, purpose, with or without seismic design, etc. So it is not reasonable to estimate the losses from future earthquakes and price CAT bonds through historical data only, the engineering characters should be represented in pricing models.
Obviously, both earthquake insurance and its derivatives widen the mitigation channel against earthquake disasters. There might be a complementary relation between the parameters of these instruments to balance the issuer’s seismic risk. To show the complementarity, our setup in the final chapter makes an insurer who provides with earthquake insurance policy also, the issuer of a CAT bond. If the insured amount is high, the circulation of the CAT bond needs to be increased to handle high claim payments; otherwise, the circulation to be low to reduce interest payment. Seismic hazard assessment is characterized by scenario earthquakes, and the probability of earthquake catastrophe occurrence estimated by the improved seismic risk assessment method mentioned above. A pricing model for the CAT bond, according to the equilibrium between the incomes of investors and the issuer of the CAT bond, is built. In this model, a stochastic process, Geometric Brownian Motion, is adopted to describe the earning of earthquake insurance premium and the payout of reinsurance premium with two different sets of parameters respectively. The probability of earthquake catastrophe occurrence is an input of the model, ratio and yield of reinvestment, principal protected ratio, issuance fee, circulation and maturity are designed as factors. A partial equilibrium circumstances among the cash flows of the issuer on earthquake risk is built to price the CAT bond in complete market firstly.
However, for earthquake risk, the market is incomplete because the insurance premium will jump after a destructive earthquake or effected by some social factors, not a catastrophe. The jump of premium is proved in some cases, like several earthquakes in Japan. In this dissertation, it is also proved from a certain angle by the influence of fire losses on property insurance premium in the next year, the correlation coefficient is 0.9083. It is supposed that the market is arbitrage-free, and Jump-Diffusion process with two different sets of parameters is adopted to describe the earning of insurance premium and the payout of reinsurance premium in incomplete market. The jump size is supposed as standard log-normal distribution, and the jump times follow an independent Poisson process.
For explanation, a region in Northeast of China is adopted as an example. Since earthquake insurance has not been launched in large-scale in China now, related data and information is not enough. We have to suggest that the insurance rate in this field is 5% and the policy-holders are distributed uniformly in space. Then, the probable maximum loss of buildings from a probable maximum earthquake (M=6), assessed by the improved method, is RMB 1.556 billion (2004 value), which is discounted from 998.2 million (1994), considering the influence of interest rate and inflation. So the probable maximum claim payment is 77.79 million.
In recent years, several insurers covered earthquake risk as extraneous risk of enterprise property insurance, and the premium rate is 10% of main risk, which is limited strictly. It is supposed that 50% policy-holders of enterprise property insurance purchase the additional earthquake insurance, then the data is converted to those in the region by the ratio of annual national GDP to the affiliated city’s, in which 90% are adopted as the earning of earthquake insurance premium in the region. Earthquake reinsurance premium is adopted as 20% of the insurance premium. The expected volatility of the earning of insurance premium is 0.0544, the standard deviation is 0.0084, and those of the payout of reinsurance premium are 0.0109 and 0.0017 respectively.
According to the improved engineering seismic risk assessment method and the definition of catastrophe in this dissertation, the loss from the maximum considered earthquake is not a catastrophe, and the occurring probability of a defined earthquake catastrophe in 3 years p is 0.0003114%. If a catastrophe occurs, the loss should be 15.436 billion at least, so the claim payment of earthquake insurance is 771.8 million, and that of reinsurance is 154.4 million. If it does not occur, the claim payment of earthquake insurance is 77.79 million, and that of reinsurance is 15.56 million. Let the period of validity T be 3 years, the proportion of reinvestment k 10%, the yield r1 5%, the circulation of the CAT bond M 100 million, the risk-free interest rate r0 3.96%, the principal protected ratio n 50% and the issuance fee B 1% of the circulation. Then, the earning of insurance premium and the payout of reinsurance premium in complete and incomplete market can be obtained. The jumps of insurance and reinsurance premium from destructive earthquakes are considered in incomplete market. The annual coupon rates of the CAT bond r are 5.52% and 6.67% in complete and incomplete markets respectively. An investor will be refunded 118.01 yuan (complete market) and 122.48 yuan (incomplete market) for a coupon bond at maturity if the defined catastrophe does not occur, and the prices of a zero coupon bond, at t=0, are 84.74 yuan (complete market) and 81.64 yuan (incomplete market). In other words, the investor purchases the CAT bond at price of 84.74 yuan or 81.64 yuan and will get 100 yuan back at maturity. If the catastrophe occurs, half of the principal will be lost, that is, the investor will get 50 yuan, 42.37 yuan or 40.82 yuan back. The sensitivity of the annual coupon rate, the interest at maturity and the price on four factors are analyzed, the order, from high to low, is the period of validity, circulation, the yield of reinvestment and the proportion of reinvestment.
By comparing with the pricing model in the author’s Master thesis, this model is improved in the following areas: (1) M, r1, n, B and p are the factors in the primary model. In this model, the character of CAT bonds as a catastrophe insurance derivative is emphasized, the cash flows of earthquake (re)insurance premium are considered. (2) There are two states in the primary one, the catastrophe occurs or not. The influence of devastating earthquakes, not catastrophes, on the cash flows is considered in this one. (3) All parameters in the primary one are constant. This model is time-depended, although the interest rate of the CAT bond is calculated at maturity. For pricing in T, this model can be referred. For example, if a catastrophe occurs early in the period, the premium is not accumulated enough, M needs to be increased with other conditions unchanged. (4) The proportion of reinvestment is improved. 10% of funds financing from the CAT bond and the initial premium is venture investment, 90% is risk-free investment. The stability of insurance funds is assured and proceeds are increased. Let r1 is 5%, 6% or 7%, r is calculated by these two model. The comparison shows the rate from the primary one is underestimated if the yield is low. For example, r is just higher than r0 0.04% if r1 is 5%. So the attraction of the CAT bond will be lost if the factors are considered simply.
By comparing with current models, this model is characterized as: (1) Combined with engineering seismic risk assessment. The expected losses from future earthquakes are estimated by seismic hazard analysis and vulnerability estimation. The exceeding probability curve of loss rate is constructed to obtain the occurring probability of a catastrophe. The expected claim payment of insurers can be estimated by the assessment, if the earthquake insurance information is sufficient. (2) Geometric Brownian Motion and Jump-Diffusion process are adopted to describe the cash flow of insurance premium in complete and incomplete markets respectively, since it is irrational to describe catastrophic losses, insurers’loss or loss index etc by stochastic processes in current ones. (3) The complementarity between CAT bond and earthquake insurance is built to balance the capital of the issuer on earthquake risk. (4) A partial equilibrium circumstances among the cash flows of the issuer on earthquake risk is built in complete market. In incomplete market, a jump process is added in the circumstances. (5) According to the definition, the loss or occurrence of a catastrophe is not correlated with the operation of insurers directly, the moral hazard of this model can be avoided to a certain extent. The basis risk can be reduced also, since the loss of the issue in a catastrophe can be estimated by seismic risk assessment and the distribution of the policyholders.
引文
[1] 北京大学课题组.中国巨灾风险管理:再保险的角色.2003.
[2] 蔡明超译.Joseph Stampfli and Victor Goodman 著.金融数学.北京:机械工业出版社.2004.
[3] 陈浩武,唐元虎.浅析期权定价理论.技术经济与管理研究,2003,(4):23-24.
[4] 陈宏.刍议国际地震保险投保率.山西地震,2005,第 1 期(总第 120 期):25-26.
[5] 陈厚群,李敏,石玉成.基于设定地震的重大工程场地设计反应谱的确定方法.水利学报,2005,36(12):1399-1404.
[6] 陈厚群,李敏,张艳红.地震危险性分析和地震动输入机制研究. 水力发电,2001,(8):48-50.
[7] 陈辉煌. 巨灾指数选择权价值.实践大学企业管理研究所硕士学位论文,2001.
[8] 陈金龙.实物期权定价理论与方法研究.天津大学博士学位论文,2003.
[9] 陈寿梁,魏琏主编.抗震防灾对策.河南:河南科学技术出版社.1988.
[10] 陈心虹, 林 纯真.台湾产险市场概况.台湾:财团法人保险事业发展中心.2003.
[11] 陈信宪,洪丽琴,钟佳伶.剖析台湾巨灾债券之需求性.证券柜台,2004,n99-n102 合订本:63-71.
[12] 陈章立.我国地震科技进步的回顾与展望(二).中国地震,2001,17(4):326-341.
[13] 陈章立.我国地震科技进步的回顾与展望(三).中国地震,2002,18(1):1-14.
[14] 程小久.大型电力工程场地地震危险性研究现状与展望.华南地震,2000,20(1):71-78.
[15] 池重阳.巨灾债券初探.中央财经大学硕士学位论文,2003.
[16] 大庆石油管理局油田建设设计研究院,中国地震局工程力学研究所.大庆油田抗震防灾信息及辅助决策系统研究报告(内部).1998.
