风险框架下的证券投资基金资产配置研究
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摘要
资产配置是以不同资产类别的收益情况与投资人风险偏好为基础，构造基于一定风险水平的最适投资组合，是证券投资基金投资管理及决策过程中的决定性环节。同样，风险是影响证券投资基金资产配置决策的重要因素，有效的风险管理可以降低基金投资风险，控制发生极端投资损失的可能性。因此，从本质上讲，证券投资基金管理就是通过优化资产配置，构建最佳的投资组合，并利用有效地控制和分散风险的方式来获取稳定或超额的收益。本文正是通过理论和实证的研究方法，探讨如何在不同的风险框架下构建最优的证券投资基金资产配置模型及投资管理决策体系。
证券投资基金资产配置一般划分为战略资产配置(SAA)和战术资产配置(TAA)两个层面的管理类型。战略资产配置又叫政策性资产配置，可以理解为一种长期的资产配置决策，即通过寻求一种长期并在各种可选择的资产类别上分配投资比例来控制风险和增加收益，以实现投资目标。一般认为，战略资产配置是实现投资目标的重要保证，是证券投资基金最首要的最基本的业绩源泉。战术资产配置则是指基金经理通过对市场波动性的有效预测并在中期或短期内对长期资产配置比例的某种偏离，尽而获取额外的收益，因此，在短期的市场波动中，战术资产配置就成了基金在市场中获利的重要技术手段，是证券投资基金中短期投资管理过程中的决定环节。
Markowitz(1952)的现代资产组合理论(MPT)和W．Sharpe(1962)的资本资产定价理论(CAPM)，是最具有影响和指导意义的现代投资决策理论的基础。在投资组合理论中，投资者往往通过对各种资产的有效配置以达到分散风险并获取最大的投资回报目标，同时利用不得买空及要求必要回报率的条件下，将投资组合风险最小化，并据此形成有效前沿。为更精确度量风险的回报能力，随后Sharpe(1966，1975)利用均值—方差法(M-V)的框架，并用标准差作为风险的衡量值，在每单位风险下，求其期望回报的最大化，为此均值—方差(M-V)优化方法和模型奠定了资产配置决策的一般框架。然而，方差标准差并非是投资者在面对各种风险时最为精确的衡量方法，投资者往往凭借着对各种投资收益与现实情况的判断，对风险度量有特定的需求。而且，证券投资市场风险的来源变得越来越复杂，证券投资基金的任何投资组合都是在承担一定风险的前提下获得收益的，证券投资基金在进行资产配置决策时，必须同时对其投资组合所承担的风险进行科学度量，选择有效的风险度量方法在证券投资基金资产配置管理决策中也变得越来越重要。因此，考虑到在不同的风险框架下，满足证券投资基金资产管理者对风险的不同特定需求，构建最优资产配置决策模型，让投资回报最大化，便是本文研究的目的所在。
基于此，本文在研究风险框架下的证券投资基金资产配置时，主要考虑了证券投资基金资产配置管理中具有实用性和广泛性的风险度量要素，即风险厌恶系数(γ)、系统性风险(β)值、跟踪误差(TEV)、风险在险值(VaR)、下偏矩(LPMs)和风险预算(Risk-Budgeting)，从而构建了证券投资基金的风险框架体系。然后，以Markowitz的投资组合理论-均值方差MV模型为基本资产配置决策框架，将模型的风险框架体系延伸到多个角度，并进行理论、实证和比较分析。
在本文的实证研究中，以中国股票市场和债券市场为证券投资基金的主要资产选择对象，即资产的风险和收益标的，然后通过构建资产配置模型并进行实证分析得出了如下结论：
(1)以考虑基金投资人对风险的偏好程度为风险约束因素，选择投资人的风险厌恶系数(γ)为风险约束指标，构建最适的基金资产配置模型。结果发现，投资者的风险厌恶水平γ对证券投资基金的资产配置有着显著的影响，投资者风险厌恶水平高，则高风险资产的配置比例相对较低；
(2)以市场风险为证券投资基金的主要风险来源，选择系统性风险(β)值为风险约束指标，构建最适的基金资产配置模型。结果发现，运用β系数对证券投资基金投资组合的风险加以测度，并在此基础上建立基金资产配置决策更具有相对的合理性；
(3)以基金经理的积极投资风险为风险控制目标，选择跟踪误差(TEV)为风险约束指标，构建最适的基金资产配置模型。结果发现，当对证券投资基金投资组合总风险额外加以限制为固定常数时，在此基础上求得其最优资产配置及有效前沿，这样就使得在基于TEV约束框架下的资产配置决策能够显著提高基金投资组合的业绩；
