基于vine copula的股市风格资产组合风险预警研究

近年来,风格投资已成为一种重要的投资模式。在此背景下,如何有效地对风格资产组合风险进行刻画与预警具有重要的理论与现实意义。本文选取股市风格资产构建投资组合,分别基于传统分布假设与EVT极值理论构建边缘分布;运用三类vine copula模型(C-vine、D-vine、R-vine)刻画风格资产间的相依关系;进一步运用滚动时间窗的蒙特卡罗模拟方法进行组合风险的动态测度,并通过返回测试比较不同风险模型的测度效果;最后基于在险价值(VaR)的预测结果对组合风险预警系统进行构建。研究结果表明:R-vine模型能够相对灵活地刻画风格资产间的相依结构,并取得更高的组合风险测度精度;相较于传统资产分布假设,引入EVT极值理论的风险模型能够更为有效地预测风格资产组合风险状况;基于在险价值所构建出的风险预警系统能够较好地对组合风险进行分级预警,从而为投资者与市场监管部门提供决策参考。     

基于GARCH-EVT-Copula的社保基金投资组合风险测度研究

社保基金是社会保障事业健康发展的物质基础,安全性是其投资的首要原则。文章基于GARCH-EVT-Copula方法测度了社保基金投资组合的VaR。首先,基于GARCH、EVT对投资组合中各金融资产收益的边缘分布建模,然后,采用极大似然估计法和Bootstrap方法估计尾部的分布函数,接着,基于Copula方法研究组合中金融资产间的相关结构,最后,运用Monte Carlo方法测度投资组合的VaR。Kupiec检验表明,基于GARCH-EVT-Copula模型测度社保基金投资组合的风险是合适的。     

中国股市整合风险测度研究——基于VaR框架和相关结构的分析

针对现有流动性风险与市场风险的整合风险测度方法忽略两者相关结构的问题,在运用ARMA-GARCH-t模型对中国股市市场风险因子和流动性风险因子的边缘分布进行刻画的基础上,引入7种Copula函数来考察两者的相关结构,并运用Monte Carlo方法测度出整合风险。以沪深300指数为研究对象的实证检验结果表明:中国股市市场风险与流动性风险之间更符合动态的相关结构;在考虑了两风险的相关结构之后,基于时变t Copula函数的风险测度模型最能准确测度两风险的整合风险。     

股票市场和外汇市场间风险溢出效应研究——基于GARCH-时变Copula-CoVaR模型的分析北大核心CSSCISSCI

在全球经济增速放缓、国内外经济环境动荡加剧的大背景下,金融市场间的风险溢出效应备受关注。本文采用偏t分布的GARCH-时变Copula-Co Va R模型测度了内地和香港两地股市和汇市四个市场两两间的风险溢出效应。研究结果表明,同一类别金融市场间的风险溢出效应最大,同一地区(不同市场)间的风险溢出次之,跨地区、跨市场的最小;长期来看,股市和汇市间的风险溢出方向是变化的。2015年"8·11"汇改以后,股市和汇市间都存在正向的风险溢出,即一方发生风险事件时,会导致另一方的风险显著上升;不管是在汇改前,还是汇改后,离岸人民币市场对在岸市场的风险溢出始终大于反方向的溢出,且在汇改以后两个市场间的联动更加密切。     

中国金属期货市场的风险度量及其Backtesting分析

以上海期货交易所的3种代表性金属期货价格指数为例,首先对其价格变化的动力学特征及波动模式进行了全面深入的考察,然后运用严谨系统的后验分析(Backtesting analysis)方法,分别在多头和空头两种头寸状况以及5种不同分位数水平下,实证对比了8种风险测度模型对VaR(Value at Risk)和ES(Excepted shortfall)两种不同风险指标估计的精度差异。研究结果表明:在综合考虑了模型对金属期货价格变化动力学的刻画效果以及对不同风险指标的测度精度等因素后,基于有偏学生t分布的APGARCH模型是一个相对合理的风险测度模型选择。     

国际原油市场极端风险的测度模型及后验分析

采用可以捕捉收益分布尾部极端风险的ES(Excepted Shortfall)指标,同时基于时变高阶矩波动模型和常规GARCH族模型建立风险测度模型,并在多、空头寸共20个分位数水平下,综合对比了不同模型在国际原油市场风险测度中表现出的精确性差异。研究结果表明:时变高阶矩波动模型可以刻画原油市场收益分布中的时变偏度和时变峰度特征,更好地测度原油市场的极端风险,同时GARCHSK-M模型表现出了相对最高的风险测度精确性,可以作为测度原油市场极端风险相对合理的模型选择。     