[17] 邓雪.Black-Scholes 期权定价模型求解的有限差分方法及其应用的研究.东北大学硕士学位论文,2000.
[18] 冯广波.期权定价有关问题的探讨.中南大学博士学位论文,2002.
[19] 冯占军.保险连接证券的发展及对保险业的影响.江西财经大学学报,2005,第 2 期(总第 38 期):36-40.
[20] 辅仁大学企业管理学系第三十六届第四组.巨灾风险转移与巨灾债券.财务管理专题报告.2002.
[21] 高孟潭.基于泊松分布的地震烈度发生概率模型。中国地震,1996,12(6):195-201
[22] 高孟潭.新的国家地震区划图.地震学报,2003,25(6):630-636.
[23] 管雪峰,解康.巨灾的保险衍生工具.上海金融报,2003.7.26,第 008 版.
[24] 国家地震局.中国地震烈度区划图(1990)概论.北京:地震出版社.1996.
[25] 国家地震局工程力学研究所.大庆石油管理局震害预测报告-地震灾害及经济损失预测总报告(内部).1994.
[26] 国内巨灾债券机制启动.现代保险金融理财杂志.2003.
[27] 郭星全,赵文星,雷建设.设定地震的分类及其确定方法.西北地震学报,1999,21(1):103-106.
[28] 郭增建,陈鑫连主编.地震对策.北京:地震出版社.1986.
[29] 韩立岩,支昱.巨灾债券与政府灾害救助.自然灾害学报,2006,15(1):17-22.
[30] 韩天雄,陈建华.巨灾风险证券化产品的定价问题,保险研究,2003,(12):31-33.
[31] 何问陶,徐华云.期权定价理论在风险管理中的应用.商业研究,2004,总第 297 期:115-118.
[32] 洪哲.保险风险证券化初探.现代财经,2004,24(3):15-18.
[33] 胡聿贤.地震工程学(第二版).北京:地震出版社.2006
[34] 胡支军.标的资产服从一般 Ito 随机过程的期权定价模型.贵州大学学报(自然科学版),2003,20(1):20-23.
[35] 黄斌.巨灾风险债券化的经济学分析.江西财经大学学报,2003,第 1 期(总第 25 期):29-33.
[36] 黄凯溥.台湾地区巨灾债券应用之价格分析.实践大学企业管理研究所硕士学位论文.2003.
[37] 黄伟民. 布朗运动理论向金融经济领域的延拓.大学物理,1999,(1):41-43.
[38] 江西一保险公司推出全国首个“地震险”.国际地震动态,2006,第 12期(总第 336 期):14.
[39] 金波.分布式防震减灾信息和辅助决策系统. 中国地震局工程力学研究所博士学位论文,2005.
[40] 金融新产品开发课题组.分散保险风险的资本市场创新工具.当代经济科学,2003,25(1):62-67.
[41] 孔学兵.证券定价理论及其分析方法. 东南大学硕士学位论文,2003.
[42] 寇锦玮,代佳,冯莉花等. 巨灾保险风险分散工具-指数巨灾债券和再保险. 保险制度创新专题研究.北京:北京大学经济学院风险管理与保险学系.2004.
[43] 李娜.巨灾风险证券化研究.北京工商大学硕士学位论文,2003.
[44] 李延明,吴燕.巨灾债券在巨灾风险管理中的应用.商业研究,2005,总第313 期:78-81.
[45] 李永.构建和谐社会中的巨灾风险防范—我国地震巨灾风险证券化的实证分析.华北地震科学,2005,23(4):45-49.
[46] 李悦,王雪峰.中国保险业应对巨灾风险的经营策略分析.哈尔滨商业大学学报(自然科学版),2003,19(5):535-538.
[47] 李珍颖.建立台湾综合天然灾害风险管理与保险规划之研究. 国立高雄第一科技大学硕士学位论文,2002.
[48] 梁利.金融资产及其衍生证券定价理论及应用.辽宁大学硕士学位论文,2002.
[49] 梁雪辉.风险债券化的交易成本分析.经济问题,2004,(4):7-9.
[50] 林 文昌,王祝三, 林 佩纯.巨灾链接权益卖权契约与评价.风险管 理学报,2005,7(1):29-51.
[51] 刘海龙,吴冲锋.非完全市场期权定价的ε-套利方法.预测,2001,(4):17-19.
[52] 刘海龙,吴冲锋.期权定价方法综述.管理科学学报,2002,5(2):67-73.
[53] 刘莉.巨灾风险证券化研究.湖南大学硕士学位论文,2003.
[54] 刘璐璐.巨灾风险证券化之研究.淡江大学硕士学位论文,2003.
[55] 刘倩.套利定价理论及保险精算方法在期权定价中的应用. 陕西师范大学硕士学位论文,2004.
[56] 刘倩,刘新平.外汇期权定价的新方法-保险精算方法.贵州大学学报(自然科学版),2004,21(1):43-58.
[57] 刘如山,王自法,朱敏.地震保险中经济损失和赔付问题的研究.地震学报,2006,28(2):197-205.
[58] 陆珩,陈伟忠.保险风险证券化-巨灾债券.华东经济管理,2003,17(4):111-113.
[59] 罗奇峰.概率一致设定地震及其估计方法.地震工程与工程振动,1996,16(3):22-29.
[60] 马超群,陈牡妙.标的资产服从混合过程的期权定价模型.系统工程理论与实践,1999,19(4):41-46.
[61] 马俊海.金融衍生证券定价的数值分析方法研究.天津大学博士学位论文,2000.
[62] 毛二万编著.金融经济学.辽宁:辽宁教育出版社.2002.
[63] 孟利锋,张世英,何信.金融衍生产品定价理论及其应用研究.价格理论与实践,2003,(9):52-53.
[64] 米宏亮,李洋,侯建盛.2005 年中国大陆地震灾害述评.自然灾害学报,2006,15(3):164-168.
[65] 聂彩仁.Black-Scholes 期权定价模型的推广、应用及数值解.云南大学硕士学位论文,2003.
[66] 聶建中,陳芾文,王友珊.金融機構承做選擇權的腜惋L險與市場風險.風險管理學報，2003,5(3):295-317.
[67] 宁丽娟.股票价格服从跳-扩散过程的期权定价模型. 陕西师范大学硕士学位论文,2004.
[68] 沈建文,石树中.一致概率谱与规范谱. 地震学报,2004,26(1):94-101.
[69] 沈湛.中国巨灾保险制度研究.厦门大学硕士学位论文,2003.
[70] 施方.巨灾保险证券化的探讨.上海大学学报(社会科学版),2004,11(1):55-58.
[71] 史培军主编.中国自然灾害系统地图集.北京:科学出版社技术分社.2003.
[72] 时振梁,李裕澈.中国地震区划.中国工程科学,2001,3(6):65-68.
[73] 苏卫东.金融波动模型及其在中国股市的应用.天津大学博士学位论文,2002.
[74] 孙良,张俊国,潘德惠.金融衍生证券定价理论进展评述.东北大学学报(自然科学版),1998,19(5):25-28.
[75] 孙宁华.金融衍生工具定价理论述评.经济学研究,2004,第 9 期:33-38.
[76] 孙雅静.巨灾风险管理中金融工具的创新.理论月刊,2002,第 9 期:72-78.
[77] 汤爱平.城市灾害管理和震后应急反应辅助决策研究. 中国地震局工程力学研究所博士学位论文,1999.
[78] 陶夏新,周锡元.地震危险性评定.中国工程抗震四十年.北京:地震出版社.1989.
[79] 陶夏新.我国新的地震区划编图和中国地震烈度区划图(1990).自然灾害学报,1992,1(1):99-107.
[80] 陶夏新.对新的全国地震区划中概率计算公式表达的讨论.世界地震工程,1992,2:33-36.
[81] 陶夏新.中国地震工程地质学之发展.世界地震工程.2000,22(4):1-6.
[82] 陶夏新,陶正如.我国地震灾害管理和金融手段.全国首届防震减灾工程学术研讨会论文集.2005.18-24.
[83] 陶正如,陶夏新.地震巨灾债券雏议.自然灾害学报,2004,13(6):138-144.
[84] 陶正如,陶夏新.基于工程地震风险评估的地震保险费率厘定. 自然灾害学报,2004,13(2):112-118.
[85] 陶正如.城市地震风险评估和管理中的两个问题.中国地震局工程力学研究所硕士论文.2003
[86] 仝春建.巨灾风险模型巨头紧盯中国市场.中国保险报,2005.12.2,第003 版.
[87] 王春峰著.金融市场风险管理.天津:天津大学出版社.2001.
[88] 王俊华,刘猛.论巨灾期权在我国的发展.重庆交通学院学报(社科版),2005,5(4):67-69.
[89] 王应贵.期权定价方法及其应用.复旦大学博士学位论文,1999.
[90] 魏海港,刘汉进.保险公司巨灾风险管理的新进展及其启示.中国软科学,2005,(6):82-85.
[91] 魏敏杰,田玲.巨灾风险证券化及在中国保险市场的应用.科技进步与对策,2003,10 月号:173-175.
