摘要
金融风险度量的核心是价格波动性的估计和预测。本文以金融市场波动的区制关联性与风险度量为题,在对金融市场波动性模型的理论与方法综述的基础上,对金融市场波动的相关性、区制关联性、风险度量问题进行了研究。首先,以我国股市波动为研究对象,考虑了交易制度的变迁,采用不同频率不同阶段的数据样本,运用马尔可夫区制转移模型系统分析了我国股市波动区制在风险识别上的应用。其次,对2005年我国汇率形成机制的市场化改革和证券市场的股权分置改革以来的日度数据样本,基于单变量马尔可夫区制转移模型对股市同利率、汇率波动的区制关联性进行分析和比较,并采用了两种方法度量市场间波动的区制相关性。第三,基于二元向量区制转移模型,分别分析了汇率、利率与股市波动率的区制关联性并对其显著性进行了检验,进而比较了波动性预测的效果。最后,以我国股市波动为例对VaR度量结果进行了系统的比较与分析。本文对我国金融市场波动性分析与风险度量的创新应用是:采用了单变量和二元向量的马尔可夫区制转移模型,按交易制度分阶段对我国股市波动的区制性和风险识别进行了分析;研究了股市和汇率波动的区制关联性在波动预测和风险度量方面的应用;并对股市波动VaR度量的双向影响进行了深入研究。这些研究对我国金融市场风险的度量与预警以及金融市场的多样化和国际化均具有重要的理论和现实意义。
China's stock market, bond market, foreign exchange market, money market has gradually improve and strengthen the securities market, the successful reform of non-tradable shares, the formation mechanism of exchange rate marketization reform deeply, various financial product development and innovation of China's financial markets, to enjoy unprecedented development. Financial market volatility system analysis and characterization, risk measurement is an important problem of risk management, use econometric model on the financial market fluctuations process and risk degree of empirical analysis, research situation of China's financial market turmoil has important significance and characteristics.
Financial risk measurement is the core of the estimated price volatility and prediction. Based on the model of financial market volatility theory and method, on the basis of the review of the financial market fluctuations, the correlation relationship risk measurement system, the problem is studied. From two aspects, one is to study the single market fluctuations cycle system, depicting the market operation cycle and the corresponding risk characteristics, the other is established between multiple market system, and describes the impact between market conduction and interconnected.
The specific contents and main conclusions are as follows:
The first chapter, includes the introduction. Mainly discussed the background and significance of topic, For the domestic and foreign relevant literatures were reviewed, And the risk management method and measure technology for review and overview, The paper gives content and structure, Clearly this in order to solve the problem and innovation.
The second chapter, financial market volatility model theory development and methods. This chapter presents the text involved in the empirical research method. The first day is one of the financial market volatility model research progress, The second section of GARCH model and SV model theory and method, The third section presents markov switching model theory and method.
The third chapter, China's stock market volatility of system analysis and empirical research. In China's stock market volatility for research object, by choosing different frequency of different sections of data sample, markov system of different structure and markov switching model estimation method is different, and the system are analyzed in detail fluctuations in risk identification system, the application that: (1) on the research of data weeks, considering the system structure of the model, the ARCH effect and non-normal sex are not significant. (2) for our market monthly and weekly return volatility Gibbs sequence, sampling method is the maximum likelihood estimation method for system changes reflect risk has better recognition. (3) in our country after 1995 stock market fluctuations on data and data of the system, the system is more suitable to two regime. The year of 1995 is China's trading system is the important points and temporal variation of structural change significantly breakpoint. Gibbs (4) sampling estimation method of China's stock market for the whole week yields sample data and better fitting.
The fourth chapter, the Chinese stock market fluctuations in currency exchange market, with the analysis of system and empirical research. In this paper based on single variable markov switching model with money, foreign exchange market volatility system analysis and comparison, the two methods of measurement and stock markets and foreign currency market volatility and the regime correlation. Select our July 2005 to the exchange rate, the interest rate since the length of the same sample with the stock market volatility data, an empirical research. By comparison, rates faster appreciation and raising interest rates as the control policy for the stock market fluctuation, and the high variance, from exchange rate fluctuations regime correlation is the interest rate volatility area has greater impact, namely currency controls. Specific found on Chinese stock market respectively, and the interest rate volatility in the area of relevance, (1) stock and interest rate volatility of high variance area from correlation coefficient sequences from low variance area correlation coefficients sequence, (2) stock and interest rate volatility regime correlation probability correlation coefficient sequences original sequence correlation coefficients. About the Chinese stock market and the rate of exchange rate fluctuations regime correlation, (1) the relationship exchange rate fluctuation rate compared with the stock market fluctuations can make high variance correlation zone; (2) when selecting the appropriate models to estimate the structure, the stock market and the high rate of exchange rate fluctuations regime variance correlation coefficients also can make area than low variance correlation coefficient sequences, (3) stock and currency volatility multi-member regime probability correlation coefficient sequences original sequence correlation coefficients.
Chapter 5, based on vector system of Chinese stock market volatility markov switching model of forecasting and compared. Based on the binary vector markov switching model are analyzed, the stock exchange, the interest rate volatility and the system and inspection and compared in this basis, the four models to predict the stock market volatility structure comparison, mainly as follows: (1) to found in July 2005, since it's data, exchange rate volatility degree of area and the variance of stock market volatility relationship between system by significant test, the average rate of exchange rate fluctuations of market system changes of variance volatility over 80% of the vector, both for stock markov switching model of the sequence of volatility fitting effect is good. (2) on July 2005 to the degree of data, since the interest rate volatility of the stock system and mean variance volatility multi-member regime relevance not through significant test, interest rate volatility of the stock market fluctuation multi-member regime average rate of no significant effect of variance area, two vector markov switching model of stock market volatility and forecasting effect are inferior to exchange rate and the stock market volatility vector markov switching model. This chapter (3) from the perspective of relevance that area from July 2005 to type in the price of the monetary policy and exchange rate, the interest rate on the stock market tool to predict the effects of volatility multi-member regime, significant correlation. (4) structure of exchange rate fluctuations average rate for the stock market volatility multi-member regime and the variance of vector multi-member regime area of our country, and markov switching model of different sample data, degrees, estimated interval prediction effect, the stability is good.