中美主要金融市场相关结构及风险传导路径研究——基于Copula理论与方法

金融危机背景下的股市表现出更加复杂的动荡性,本文在传统GARCH模型的基础上引入了风险值对收益率的影响因素,运用GARCH-M模型来刻画股票收益率序列边缘分布,通过构建GARCH-M-t边缘分布过滤模型获取收益率残差序列,最后采用Copula函数对边缘分布拟合后的残差序列建模构建出Copula-GARCH-M-t相关结构模型。经过参数估计及多种Copula函数的拟合优度检验,最终成功刻画出中美金融市场五大证券交易中心股票收益率之间的相关结构模型。通过秩相关系数、尾部相关系数等相关性度量工具对中美两国金融市场的相关性进行分析,最后通过对不同股票市场之间的尾部相关性分析确定两国金融市场之间风险传导路径。     

基于Copula函数的国际原油价格与股票市场收益的相关性研究

针对国际原油价格与金砖五国股票市场收益之间的相关性问题,使用 AR(p)-GARCH(1,1)-Copula 模型进行检验。运用广义误差分布(GED)获取收益残差序列,对 WTI 原油价格和金砖五国股市收益之间的相关性进行实证分析。研究结果表明,国际原油价格与中国股市收益呈现微弱的相关关系,而与其他四国股市收益的相关关系较为明显。用时变 SJC Copula 模型刻画国际原油价格与金砖五国股票市场收益的相关性最为合适。     

基于半参数GARCH的沪深两市收益率波动和风险的实证研究

本文基于2005年1月4日至2009年11月30日的数据，运用GARCH模型研究上海和深圳两个股市间的收益率及波动性对于分析股市结构和判断股市走势及风险传递的意义。并运用半参数模型计算VaR值来测度金融市场风险。     

基于动态条件得分模型的风险测度和MCS检验

本文在研究动态条件得分模型(DCS)的基础上,通过构建风险测度模型对我国股市的风险进行了度量。     

Financial crisis,Value-at-Risk forecasts and the puzzle of dependency modeling

Forecasting Value-at-Risk (VaR) for financial portfolios is a crucial task in applied financial risk management. In this paper, we compare VaR forecasts based on different models for return interdependencies: volatility spillover (Engle & Kroner, 1995), dynamic conditional correlations (Engle, 2002, 2009) and (elliptical) copulas (Embrechts et al., 2002). Moreover, competing models for marginal return distributions are applied. In particular, we apply extreme value theory (EVT) models to GARCH-filtered residuals to capture excess returns.Drawing on a sample of daily data covering both calm and turbulent market phases, we analyze portfolios consisting of German Stocks, national indices and FX-rates. VaR forecasts are evaluated using statistical backtesting and Basel II criteria. The extensive empirical application favors the elliptical copula approach combined with extreme value theory (EVT) models for individual returns. 99% VaR forecasts from the EVT-GARCH-copula model clearly outperform estimates from alternative models accounting for dynamic conditional correlations and volatility spillover for all asset classes in times of financial crisis.     

Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach

We propose a method for estimating Value at Risk (VaR) and related risk measures describing the tail of the conditional distribution of a heteroscedastic financial return series. Our approach combines pseudo-maximum-likelihood fitting of GARCH models to estimate the current volatility and extreme value theory (EVT) for estimating the tail of the innovation distribution of the GARCH model. We use our method to estimate conditional quantiles (VaR) and conditional expected shortfalls (the expected size of a return exceeding VaR), this being an alternative measure of tail risk with better theoretical properties than the quantile. Using backtesting of historical daily return series we show that our procedure gives better 1-day estimates than methods which ignore the heavy tails of the innovations or the stochastic nature of the volatility. With the help of our fitted models we adopt a Monte Carlo approach to estimating the conditional quantiles of returns over multiple-day horizons and find that this outperforms the simple square-root-of-time scaling method.     

Managing extreme risks in tranquil and volatile markets using conditional extreme value theory

Financial risk management typically deals with low-probability events in the tails of asset price distributions. To capture the behavior of these tails, one should therefore rely on models that explicitly focus on the tails. Extreme value theory (EVT)-based models do exactly that, and in this paper, we apply both unconditional and conditional EVT models to the management of extreme market risks in stock markets. We find conditional EVT models to give particularly accurate Value-at-Risk (VaR) measures, and a comparison with traditional (Generalized ARCH (GARCH)) approaches to calculate VaR demonstrates EVT as being the superior approach both for standard and more extreme VaR quantiles.     