[92] 温瑞智.基于GIS的防震减灾系统. 中国地震局工程力学研究所博士学位论文,1999.
[93] 吴军,孙晓骏.论巨灾债券在我国的发展.保险研究,2004,(11):19-21.
[94] 奚炜.Gamma 时变过程与 Black-Scholes 期权定价的定价偏差纠正.管理工程学报,2004,18(1):111-113.
[95] 谢丹.巨灾保险证券化与巨灾债券在我国的应用.对外经济贸易大学硕士学位论文,2004.
[96] 熊华,罗奇峰.国内外地震保险概况.灾害学,2003,18(3):61-65.
[97] 徐爱荣.巨灾债券的一种定价模型.统计与决策,2005,第 10 期(总第 199期):15-16.
[98] 薛红.金融衍生证券定价理论及其应用研究.西安交通大学博士学位论文,2000.
[99] 闫海峰,刘三阳.带有 Poisson 跳的股票价格模型的期权定价.工程数学学报,2003,20(2):35-40.
[100] 闫海峰,刘三阳.广义 Black-Scholes 模型期权定价新方法-保险精算方法.应用数学和力学,2003,24(7):730-738.
[101] 鄢家全.地震危险性分析的困惑与展望.国际地震动态,2005,第 6 期(总第 318 期):14-24.
[102] 杨宝华.中国发展巨灾保险债券的构想.上海师范大学学报(哲学社会科学版),2005,34(2):23-28.
[103] 杨恩斌.期权定价理论及其在我国的应用.中国海洋大学硕士学位论文,2003.
[104] 杨培涛.期权在保险中的应用研究.中南林学院硕士学位论文,2004.
[105] 杨招军,黄立宏.随机波动率与跳组合情形的期权问题闭式解.应用概率统计,2004,12(3):229-233.
[106] 姚壬元.巨灾风险证券化研究.中南财经政法大学学报,2004,第 5 期(总第 146 期):102-108.
[107] 易立新,胡晓,钟菊芳.基于 EPA 的重大工程设计地震动确定.地震研究,2004,27(3):271-276.
[108] 尹之潜.震害与地震损失的估计方法.地震工程与工程振动,1990,10(1):99-107.
[109] 尹之潜.地震灾害损失预测研究. 地震工程与工程振动,1991,11(4):87-96.
[110] 尹之潜著.地震灾害及损失预测方法.北京:地震出版社.1995.
[111] 应坚刚,金蒙伟编著.随机过程基础.上海:复旦大学出版社.2005.
[112] 战雪丽.期权及其定价的应用研究.天津大学硕士学位论文,2004.
[113] 张洪涛,郑功成主编.保险学.北京:中国人民大学出版社.2002.
[114] 张萍.债券期权定价的近似方法.东北大学硕士学位论文,2003.
[115] 张平,张晓晶.直面符号经济.北京:社会科学文献出版社.2003.
[116] 张淑英,张世英.非完全市场与完全市场证券组合比较研究.管理工程学报,2000,14(3):48-51.
[117] 张淑蓉.二项式巨灾权益卖权订价模型.台湾逢甲大学保险学系硕士论文,2004.
[118] 张伟忠.巨灾债券之理论与实际.国立中央大学财务管理研究所硕士学位论文.2000.
[119] 张振伟.关于期权定价理论和应用的问题研究.南京理工大学硕士学位论文,2000.
[120] 张志明.保险公司巨灾保险风险证券化初探.东北财经大学学报,2006,第3 期(总第 45 期):60-63.
[121] 赵红,李洋,张岚.我国保险资金投资渠道与模式探讨.北方经贸,2003,(5):43-45.
[122] 中国保险监督管理委员会.保险公司管理规定,保监会[2004] 3 号令.2004.
[123] 中国地震局工程力学研究所.上海市浦东新区震害预测报告(内部).2002.
[124] 中国地震局工程力学研究所.长春第三热电厂工程场地地震安全性评价报告(内部).2005.
[125] 中华人民共和国国家标准,GB17741-2005.工程场地地震安全性评价. 北京:中国标准出版社,2005.
[126] 钟菊芳,胡晓,易立新,吴胜兴.重大工程设定地震方法研究进展.水力发电,2005,31(4):22-24.
[127] 周少甫,张子刚,程斌武.期权定价理论和倒向随机微分方程.科技进步与对策,2000,17(10):100-102.
[128] 周荣义,廖可兵.GIS在巨灾保险风险管理中的应用研究.中国安全科学学报, 2004,14(9):56-59.
[129] 左惠强.基于GIS的地震危险性分析.中国地震局工程力学研究所博士学位论文,1996.
[130] Alan Punter. Securitization of insurance risk, AON. 1999.
[131] Alexander Muermann. Pricing cat insurance derivatives. Financial Markets Group Discussion Paper, 2001, No.400.
[132] Alexander Muermann. Actuarially Consistent Valuation of Catastrophe Derivatives. Wharton Working Paper, 2003, No.03-18.
[133] American Academy of Actuaries, Index Securitization Task Force With Research and Input From Casualty Actuarial Society & Valuation, Finance and Investments Committee. Evaluating the effectiveness of index-based insurance derivatives in hedging property/casualty insurance transactions. 1999.
[134] Andreas Müller. Integrated risk management. Munich Re ART Solutions, Munich Re Group. 2000.
[135] Andreas Richter. Catastrophe risk management-implications of default risk and basis risk. Proc. of 9th Symposium on Finance, Banking, and Insurance. 2002.
[136] Angelika Sch?chlin. On the market price of catastrophe risk: empirical evidence from catastrophe bonds. Switzerland: the University of St. Gallen, 2001.
[137] Angelos Dassios, Ji-Wook Jang. The pricing of catastrophe reinsurance contract & insurance derivatives using the Cox process with shot noise process as its intensity. Finance and Stochastics,2003, 7(1): 73-95.
[138] Anton Zaicenco, Vasile Alkaz. Urban seismic risk studies with utilization of GIS. Presentation on NATO Advanced ResearchWorkshop. 2005.
[139] Aurelia Zanetti, Rudolf Enz, Pamela Heck, Jay Green, Sonja Suter. Natural catastrophes and man-made disasters in 2003: many fatalities, comparatively moderate insured losses. Sigma, 2004, No.1.
[140] Aurelia Zanetti, Susanna Schwarz. Natural catastrophes and man-made disasters in 2005: high earthquake casualties, new dimension in windstorm losses. Sigma, 2006, No.2.
[141] B. Jensen. Option pricing in the jump-diffusion model with a randomjump amplitude: a complete market approach. Center for analytical finance, University of AARHUS-AARHUS School of Business. 1999. Working Paper Series No.42.
[142] C.Vorm Christensen. A new model for pricing catastrophe insurancederivatives. Center for analytical finance, University of AARHUS-AARHUS School of Business. 1999. Working Paper Series No. 28.
[143] Carol Hayek, Roger Ghanem. Impact of uncertainty in catastrophe losses on insurance derivatives. Proc. of 15th ASCE Engineering Mechanics Conference. 2002.
[144] Christofides Stavros. Pricing of catastrophe linked securities. ASTIN Colloquia, Norway. 2004.
[145] Christopher McGhee. Market update: the catastrophe bond market at year-end 2003. Guy Carpenter & Company Inc. and MMC Securities Corp. 2004.
[146] Cornell C. A.. Engineering seismic risk analysis. Bull. Seis. Soc. Amer., 1968, 58(3):1583-1606.
[147] David C. Croson, Howard C. Kunreuther. Customizing reinsurance and cat bonds for natural hazard risks. Proc. of the EuroConference on global change and catastrophic risk management. 1999.
[148] David Croson, Andreas Richter. Sovereign cat bonds and infrastructure project financing. Risk Analysis, 2003, 23(3): 611-626.
[149] David K. Bradford. Earthquake: the other insurance crisis. An Advisen QuickNote, Advisen Ltd, 2006.
[150] David S. Laster, Mayank Returi. Capital market innovation in the insurance industry. Sigma, 2001, No.3.
[151] Doherty N. Financial innovation in the management of catastrophe risk. Wharton Working Paper, 1997, No.98-12.
[152] Dwight M. Jaffee, Thomas Russell. Catastrophe insurance, capitalmarkets and uninsurable risks. Wharton Working Paper, 1995, No.96-12.
[153] Earthquake Engineering Research Institute. Securing society against catastrophic earthquake losses, A research and outreach plan in earthquake engineering. USA: Earthquake Engineering Research Institute. 2003.
[154] Eduardo Canabarro, Markus Finkemeier, Richard R. Anderson, Fouad Bendimerad. Analyzing insurance-linked securities. Fixed Income Research. Editor: Ronald A. Krieger. Goldman, Sachs & Co. 1998.
[155] Francis Parisi, Helene Herlihy. Modeling catastrophe reinsurance risk: implications for the CAT bond market. Standard & Poor’sStructured Finance Special Report. 1999.
[156] Franz Sauter .EERI committee on seismic risk, glossary of terms for probabilistic seismic-risk and hazard analysis. EarthquakeSpectra, 1984, (1): 33-40.