Chapter 6, based on the nonlinear model of Chinese stock market volatility VaR measurement and compared. Based on Chinese stock market volatility VaR measurement results from the basic theory, VaR by choosing different frequency interval samples and data to construct VaR model back-test system for the Chinese stock market is the most representative of the Shanghai index in 95%, 97.5% and 99% VaR model measurement results of the comparison and analysis, we find that for the degree of data, (1) in volatility estimates on the choice of model, considering the ARCH effect and non-normal sex assumptions on the efficiency of the VaR model can have certain. (2) for the Chinese stock market volatility VaR measurement system, the ARCH effect weakens when adopting markov switching model, the measurement results of the model of MS variance are basically the same as GARCH model. (3) for our market volatility VaR measurement system, using vector model including asymmetric coefficient of the ARCH effect, and are not significant in the sample under the condition of the shorter length measurement results, it can return through inspection, relative to other model has obvious advantages. For weeks, (1) degree of data on Chinese stock market volatility VaR measurement, considering one-way wave SWARCH model for risk, and MS variance model results are basically the same, (2) for the Chinese stock market volatility VaR measurement can be empty in margin trading mechanism as low-class system should be considered for the risk of two-way fluctuation, MS variance and SWARCH model of measurement results are good model.
Based on the system of financial market fluctuations in collecting, sorting out the area of relevance theory and empirical risk measurement and, on the basis of the research literatures in Chinese stock market as samples, the main research with foreign exchange market and money market fluctuations, and the system based on VaR measurement and compared. Based on the method of comparison on the main work is: (1) for the Chinese stock market volatility and prediction of the single variables on markov switching model and the MLE or GIBBS sampling estimation method in identifying the fluctuation of the different areas. (2) for our stock exchange in the currency market fluctuation, and the correlation analysis, the dual vector system markov switching model of mean and variance multi-member regime area of two kinds of structure, and examines the relationship between their multi-member regime. (3) in the study of China's stock market volatility and foreign exchange, money markets, not the relevance to the original sequence of correlation analysis, probability analysis and forecast for the thought that the area between the market research, and then used for making risk measurement analysis.
Based on China's financial market volatility, system analysis and correlation of the main risk measurement results and innovative applications are: (1) through the SWARCH model and parameters of the model, MS variance MLE estimation and Gibbs sampling method in the financial market volatility description on the comparison of difference and the quality, there is significant in Chinese stock market transition phases and characteristics. (2) in China, foreign currency market shares and the operation characteristic, the three measures market operation cycle and the main area of the relationship between, By selecting and measure liquidity to markets and volatility of dynamic indexes, the study found that the exchange rate of the stock market volatility variance explained, and then makes a stronger ability of model prediction ability greatly improved. (3) on Chinese stock market fluctuations in currency exchange, and the correlation analysis, the dual vector switching model of mean or variance regime structure, and tests the relationship between market volatility regime of significant. (4) through the segment and choose a different frequency of data transfer system, vector model has better in VaR measurement, can apply the effect after the financial crisis of the volatile market conditions of reality. And according to the risk degree and the Chinese financial market operation characteristic of the important facts, countermeasures and Suggestions are experienced in China, further turbulence in financial markets and economic growth cycle lays the foundation of relevance.
引文
14 Artzner P, Delbaen F, et al. Coherent measures of risk [J]. Math Finance, 1999, 9(3):203～228.
15 Jaschke, Stefan R., 2001, Quantile-VaR is the Wrong Measure to Quantify Market Risk for Regulatory Purposes, Working Paper, Weierstrass-Institut fur Angewandte Analysis and Stochastic.
16赖柏志.关联结构(Copula)在信用风险管理之应用,风险研究小组,Working Paper,2004.
17Lopez & Marc. Evaluating Credit Risk Models. Journal of Banking and Finance, 1999, 24: 151~165.
18敬永康.信用风险之模型效果检验-以台湾金融业为例.货币观测与信用评等,2002,35:20~126.
[1] A kging V. Distribution properties of Latin American black market exchange rates[J]. Journal of international Money and Finance,1998,7: 37-48
[2] Abiad, Abdul. Early Warning Systems for Currency Crises: A Survey and a Regime- Switching Approach [R]. IMF Working Paper, 2003, 03-32.
[3] Artzner P, Delbaen F, et al. Coherent measures of risk [J]. Math Finance, 1999, 9(3):203-228.
[4] Artzner P, Delbaen F, et al. Thinking coherently [J]. Risk,1997,10(11):68-71.
[5] Basle Committee on Banking Supervision, Amendment to the capital accord to incorporate market risks. Basle: Bank for International Settlements, 1996b.
[6] Basle Committee on Banking Supervision, Supervisory Framework for the use of "Backtesting" in Conjunction with the Internal Models Approach to Market Risk Capital Requirements, BIS, Basle, Switzerland, 1996a.
[7] Baumol, W. An expected gain confidence limit criterion for portfolio selection [J]. Management Science, 1963,10(1).
[8] Baumol, W.J. The transactions demand for cash: An inventory theoretic approach. Quarterly Journal of Economics, 66, 1952, 545-556.
[9] Bekaert G.and Grenadier S.R.Stock and Bond Pricing in an Affine Economy.Columbia Business School Working Paper,2000.
[10] Bernell, Kenneth, Stene, Risk Returnand Equilibriium[M]. The MIT press Cambridge, Massachusetts, and London, England, 1970.