(4)以研究基金资产的可能损失为风险度量基础，选择风险在险值(VaR)为风险约束指标，构建最适的基金资产配置模型。结果发现，当证券投资基金的资产配置模型在满足了投资人针对风险在险值(VaR)的约束需求时，提供了一种在实践中较为有效的资产配置决策方法；
(5)以满足下方风险厌恶者的投资需求，选择下偏矩(LPMs)为风险约束指标，构建最适的基金资产配置模型。结果发现，下偏距LPMs约束框架下的证券投资基金资产配置决策对于下方风险厌恶的投资者来说，至少与传统的均值-方差(MV)优化技术方法一样有效；
(6)以研究如何在度量和分解证券投资基金总风险的基础上进行资产配置决策，选择和应用风险预算(Risk-Budgeting)作为风险框架。研究认为，度量和分解证券投资基金总风险并加入风险预算(Risk-Budgeting)约束时，进行证券投资基金资产配置是控制证券投资基金总风险的有效方法。
最后，本文以中国证券市场中的封闭式和开放式基金为对象，对中国证券投资基金的资产配置风格和资产配置政策进行了实证检验和分析，目的是从证券投资基金管理者的资产配置的行为特征和资产配置政策对基金收益的贡献特征两个方面，来研究证券投资基金的资产配置决策体系、应用模型和风险特征。研究结论如下：
(1)通过基于组合的风格分析方法，对30只股票型证券投资基金的资产配置风格进行了实证检验，研究结果表明，中国证券投资基金资产配置严重趋同，风格特征也呈现趋同化，而且投资风险分散化特征较弱，尚未形成特色鲜明的资产配置风格特征。证券投资基金资产配置风格已广泛被基金管理者和投资者所接受，基金经理可根据风格分析进行资产配置和风险监控活动，也就是说控制资产配置风格也就成为基金经理风险监控和投资决策的重要方面。
(2)通过对94只中国证券投资基金的政策配置政策进行实证分析，可以看出资产配置政策对中国证券投资基金的业绩贡献并不明显，而积极管理(选股和择时)却是提高基金业绩的重要因素。这一点与一般所认为的资产配置政策是一种长期资产配置决策模型的应用和证券投资基金业绩的重要贡献来源完全不同。中国证券投资基金管理者需要尽快完善资产配置管理及决策体系，制定并执行适合于基金自身投资特点，且又具有投资指导意义的资产配置决策模型，从而形成稳健的资产配置风格和投资理念，以满足基金投资者投资需求。
总之，本文主要针对证券投资基金的属性和资产配置特征，从风险角度出发研究了不同风险因素对证券投资基金资产配置决策的影响，通过构建基于不同风险框架下的资产配置模型，获得了理论和实证上的研究结论。
Asset Allocation is the process in optimizing investment portfolio at certain risk level based on the return of assets class and the investor's risk attitude, and which is the key determinant in investment management of mutual fund. Also the market risk is a more important factor in assets allocation decision, the risk-managering can efficiently lowered investment risk, meet investor's risk preference and control the probability of extreme losing happenning. To the most extent, mutual fund managers could get long-term and abnormal return by optimizing investment portfolios and diversifing risk. Used theoretical and empirical methods this paper focus on how to construct a best asset allocation model and investment decision system at different risk framework.