[157] Gene C.Lai. The future trends in risk management and insuranceworldwide: integration and globalization of risk management. Journal of Risk Management, 1999, 1(2): 1-13.
[158] Gao Mengtan. Probabilistic model of estimation of the expected loss from earthquakes based on the frequency of intensity. Proc. of 5th International conference on seismic zonation, Nice France. 1995. 249-256.
[159] Glenn Meyers. Managing the catastrophe risk. Casualty Actuarial Society Discussion Paper Program. Arlington, Virginia: Casualty Actuarial Society. 1995. 111-150.
[160] Glenn G. Meyers, John J. Kollar. On the cost of financing catastrophe insurance. Casualty Actuarial Society Discussion Paper Program. Arlington, Virginia: Casualty Actuarial Society. 1998. 119-148.
[161] Glenn Meyers, John Kollar. Catastrophe risk securitization: insurer and investor perspectives. Casualty Actuarial Society Discussion Paper Program. Arlington, Virginia: Casualty Actuarial Society. 1999. 223-272.
[162] Harrington Scott, Mann Steven, Niehaus Greg. Basis risk with catastrophe insurance derivative contracts. Proc. of 5th International Conference on Insurance Solvency and Finance, London. 1997.
[163] Henri Loubergé, Evis Kellezi, Manfred Gilli. Using catastrophe-linked securities to diversify insurance risk: afinancial analysis of cat bonds. Journal of Insurance Issues, 1999, 22(2): 125-146.
[164] Howard Kunreuther. Mitigation and financial risk management for natural hazards. Proc. of 6th International Conference on SeismicZonation. 2000. No.00099.
[165] Ishikawa Y., Kameda H. Probability-based determination of specific scenario earthquakes. Proc. of 4th International Conference on Seismic Zonation. 1991. 3-10.
[166] J. David Cummins, Hélyette Geman. An Asian Option Approach to theValuation of Insurance Futures Contracts, Wharton working paper, No.94-03
[167] J. David Cummins, Christopher Lewis, Richard D. Phillips. Pricing excess-of-loss reinsurance contracts against catastrophic loss. Wharton Working Paper, 1997, No.98-09.
[168] J. David Cummins, Richard D. Phillips. Applications of financial pricing models in property-liability insurance. The Handbook of Insurance Economics. Edited by Georges Dionne. Boston: Kluwer Academic Publishers. 1999.
[169] J. David Cummins, David Lalonde, Richard D. Phillips. The basis risk of catastrophic-loss index securities. Wharton Working Paper, 1999, No.00-22.
[170] J. David Cummins, Neil Doherty, Anita Lo. Can insurers pay for the “Big One”?, Measuring the capacity of an insurance market to respond to catastrophic losses. Journal of Banking & Finance, 2002, (26): 557-583.
[171] J. David Cummins, David Lalonde, Richard D. Phillips. Managing risk using index-linked catastrophic loss securities, Alternative risk strategies. Morton Lane Ed. London, England: Risk Books. 2002. 19-46.
[172] Kenneth A.Froot, The limited financing of catastrophe risk: an overview, NBER working paper series, No.6025, National Bureau of economic research,1997:
[173] Kevin P. Sheehan. Catastrophe securities and the market sharing of deposit insurance risk. FDIC Banking Review, 2003, 15(1): 1-16.
[174] Knut K Aase. An equilibrium model of catastrophe insurance futures and spreads. The Geneva Papers on Risk and Insurance Theory, 1999, 24(1): 69-96.
[175] Krzysztof Burnecki. Pricing catastrophe bonds in a compoundnon-homogeneous Poisson model with left-truncated loss distributions. Presentation on Mathematics in Finance Conference.2005.
[176] Lee Jin-ping. Pricing CAT bonds with moral hazard and basis risk.Doctor Thesis, Department of Finance, National Central University, 2001.
[177] Lee Jin-Ping, Yu Min. The pricing default-risky cat bonds with moralhazard and basis risk. The Journal of Risk and Insurance, 2002, 69(1): 25-44.
[178] Lewis V. Augustine. Catastrophe risk mitigation: a survey of methods. Casualty Actuarial Society Discussion Paper Program.Arlington, Virginia: Casualty Actuarial Society. 1998. 323-342.
[179] Marcel Grandi, Andreas Müller. Insurance derivatives –Convergence of capital markets and insurance markets. Munich Re ART Solutions, Munich Re Group. 1999.
[180] Martin Nell, Andreas Richter. Catastrophe index-linked securitiesand reinsurance as substitutes. Working paper series: finance & accounting, 2000, No.56.
[181] Martínez Torre-Enciso I., Laye J. Financing catastrophe risk in the capital markets. Int. J. Emergency Management, 2001, 1(1): 61-69.
[182] Michael Bayard Smith, L. Jaimie Pickles. An introduction to catastrophe insurance futures. Proc. of 4th AFIR International Colloquium. 1994. 817-844.
[183] Michael Monoyios. Lecture in Mathematical Finance,Department of Economic and Finance,Brunel Universuty. 2000.
[184] Morton N. Lane. Price, risk and rating for insurance-linked notes: evaluating their position in your portfolio. Sedgwick Lane Financial, LLC.1998.
[185] Morton N. Lane. Risk cubes or price, risk and ratings (PartII).Sedgwick Lane Financial, LLC.1999.
[186] Morton N. Lane. Pricing risk transfer transactions. ASTIN BULLETIN, 2000, 30(2): 259-293.
[187] Morton N. Lane. Analyzing the pricing of the 2001 risk-linked securities transactions. Lane Financial, LLC. 2001.
[188] Morton N. Lane. Rational and results with the LFC CAT bonds pricing model. Lane Financial, LLC. 2003.
[189] Morton N. Lane. The viability and likely pricing of “CAT Bonds”for developing countries. Lane Financial, LLC. 2004.
[190] Munich Re Group. World of Natural Hazards. 2000.
[191] Munich Re Group. Finite Risk Reinsurance and Risk Transfer to the Capital Markets-A Complement to Traditional Reinsurance. Munich Re ART Solutions, A series of Munich Re ART publications. 2000.
[192] Munich Re Group. Risk transfer to the capital market-Using the Capital Markets in Insurance Risk Management. Munich Re ART Solutions, A series of Munich Re ART publications. 2001.
[193] Munich Re Group. Annual review: Natural catastrophes 2005. Topics Geo, Knowledge series, 2006.
[194] Munich Re Group. The 1906 earthquake and Hurricane KatrinaSimilarities and differences-Implications for the insurance industry. Germany: WKD-Offsetdruck GmbH, Oskar-Messter-Strasse 16, 85737 Ismaning. 2006.
[195] Patricia Grossi, Domenoco del Re and Zifa Wang. The 1976 Great Tangshan Earthquake 30-year retrospective. Institute of engineering mechanics and Risk management solutions, Inc. 2006.
[196] Patricia Grossi. The 1679 Sanhe-Pinggu Earthquake implications for the modern-day Beijing region. Risk management solutions, Inc. 2007.
[197] Patrizia Baur, Rudolf Enz. Solvency II: an integrated risk approach for European insurers. Sigma, 2006, No.4.
[198] Peter Zimmerli, Junhua Zhou. Natural hazard in China-Ensuring long-term stability. Focus report, Swiss Re. 2006.
[199] Rainer Helfenstein, Thomas Holzheu. Securitization-new opportunities for insurers and investors. Sigma, 2006, No.7.
[200] Richard W. Gorvett. Insurance securitization: the development of a new asset class. Casualty Actuarial Society “Securitization of Risk” Discussion Paper Program. 1999.
[201] Richard W. Gorvett. Foreign exchange rate risk: institutional issues and stochastic modeling. Discussion Paper Program on Financial and Accounting Systems and Issues Associated with the Globalization of Insurance, 2001, 19-52.
[202] Robert E. Hoyt and Kathleen A. McCullough,Catastrophe Insurance Options: Are They Zero-Beta Assets?, Journal of Insurance Issues, 1999, 22, 2, pp147–163.
[203] Robert P. Eramo. Insurance Catastrophe Futures. Casualty Actuarial Society Discussion Paper Program. Arlington, Virginia: Casualty Actuarial Society. 1996. 47-60.
[204] Robin K.McGuire. Probabilistic seismic hazard analysis and design earthquakes: closing the loop. Bulletin of the Seismological Socirty of America, 1995, 85(5):1275-1284.
[205] Samuel H. Cox, Joseph R. Fairchild, Hal W. Pederse. Economic aspects of securitization of risk. ASTIN BULLETIN, 2000,30(1): 157-193.
[206] Samuel H. Cox, Hal W. Pedersen. Catastrophe risk bonds. NorthAmerican Actuarial Journal, 2001, 4(4): 56-82.
[207] Sara Borden, Asani Sarkar. Securitizing property catastrophe risk. Current issues in economics and finance, 1996, 2(9): 1-6.
