基于VaR的开放式股票型基金市场风险的测量与评价

通过采用半参数法计算投资组合VaR,得到相应VaR的近似置信区间,并结合成分VaR、边际VaR对投资组合vaR进行分解,结果发现,VaR作为风险管理工具同样可以有效应用于开放式股票型基金市场风险的测量与评价.     

基于加权CVaR下具有不确定退出时间的最优投资组合研究

一、引言 VaR是指在一定的概率(置信)水平下,某一金融资产或投资组合在未来特定的一段时间内遭受的最大可能损失.但是经过后来的研究,发现了VaR具有很多不足之处,于是Artzner(1997)提出了CVaR,CVaR是指投资组合的损失大于某个给定VaR值的条件下,该投资组合的损失平均值.2000年Rockafeller等人证明了基于CVaR的投资组合优化必定存在最小风险的解.CVaR和VaR作为风险测量工具在文献[3]中作了广泛的比较,CVaR现在被认为是比VaR更好的一致性风险度量.     

投资组合行业配置的动态VaR研究——基于时变Copula-GJR-Skewed-t模型

用时变Copula-GJR-Skewed-t模型研究了深证22个行业分类指数中任意两个投资组合的动态VaR,并与静态VaR比较。实证结果表明,时变t-Copula函数在众多Copula函数中对行业投资组合的拟合效果最优,给出了模型估计出的VaR最大和最小的5对行业组合,不同行业组合的动态VaR在股市周期各阶段关系相对稳定,同一行业组合的动态VaR和静态VaR关系相对稳定,且略高于静态VaR。     

基于贝叶斯方法与时变Copula模型的基金风险的度量

基于贝叶斯理论的 MCMC方法对单个基金收益率进行 GARCH 建模,以及对投资组合权重进行后验模拟.进一步结合时变Copula理论计算基金投资组合的 VaR,与基于极大似然法的结果进行比较.实证结果表明基于贝叶斯理论的时变Copula的 VaR方法,能够更有效的度量开放式基金投资组合的风险.     

股票交易组合风险的实证分析——基于VAR法

本文在介绍VaR基本概念的基础上,着重分析VaR的三种获取方法,并以马钢股份(600808)和交通银行(601328)组合为例,对VaR方法在我国证券市场上的投资组合应用进行分析。     

中国开放式基金投资组合风险值的实证——基于Copula-GARCH的分析

结合Copula技术和GARCH模型,建立了投资组合风险分析的Gopula-GARCH模型.由于该模型可以捕捉金融市场间的非线性相关性,因而可用于投资组合VaR的分析.利用这个模型,结合Monte Carlo,模拟技术,对我国第一支开放式基金一华安创新基金的投资组合进行了风险分析.     

基于加权CVaR下具有不确定退出时间的最优投资组合研究

一、引言VaR是指在一定的概率(置信)水平下,某一金融资产或投资组合在未来特定的一段时间内遭受的最大可能损失。但是经过后来的研究,发现了VaR具有很多不足之处,于是Artzner(1997)提出了CVaR,CVaR是指投资组合的损失大于某个给定VaR值的条件下,该投资组合的损失平均值。2000年     

中国股市中板块间动态关联效应及组合在险价值的测度——以地产板块为例

首先,本文采用多元GARCH模型,对中国股票市场上与房地产行业板块相关联的三个板块收益率的统计特征进行研究,然后应用DCC-MVGARCH模型计算房地产板块与其他三个板块的相关系数.最后构造了房地产扳块和建筑板块的一个投资组合,比较在不同信息影响下,由动态相关系散和常相关系数所估算出的投资组合的VaR值的大小,并由此得出结论：与常相关系数下的VaR相比,动态相关系数下的VaR能更好地描述实际风险,特别是在市场波动剧烈时期.     