In general, typical asset allocation for mutual funds defined by classification mainly included Strategy Asset Allocation (SAA), Tactical Asset Allocation (TAA) in this paper. Strategy asset allocation also named as asset allocation policy, and also it is a long-term asset allocation policy, assigned by asset class proportion among the investment asset in long-term horizon to gain maximum returns at a certain risk level. Strategy asset allocation is the important insurance to reach the object of investment for investors and the basis resource of the investment's performance. Tactical asset allocation is often a bias from the long-term benchmark to gain the excess returns, according to forecast market variability in long-term or short-term asset assignment. In the short-term investment horizon, tactical asset allocation is a more important method to gain excess return from market.
Markowitz (1952)'s Modern Portfolio Theory (MPT) and W.Sharpe (1962)'s Capital Asset Price Theory (CAPM) all are the most important investment decision theory as the investment guide for fund managers. As for Modern Portfolio Theory, investors have the efficient asset allocation among the asset calss for the object investment to gain the maximum returns and to diversify risk, while a return rate is required and short sales is limited, get the efficient frontier graph consisted of portfolios sets of asset calss for investment. Then Sharpe (1966, 1975) in mean-variance (MV) framework got the maximum returns at a unit risk. But both the Variance and Standard-deviation are not the best risk measurement for the mutual fund investors, because investors have more and more specified risk aversion and required. The source of securities market risk is becoming more and more complex and any investment portfolio must be the return tradeoff at a certain level risk, furthermore we evaluate the investment performance at a certain level risk. It's becoming more and more important to select a risk measure properly for asset allocation of mutual fund, and regarding on a certain risk level constraints specified, the purpose in this paper is to set up asset allocation model to meet the investors risk required. We will set up asset allocation model based on risk constraints framework to analyze and examine what is the difference for these asset allocation models regarding the different risk constraints.
Based on the Markowitz Portfolio Theory and M-V (Mean-Variance) model regarding as a basis asset allocation decision framework, then extending the risk framework to more risk sector, i.e. in perspectively to measure investor's risk aversion (γ), Systemic Risk (β), Tracing Error Variation (TEV), Value at Risk (VaR), Lower Partial Moments (LPMs) and Risk budgeting as the risk framework. We input these risk frameworks as a constraints to asset allocation model and optimization system in theoretical and empirical analysis.
In empirical analysis, we examined China stock and bond market as investment asset of mutual fund, i.e. the object of investment asset return and risk benchmark, and have gotten these results as inflows:
(1) Slected Investor's risk aversion of mutual fund as a risk constraint, and constructed most optimization asset allocation model, we found the different risk aversion (γ) level of investors could differently impacting on the asset allocation decision, and higher aversion with more lower risk assets;
(2) Only focus on the risk from market and selected systemtic bate(β) value as a risk constraint, then constructed most optimization asset allocation model, we found that Beta (β) value as risk measurement for asset allocation decision is more reseaonable to use in practically compared to Variance and Standard Deviation;
(3) For the risk from the fund manager's active investment, we selected Tracking Error Variation (TEV) as a risk constraint, then constructed most optimization asset allocation model, and we found that the total risk as a fixed constraints as a constant, and in TEV constraints framework, the performance can be improved significantly;
(4) For the possible of loss in the asset of mutual fund, we selected Value at risk (VaR) as a risk constraint, then constructed most optimization asset allocation model, and found that the asset allocation model met the investor's downside risk VaR constraints, also it's a more better and more efficient asset allocation decision methods;
(5) To meet investor's downside risk aversion, we selected Lower Partial Matrix (LPM) as a risk constraint, then constructed most optimization asset allocation model, and found that Asset allocation decision in LPMs framework for the downside risk aversion's investor, is less than or the same as the MV optimal model;
(6) For only focus on the risk and ignore the return, we selected risk budgeting as a risk framework, and based on measuring and decomposing the total risk to do asset allocation whithin risk budgeting constraints, which could efficiently control the total risk of mutual fund.
Finally, researching on the fund manger investment style and asset allocation policy, we also examined the asset allocation style and the asset allocation policy of China mutual funds, the result as inflow:
(1) Used the holding based analysis method to examine 40 China stock investment funds, and we found that most of China stock mutual fund mostly had the same investment style in different investment period and could not diversify the risk from securities market.