[11] Bollerslev T. Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 1986, 31:307-327
[12] Bossaerts,P.Common nonstationary components of asset prices[J].Journal of Economic Dynamics and Control,1988,(12):347-364。
[13] Campbell,J.and G.Taksler.Equity volatility and corporate bond yields[J].Journal of Finance,2002,57:1132-1169.
[14] Campbell,John Y.,and Ammer,John.What Moves the Stock and Bond Markets?A Variance Decomposition for Long-Term Asset Returns[J].Journal of Finance,1993,48(1):3-37.
[15] Chan,Kam C.,Norrbin,Stefan C.and Lai,Pikki.Are stock and bond prices collinear in the long run? [J].International Review of Economics&Finance,1997,6(2):193-201.
[16] Clayton D.G. A Model for Association in Bivariate Life Tables and Its Application inEpidemiological Studies of Familial Tendency in Chronic Disease Incidence [J]. Biometrika,1978,(65).
[17] Danielsson J, Hartmann P, C G de Vries. The cost of conservatism: Extreme returns, value-at -risk, and the Basle 'Multiplication factor[J]. Risk, 1998, 11:101-103.
[18] David Alexander and Pietro Veronesi.Inflation and Earnings Uncertainty and the Volatility of Asse Prices:An Empirical Investigation.University of Chicago Working Paper,1999.
[19] David G.Luenberger. Investment Scienc[M]. Oxford University Press 1998
[20] Davision R A, Resnick S I. Tail estimates motivated by extreme value theory. Ann. Statist. 1984,12: 1467 -1487.
[21] Diebold F X. Horizon problems and extreme events in financial risk management[R]. University of Pennsylva-nia,1998.
[22] Dirk Baur and Brian M.Lucey.Flight-to-quality or Contagion?An Empirical Analysis of Stock-bond Correlations.SSRN Working Paper,2006.
[23] Dodd E L. The greatest and least variate under general laws of error [J] .Trans,Amer,Math,Soc,1923,25:525-539.
[24] Donald S Watson & Mary A Holman. Price Theory and Its Uses. Houghton Mifflin Company, Boston, 1997.
[25] Dowd, K. Measuring market risk [M]. Wiley finance series. John Wiley&Sons Ltd. 2002.
[26] Embrechets, P., A. Mcneil, and D. Straumann, Correlation and Dependence in Risk Management: Properties and Pitfalls. 1999.
[27] Embrechts P, Resnick S, Samorodnitsky G. EVT as a risk management tool[C].The thirth International Astin Colloquium in Cairns,1996.
[28] Embrechts P, Resnick S, Samorodnitsky G. Living on the edge[J]. Risk ,1998,(1):96-100.
[29] Engle , Robert F. and Victor K., Ng Measuring and Test the impact of News on Volatility[J], Journal of Finance , 1993 ,48: 1022-1082.
[30] Engle and K.Kroner. Multivariate Simultaneous Generalized ARCH Econometric Theory, 1993, 11, 122-150.
[31] Engle R. Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation [J]. Journal of Finance, 1982, 50(3): 821-851
[32] Engle, R. F., Autoregressive conditional heteroscedasticity with estimate of the variance of United Kingdom inflation[J], Econometrica, 1982, 50, 987-1008.
[33] Engle, RF, Manganelli, S., 1999. CAViaR: conditional value at risk by quantile regression. NBER. Working Paper No. 7341.
[34] Engle, Robert F., and C.W.J.Granger.1987. Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55:251-76.
[35] Engle,R.F. Susmel ,R. Common volatility in international equity markets. Journal of Business and Economic Statistics,1993,11,167-176
[36] F Black ,M C Jensen and M Scholas . The Capital Asset Pricing Model: Some Em-pirical Tests. In Ed: M C Jensen. Studies in Theory of Capital Markets. New York: praeger, 1972.
[37] Fallon W. Calculating Value-at-Risk[R]. Working paper. The Wharton Financial Institutions Center. University of Pennsylvania, 1996,96-49
[38] Farrar, Donald E. The Investment Decision Under Uncertainty[M]. Englewood Cliffs, N.J, : Prentice-Hall,1962.
[39] Fisher R A, Tippett L H C. Limiting forms of the frequency distributions of the largest of smallest member of a sample [J].Proceeding of Cambridge Philosophical Society,1928,24:180-190
[40] Fleming Jeff,Chris Kirby and Barbara Ostdiek.Information and Volatility Linkages in the Stock,Bond,and Money Market[J].Journal of Financia Economics,1998,49:111-137.
[41] Fornari, F. and Mele, A., Sign-And Volatility-Switching Arch Models : Theory and Applications To International Stock Markets[J] , Journal of Applied Econometrics, 1997, 12, 49-65.
[42] Frechet M. Sur la loi de probabili de l'ecart. maximum [J].Ann ,Soc,Polonaise Math,Cracow,1927,(6):93-116.
[43] Friedman, Milton, and L.J. Savage. The Utility Analysis of Choices[M]. Journal of Political Economy,August 948.
[44] Frye, J. Monte Carlo by day.Risk,1998,(11),66-71
[45] Garbade K .Assessing risk and capital adequacy for treasury securities[R]. Topics Money and Securities Markets. Bankers Trust, 1986
[46] Genest and Rivest, Statistical inference procedures for bivariate Archimedean Copulas [J]. Journal of the American Statistical Association,1995.
[47] Genest C. Frank’s family of bivariate distributions [J]. Biometrika,1987,(74).
[48] Gerald P Dwyer & R W Hafer. The Stock Market .Bubbles Volatility and Chaos, NBER, 1983.
[49] Gilbert, C. L. , Professor Hendry’s Econometric Methodology[M], Oxford Bulletin of Economics and Statistics , 1986, 48, 283-307.