[208] Sebastian Jaimungal, Tao Wang. Catastrophe options with stochastic interest rates and compound Poisson losses. Insurance: Mathematics and Economics,2006, 38(3): 469-483.
[209] Sergei Esipov, Dajiang Guo. Portfolio based pricing of residualbasis risk with application to the S&P500 put options. Discussion Paper Program on Insurance in the Next Century, 2000, 43-66.
[210] Shah Hemant, Nakada Peter. Managing and financing catastrophe risk: the need for an enterprise-wide, value-oriented approach. Published in Alternative Insurance Capital, Issue 95, 1999.
[211] Shaun S. Wang. A universal framework for pricing financial and insurance risks. ASTIN BULLETIN, 2002, 32(2): 213-234.
[212] Shaun S. Wang. Cat Bond Pricing Using Probability Transforms. The Geneva Papers: Etudes et Dossiers, special issue on “Insurance and the State of the Art in Cat Bond Pricing”, 2004, No.278, 19-29.
[213] Shigeyuki Goto. Study on the interactive approach between insurance and capital markets for catastrophe risks. Center on Japanese Economy and Business, Columbia Business School, 1997, Working Paper 140.
[214] Stephen P. D'Arcy, Virginia Grace France, Richard W. Gorvett. Pricing catastrophe risk: could cat futures have coped with Andrew?. Casualty Actuarial Society “Securitization of Risk” Discussion Paper Program. 1999.
[215] Steven Kaplan, Guillaume Lefebvre. CAT bonds: tax treatment of an innovative financial product. Journal of Taxation of Financial Institutions, 2003, 16(4): 5-16.
[216] Swiss Re. Alternative risk transfer (ART) for corporations: apassing fashion or risk management for the 21st Century?. Sigma, 1999, No.2.
[217] T. Saeki, H. Tsubokawa, S. Midorikawa. Estimation of earthquake economic loss to residents using GIS. Proc. of 7th National Conference on Earthquake Engineering. 2002. No.00055.
[218] Takada T, Okumura T, Hirose J, Muramatsu K, Taki S, Ishii K.Probabilistic scenario earthquakes for seismic design-comparison of two identification procedures. Proc. of OECD-NEA workshop on Seismic Risk. 1999.
[219] Thomas Holzheu, Kurt Karl, Mayank Raturi. The picture of ART. Sigma, 2003, No.1.
[220] Thunderbird. Catastrophe insurance exposure and hedging: structure and issues. The American Graduate School of International Management.2000. No.B06-00-0023.
[221] Torben J. Andersen. Innovative financial instruments for natural disaster risk management. Technical Papers Series of Inter-American Development Bank, Sustainable Development Department, Washington, D. C. 2002.
[222] United States General Accounting Office. The role of risk-linkedsecurities and factors affecting their use. Report to the Chairman, Committee on Financial Services, House of Representative. 2002. GAO-02-941.
[223] Vivek J. Bantwal,Howard C. Kunreuther. A cat bond premium puzzle?. Wharton Working Paper, 1999, No.99-26.
[224] Walter Kielholz, Alex Durrer. Insurance derivatives and securitization, New hedging perspectives for the US cat insurance market. The Geneva Papers on Risk and Insurance, 1997, 22(No.82):3-16.
[225] William Dubinsky, David Laster. Insurance-linked securities. Swiss Re Capital Market Corporation. 2003.
[226] Xie L., Tao X. et al. A GIS based earthquake losses assessment and emergency response system for Daqing oil field. Proc. of 12th WorldConference on Earthquake Engineering. 2000. No.0091.
[227] Yu. Baryshnikov, A. Mayo, D. R. Taylor. Pricing of CAT bonds. Working paper of School of Computational and Applied Mathematics. 2001.
[2] 蔡明超译.Joseph Stampfli and Victor Goodman 著.金融数学.北京:机械工业出版社.2004.
[3] 陈浩武,唐元虎.浅析期权定价理论.技术经济与管理研究,2003,(4):23-24.
[4] 陈宏.刍议国际地震保险投保率.山西地震,2005,第 1 期(总第 120 期):25-26.
[5] 陈厚群,李敏,石玉成.基于设定地震的重大工程场地设计反应谱的确定方法.水利学报,2005,36(12):1399-1404.
[6] 陈厚群,李敏,张艳红.地震危险性分析和地震动输入机制研究. 水力发电,2001,(8):48-50.
[7] 陈辉煌. 巨灾指数选择权价值.实践大学企业管理研究所硕士学位论文,2001.
[8] 陈金龙.实物期权定价理论与方法研究.天津大学博士学位论文,2003.
[9] 陈寿梁,魏琏主编.抗震防灾对策.河南:河南科学技术出版社.1988.
[10] 陈心虹, 林 纯真.台湾产险市场概况.台湾:财团法人保险事业发展中心.2003.
[11] 陈信宪,洪丽琴,钟佳伶.剖析台湾巨灾债券之需求性.证券柜台,2004,n99-n102 合订本:63-71.
[12] 陈章立.我国地震科技进步的回顾与展望(二).中国地震,2001,17(4):326-341.
[13] 陈章立.我国地震科技进步的回顾与展望(三).中国地震,2002,18(1):1-14.
[14] 程小久.大型电力工程场地地震危险性研究现状与展望.华南地震,2000,20(1):71-78.
[15] 池重阳.巨灾债券初探.中央财经大学硕士学位论文,2003.
[16] 大庆石油管理局油田建设设计研究院,中国地震局工程力学研究所.大庆油田抗震防灾信息及辅助决策系统研究报告(内部).1998.
[17] 邓雪.Black-Scholes 期权定价模型求解的有限差分方法及其应用的研究.东北大学硕士学位论文,2000.
[18] 冯广波.期权定价有关问题的探讨.中南大学博士学位论文,2002.
[19] 冯占军.保险连接证券的发展及对保险业的影响.江西财经大学学报,2005,第 2 期(总第 38 期):36-40.
[20] 辅仁大学企业管理学系第三十六届第四组.巨灾风险转移与巨灾债券.财务管理专题报告.2002.
[21] 高孟潭.基于泊松分布的地震烈度发生概率模型。中国地震,1996,12(6):195-201
[22] 高孟潭.新的国家地震区划图.地震学报,2003,25(6):630-636.
[23] 管雪峰,解康.巨灾的保险衍生工具.上海金融报,2003.7.26,第 008 版.
[24] 国家地震局.中国地震烈度区划图(1990)概论.北京:地震出版社.1996.
[25] 国家地震局工程力学研究所.大庆石油管理局震害预测报告-地震灾害及经济损失预测总报告(内部).1994.
[26] 国内巨灾债券机制启动.现代保险金融理财杂志.2003.
[27] 郭星全,赵文星,雷建设.设定地震的分类及其确定方法.西北地震学报,1999,21(1):103-106.
[28] 郭增建,陈鑫连主编.地震对策.北京:地震出版社.1986.
[29] 韩立岩,支昱.巨灾债券与政府灾害救助.自然灾害学报,2006,15(1):17-22.
[30] 韩天雄,陈建华.巨灾风险证券化产品的定价问题,保险研究,2003,(12):31-33.
[31] 何问陶,徐华云.期权定价理论在风险管理中的应用.商业研究,2004,总第 297 期:115-118.
[32] 洪哲.保险风险证券化初探.现代财经,2004,24(3):15-18.
[33] 胡聿贤.地震工程学(第二版).北京:地震出版社.2006
[34] 胡支军.标的资产服从一般 Ito 随机过程的期权定价模型.贵州大学学报(自然科学版),2003,20(1):20-23.
[35] 黄斌.巨灾风险债券化的经济学分析.江西财经大学学报,2003,第 1 期(总第 25 期):29-33.
[36] 黄凯溥.台湾地区巨灾债券应用之价格分析.实践大学企业管理研究所硕士学位论文.2003.
[37] 黄伟民. 布朗运动理论向金融经济领域的延拓.大学物理,1999,(1):41-43.
[38] 江西一保险公司推出全国首个“地震险”.国际地震动态,2006,第 12期(总第 336 期):14.
[39] 金波.分布式防震减灾信息和辅助决策系统. 中国地震局工程力学研究所博士学位论文,2005.
[40] 金融新产品开发课题组.分散保险风险的资本市场创新工具.当代经济科学,2003,25(1):62-67.
[41] 孔学兵.证券定价理论及其分析方法. 东南大学硕士学位论文,2003.
[42] 寇锦玮,代佳,冯莉花等. 巨灾保险风险分散工具-指数巨灾债券和再保险. 保险制度创新专题研究.北京:北京大学经济学院风险管理与保险学系.2004.
[43] 李娜.巨灾风险证券化研究.北京工商大学硕士学位论文,2003.
[44] 李延明,吴燕.巨灾债券在巨灾风险管理中的应用.商业研究,2005,总第313 期:78-81.
[45] 李永.构建和谐社会中的巨灾风险防范—我国地震巨灾风险证券化的实证分析.华北地震科学,2005,23(4):45-49.
[46] 李悦,王雪峰.中国保险业应对巨灾风险的经营策略分析.哈尔滨商业大学学报(自然科学版),2003,19(5):535-538.