(2) Used the time series regression analysis to examine 94 China investment funds, also found that all fund can be explained with a lower level by assets allocation policy, but active management (Stock picking and Market timing) can significantly improve the performance these funds. Mutual funds in China still have not set up a better asset allocation model and system, and need to improve it in the long-term development.
To sum up, this paper mainly focused on the theoretical and empirical analysis of the assets allocation of mutual funds based on certain risk framework.
证券投资基金资产配置一般划分为战略资产配置(SAA)和战术资产配置(TAA)两个层面的管理类型。战略资产配置又叫政策性资产配置，可以理解为一种长期的资产配置决策，即通过寻求一种长期并在各种可选择的资产类别上分配投资比例来控制风险和增加收益，以实现投资目标。一般认为，战略资产配置是实现投资目标的重要保证，是证券投资基金最首要的最基本的业绩源泉。战术资产配置则是指基金经理通过对市场波动性的有效预测并在中期或短期内对长期资产配置比例的某种偏离，尽而获取额外的收益，因此，在短期的市场波动中，战术资产配置就成了基金在市场中获利的重要技术手段，是证券投资基金中短期投资管理过程中的决定环节。
Markowitz(1952)的现代资产组合理论(MPT)和W．Sharpe(1962)的资本资产定价理论(CAPM)，是最具有影响和指导意义的现代投资决策理论的基础。在投资组合理论中，投资者往往通过对各种资产的有效配置以达到分散风险并获取最大的投资回报目标，同时利用不得买空及要求必要回报率的条件下，将投资组合风险最小化，并据此形成有效前沿。为更精确度量风险的回报能力，随后Sharpe(1966，1975)利用均值—方差法(M-V)的框架，并用标准差作为风险的衡量值，在每单位风险下，求其期望回报的最大化，为此均值—方差(M-V)优化方法和模型奠定了资产配置决策的一般框架。然而，方差标准差并非是投资者在面对各种风险时最为精确的衡量方法，投资者往往凭借着对各种投资收益与现实情况的判断，对风险度量有特定的需求。而且，证券投资市场风险的来源变得越来越复杂，证券投资基金的任何投资组合都是在承担一定风险的前提下获得收益的，证券投资基金在进行资产配置决策时，必须同时对其投资组合所承担的风险进行科学度量，选择有效的风险度量方法在证券投资基金资产配置管理决策中也变得越来越重要。因此，考虑到在不同的风险框架下，满足证券投资基金资产管理者对风险的不同特定需求，构建最优资产配置决策模型，让投资回报最大化，便是本文研究的目的所在。
基于此，本文在研究风险框架下的证券投资基金资产配置时，主要考虑了证券投资基金资产配置管理中具有实用性和广泛性的风险度量要素，即风险厌恶系数(γ)、系统性风险(β)值、跟踪误差(TEV)、风险在险值(VaR)、下偏矩(LPMs)和风险预算(Risk-Budgeting)，从而构建了证券投资基金的风险框架体系。然后，以Markowitz的投资组合理论-均值方差MV模型为基本资产配置决策框架，将模型的风险框架体系延伸到多个角度，并进行理论、实证和比较分析。
在本文的实证研究中，以中国股票市场和债券市场为证券投资基金的主要资产选择对象，即资产的风险和收益标的，然后通过构建资产配置模型并进行实证分析得出了如下结论：
(1)以考虑基金投资人对风险的偏好程度为风险约束因素，选择投资人的风险厌恶系数(γ)为风险约束指标，构建最适的基金资产配置模型。结果发现，投资者的风险厌恶水平γ对证券投资基金的资产配置有着显著的影响，投资者风险厌恶水平高，则高风险资产的配置比例相对较低；
(2)以市场风险为证券投资基金的主要风险来源，选择系统性风险(β)值为风险约束指标，构建最适的基金资产配置模型。结果发现，运用β系数对证券投资基金投资组合的风险加以测度，并在此基础上建立基金资产配置决策更具有相对的合理性；
(3)以基金经理的积极投资风险为风险控制目标，选择跟踪误差(TEV)为风险约束指标，构建最适的基金资产配置模型。结果发现，当对证券投资基金投资组合总风险额外加以限制为固定常数时，在此基础上求得其最优资产配置及有效前沿，这样就使得在基于TEV约束框架下的资产配置决策能够显著提高基金投资组合的业绩；
(4)以研究基金资产的可能损失为风险度量基础，选择风险在险值(VaR)为风险约束指标，构建最适的基金资产配置模型。结果发现，当证券投资基金的资产配置模型在满足了投资人针对风险在险值(VaR)的约束需求时，提供了一种在实践中较为有效的资产配置决策方法；
(5)以满足下方风险厌恶者的投资需求，选择下偏矩(LPMs)为风险约束指标，构建最适的基金资产配置模型。结果发现，下偏距LPMs约束框架下的证券投资基金资产配置决策对于下方风险厌恶的投资者来说，至少与传统的均值-方差(MV)优化技术方法一样有效；
