基于GARCH模型的VaR方法对我国开放式基金风险的分析

本文从我国开放式基金收益率序列的分布与波动性两方面建立了一个估计基金风险的VaR-GARCH模型,在正态分布和能够刻画收益率的尖峰厚尾特征的t分布GED分布三种不同的分布假设下,对基金的VaR值进行估计,并应用Kupiec失败频率检验方法对VaR模型的准确性进行了返回检验。结果显示,基于GED分布的GARCH模型计算的VaR值比基于正态分布和t分布GARCH模型计算的VaR值更真实地反映了基金的风险。     

对我国开放式基金风险的实证研究——基于GARCH模型的VaR方法

从我国开放式基金收益率序列的分布、波动性和杠杆效应三方面考虑,在正态分布、t分布和GED分布的假设下,-建立了估计基金风险的VaR—GARCH、VaR—EGARCH模型,选择合适的模型对各只基金及不同类型基金的VaR值进行估计,并应用Kupiec方法对VaR模型的准确性进行了返回检验。结果显示,基于GED分布的GARCH模型计算的VaR值比基于t分布的GARCH模型计算的VaR值更真实地反映了基金的风险,不同投资类型和投资风格的基金的风险也不尽相同。     

基于GARCH模型的VaR方法的实证分析——以开放式基金为研究对象

从我国开放式基金收益率序列的分布、波动性和杠杆效应三方面考虑,在正态分布、t分布和GED分布的假设下,建立了估计基金风险的VaR-GARCH、VaR-EGAKCH模型.选择合适的模型对各只基金及不同类型基金的VaR值进行估计,最好根据结果得出相应的结论.     

我国开放式基金市场收益率波动性研究

自从2001年首只开放式基金发行以来,我国开放式基金迅速发展,逐渐占据基金主体地位,但有关开放式基金市场收益率波动性研究的文献并不多。一般认为,自回归问题是时间序列数据所特有,而异方差性是横截面序列数据的特点,本文通过对2003年以来我国开放式基金市场收益率序列数据进行研究,发现我国开放式基金市场收益率序列不服从正态分布,具有明显的“尖峰厚尾”特征,并且收益率的波动存在明显的集聚性。运用GARCH（1,1）模型较好的描述了这一波动特征。     

我国开放式基金市场收益率波动性研究

自从2001年首只开放式基金发行以来,我国开放式基金迅速发展,逐渐占据基金主体地位,但有关开放式基金市场收益率渡动性研究的文献并不多.一般认为,自回归问题是时间序列数据所特有,而异方差性是横截面序列数据的特点,本文通过对2003年以来我国开放式基金市场收益率序列数据进行研究,发现我国开放式基金市场收益率序列不服从正态分布,具有明显的"尖峰厚尾"特征,并且收益率的波动存在明显的集聚性.运用GARCH(1,1)模型较好的描述了这一波动特征.     

Asymmetric-Laplace-AGARCH-M模型下的股指风险研究

本文采用非对称Laplace分布来拟合具有尖峰、厚尾、有偏特征的金融收益率残差序列,利用AGARCH-M模型来描述金融序列波动的＂杠杆效应＂,提出了准确有效的Asym-metric-Laplace-AGARCH-M模型来度量金融收益序列的ES。通过实证分析表明,Asymmetric-Laplace-AGARCH-M模型能很好地度量金融收益风险。     

基于FIGARCH模型的中国房地产指数的实证分析

在T分布和正态分布假设下采用GARCH模型和FIGARCH模型对上证地产股指数日收益率序列进行建模分析,结果表明,上证地产股指数日收益率序列的波动具有显著的长记忆性,表明外部冲击对波动有着长期的影响。因此,采用FIGARCH模型建模的效果优于采用GARCH模型建模的效果,并且在T分布假设下拟合模型,其效果优于在正态分布假设下拟合的模型。     