VaR理论模型在证券组合投资中实证分析

目前我国的证券投资市场还存在众多不规范的地方,加强金融市场尤其是证券交易市场的风险管理势在必行。目前在国际市场上存在着很多的风险测量的方式,其中VaR模型已成为金融市场上非常重要的测量方式。首先介绍了关于证券组合投资的基本概念及面临的主要风险,再介绍了VaR模型的主要计算方式、优缺点以及VaR模型主要的获取方法。最后重点分析了在VaR约束下使用方差-协方差法的投资组合决策。     

VaR理论模型在证券组合投资中实证分析

目前我国的证券投资市场还存在众多不规范的地方,加强金融市场尤其是证券交易市场的风险管理势在必行。目前在国际市场上存在着很多的风险测量的方式,其中VaR模型已成为金融市场上非常重要的测量方式。首先介绍了关于证券组合投资的基本概念及面临的主要风险,再介绍了VaR模型的主要计算方式、优缺点以及VaR模型主要的获取方法。最后重点分析了在VaR约束下使用方差-协方差法的投资组合决策。     

Portfolio selection based on the mean–VaR efficient frontier

Value-at-Risk (VaR) has become one of the standard measures for assessing risk not only in the financial industry but also for asset allocations of individual investors. The traditional mean–variance framework for portfolio selection should, however, be revised when the investor's concern is the VaR instead of the standard deviation. This is especially true when asset returns are not normal. In this paper, we incorporate VaR in portfolio selection, and we propose a mean–VaR efficient frontier. Due to the two-objective optimization problem that is associated with the mean–VaR framework, an evolutionary multi-objective approach is required to construct the mean–VaR efficient frontier. Specifically, we consider the elitist non-dominated sorting Genetic Algorithm (NSGA-II). From our empirical analysis, we conclude that the risk-averse investor might inefficiently allocate his/her wealth if his/her decision is based on the mean–variance framework.     

How does the choice of Value-at-Risk estimator influence asset allocation decisions?

Considering the growing need for managing financial risk, Value-at-Risk (VaR) prediction and portfolio optimisation with a focus on VaR have taken up an important role in banking and finance. Motivated by recent results showing that the choice of VaR estimator does not crucially influence decision-making in certain practical applications (e.g. in investment rankings), this study analyses the important question of how asset allocation decisions are affected when alternative VaR estimation methodologies are used. Focusing on the most popular, successful and conceptually different conditional VaR estimation techniques (i.e. historical simulation, peak over threshold method and quantile regression) and the flexible portfolio model of Campbell et al. [J. Banking Finance. 2001, 25(9), 1789–1804], we show in an empirical example and in a simulation study that these methods tend to deliver similar asset weights. In other words, optimal portfolio allocations appear to be not very sensitive to the choice of VaR estimator. This finding, which is robust in a variety of distributional environments and pre-whitening settings, supports the notion that, depending on the specific application, simple standard methods (i.e. historical simulation) used by many commercial banks do not necessarily have to be replaced by more complex approaches (based on, e.g. extreme value theory).     

基于Copula理论的商业银行的市场风险研究

在投资者看好银行股的背景下,结合t-EGARCH模型和极值理论,利用Copula方法对14家上市银行股票进行分析,并通过蒙特卡洛模拟计算单只股票以及投资组合的VaR.结果表明,此方法能很好地量化风险,有助于衡量市场风险.     

Value-at-risk capital requirement regulation,risk taking and asset allocation: a mean–variance analysis

In this study, the mean–variance framework is employed to analyze the impact of the Basel value-at-risk (VaR) market risk regulation on the institution's optimal investment policy, the stockholders’ welfare, as well as the tendency of the institution to change the risk profile of the held portfolio. It is shown that with the VaR regulation, the institution faces a new regulated capital market line, which induces resource allocation distortion in the economy. Surprisingly, only when a riskless asset is available does VaR regulation induce the institution to reduce risk. Otherwise, the regulation may induce higher risk, accompanied by asset allocation distortion. On the positive side, the regulation implies an upper bound on the risk the institution takes and it never induces the firm to select an inefficient portfolio. Moreover, when the riskless asset is available, tightening the regulation always increases the amount of maintained eligible capital and decreases risk.     

Applying VaR to REITs: A comparison of alternative methods

This study employs five methods to calculate the VaR of twelve REITs portfolios and evaluates the accuracy of these methods. Firstly, we find that the VaR varies among individual portfolios. The Hotel REITs has consistently the largest VaR. The low-leveraging portfolio tends to have the largest VaR measured by the parametric methods, while the high leveraging portfolio has the largest VaR calculated by the non-parametric methods. Secondly, each method performs differently at different confidence levels, and no method dominates the others. At the 95% confidence level, the EWMA method performs relatively well. The EQWMA and the two non-parametric methods perform equivalently and slightly overestimate VaRs. The EQWMA_T method ranks the bottom and significantly overestimates VaRs for all portfolios. At the 99% confidence level, the EQWMA method performs the best. The EQWMA_T and the two non-parametric methods perform equivalently and may overestimate VaR for all portfolios. The EWMA method turns out to be the worst and tends to underestimate the VaR. These findings may provide more insights for institutional real estate investors.     