[50] Gnedenko. Sur la distribution limite du maximum d’une serie aleatoire[J] Ann. Math, 1943,44 :423-53.
[51] Gumbel E J .Statistics of extremes [N]. New York: Columbia University Press, 1958.
[52] Gumbel E.J. Bivariate exponential distributions [J]. Journal of the American Statistical Association,1960,(55).
[53] Hann L D.On regular variation and its application to the weak convergence of sample extremes[J].Mathematical Centre Tracts,1970,32: 523-528.
[54] Harmo,Y., and Masulis ,R. W. , Correlations in price changes and volatility across international stock markets Review of Financial Studies,1990,3,281-307
[55] Harry Joe. Multivariate Models and Dependence Concepts [M]. Chapaman&Hall: 1997.
[56] Hosking J R M, Wallis J R, Parameter and quantile estimation for the generalized Pareto distribution [J] .Techno metrics, 1987, 29: 339-2349
[57] Hsieh D. Implications of nonlinear dynamics for financial risk management [J]. Journal of Financial and Quantitative Analysis, 1993, 28(1): 41-64
[58] J.P. Morgan/Reuters. RiskMetrics- Technical Document: 4th edition [M]. New York: RiskMetrics Group, 1996.
[59] Jackel, P., Monte Carlo Methods in Finance. Wiley finance series. 2002: JOHN WILEY&SONS,LTD. 219.
[60] James D. Hamilton, Raul Susmel. Autoregressive conditional heteroskedasticity and changes in regime [J]. Journal of Econometrics. 1994,(64).
[61] James D. Hamilton. Time Series Analysis [M]. The Princeton University Press,1994.
[62] Jansen D W,Vries C G D. On the frequency of large stock perspective[J].The Review of Economics and Statistics,1990,(5):18-24.
[63] Jenkinson A.F. The frequency distribution of the annual maximum (or minimum) values of meteorological elements [J]. Quarterly Journal of the Royal Meteorological Society, 1955,87: 158-171.
[64] Jeon,B.N.,& Von Furstenberg ,G.M. Growing international co-movement in stock price indexes. Quarterly Review of Economics and Business,1990,30,15-30
[65] Joe, H., Multivariate Models and Dependence Concepts. 1997: Chapaman&Hall.
[66] Jorion P. Value at Risk: The new benchmark for controlling market risk. New York: McGraw-Hill Companies, Inc, 1997
[67] JP Morgan. Risk Metrics- Risk Metrics monitor.1st Quarter, 1996
[68] JP Morgan. Risk Metrics- Risk Metrics monitor.2nd Quarter, 1996
[69] JP Morgan. Risk Metrics- Risk Metrics monitor.3rd Quarter, 1996
[70] Kahneman D. and Tversky A., Prospect Theory : An Analysis of Decision Making under Risk[J], Econometrica , 1979, Vol. 47 , No. 2 , March: 263-291.
[71] Kam C.,Chan,Stefan C.Norrbin and Pikki Lai.Are Stock and Bond Prices Collinear in the Long Run? [J].International Review of Economics and Finance,1997,6(2):193-201.
[72] Kim, Chang Jin, and Charles R. Nelson. State-Space Models with Regime Switching[M]. Cambridge, Massachusetts: MIT Press, 1999, 209-219.
[73] Koedijk K G. The estimates of East European exchange rates[J].Journal of Business & Economic Statistics,1992,(10): 83-89.
[74] Koedijk K G. The tail index of exchange rate returns[J].Journal of International Economics,1990,29:93-108.
[75] Kristin J.Forbes and Menzie D.Chinn.A Decomposition of Global Linkages in Financial Markets over Time.NBER Working Paper,2003.
[76] Krolzig, H.-M.. Markov Switching Vector Autoregressions: Modelling, Statisitical Inference and Application to Business Cycle Analysis [M]. Berlin: Springer, 1997.
[77] Kupiec.P: Techniques for Verifying the Accuracy of Risk Measurement Models,Journal of Derivatives,2,(1995),73-84 .
[78] Kwan·S.Firm-specific Information and the Correlation between Individual Stocks and Bonds[J].Journal of Financial Economics,1996,40:63-80.
[79] Kyung-Chun Mun. Volatility and correlation in international stock markets and the role of exchange rate fluctuations. Journal of international financial markets, 2007, 17:25-41.
[80] Lamoureux, C.G., Lastrapes, W. D. Persistence in variance, structural change and the GARCH model [J]. Journal of Business and Economic Statistics, 1990, 8, 225-234.
[81] Latha Ramchand, Raul Susmel. Volatility and cross correlation across major stock markets[J]. Journal of Empirical Finance, 1998, 5: 397–416
[82] Lester G.Telser. Safety First and Hedging. Review of Economic Studies,23,1955,1-16
[83] Lindskog, F., Modeling Dependence with Copulas and Applications to Risk Management. 2000.
[84] Linterner J., Security Prices,Risk,and Maximal Gains From Diversification[J], The Fournal Finaco 1965,No.4.
[85] Longin F M. From value at risk to stress testing: the extreme value Approach [J].Journal of Banking and Finance, 2000, 24:1097-1130
[86] Longin F M. The asymptotic distribution of extreme stock market returns [J].Journal of business, 1996, 69(3):383-408
[87] Longin FM. From Valuate at Risk to stresstesting: the extreme value approach[J]. Journal of Banking and Finance, 2000, 24:1097-1130.
[88] Markowitz H.Portfolio Selection. Journal of Finance, 1952(7):77-91.
[89] Mcneil A , Frey R. Estimation of financial time series: an extreme value approach[R].Working paper ETHZ,1998.
[90] Mcneil A. Estimating the tails of loss severity distributions using EVT[J]. AstinBulletin,1997,27:117-137.