我国指数型开放式基金业绩实证研究

分析评价指数型开放式基金业绩,是一个现实而有意义的课题。其中的实证设计直接涉及如下主要因素:样本基金的选择、基准组合的选取、无风险利率和基金周收益率的计算、数据来源及应用软件的确定等。在此基础上,通过运用收益率评价法和风险调整收益法,对所选10只样本基金业绩进行对比研究后发现:样本基金的业绩与证券市场行情密切相关,受到股票市场环境的主导性影响;这种主导性影响主要体现在对开放式基金投资收益和风险的影响上。研究同时表明,指数型基金在我国目前市场状况下是一个比较理想的选时投资工具。     

不同分布下中国股市的VaR度量——基于ARFIMA-FIGACH模型

基于正态分布、学生-t分布、GED分布和Skewed-t分布四种不同分布,采用ARFIMA-FIGARCH模型对深圳股市收益率的风险值进行了动态建模。通过模型实证参数估计,发现深圳股市收益率序列存在双长记忆性特征;通过模型预测的VaR值的DQ和LR测试,发现在Skewed-t分布下,ARFIMA-FIGARCH模型能更有效地捕捉深圳股市收益率序列的特性,能够较好地反映金融收益率的实际风险。     

基于混合正态分布的风险价值度量

利用参数方法计算VaR的关键在于对收益率分布形式的假定是否合理.为了充分反映金融收益的统计特性,并更好地刻画厚尾特征,本文在利用ARMA-GARCH模型过滤了收益序列的自相关和波动聚类特性后,采用混合正态分布模型分析资产收益的VaR度量,并对上证综指获得的日收益率序列进行了实证研究.比较分析混合正态分布和正态分布两种假定下的VaR.结果表明:混合正态分布假设能够反映收益分布5%的厚尾特征并准确地刻画1%的厚尾部分.避免了正态分布假设低估风险的缺陷,保证了VaR的准确性.     

基于GARCH模型的证券投资基金VaR计算与实证研究

基于统计技术的度量金融市场风险值(Value at Risk,VaR)已成为测量市场风险的新标准和新方法。鉴此,如何高效、准确地进行VaR的计算将是问题所在。基于GARCH模型,讨论了对数收益率时间序列在正态、学生t和广义误差(GED)三种不同分布下的VaR计算方法,对样本基金的市场风险进行估计,并通过返回检验来检验模型的准确性。研究结果表明,基于GED分布的GARCH模型计算的VaR值最能真实地反映基金风险。     

Energy portfolio risk management using time-varying extreme value copula methods

This work is concerned with the statistical modeling of the dependence structure between three energy commodity markets (WTI crude oil, natural gas and heating oil) using the concept of copulas and proposes a method for estimating the Value at risk (VaR) of energy portfolio based on the combination of time series models with models of the extreme value theory before fitting a copula. Each return series is modeled by AR-(FI) GARCH univariate model. Then, we fit the GPD distribution to the tails of the residuals to model marginal residuals distributions. The extreme value copula to the iid residuals is fitted and we simulate from it to construct N portfolios and estimate VaR. As a first step, the method is applied to a two-dimensional energy portfolio. In second step, we extend method in trivariate context to measure VaR of three-dimensional energy portfolio. Dependences between residuals are modeled using a trivariate nested Gumbel copulas. Methods proposed are compared with various univariate and multivariate conventional VaR methods. The reported results demonstrate that GARCH-t, conditional EVT and FIGARCH extreme value copula methods produce acceptable estimates of risk both for standard and more extreme VaR quantiles. Generally, copula methods are less accurate compared with their predictive performances in the case of portfolio composed of exchange market indices.     

Option Pricing Based on Alternative Jump Size Distributions

It is well known that volatility smirks and heavy-tailed asset return distributions are two violations of the Black-Scholes model. This paper investigates the role of jump size distribution played in explaining these two abnormalities. We consider a jump-diffusion model with Laplace jump size distribution, in comparison to the conventional normal distribution. In addition, our analysis is built upon a pure exchange economy, in which the representative agent’s risk preference shows a fanning characteristic. We find that, when a fanning effect is present, Laplace model produces a more remarkable leptokurtic pattern of the risk-neutral distribution implied by options, as well as generating more pronounced volatility smirks than the normal model.     