(6)以研究如何在度量和分解证券投资基金总风险的基础上进行资产配置决策，选择和应用风险预算(Risk-Budgeting)作为风险框架。研究认为，度量和分解证券投资基金总风险并加入风险预算(Risk-Budgeting)约束时，进行证券投资基金资产配置是控制证券投资基金总风险的有效方法。
最后，本文以中国证券市场中的封闭式和开放式基金为对象，对中国证券投资基金的资产配置风格和资产配置政策进行了实证检验和分析，目的是从证券投资基金管理者的资产配置的行为特征和资产配置政策对基金收益的贡献特征两个方面，来研究证券投资基金的资产配置决策体系、应用模型和风险特征。研究结论如下：
(1)通过基于组合的风格分析方法，对30只股票型证券投资基金的资产配置风格进行了实证检验，研究结果表明，中国证券投资基金资产配置严重趋同，风格特征也呈现趋同化，而且投资风险分散化特征较弱，尚未形成特色鲜明的资产配置风格特征。证券投资基金资产配置风格已广泛被基金管理者和投资者所接受，基金经理可根据风格分析进行资产配置和风险监控活动，也就是说控制资产配置风格也就成为基金经理风险监控和投资决策的重要方面。
(2)通过对94只中国证券投资基金的政策配置政策进行实证分析，可以看出资产配置政策对中国证券投资基金的业绩贡献并不明显，而积极管理(选股和择时)却是提高基金业绩的重要因素。这一点与一般所认为的资产配置政策是一种长期资产配置决策模型的应用和证券投资基金业绩的重要贡献来源完全不同。中国证券投资基金管理者需要尽快完善资产配置管理及决策体系，制定并执行适合于基金自身投资特点，且又具有投资指导意义的资产配置决策模型，从而形成稳健的资产配置风格和投资理念，以满足基金投资者投资需求。
总之，本文主要针对证券投资基金的属性和资产配置特征，从风险角度出发研究了不同风险因素对证券投资基金资产配置决策的影响，通过构建基于不同风险框架下的资产配置模型，获得了理论和实证上的研究结论。
Asset Allocation is the process in optimizing investment portfolio at certain risk level based on the return of assets class and the investor's risk attitude, and which is the key determinant in investment management of mutual fund. Also the market risk is a more important factor in assets allocation decision, the risk-managering can efficiently lowered investment risk, meet investor's risk preference and control the probability of extreme losing happenning. To the most extent, mutual fund managers could get long-term and abnormal return by optimizing investment portfolios and diversifing risk. Used theoretical and empirical methods this paper focus on how to construct a best asset allocation model and investment decision system at different risk framework.
In general, typical asset allocation for mutual funds defined by classification mainly included Strategy Asset Allocation (SAA), Tactical Asset Allocation (TAA) in this paper. Strategy asset allocation also named as asset allocation policy, and also it is a long-term asset allocation policy, assigned by asset class proportion among the investment asset in long-term horizon to gain maximum returns at a certain risk level. Strategy asset allocation is the important insurance to reach the object of investment for investors and the basis resource of the investment's performance. Tactical asset allocation is often a bias from the long-term benchmark to gain the excess returns, according to forecast market variability in long-term or short-term asset assignment. In the short-term investment horizon, tactical asset allocation is a more important method to gain excess return from market.