[91] Melchiori, M.R., Which Archimedean Copula is the right one? YieldCurve, 2003.
[92] Mendes, B.V.d.M. and R.M.d. Souza, Measuring financial risks with copulas. Elsevier, 2004.
[93] Mises L. La Theorie dite Autrichienne du cycle economique. Bulletin Periodique. Societe Belge d'Etudes et d'Expansion.,1936, 103.
[94] Montfort V, Witter J V. Testing exponentially against generalized Pareto distribution. Journal of Hydrology, 1985,78: 305-315.
[95] Morgan Guaranty Trust. RiskMetrics– Technical Document[M]. 3rded. NewYork: Morgan Guaranty Trust, 1995
[96] Neil D. Pearson, Charles Smithon. VaR the state of play. Review of Financial Economics,2002,11: 175-189
[97] Nelsen, An Introduction to Copulas. 1999: Springer.
[98] Nelsen, D.B. Conditional heteroscedasticity in asset returns: a new approach [J]. Econometrica, 1991, 59, 347-370.
[99] Nelson D B., Stationary and Persistence in the GARCH(1,1)model[J], Econometric Theory, (6):318-334.
[100] P H Cootner. The Random Character of Stock Market Prices. Cambridge, Mass:MIT Press, 1964.?
[101] Peter De Goeij and Wessel Marquerig.Modeling the Conditional Covariance between Stock and Bond Returns:A Multivariate GARCH Approach.SSRNWorking Paper,2002.
[102] Pickands J. Statistical inference using extreme order statistics[J].Annals of statistics, 1975,(3):119-131.
[103] Pyle D. H. and S. J. Turnovsky, Safety First and Expected Utility Maximization in Mean-Standard Deviation Portfolio Analysis. Review of Economics and Statistics, 52, 1970,75-81.
[104] Rahman M.and Mustafa M.Dynamic linkages and Granger causality between short-term US corporate bond and stock market[J].Applied Economics Letters,1997,(4):9-91.
[105] Rank, D.J. Copulas in Financial Risk Management, in A thesis submitted for the diploma in Mathematical Finance [Z]. Oxford, Editor. 2000.
[106] Risk Metrics 1996, Technical Document, Morgan Guaranty Trust Company of New York.
[107] Robert Connolly,Chris Stivers and Licheng Sun.An International Analysis of Stock-to-Bond and Stock-to-Stock Return Comovement with Time-varying Uncertaity.SSRN Working Paper,2003.
[108] Robert F. Engle , David M. Lilien , and Russell P. Robins , Estimating Time VaryingRisk Premia in the Term Structure: the ARCH-M model[J], Econometrica, 1987,(55):391-407.
[109] Roy A. D. Safety First and the Holding of Assets. Econometrica,20,1952,431-449.
[110] S A Ross, R W Westerfield and J F Jaffe. Corporate Finance. Richard D. IRWIN, Inc, 1990.
[111] Sebastian Edwards, Raul Susmel. Volatility dependence and contagion in emerging equity markets[J], Journal of Development Economics, 2001, 66: 505-532
[112] Sharpe W F. A Linear Programming Approximation for the General Portfolio Selection Problem. Journal of financial and Quantitative Analysis, December, 1971.
[113] Sklar A. Functions de repartition a n dimensions et leurs marges [J]. Publications de l’Institut deStatistique de l’Universite de Paris, 1959,8:229-31.
[114] Smith, M.D., Modeling sample selection using Archimedean copulas. 2002.
[115] Speman C. The proof and measurement of association between two things [J]. American Journal of Psychology,1904,(15).
[116] Splus 6 S+FinMetrics User's Guide [M]. The Insightful Corporation, 2003.
[117] Statistics Toolbox User’s Guide [M]. The MathWorks, Inc. 2002.
[118] Stivers Chris and Licheng Sun.Stock Market Uncertainty and Bond Returns:Evidence of Flight-to-Quality?University of Georgia Working Paper,2002.
[119] Suk-Joong Kim,Fari Moshirian and Eliza Wu.Evolution of International Stock and Bond Market Integration:Influence of European Monetary Union[J].Journal of Banking and Finance,2006,30(5):1507-1534.
[120] Terence C.Mills, The Econometric Modelling of Finacial Time Series, Original english Language Edition Published By Cambridge University Preess,1999.
[121] Torben G. Andersen, Tim Bollerslev,Peter F. Christoffersen, Francis X. Diebold, Volatility Forecasting, http://www.nber.org/papers/w11188.
[122] Vilson,T.C.(1998): Calculating Risk Capital. The handbook of risk management and analysis ,John Wiley &Sons. New York 1993-232 .
[123] Vincent Bodarta, Paul Reding. Exchange rate regime, volatility and international correlations on bond and stock markets[J], Journal of International Money and Finance, 1999, 18: 133-151.
[124] W F Sharpe. Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk .Journal of Finance, 1964(19):425-442.
[125] Weibull W.A statistical distribution of wide applicability[J].J. Appl. Mechanics ,1951,18:293-297.
[126] Weibull W.A statistical theory of strength of materials[C].Proc,Ing,Vetenskapsakad,1939,151
[127] William H. Greene. Econometric Analysis,5th edition [M]. Prentice Hall,2003.
[128] William R.Gebhardt,Soeren Hvidkjaer and Bhaskaran Swaminathan.Stock and Bond Market Interaction:Does Momentum Spill over?[J]. Journal of Financial Economics, 2005,75:651-690.
[129] Wilson T. Plugging the GAP[J]. Risk, 1994, 7(10): 74-80.
[1]毕永松.后WTO背景下的我国商业银行市场风险分析及其管控设计[J].华东经济管理, 2007(6).
[2]陈工孟,芮萌.中国股票市场的股票收益与波动关系研究[J].系统工程理论与实践,2003,(10):12-21.