[47] 李珍颖.建立台湾综合天然灾害风险管理与保险规划之研究. 国立高雄第一科技大学硕士学位论文,2002.
[48] 梁利.金融资产及其衍生证券定价理论及应用.辽宁大学硕士学位论文,2002.
[49] 梁雪辉.风险债券化的交易成本分析.经济问题,2004,(4):7-9.
[50] 林 文昌,王祝三, 林 佩纯.巨灾链接权益卖权契约与评价.风险管 理学报,2005,7(1):29-51.
[51] 刘海龙,吴冲锋.非完全市场期权定价的ε-套利方法.预测,2001,(4):17-19.
[52] 刘海龙,吴冲锋.期权定价方法综述.管理科学学报,2002,5(2):67-73.
[53] 刘莉.巨灾风险证券化研究.湖南大学硕士学位论文,2003.
[54] 刘璐璐.巨灾风险证券化之研究.淡江大学硕士学位论文,2003.
[55] 刘倩.套利定价理论及保险精算方法在期权定价中的应用. 陕西师范大学硕士学位论文,2004.
[56] 刘倩,刘新平.外汇期权定价的新方法-保险精算方法.贵州大学学报(自然科学版),2004,21(1):43-58.
[57] 刘如山,王自法,朱敏.地震保险中经济损失和赔付问题的研究.地震学报,2006,28(2):197-205.
[58] 陆珩,陈伟忠.保险风险证券化-巨灾债券.华东经济管理,2003,17(4):111-113.
[59] 罗奇峰.概率一致设定地震及其估计方法.地震工程与工程振动,1996,16(3):22-29.
[60] 马超群,陈牡妙.标的资产服从混合过程的期权定价模型.系统工程理论与实践,1999,19(4):41-46.
[61] 马俊海.金融衍生证券定价的数值分析方法研究.天津大学博士学位论文,2000.
[62] 毛二万编著.金融经济学.辽宁:辽宁教育出版社.2002.
[63] 孟利锋,张世英,何信.金融衍生产品定价理论及其应用研究.价格理论与实践,2003,(9):52-53.
[64] 米宏亮,李洋,侯建盛.2005 年中国大陆地震灾害述评.自然灾害学报,2006,15(3):164-168.
[65] 聂彩仁.Black-Scholes 期权定价模型的推广、应用及数值解.云南大学硕士学位论文,2003.
[66] 聶建中,陳芾文,王友珊.金融機構承做選擇權的腜惋L險與市場風險.風險管理學報，2003,5(3):295-317.
[67] 宁丽娟.股票价格服从跳-扩散过程的期权定价模型. 陕西师范大学硕士学位论文,2004.
[68] 沈建文,石树中.一致概率谱与规范谱. 地震学报,2004,26(1):94-101.
[69] 沈湛.中国巨灾保险制度研究.厦门大学硕士学位论文,2003.
[70] 施方.巨灾保险证券化的探讨.上海大学学报(社会科学版),2004,11(1):55-58.
[71] 史培军主编.中国自然灾害系统地图集.北京:科学出版社技术分社.2003.
[72] 时振梁,李裕澈.中国地震区划.中国工程科学,2001,3(6):65-68.
[73] 苏卫东.金融波动模型及其在中国股市的应用.天津大学博士学位论文,2002.
[74] 孙良,张俊国,潘德惠.金融衍生证券定价理论进展评述.东北大学学报(自然科学版),1998,19(5):25-28.
[75] 孙宁华.金融衍生工具定价理论述评.经济学研究,2004,第 9 期:33-38.
[76] 孙雅静.巨灾风险管理中金融工具的创新.理论月刊,2002,第 9 期:72-78.
[77] 汤爱平.城市灾害管理和震后应急反应辅助决策研究. 中国地震局工程力学研究所博士学位论文,1999.
[78] 陶夏新,周锡元.地震危险性评定.中国工程抗震四十年.北京:地震出版社.1989.
[79] 陶夏新.我国新的地震区划编图和中国地震烈度区划图(1990).自然灾害学报,1992,1(1):99-107.
[80] 陶夏新.对新的全国地震区划中概率计算公式表达的讨论.世界地震工程,1992,2:33-36.
[81] 陶夏新.中国地震工程地质学之发展.世界地震工程.2000,22(4):1-6.
[82] 陶夏新,陶正如.我国地震灾害管理和金融手段.全国首届防震减灾工程学术研讨会论文集.2005.18-24.
[83] 陶正如,陶夏新.地震巨灾债券雏议.自然灾害学报,2004,13(6):138-144.
[84] 陶正如,陶夏新.基于工程地震风险评估的地震保险费率厘定. 自然灾害学报,2004,13(2):112-118.
[85] 陶正如.城市地震风险评估和管理中的两个问题.中国地震局工程力学研究所硕士论文.2003
[86] 仝春建.巨灾风险模型巨头紧盯中国市场.中国保险报,2005.12.2,第003 版.
[87] 王春峰著.金融市场风险管理.天津:天津大学出版社.2001.
[88] 王俊华,刘猛.论巨灾期权在我国的发展.重庆交通学院学报(社科版),2005,5(4):67-69.
[89] 王应贵.期权定价方法及其应用.复旦大学博士学位论文,1999.
[90] 魏海港,刘汉进.保险公司巨灾风险管理的新进展及其启示.中国软科学,2005,(6):82-85.
[91] 魏敏杰,田玲.巨灾风险证券化及在中国保险市场的应用.科技进步与对策,2003,10 月号:173-175.
[92] 温瑞智.基于GIS的防震减灾系统. 中国地震局工程力学研究所博士学位论文,1999.
[93] 吴军,孙晓骏.论巨灾债券在我国的发展.保险研究,2004,(11):19-21.
[94] 奚炜.Gamma 时变过程与 Black-Scholes 期权定价的定价偏差纠正.管理工程学报,2004,18(1):111-113.
[95] 谢丹.巨灾保险证券化与巨灾债券在我国的应用.对外经济贸易大学硕士学位论文,2004.
[96] 熊华,罗奇峰.国内外地震保险概况.灾害学,2003,18(3):61-65.
[97] 徐爱荣.巨灾债券的一种定价模型.统计与决策,2005,第 10 期(总第 199期):15-16.
[98] 薛红.金融衍生证券定价理论及其应用研究.西安交通大学博士学位论文,2000.
[99] 闫海峰,刘三阳.带有 Poisson 跳的股票价格模型的期权定价.工程数学学报,2003,20(2):35-40.
[100] 闫海峰,刘三阳.广义 Black-Scholes 模型期权定价新方法-保险精算方法.应用数学和力学,2003,24(7):730-738.
[101] 鄢家全.地震危险性分析的困惑与展望.国际地震动态,2005,第 6 期(总第 318 期):14-24.
[102] 杨宝华.中国发展巨灾保险债券的构想.上海师范大学学报(哲学社会科学版),2005,34(2):23-28.
[103] 杨恩斌.期权定价理论及其在我国的应用.中国海洋大学硕士学位论文,2003.
[104] 杨培涛.期权在保险中的应用研究.中南林学院硕士学位论文,2004.
[105] 杨招军,黄立宏.随机波动率与跳组合情形的期权问题闭式解.应用概率统计,2004,12(3):229-233.
[106] 姚壬元.巨灾风险证券化研究.中南财经政法大学学报,2004,第 5 期(总第 146 期):102-108.
[107] 易立新,胡晓,钟菊芳.基于 EPA 的重大工程设计地震动确定.地震研究,2004,27(3):271-276.
[108] 尹之潜.震害与地震损失的估计方法.地震工程与工程振动,1990,10(1):99-107.
[109] 尹之潜.地震灾害损失预测研究. 地震工程与工程振动,1991,11(4):87-96.
[110] 尹之潜著.地震灾害及损失预测方法.北京:地震出版社.1995.
[111] 应坚刚,金蒙伟编著.随机过程基础.上海:复旦大学出版社.2005.
[112] 战雪丽.期权及其定价的应用研究.天津大学硕士学位论文,2004.
[113] 张洪涛,郑功成主编.保险学.北京:中国人民大学出版社.2002.
[114] 张萍.债券期权定价的近似方法.东北大学硕士学位论文,2003.
[115] 张平,张晓晶.直面符号经济.北京:社会科学文献出版社.2003.
[116] 张淑英,张世英.非完全市场与完全市场证券组合比较研究.管理工程学报,2000,14(3):48-51.
[117] 张淑蓉.二项式巨灾权益卖权订价模型.台湾逢甲大学保险学系硕士论文,2004.
[118] 张伟忠.巨灾债券之理论与实际.国立中央大学财务管理研究所硕士学位论文.2000.
[119] 张振伟.关于期权定价理论和应用的问题研究.南京理工大学硕士学位论文,2000.
[120] 张志明.保险公司巨灾保险风险证券化初探.东北财经大学学报,2006,第3 期(总第 45 期):60-63.
[121] 赵红,李洋,张岚.我国保险资金投资渠道与模式探讨.北方经贸,2003,(5):43-45.