Conditional VaR using EVT - Towards a planned margin scheme

This paper constructs a robust Value-at-Risk (VaR) measure for the Indian stock markets by combining two well-known facts about equity return time series — dynamic volatility resulting in the well-recognized phenomenon of volatility clustering, and non-normality giving rise to fat tails of the return distribution. While the phenomenon of volatility dynamics has been extensively studied using GARCH model and its many relatives, the application of Extreme Value Theory (EVT) is relatively recent in tracking extreme losses in the study of risk measurement. There are recent applications of Extreme Value Theory to estimate the unexpected losses due to extreme events and hence modify the current methodology of VaR. Extreme value theory (EVT) has been used to analyze financial data showing clear non-normal behavior. We combine the two methodologies to come up with a robust model with much enhanced predictive abilities. A robust model would obviate the need for imposing special ad hoc margins by the regulator in times of extreme volatility. A rule based margin system would increase efficiency of the price discovery process and also the market integrity with the regulator no longer seen as managing volatility.     

El efecto de la volatilidad del peso mexicano en los rendimientos y riesgo de la Bolsa Mexicana de Valores

The effect of heavy tails due to rare events and different levels of asymmetry associated with high volatility clustering in the emerging financial markets requires sophisticated models for statistical modelling of such stylized facts. This article applies extreme value theory (EVT) to quantify tail risk on the daily returns of Mexican stock market under aggregation of foreign exchange rate risk from January 1971 to December 2010. This study focuses on the maximum-block method and generalized extreme value distribution (GEVD) to model the asymptotic behavior of extreme returns in US dollars. The empirical results show that EVT-Based VaR measured at high confidence levels performs better than simulation historical and delta-normal VaR models on capturing fat-tails in the returns of highly volatile stock markets. Additionally, international investors holding long positions in Mexican stock market are more prone to experience larger potential losses than investors with short positions during local currency depreciation and financial crisis periods.     

A Generalized Extreme Value Approach to Financial Risk Measurement

This paper develops an unconditional and conditional extreme value approach to calculating value at risk (VaR), and shows that the maximum likely loss of financial institutions can be more accurately estimated using the statistical theory of extremes. The new approach is based on the distribution of extreme returns instead of the distribution of all returns and provides good predictions of catastrophic market risks. Both the in-sample and out-of-sample performance results indicate that the Box–Cox generalized extreme value distribution introduced in the paper performs surprisingly well in capturing both the rate of occurrence and the extent of extreme events in financial markets. The new approach yields more precise VaR estimates than the normal and skewed t distributions.     

Risk management under extreme events

This article presents two applications of extreme value theory (EVT) to financial markets: computation of value at risk (VaR) and cross-section dependence of extreme returns (i.e., tail dependence). We use a sample comprised of the United States, Europe, Asia, and Latin America. Our main findings are the following. First, on average, EVT gives the most accurate estimate of VaR. Second, tail dependence of paired returns decreases substantially when both heteroscedasticity and serial correlation are filtered out by a multivariate GARCH model. Both findings are in agreement with previous research in this area for other financial markets.     

Estimation of tail‐related risk measures in the Indian stock market: An extreme value approach

The purpose of the study is to estimate tail-related risk measures using extreme value theory (EVT) in the Indian stock market. The study employs a two stage approach of conditional EVT originally proposed by McNeil and Frey (2000) to estimate dynamic Value at Risk (VaR) and expected shortfall (ES). The dynamic risk measures have been estimated for different percentiles for negative and positive returns. The estimates of risk measures computed under different quantile levels exhibit strong stability across a range of the selected thresholds, implying the accuracy and reliability of the estimated quantile based risk measures.     

基于GARCH族模型的黄金市场的风险度量与预测研究

本文以上海和伦敦黄金市场的现货交易为对象,比较研究了不同分布假定下RiskMetrics、GARCH族及其衍生模型度量VaR值的精确程度,并对超前一天预测的VaR值进行了失败率检测和动态分位数测试。结果表明:两个市场的收益率分布均具有尖峰厚尾、波动集聚和长记忆性等特征;学生t分布很好的刻画了上海黄金市场的风险特征,而正态分布则适合描述伦敦黄金市场特征;上海黄金市场相比于伦敦黄金市场风险更大。