[3]陈敏.中国股票市场弱有效性的统计套利检验[J].系统工程理论及其应用,2007。
[4]陈千里,周少甫.上证指数收益的波动性研究[J].数量经济技术经济研究,2002,(6):122-125.
[5]陈守东,陈雷,刘艳武.中国沪深股市收益率及波动性相关分析[J].金融研究,2003,(7):80-85.
[6]陈守东,韩广哲,荆伟.主要股票市场指数与我国股票市场指数间的协整分析[J].数量经济技术经济研究,2003,(5):124-129.
[7]陈守东,马辉,才元.上海证券市场分阶段收益率与波动性的实证分析[J].吉首大学学报(社会科学版), 2006(4).
[8]陈守东,马辉,才元.中国房地产业与经济增长的实证研究[J].未来与发展,2006(7).
[9]陈守东,马辉,穆春舟.中国金融风险预警的MS-VAR模型与区制状态研究[J].吉林大学社会科学学报,2009(1).
[10]陈守东,马辉,王晨.中国金融脆弱性指数的合成及风险预警系统的建立[R]. 21世纪数量经济学第八卷,北京方志出版社,2008年版.
[11]陈守东,孟庆顺,杨兴武.中国股票市场的有效性检验与分析[J].吉林大学社会科学学报,1998(2):69-74
[12]陈守东,杨莹,马辉.中国金融风险预警研究[J].数量经济技术经济研究,2006(7).
[13]陈守东,俞世典.基于GARCH模型的VaR方法——对中国股市的分析[J].吉林大学社会科学学报,2002,4:11-17.
[14]陈守东,俞世典.基于GARCH模型的VaR方法对中国股市的分析[J].吉林大学社会科学学报,2002,(4):11-17
[15]陈守东,王晨,孙叶萌.中国股市波动性的MS方差模型和SWARCH模型比较研究[C].中国现场统计研究会第十三届学术年会论文集,武汉,2007.
[16]陈松林.金融风险监测与预警研究[J].经济科学,1997(3).
[17]陈卫华,张睿.基于“可能—满意度”法的金融危机预警系统的构建与应用[J].国际金融研究,2007年(4).
[18]陈学军.金融危机早期预警模型综述[J].金融纵横,2008(7).
[19]陈云.美国次级按揭贷款危机及对我国金融风险管理的启示[J].生产力研究, 2008(21).
[20]丁志国.证券市场有效性研究[D].吉林大学博士学位论文, 2004.
[21]董小君,李宇航.中国金融体系脆弱性与系统性金融风险[J].国家行政学院学报,2006(5).
[22]董彦岭,张继华.货币危机与银行危机共生因子实证分析——国别比较的视角[J].财经研究,2009(1).
[23]范英. VaR方法及其在股市风险分析中的应用初探[J].中国管理科学,2000(3):26-29.
[24]冯芸、吴冲锋.货币危机早期预警系统[J].系统工程理论方法应用,2002(11).
[25]付江涛,王方华.货币危机预警指标体系的构建及实证分析[J].世界经济研究,2004(12).
[26]葛新权.泡沫经济计量模型研究与应用[J].数量经济技术经济研究,2005(5).
[27]顾锋锋.经济转轨国家银行业危机问题研究[D].华东师范大学大学博士论文,2006.
[28]顾海兵.宏观金融预警系统:警源与警兆分析[J].浙江社会科学,2000(3).
[29]顾海兵.经济预警新论[J].数量经济技术经济研究,1994(1).
[30]胡日东,苏芳,李立柱.中国沪深股市均值回复效应实证研究-基于GIBBS抽样方法.宁波大学学报(人文科学版),2008,(11):70:77.
[31]胡铮洋.证券市场风险度量—时变Copula和极值Copula的应用研究[D].吉林大学博士学位论文, 2009.
[32]惠晓峰,胡运权,胡伟.基于遗传算法的BP神经网络在汇率预测中的应用研究[J].数量经济技术经济研究, 2002(2).
[33]贾涛,靳玉英.货币危机传染性研究述评[J].上海财经大学学报,2007(10).
[34]江启堂著.外汇买卖实务.深圳:海天出版社, 1993.?
[35]金洪飞.关于货币危机传染文献综述[J].经济学动态,2001(7).
[36]金洪飞.货币危机理论文献综述[J].当代经济科学,2001(5).
[37]金洪飞.货币危机理论综述[J].当代经济科学,2001(9).
[38]孔繁利.金融市场风险的度量—基于极值理论和Copula的应用研究[D].吉林大学博士学位论文, 2006.
[39]李纲,杨辉耀,郭海燕.基于极值理论的风险价值度量[J].决策借鉴,2002,15(5):40-44.
[40]李长青,王春生.汇率变动与证券市场价格波动的相关性分析[J].山西财经大学学报,1999,(1):55-56.
[41]林谦,王宇.金融风险预警系统及发展[J].统计与决策,2007(7).
[42]刘赣州,安琨.人民币汇率波动与证券市场价格波动相关性的实证分析[J].2007(24):35-47.
[43]刘建春.股票与债券关联性研究及其启示[J].商业研究, 2005(10).
[44]刘金全,崔畅.中国沪深股市收益率和波动性的实证分析[J].经济研究(季刊),2002,(1):885-898.
[45]刘金全,刘志刚.我国股票市场收益波动性的区制转移分析[C].中国现场统计研究会第12届学术年会:2005.
[46]刘莉亚,任若恩.银行危机与货币危机共生性关系的实证研究[J].经济研究,2003(10).
[47]刘明康.全面提高我国银行业的市场风险管控能力[J].中国金融, 2006(12).
[48]刘志强.国外预警金融危机的方法评介[J].世界经济,2000(7).
[49]刘志强.金融危机预警指标体系研究[J].世界经济,1999(4).