[122] 中国保险监督管理委员会.保险公司管理规定,保监会[2004] 3 号令.2004.
[123] 中国地震局工程力学研究所.上海市浦东新区震害预测报告(内部).2002.
[124] 中国地震局工程力学研究所.长春第三热电厂工程场地地震安全性评价报告(内部).2005.
[125] 中华人民共和国国家标准,GB17741-2005.工程场地地震安全性评价. 北京:中国标准出版社,2005.
[126] 钟菊芳,胡晓,易立新,吴胜兴.重大工程设定地震方法研究进展.水力发电,2005,31(4):22-24.
[127] 周少甫,张子刚,程斌武.期权定价理论和倒向随机微分方程.科技进步与对策,2000,17(10):100-102.
[128] 周荣义,廖可兵.GIS在巨灾保险风险管理中的应用研究.中国安全科学学报, 2004,14(9):56-59.
[129] 左惠强.基于GIS的地震危险性分析.中国地震局工程力学研究所博士学位论文,1996.
[130] Alan Punter. Securitization of insurance risk, AON. 1999.
[131] Alexander Muermann. Pricing cat insurance derivatives. Financial Markets Group Discussion Paper, 2001, No.400.
[132] Alexander Muermann. Actuarially Consistent Valuation of Catastrophe Derivatives. Wharton Working Paper, 2003, No.03-18.
[133] American Academy of Actuaries, Index Securitization Task Force With Research and Input From Casualty Actuarial Society & Valuation, Finance and Investments Committee. Evaluating the effectiveness of index-based insurance derivatives in hedging property/casualty insurance transactions. 1999.
[134] Andreas Müller. Integrated risk management. Munich Re ART Solutions, Munich Re Group. 2000.
[135] Andreas Richter. Catastrophe risk management-implications of default risk and basis risk. Proc. of 9th Symposium on Finance, Banking, and Insurance. 2002.
[136] Angelika Sch?chlin. On the market price of catastrophe risk: empirical evidence from catastrophe bonds. Switzerland: the University of St. Gallen, 2001.
[137] Angelos Dassios, Ji-Wook Jang. The pricing of catastrophe reinsurance contract & insurance derivatives using the Cox process with shot noise process as its intensity. Finance and Stochastics,2003, 7(1): 73-95.
[138] Anton Zaicenco, Vasile Alkaz. Urban seismic risk studies with utilization of GIS. Presentation on NATO Advanced ResearchWorkshop. 2005.
[139] Aurelia Zanetti, Rudolf Enz, Pamela Heck, Jay Green, Sonja Suter. Natural catastrophes and man-made disasters in 2003: many fatalities, comparatively moderate insured losses. Sigma, 2004, No.1.
[140] Aurelia Zanetti, Susanna Schwarz. Natural catastrophes and man-made disasters in 2005: high earthquake casualties, new dimension in windstorm losses. Sigma, 2006, No.2.
[141] B. Jensen. Option pricing in the jump-diffusion model with a randomjump amplitude: a complete market approach. Center for analytical finance, University of AARHUS-AARHUS School of Business. 1999. Working Paper Series No.42.
[142] C.Vorm Christensen. A new model for pricing catastrophe insurancederivatives. Center for analytical finance, University of AARHUS-AARHUS School of Business. 1999. Working Paper Series No. 28.
[143] Carol Hayek, Roger Ghanem. Impact of uncertainty in catastrophe losses on insurance derivatives. Proc. of 15th ASCE Engineering Mechanics Conference. 2002.
[144] Christofides Stavros. Pricing of catastrophe linked securities. ASTIN Colloquia, Norway. 2004.
[145] Christopher McGhee. Market update: the catastrophe bond market at year-end 2003. Guy Carpenter & Company Inc. and MMC Securities Corp. 2004.
[146] Cornell C. A.. Engineering seismic risk analysis. Bull. Seis. Soc. Amer., 1968, 58(3):1583-1606.
[147] David C. Croson, Howard C. Kunreuther. Customizing reinsurance and cat bonds for natural hazard risks. Proc. of the EuroConference on global change and catastrophic risk management. 1999.
[148] David Croson, Andreas Richter. Sovereign cat bonds and infrastructure project financing. Risk Analysis, 2003, 23(3): 611-626.
[149] David K. Bradford. Earthquake: the other insurance crisis. An Advisen QuickNote, Advisen Ltd, 2006.
[150] David S. Laster, Mayank Returi. Capital market innovation in the insurance industry. Sigma, 2001, No.3.
[151] Doherty N. Financial innovation in the management of catastrophe risk. Wharton Working Paper, 1997, No.98-12.
[152] Dwight M. Jaffee, Thomas Russell. Catastrophe insurance, capitalmarkets and uninsurable risks. Wharton Working Paper, 1995, No.96-12.
[153] Earthquake Engineering Research Institute. Securing society against catastrophic earthquake losses, A research and outreach plan in earthquake engineering. USA: Earthquake Engineering Research Institute. 2003.
[154] Eduardo Canabarro, Markus Finkemeier, Richard R. Anderson, Fouad Bendimerad. Analyzing insurance-linked securities. Fixed Income Research. Editor: Ronald A. Krieger. Goldman, Sachs & Co. 1998.
[155] Francis Parisi, Helene Herlihy. Modeling catastrophe reinsurance risk: implications for the CAT bond market. Standard & Poor’sStructured Finance Special Report. 1999.
[156] Franz Sauter .EERI committee on seismic risk, glossary of terms for probabilistic seismic-risk and hazard analysis. EarthquakeSpectra, 1984, (1): 33-40.
[157] Gene C.Lai. The future trends in risk management and insuranceworldwide: integration and globalization of risk management. Journal of Risk Management, 1999, 1(2): 1-13.
[158] Gao Mengtan. Probabilistic model of estimation of the expected loss from earthquakes based on the frequency of intensity. Proc. of 5th International conference on seismic zonation, Nice France. 1995. 249-256.
[159] Glenn Meyers. Managing the catastrophe risk. Casualty Actuarial Society Discussion Paper Program. Arlington, Virginia: Casualty Actuarial Society. 1995. 111-150.
[160] Glenn G. Meyers, John J. Kollar. On the cost of financing catastrophe insurance. Casualty Actuarial Society Discussion Paper Program. Arlington, Virginia: Casualty Actuarial Society. 1998. 119-148.
[161] Glenn Meyers, John Kollar. Catastrophe risk securitization: insurer and investor perspectives. Casualty Actuarial Society Discussion Paper Program. Arlington, Virginia: Casualty Actuarial Society. 1999. 223-272.
[162] Harrington Scott, Mann Steven, Niehaus Greg. Basis risk with catastrophe insurance derivative contracts. Proc. of 5th International Conference on Insurance Solvency and Finance, London. 1997.
[163] Henri Loubergé, Evis Kellezi, Manfred Gilli. Using catastrophe-linked securities to diversify insurance risk: afinancial analysis of cat bonds. Journal of Insurance Issues, 1999, 22(2): 125-146.
[164] Howard Kunreuther. Mitigation and financial risk management for natural hazards. Proc. of 6th International Conference on SeismicZonation. 2000. No.00099.
[165] Ishikawa Y., Kameda H. Probability-based determination of specific scenario earthquakes. Proc. of 4th International Conference on Seismic Zonation. 1991. 3-10.
[166] J. David Cummins, Hélyette Geman. An Asian Option Approach to theValuation of Insurance Futures Contracts, Wharton working paper, No.94-03
[167] J. David Cummins, Christopher Lewis, Richard D. Phillips. Pricing excess-of-loss reinsurance contracts against catastrophic loss. Wharton Working Paper, 1997, No.98-09.
[168] J. David Cummins, Richard D. Phillips. Applications of financial pricing models in property-liability insurance. The Handbook of Insurance Economics. Edited by Georges Dionne. Boston: Kluwer Academic Publishers. 1999.
[169] J. David Cummins, David Lalonde, Richard D. Phillips. The basis risk of catastrophic-loss index securities. Wharton Working Paper, 1999, No.00-22.
[170] J. David Cummins, Neil Doherty, Anita Lo. Can insurers pay for the “Big One”?, Measuring the capacity of an insurance market to respond to catastrophic losses. Journal of Banking & Finance, 2002, (26): 557-583.
[171] J. David Cummins, David Lalonde, Richard D. Phillips. Managing risk using index-linked catastrophic loss securities, Alternative risk strategies. Morton Lane Ed. London, England: Risk Books. 2002. 19-46.
[172] Kenneth A.Froot, The limited financing of catastrophe risk: an overview, NBER working paper series, No.6025, National Bureau of economic research,1997:
[173] Kevin P. Sheehan. Catastrophe securities and the market sharing of deposit insurance risk. FDIC Banking Review, 2003, 15(1): 1-16.
[174] Knut K Aase. An equilibrium model of catastrophe insurance futures and spreads. The Geneva Papers on Risk and Insurance Theory, 1999, 24(1): 69-96.
