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.     

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。     

CVaR风险度量及投资组合优化的实证分析

CVaR法是在VaR法基础上优化改进而来的风险度量方法。通过CVaR法对金融资产风险进行评估,对投资组合资产配置进行优化等成为了金融风险管理的重要内容。本文首先对比了CVaR和VaR法的区别,并通过比较A股和H股收益分布的特征得出A股更适用CVaR模型的结论。通过多因子选股模型选出投资组合成分,再使用均值-CVaR模型对投资组合进行优化,最终得到不同置信度和阀值下投资组合的优化结果。从而证明了该模型的有效性,突出了CVaR法在国内证券市场的使用价值和意义。     

基于Kernel-Copula函数的VaR

VaR是评估资产组合风险的有效手段之一。已有的VaR估计方法依赖于总体分布,且无法全面描述不同资产间的风险相关性,导致风险常常被低估。为此,本文采用Kernel-Copula函数,估计资产组合的VaR,提高估计结果的精度和可信度。最后,利用新方法估算沪深300和标普500构成的资产组合VaR。     

VaR的含义与应用

通俗地讲,VaR是指在市场正常波动下,某一金融资产或投资组合的最大可能损失;更为确切地讲,是在一定概率(置信水平)下,某一金融资产或投资组合价值在未来特定时期内的最大可能损失。VaR的魅力就在于,它只用一个数字就能够明确地表示出一个组合所面临的全部市场风险。例如,某银行     

Copula的投资组合选择模型的应用研究

结合Copula技术和GARCH模型,建立了投资组合的Copula-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的风险分析中。利用这个模型,并结合Markowitz的投资组合选择模型,对我国的一支开放式基金——中信红利精选股票型证券投资基金投资组合的选择进行了优化,本文应用lingo 8.0,在收益率一定的情况下,得到了风险(VaR)最小的投资组合。     

基于VaR方法的证券投资基金风险管理实证分析

近年来,我国证券投资基金发展迅猛,基金总规模和数量都快速增长,已经为成我国证券市场的重要组成部分。然而,由于我国证券市场起步较晚,发展时间短暂,目前还不是一个成熟的市场。存在着投资品种混杂、投机氛围浓厚、市场风险大等诸多问题。本文通过运用VaR模型对基金投资组合实例进行风险分析及管理,希望能尽一文之力推动VaR在我国证券投资基金中的运用。     

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.     

Portfolio frontiers with restrictions to tracking error volatility and value at risk

Asset managers are often given the task of restricting their activity by keeping both the value at risk (VaR) and the tracking error volatility (TEV) under control. However, these constraints may be impossible to satisfy simultaneously because VaR is independent of the benchmark portfolio. The management of these restrictions is likely to affect portfolio performance and produces a wide variety of scenarios in the risk-return space. The aim of this paper is to analyse various interactions between portfolio frontiers when risk managers impose joint restrictions upon TEV and VaR. Specifically, we provide analytical solutions for all the intersections and we propose simple numerical methods when such solutions are not available. Finally, we introduce a new portfolio frontier.     

基于Copula-GARCH-EVT的中国开放式基金投资组合风险度量

文章结合CARCH模型和EVT理论刻画了单个金融资产收益率的波动性和尾部分布,并将Copula函数和Monte Carlo技术应用于证券投资组合的VaR计算方法.通过对光大红利基金的实证研究,得到前十大重仓中单只股票及其投资组合的风险值,结果表明,基于Copula-GARCH-EVT的VaR方法具有重要的经济应用价值.     

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 Measures for Hedge Funds: a Cross‐sectional Approach

This paper analyses the risk‐return trade‐off in the hedge fund industry. We compare semi‐deviation, value‐at‐risk (VaR), Expected Shortfall (ES) and Tail Risk (TR) with standard deviation at the individual fund level as well as the portfolio level. Using the Fama and French (1992) methodology and the combined live and defunct hedge fund data from TASS, we find that the left‐tail risk captured by Expected Shortfall (ES) and Tail Risk (TR) explains the cross‐sectional variation in hedge fund returns very well, while the other risk measures provide statistically insignificant or marginally significant results. During the period between January 1995 and December 2004, hedge funds with high ES outperform those with low ES by an annual return difference of 7%. We provide empirical evidence on the theoretical argument by Artzner et al. (1999) that ES is superior to VaR as a downside risk measure. We also find the Cornish‐Fisher (1937) expansion is superior to the nonparametric method in estimating ES and TR.     

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.     

Emerging markets and portfolio foreign exchange risk: An empirical investigation using a value-at-risk decomposition technique

The correlation between a portfolio's equity and foreign exchange components plays a role in reducing foreign exchange exposure. Investors must account for this correlation when determining the extent of foreign exchange risk in emerging market equity portfolio investments. This study employs a VaR risk factor mapping technique, under the variance–covariance VaR approach, to decompose portfolio risk in Argentina, Brazil, China, India, Mexico and Russia. For comparison purposes, the same technique is used to decompose portfolio risk in the US. The study is conducted from the perspective of a European equity investor with a portfolio of equities in each country. By employing the VaR decomposition technique, the correlation between a portfolio's equity and foreign exchange components is taken into account and portfolio foreign exchange risk is extracted from portfolio systematic risk. Our results uniquely demonstrate significant variation in foreign exchange risk in emerging markets.     

基于VaR模型的商业银行体系系统性风险研究

本文以中国2016年之前上市商业银行作为中国银行业的代表,测算银行业系统性 风险VaR。整体来讲,我国银行业系统性风险较低,但VaR在2015年较高。虽如此,我国银行业资本持有量能够抵御银行体系的系统性风险。在系统性风险VaR贡献度方面,本文实证分析表明,在样本期间内,浦发银行、中国银行、农业银行、交通银行贡献度较高。银行体系系统 性风险VaR受GDP增长率和沪深300指数收益率的显著影响。     

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.     

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 in a market subject to regime switching

Many empirical researches report that value-at-risk (VaR) measures understate the actual 1% quantile, while for Inui, K., Kijima, M. and Kitano, A., VaR is subject to a significant positive bias. Stat. Probab. Lett., 2005, 72, 299–311. proved that VaR measures overstate significantly when historical simulation VaR is applied to fat-tail distributions. This paper resolves the puzzle by developing a regime switching model to estimate portfolio VaR. It is shown that our model is able to correct the underestimation problem of risk.