[50]刘子斐,史敬. VaR模型比较技术及其评价——理论、实证回顾及其应用初探.金融研究, 2008(5).
[51]马德功,黄娟.构建我国银行危机预警指标体系的若干思考[J].国际经济合作,2007年(10).
[52]马德功,张畅,马敏捷.货币危机预警模型理论与中国适用[J].上海金融,2007(12) .
[53]马辉,陈守东,才元.当前中国房地产泡沫的实证分析[J].经济研究参考,2008(34).
[54]马辉,陈守东.中国股市对居民消费行为影响的实证分析[J].消费经济,2006(8).
[55]牟晓云.外汇危机预警模型及在我国的实证研究[D].吉林大学硕士论文,2004.
[56]穆春舟.中国金融风险评估与风险预警研究[D].吉林大学硕士论文,2008.
[57]潘婉彬,缪柏其,靳韬.银行间国债市场与交易所国债市场相关性研究[J].数理统计与管理,2007,(3):28-34.
[58]庞晓波,孙叶萌,王晨.基于ANN方法对汇率波动非线性的检验与预测比较[J],吉林大学社会科学学报,2008(1).
[59]濮卫东.货币危机理论与实证[D].复旦大学博士论文,2002.
[60]乔桂明.货币危机预警理论及实证比较研究——兼对中国的模拟分析及启示[J].财经研究,2007(12) .
[61]邱丕群.金融预警系统初探[J].统计与信息论坛,1997(2).
[62]屈颖爽,陈守东,王晨.跟踪误差约束下指数化投资组合优化的实证分析,工业技术经济,2008(1).
[63]邵欣炜.基于VaR的金融风险度量与管理[D].吉林大学博士学位论文, 2004.
[64]沈中华.银行危机与货币危机真是共生的吗?[J],金融研究,2000(6).
[65]石柱鲜,牟晓云.关于中国外汇风险预警研究——利用三元Logit模型[J].金融研究,2005(7).
[66]史道济,关静.沪深股市风险的相关性分析[J].统计研究,2003,(10):45-48.
[67]史芳丽,胡啸兵.股指期货仿真交易市场与现货市场关联问题研究[J].郑州航空工业管理学院学报,2009,(3)85-89.
[68]史敬.各类VaR方法的比较:基于中国股市的实证研究[D].湖南大学硕士学位论文, 2005.
[69]司继文,杨智勤,龚朴.广义Pareto分布下风险值估计[J].华中科技大学学报(城市科学版),2004,(2):12-15.
[70]孙叶萌.汇率决定理论和汇率预测[D].吉林大学博士学位论文, 2008.
[71]孙叶萌,王晨.差别资本充足率对银行影响几何[N].上海证券报(理论版),2009-9-1.
[72]孙叶萌,王晨.基于马尔可夫区制转移模型的风险价值度量-我国股市波动区制的识别与预警[J].经济纵横,2010(3)99-101.
[73]唐齐鸣,陈健.中国股市的ARCH效应分析[J].世界经济,2001, (3):29-36
[74]唐旭,张伟.论建立中国金融危机预警系统[J].经济学动态,2002(6).
[75]田华,曹家和.中国股票市场报酬与波动的GARCH-M模型[J].系统工程理论与实践,2003,(8):81-86.
[76]田新时,郭海燕.极值理论在风险度量中的应用—基于上证180指数[J].运筹与管理,2004,(1):106-111.
[77]汪利娜.美国次级抵押贷款危机的警示[J].财经科学,2007(10).
[78]王晨.后金融危机时期城市商业银行风险管理研究[J].经济纵横,2010(2)88-91.
[79]王春峰.金融市场风险测量模型—VaR[J].系统工程学报,2000(1):67-70.
[80]王东风.新兴市场金融脆弱性研究[D].辽宁大学博士论文,2007.
[81]王静,韩金华.论货币市场的功能[J].中央财经大学学报, 2001(6).
[82]王军波,邓述慧.利率、成交量对股价波动的影响[J].系统工程理论与实践, 1999(9):4-12.
[83]王喜平.货币危机理论与模型:文献综述[J].北京航空航天大学学报(社会科学版),2004(12)。
[84]王育宝.货币危机预警理论研究的新进展[J].国际金融研究,2003(12).
[85]温家宝.关于深入贯彻落实科学发展观的若干重大问题[J].求是,2008(21).
[86]汪寿阳,余乐安,黎建强. TEI@I方法论及其在外汇汇率预测中的应用[J].管理学报, 2007(1).
[87]谢赤,戴克维,刘潭秋.基于STAR模型的人民币实际汇率行为的描述[J].金融研究, 2005(5).
[88]谢赤,杨小帆.汇率预测的理论与方法及其最新进展[J].湖南大学学报(社会科学版), 2004(5).
[89]吴长风.利用回归GARCH模型对我国沪深股市的分析[J].预测,1999,(4):46-47.
[90]吴军.当代金融预警方法述评[J].世界经济文汇,2006(6).
[91]吴丽华,傅春.石油价格与汇率的相关性研究[J].福建金融,2007(3):15-17.
[92]吴世农,陈斌.风险度量方法与金融资产配置模型的理论和实证研究[J].经济研究,1999(9):30-32.
[93]伍志文.中国金融脆弱性:综合判断及对策建议[J].国际金融研究,2002(8).
[94]徐林.我国股市与债市(国债)相关性研究[D].西南财经大学博士学位论文, 2006.
[95]杨宗原.中国证券市场发展与银行利润的相关性分析[J].集美大学学报(哲学社会科学版),2006,(3):3-6.
[96]于震.中国金融发展对经济增长作用的途径与机制研究[D].吉林大学博士论文,2007.
[97]余永定.美国次贷危机:背景、原因与发展[J].当代亚太,2008(5).