[175] Krzysztof Burnecki. Pricing catastrophe bonds in a compoundnon-homogeneous Poisson model with left-truncated loss distributions. Presentation on Mathematics in Finance Conference.2005.
[176] Lee Jin-ping. Pricing CAT bonds with moral hazard and basis risk.Doctor Thesis, Department of Finance, National Central University, 2001.
[177] Lee Jin-Ping, Yu Min. The pricing default-risky cat bonds with moralhazard and basis risk. The Journal of Risk and Insurance, 2002, 69(1): 25-44.
[178] Lewis V. Augustine. Catastrophe risk mitigation: a survey of methods. Casualty Actuarial Society Discussion Paper Program.Arlington, Virginia: Casualty Actuarial Society. 1998. 323-342.
[179] Marcel Grandi, Andreas Müller. Insurance derivatives –Convergence of capital markets and insurance markets. Munich Re ART Solutions, Munich Re Group. 1999.
[180] Martin Nell, Andreas Richter. Catastrophe index-linked securitiesand reinsurance as substitutes. Working paper series: finance & accounting, 2000, No.56.
[181] Martínez Torre-Enciso I., Laye J. Financing catastrophe risk in the capital markets. Int. J. Emergency Management, 2001, 1(1): 61-69.
[182] Michael Bayard Smith, L. Jaimie Pickles. An introduction to catastrophe insurance futures. Proc. of 4th AFIR International Colloquium. 1994. 817-844.
[183] Michael Monoyios. Lecture in Mathematical Finance,Department of Economic and Finance,Brunel Universuty. 2000.
[184] Morton N. Lane. Price, risk and rating for insurance-linked notes: evaluating their position in your portfolio. Sedgwick Lane Financial, LLC.1998.
[185] Morton N. Lane. Risk cubes or price, risk and ratings (PartII).Sedgwick Lane Financial, LLC.1999.
[186] Morton N. Lane. Pricing risk transfer transactions. ASTIN BULLETIN, 2000, 30(2): 259-293.
[187] Morton N. Lane. Analyzing the pricing of the 2001 risk-linked securities transactions. Lane Financial, LLC. 2001.
[188] Morton N. Lane. Rational and results with the LFC CAT bonds pricing model. Lane Financial, LLC. 2003.
[189] Morton N. Lane. The viability and likely pricing of “CAT Bonds”for developing countries. Lane Financial, LLC. 2004.
[190] Munich Re Group. World of Natural Hazards. 2000.
[191] Munich Re Group. Finite Risk Reinsurance and Risk Transfer to the Capital Markets-A Complement to Traditional Reinsurance. Munich Re ART Solutions, A series of Munich Re ART publications. 2000.
[192] Munich Re Group. Risk transfer to the capital market-Using the Capital Markets in Insurance Risk Management. Munich Re ART Solutions, A series of Munich Re ART publications. 2001.
[193] Munich Re Group. Annual review: Natural catastrophes 2005. Topics Geo, Knowledge series, 2006.
[194] Munich Re Group. The 1906 earthquake and Hurricane KatrinaSimilarities and differences-Implications for the insurance industry. Germany: WKD-Offsetdruck GmbH, Oskar-Messter-Strasse 16, 85737 Ismaning. 2006.
[195] Patricia Grossi, Domenoco del Re and Zifa Wang. The 1976 Great Tangshan Earthquake 30-year retrospective. Institute of engineering mechanics and Risk management solutions, Inc. 2006.
[196] Patricia Grossi. The 1679 Sanhe-Pinggu Earthquake implications for the modern-day Beijing region. Risk management solutions, Inc. 2007.
[197] Patrizia Baur, Rudolf Enz. Solvency II: an integrated risk approach for European insurers. Sigma, 2006, No.4.
[198] Peter Zimmerli, Junhua Zhou. Natural hazard in China-Ensuring long-term stability. Focus report, Swiss Re. 2006.
[199] Rainer Helfenstein, Thomas Holzheu. Securitization-new opportunities for insurers and investors. Sigma, 2006, No.7.
[200] Richard W. Gorvett. Insurance securitization: the development of a new asset class. Casualty Actuarial Society “Securitization of Risk” Discussion Paper Program. 1999.
[201] Richard W. Gorvett. Foreign exchange rate risk: institutional issues and stochastic modeling. Discussion Paper Program on Financial and Accounting Systems and Issues Associated with the Globalization of Insurance, 2001, 19-52.
[202] Robert E. Hoyt and Kathleen A. McCullough,Catastrophe Insurance Options: Are They Zero-Beta Assets?, Journal of Insurance Issues, 1999, 22, 2, pp147–163.
[203] Robert P. Eramo. Insurance Catastrophe Futures. Casualty Actuarial Society Discussion Paper Program. Arlington, Virginia: Casualty Actuarial Society. 1996. 47-60.
[204] Robin K.McGuire. Probabilistic seismic hazard analysis and design earthquakes: closing the loop. Bulletin of the Seismological Socirty of America, 1995, 85(5):1275-1284.
[205] Samuel H. Cox, Joseph R. Fairchild, Hal W. Pederse. Economic aspects of securitization of risk. ASTIN BULLETIN, 2000,30(1): 157-193.
[206] Samuel H. Cox, Hal W. Pedersen. Catastrophe risk bonds. NorthAmerican Actuarial Journal, 2001, 4(4): 56-82.
[207] Sara Borden, Asani Sarkar. Securitizing property catastrophe risk. Current issues in economics and finance, 1996, 2(9): 1-6.
[208] Sebastian Jaimungal, Tao Wang. Catastrophe options with stochastic interest rates and compound Poisson losses. Insurance: Mathematics and Economics,2006, 38(3): 469-483.
[209] Sergei Esipov, Dajiang Guo. Portfolio based pricing of residualbasis risk with application to the S&P500 put options. Discussion Paper Program on Insurance in the Next Century, 2000, 43-66.
[210] Shah Hemant, Nakada Peter. Managing and financing catastrophe risk: the need for an enterprise-wide, value-oriented approach. Published in Alternative Insurance Capital, Issue 95, 1999.
[211] Shaun S. Wang. A universal framework for pricing financial and insurance risks. ASTIN BULLETIN, 2002, 32(2): 213-234.
[212] Shaun S. Wang. Cat Bond Pricing Using Probability Transforms. The Geneva Papers: Etudes et Dossiers, special issue on “Insurance and the State of the Art in Cat Bond Pricing”, 2004, No.278, 19-29.
[213] Shigeyuki Goto. Study on the interactive approach between insurance and capital markets for catastrophe risks. Center on Japanese Economy and Business, Columbia Business School, 1997, Working Paper 140.
[214] Stephen P. D'Arcy, Virginia Grace France, Richard W. Gorvett. Pricing catastrophe risk: could cat futures have coped with Andrew?. Casualty Actuarial Society “Securitization of Risk” Discussion Paper Program. 1999.
[215] Steven Kaplan, Guillaume Lefebvre. CAT bonds: tax treatment of an innovative financial product. Journal of Taxation of Financial Institutions, 2003, 16(4): 5-16.
[216] Swiss Re. Alternative risk transfer (ART) for corporations: apassing fashion or risk management for the 21st Century?. Sigma, 1999, No.2.
[217] T. Saeki, H. Tsubokawa, S. Midorikawa. Estimation of earthquake economic loss to residents using GIS. Proc. of 7th National Conference on Earthquake Engineering. 2002. No.00055.
[218] Takada T, Okumura T, Hirose J, Muramatsu K, Taki S, Ishii K.Probabilistic scenario earthquakes for seismic design-comparison of two identification procedures. Proc. of OECD-NEA workshop on Seismic Risk. 1999.
[219] Thomas Holzheu, Kurt Karl, Mayank Raturi. The picture of ART. Sigma, 2003, No.1.
[220] Thunderbird. Catastrophe insurance exposure and hedging: structure and issues. The American Graduate School of International Management.2000. No.B06-00-0023.
[221] Torben J. Andersen. Innovative financial instruments for natural disaster risk management. Technical Papers Series of Inter-American Development Bank, Sustainable Development Department, Washington, D. C. 2002.
[222] United States General Accounting Office. The role of risk-linkedsecurities and factors affecting their use. Report to the Chairman, Committee on Financial Services, House of Representative. 2002. GAO-02-941.
[223] Vivek J. Bantwal,Howard C. Kunreuther. A cat bond premium puzzle?. Wharton Working Paper, 1999, No.99-26.
[224] Walter Kielholz, Alex Durrer. Insurance derivatives and securitization, New hedging perspectives for the US cat insurance market. The Geneva Papers on Risk and Insurance, 1997, 22(No.82):3-16.
[225] William Dubinsky, David Laster. Insurance-linked securities. Swiss Re Capital Market Corporation. 2003.
[226] Xie L., Tao X. et al. A GIS based earthquake losses assessment and emergency response system for Daqing oil field. Proc. of 12th WorldConference on Earthquake Engineering. 2000. No.0091.
[227] Yu. Baryshnikov, A. Mayo, D. R. Taylor. Pricing of CAT bonds. Working paper of School of Computational and Applied Mathematics. 2001.