Markowitz (1952)'s Modern Portfolio Theory (MPT) and W.Sharpe (1962)'s Capital Asset Price Theory (CAPM) all are the most important investment decision theory as the investment guide for fund managers. As for Modern Portfolio Theory, investors have the efficient asset allocation among the asset calss for the object investment to gain the maximum returns and to diversify risk, while a return rate is required and short sales is limited, get the efficient frontier graph consisted of portfolios sets of asset calss for investment. Then Sharpe (1966, 1975) in mean-variance (MV) framework got the maximum returns at a unit risk. But both the Variance and Standard-deviation are not the best risk measurement for the mutual fund investors, because investors have more and more specified risk aversion and required. The source of securities market risk is becoming more and more complex and any investment portfolio must be the return tradeoff at a certain level risk, furthermore we evaluate the investment performance at a certain level risk. It's becoming more and more important to select a risk measure properly for asset allocation of mutual fund, and regarding on a certain risk level constraints specified, the purpose in this paper is to set up asset allocation model to meet the investors risk required. We will set up asset allocation model based on risk constraints framework to analyze and examine what is the difference for these asset allocation models regarding the different risk constraints.
Based on the Markowitz Portfolio Theory and M-V (Mean-Variance) model regarding as a basis asset allocation decision framework, then extending the risk framework to more risk sector, i.e. in perspectively to measure investor's risk aversion (γ), Systemic Risk (β), Tracing Error Variation (TEV), Value at Risk (VaR), Lower Partial Moments (LPMs) and Risk budgeting as the risk framework. We input these risk frameworks as a constraints to asset allocation model and optimization system in theoretical and empirical analysis.
In empirical analysis, we examined China stock and bond market as investment asset of mutual fund, i.e. the object of investment asset return and risk benchmark, and have gotten these results as inflows:
(1) Slected Investor's risk aversion of mutual fund as a risk constraint, and constructed most optimization asset allocation model, we found the different risk aversion (γ) level of investors could differently impacting on the asset allocation decision, and higher aversion with more lower risk assets;
(2) Only focus on the risk from market and selected systemtic bate(β) value as a risk constraint, then constructed most optimization asset allocation model, we found that Beta (β) value as risk measurement for asset allocation decision is more reseaonable to use in practically compared to Variance and Standard Deviation;
(3) For the risk from the fund manager's active investment, we selected Tracking Error Variation (TEV) as a risk constraint, then constructed most optimization asset allocation model, and we found that the total risk as a fixed constraints as a constant, and in TEV constraints framework, the performance can be improved significantly;
(4) For the possible of loss in the asset of mutual fund, we selected Value at risk (VaR) as a risk constraint, then constructed most optimization asset allocation model, and found that the asset allocation model met the investor's downside risk VaR constraints, also it's a more better and more efficient asset allocation decision methods;
(5) To meet investor's downside risk aversion, we selected Lower Partial Matrix (LPM) as a risk constraint, then constructed most optimization asset allocation model, and found that Asset allocation decision in LPMs framework for the downside risk aversion's investor, is less than or the same as the MV optimal model;
(6) For only focus on the risk and ignore the return, we selected risk budgeting as a risk framework, and based on measuring and decomposing the total risk to do asset allocation whithin risk budgeting constraints, which could efficiently control the total risk of mutual fund.
Finally, researching on the fund manger investment style and asset allocation policy, we also examined the asset allocation style and the asset allocation policy of China mutual funds, the result as inflow:
(1) Used the holding based analysis method to examine 40 China stock investment funds, and we found that most of China stock mutual fund mostly had the same investment style in different investment period and could not diversify the risk from securities market.
(2) Used the time series regression analysis to examine 94 China investment funds, also found that all fund can be explained with a lower level by assets allocation policy, but active management (Stock picking and Market timing) can significantly improve the performance these funds. Mutual funds in China still have not set up a better asset allocation model and system, and need to improve it in the long-term development.
To sum up, this paper mainly focused on the theoretical and empirical analysis of the assets allocation of mutual funds based on certain risk framework.
引文
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