中国股票市场ES和VaR的实证比较分析

以我国股票收益率为研究对象,分别在正态分布和非正态稳定分布条件下对ES和VaR的凸性、次可加性和有效性进行了实证比较分析,发现:在非正态稳定分布条件下VaR不满足凸性和次可加性,ES满足凸性和次可加性,在正态分布条件下VaR和ES都满足凸性和次可加性;在两种分布条件下ES的有效性都高于VaR的有效性,而在非正态稳定分布条件下ES的优势更加明显。由于本文的收益率分布拟合检验表明我国的股票收益率服从非正态稳定分布,所以在我国股票市场上ES是比VaR更好的风险度量。     

大宗农产品价格风险评估——基于小波神经网络-Bootstrap方法的实证研究

构建了一个将小波神经网络与Bootstrap抽样相结合的价格风险评估模型。采用国际通用的VaR(在险价值)风险指标评估了国内小麦、水稻、玉米、大豆和棉花5种主要大宗农产品现货价格的风险水平,仿真研究了以上大宗农产品价格下跌风险和价格上涨风险的分布特征。结果表明:按价格风险水平由高到低对5种主要大宗农产品进行排序依次为棉花、大豆、玉米、小麦和水稻;从风险均值来看,我国大宗农产品价格特别是粮食价格的风险处于较低水平;从风险的经验分布来看,除大豆外,其他大宗农产品(特别是小麦、水稻和玉米)的涨价风险高于跌价风险;5种农产品的价格均存在偏度风险和峰度风险。     

Long-Memory,Asymmetry and Fat-Tailed GARCH Models in Value-at-Risk Estimation: Empirical Evidence from the Global Real Estate Markets

In this paper, we address the question of whether long memory, asymmetry, and fat-tails in global real estate markets volatility matter when forecasting the two most popular measures of risk in financial markets, namely Value-at-risk (VaR) and Expected Shortfall (ESF), for both short and long trading positions. The computations of both VaR and ESF are conducted with three long memory GARCH-class models including the Fractionally Integrated GARCH (FIGARCH), Hyperbolic GARCH (HYGARCH), and Fractionally Integrated Asymmetric Power ARCH (FIAPARCH). These models are estimated under three alternative innovation’s distributions: normal, Student, and skewed Student. To test the efficacy of the forecast, we employ various backtesting methodologies. Our empirical findings show that considering for long memory, fat-tails, and asymmetry performs better in predicting a one-day-ahead VaR and ESF for both short and long trading positions. In particular, the forecasting ability analysis points out that the FIAPARCH model under skewed Student distribution turns out to improve substantially the VaR and ESF forecasts. These results may have several potential implications for the market participants, financial institutions, and the government.

     

Value at risk estimation by threshold stochastic volatility model

This article proposes a threshold stochastic volatility model that generates volatility forecasts specifically designed for value at risk (VaR) estimation. The method incorporates extreme downside shocks by modelling left-tail returns separately from other returns. Left-tail returns are generated with a t-distributional process based on the historically observed conditional excess kurtosis. This specification allows VaR estimates to be generated with extreme downside impacts, yet remains empirically widely applicable. This article applies the model to daily returns of seven major stock indices over a 22-year period and compares its forecasts to those of several other forecasting methods. Based on back-testing outcomes and likelihood ratio tests, the new model provides reliable estimates and outperforms others.     

我国银行间同业拆借市场利率风险度量

本文利用1996年1月5日至2008年9月17日的我国银行间隔日同业拆借利率序列,通过GARCH模型对收益数据中的自相关和异方差现象进行了实证研究,采用MLE方法估计模型参数,再利用所得参数分别计算了不同收益率分布假设下的不同置信水平的VaR值;在此基础上,进行回测检验,比较了各种模型估计效果,并进一步分析了我国同业拆借利率市场的系统性风险历史波动趋势;最后提出了相关结论与政策建议。