[98]袁超;张兵;汪慧建.债券市场与股票市场的动态相关性研究[J].金融研究,2008(1):63-75.
[99]曾康霖.金融危机理论及其研究方法[J].财经科学,2000(4).
[100]曾志坚,江洲.关于我国股票市场与债券市场收益率联动性的实证研究[J].当代财经, 2007(9).
[101]战雪丽,张世英,张瑞锋.金融市场相关性分析及其度量方法改进[J].长安大学学报(社会科学版),2006,(1):51-59.
[102]张炳辉,马辉.美国次贷危机对中国房地产市场的警示[J].经济纵横,2008(7).
[103]张琳琳.证券市场下方风险测度模型及市场风险的实证研究[D].吉林大学博士学位论文, 2007.
[104]张明.透视美国次级债危机及其对中国的影响[J].国际经济评论,2007,(9).
[105]张伟.体制转换模型能预测货币危机吗?[J].经济研究,2004(7).
[106]张瀛,王浣尘.货币危机预警综合评价方法研究[J].统计研究,2004(7).
[107]赵大坤.运用Logit模型建立我国的金融危机预警系统[D].吉林大学硕士论文,2005.
[108]赵强.我国证券市场综合理论研究[D].天津大学博士学位论文, 2004.
[109]赵晓力,马辉,陈守东.股价房价对消费行为的影响[J].广州大学学报,2007(3).
[110]赵振全,陈守东.股票基本定价与定价模型的选择[J].数量经济技术经济研究2000(1).
[111]赵振全,陈守东,李夺.迈向市场经济的中国资本市场[J].数量经济技术经济研究2000年6期。
[112]赵振全,苏治,丁志国.中国证券市场波动的区制关联性[J].财贸经济,2005,(11):34-38.
[113]郑建明,桑百川,李文锋.中国外汇储备过多的风险及管理对策[R],第四届中国金融学年会论文,2008。
[114]朱波,范方志.金融危机理论与模型综述[J].世界经济研究,2005(6).
[115]朱宏泉,卢祖帝,汪寿阳.中国股市的Granger因果关系分析[J].管理科学学报,2001(5) :7-13.
[1] [美]马利亚里斯,[美]布罗克.经济学和金融学中的随机方法——现代金融方法论丛书[M].陈守东,李小军,李元译.上海:上海人民出版社,2004年4月.
[2] [美]莫里斯·戈尔茨坦,[美]格拉谢拉·L.凯宾斯基,[美]卡门·M.瑞哈特.金融脆弱性实证分析——新兴市场早期预警体系的构建[M].北京:中国金融出版社,2005年版.
[3] [美]帕德玛·德赛.金融危机,蔓延与遏制——从亚洲到阿根廷[M].王远林,王力元等译.北京:中国人民大学出版社,2006年版.
[4] [英]特里·J·沃特沙姆,基思·帕拉莫尔.金融数量方法[M].陈工孟,陈守东译.上海:上海人民出版社,2004年5月.
[5] [英]约翰·伊特韦尔,[美]默里·米尔盖特,[美]彼得·纽曼.新帕尔格雷夫经济学大辞典[M] .北京:经济科学出版社,1996年版.
[6]陈守东,赵振全.证券投资理论与分析[M].长春:吉林大学出版社,2001年。
[7]陈守东.金融资产波动模型与风险度量[M].北京:经济科学出版社,2007年。
[8]董文泉,高铁梅,姜诗章,陈磊.经济周期波动的分析与预测[M].长春:吉林大学出版社,1998年版。
[9]董小君.金融风险预警机制研究[M].北京:经济管理出版社,2004年版.
[10]范小云.繁荣的背后——金融系统性风险的本质、测度与管理[M].北京:中国金融出版社,2006年版.
[11]高铁梅主编.计量经济分析方法与建模[M].北京:清华大学出版社,2006年版.
[12]黄瑞玲.汇率稳定机制——以盯住汇率制下的货币危机为视角[M].北京:社会科学文献出版社,2005年版.
[13]李志刚.金融风险宏观与微观透视[M].北京:中国金融出版社,2006年版.
[14]刘莉亚.新兴市场国家(地区)金融危机理论研究[M].上海:上海财经大学出版社,2004年版.
[15]刘仁伍.金融稳定:机理与评价[M].北京:中国财政经济出版社,2007年版.
[16]刘锡良.中国经济转轨时期金融安全问题研究[M].北京:中国金融出版社,2004年版.
[17]刘园,王达学.金融危机的防范与管理[M].北京:北京大学出版社,1999年版.
[18]庞晓波,陈守东等编著.经济预测与决策[M].长春:吉林大学出版社, 1990.?
[19]朴明根.二元经济与我国金融危机防范研究[M].北京:经济科学出版社,2006年版.
[20]宋逢明.金融工程原理[M].北京:清华大学出版社,1999.?
[21]王春峰.金融市场风险管理[M].天津:天津大学出版社,2001年.
[22]王叙果.汇率制度安排与国家金融安全[M].北京:经济科学出版社,2006年版.
[23]吴莹,张一等.中国进出口贸易预测与分析[M].北京:科学出版社,2005年版.
[24]辛乔利,孙兆东.次贷危机[M].北京:中国经济出版社,2008年版.
[25]杨有振,王月光.中国商业银行风险预警体系的构建[M].北京:经济科学出版社,2006年版.
[26]张亦春.金融市场学[M].北京:高等教育出版社, 1999.?
[27]张晓峒. EVIEWS使用指南与案例[M].北京:机械工业出版社,2007年版.
[28]张志波.金融危机传染与国家经济安全[M].上海:上海社会科学出版社,2007年版.
[29]郑鸣.金融脆弱性论[M].北京:中国金融出版社,2007年版.
[30]周光亚,赵文,赵振全,姜诗章.多元统计方法[M].长春:吉林大学出版社,1988年.