Comment on “Optimal portfolio selection in a value-at-risk framework”

This comment discusses some errors in [Journal of Banking and Finance 25 (2001) 1789]. Given the portfolio rate of return is normally distributed, the following can be inferred. First, taking expected portfolio return rate as the benchmark of value-at-risk (VaR), the risk–return ratio collapses to a multiple of the Sharpe index. However, using risk-free rate as the benchmark, then above inference does not hold. Second, whether the benchmark of VaR is expected portfolio return rate or the risk-free rate, the optimal asset allocations for maximizing the risk–return ratio and Sharpe index are identical.     

Dynamic mean–VaR portfolio selection in continuous time

The value-at-risk (VaR) is one of the most well-known downside risk measures due to its intuitive meaning and wide spectra of applications in practice. In this paper, we investigate the dynamic mean–VaR portfolio selection formulation in continuous time, while the majority of the current literature on mean–VaR portfolio selection mainly focuses on its static versions. Our contributions are twofold, in both building up a tractable formulation and deriving the corresponding optimal portfolio policy. By imposing a limit funding level on the terminal wealth, we conquer the ill-posedness exhibited in the original dynamic mean–VaR portfolio formulation. To overcome the difficulties arising from the VaR constraint and no bankruptcy constraint, we have combined the martingale approach with the quantile optimization technique in our solution framework to derive the optimal portfolio policy. In particular, we have characterized the condition for the existence of the Lagrange multiplier. When the opportunity set of the market setting is deterministic, the portfolio policy becomes analytical. Furthermore, the limit funding level not only enables us to solve the dynamic mean–VaR portfolio selection problem, but also offers a flexibility to tame the aggressiveness of the portfolio policy.     

Risk Management for Linear and Non-Linear Assets: A Bootstrap Method with Importance Resampling to Evaluate Value-at-Risk

Many empirical studies suggest that the distribution of risk factors has heavy tails. One always assumes that the underlying risk factors follow a multivariate normal distribution that is a assumption in conflict with empirical evidence. We consider a multivariate t distribution for capturing the heavy tails and a quadratic function of the changes is generally used in the risk factor for a non-linear asset. Although Monte Carlo analysis is by far the most powerful method to evaluate a portfolio Value-at-Risk (VaR), a major drawback of this method is that it is computationally demanding. In this paper, we first transform the assets into the risk on the returns by using a quadratic approximation for the portfolio. Second, we model the return’s risk factors by using a multivariate normal as well as a multivariate t distribution. Then we provide a bootstrap algorithm with importance resampling and develop the Laplace method to improve the efficiency of simulation, to estimate the portfolio loss probability and evaluate the portfolio VaR. It is a very powerful tool that propose importance sampling to reduce the number of random number generators in the bootstrap setting. In the simulation study and sensitivity analysis of the bootstrap method, we observe that the estimate for the quantile and tail probability with importance resampling is more efficient than the naive Monte Carlo method. We also note that the estimates of the quantile and the tail probability are not sensitive to the estimated parameters for the multivariate normal and the multivariate t distribution. The research of Shih-Kuei Lin was partially supported by the National Science Council under grants NSC 93-2146-H-259-023. The research of Cheng-Der Fuh was partially supported by the National Science Council under grants NSC 94-2118-M-001-028.     

Nonlinear portfolio selection using approximate parametric Value-at-Risk

As the skewed return distribution is a prominent feature in nonlinear portfolio selection problems which involve derivative assets with nonlinear payoff structures, Value-at-Risk (VaR) is particularly suitable to serve as a risk measure in nonlinear portfolio selection. Unfortunately, the nonlinear portfolio selection formulation using VaR risk measure is in general a computationally intractable optimization problem. We investigate in this paper nonlinear portfolio selection models using approximate parametric Value-at-Risk. More specifically, we use first-order and second-order approximations of VaR for constructing portfolio selection models, and show that the portfolio selection models based on Delta-only, Delta–Gamma-normal and worst-case Delta–Gamma VaR approximations can be reformulated as second-order cone programs, which are polynomially solvable using interior-point methods. Our simulation and empirical results suggest that the model using Delta–Gamma-normal VaR approximation performs the best in terms of a balance between approximation accuracy and computational efficiency.