资产相关结构对投资组合风险测度的影响分析

文章从分析金融资产收益率的统计特征入手,以GARCH模型为基础,用非对称幂分布描述组合资产中各金融资产收益率的边缘分布函数,在多种Copula函数情形下计算组合资产的风险值VaR及ES。结果表明:基于由多元Clayton Copula和多元Gumbel Copula组成的混合Copula函数较好地刻画了多只股票的相关结构,而且ES比VaR能够较准确地估计组合资产的尾部风险。     

资产相关结构对投资组台风险测度的影响分析

文章从分析金融资产收益率的统计特征入手,以GARCH模型为基础.用非对称幂分布描述组合资产中各金融资产收益率的边缘分布函数,在多种Copula函数情形下计算组合资产的风险值VaR及ES.结果表明:基于由多元Clayton Copula和多元Gumbel Copula组成的混合Copula函数较好地刻画了多只股票的相关结构,而且ES比VaR能够较准确地估计组合资产的尾部风险.     

深成指GPD分布尾部拟合与VaR、ES风险度量

文章首先使用GARCH模型对深成指对数收益率时间序列的自相关和异方差特性进行弱化,之后运用POT方法对GARCH模型所得的残差序列进行拟合,使用极大似然方法估计了GPD分布各参数,进一步计算出收益率序列的VaR和ES值,通过后验测试比较各种估计方法优劣。     

VaR估计中的概率分布设定风险与改进

 在金融风险管理中,金融风险的事先判断具有极其重要的意义,然而金融机构金融决策事前支持技术的缺陷常常被忽略,在金融投资收益率概率分布估计方法尚未建立以前,将样本数据特征纳入风险度量的计算则不失为一种改进风险判断的有效途径。本文选择度量金融风险的主流方法—VaR技术来讨论概率分布设定风险,探讨依据数据特征改进和扩展VaR计算方法,通过对Delta-正态方法与Delta-Gamma-Cornish-Fisher扩展方法估计VaR值的比较,从实证分析角度论证了扩展方法在VaR估计中的有效性与稳健性。     

基于极值视角的沪深股市收益率的风险度量

文章以沪深股市收益率为研究对象,分别运用收益率的极值分布、收益率的POT模型以及基于收益率序列GARCH模型残差的POT方法计算沪深股市的在险价值(VaR).实证过程发现沪深股市收益率序列均存在明显的尖峰厚尾现象,实证结果表明深市潜在风险高于沪市潜在风险,并且三种方法中基于收益率序列的POT模型计算的VaR精度最高,而对收益率序列应用GARCH模型描述其波动后再对模型的残差进行POT方法计算的VaR精度最低.     

基于尾部指数回归方法的CVaR估计以及实证研究

 在险价值VaR是一种非常重要的金融风险度量方法,近期也有很多关于动态VaR以及条件VaR (CVaR) 等方面的研究。根据金融资产的收益率具有重尾特征这一事实,本文假定金融资产收益率服从重尾分布,并假定重尾分布的尾部指数随着收益率发生变化。本文基于尾部指数回归模型对重尾分布的尾部指数进行估计,进而得到收益率尾部数据所服从的条件分布,并首次运用该方法对条件VaR进行估计。本文对沪深300指数进行了实证研究,得到CVaR的估计,并对估计得到的CVaR的预测效果作出检验,并与传统VaR估计方法进行了对比,实证结果发现本文的方法的预测效果更好。     

中国股市的风险度量研究

本文从沪、深股指收益率的基本统计特征入手,用GARCH-GED模型和核估计模型分别估计了其VaR值,并对模型本身及其估计的VaR进行了较为严格的检验.结论显示GARCHGED模型能够反应股市的短期动态特征,而核估计模型估计的VaR反应了股市风险的长期特征,两个模型相互补充.     

基于核密度估计对VaR值计算方法的改进

文章从VaR方法的定义出发,首先对VaR值的两种基本计算方法进行阐述,进而基于核密度估计,提出一种改进的VaR值计算方法.该改进方法将蒙特卡罗模拟法引入到核密度估计规则,并且考虑四分位距来构造核密度估计的窗宽,对股市收益率的变异性以及高峰厚尾现象进行了更好地刻画.实证验证了改进的VaR值计算方法的有效性及优越性.     

基于ARMA—APARCH—SGED模型的原油价格风险度量研究

从原油现货市场收益率的特征分析着手,为了更好地描述原油现货市场收益率的尖峰厚尾、偏态和波动集聚等特性,利用APARCH模型来刻画收益率的波动性,同时利用SkewGED（SGED）分布来描述收益率的概率分布特征;进而运用削孙嗄A—AP灿配H—SGED模型对原油现货市场收益率的VaR进行估计和分析,并与基于Skew一t和Glm分布的ARMA—APARCH模型进行比较。通过返回检验,结果表明,AR—MA—AP魁配H—SGIm模型能更加准确地度量原油现货市场的风险价值。     

基于杠杆效应SV模型和EGARCH模型VaR的比较

文章通过对比EGARCH和杠杆效应SV模型,发现杠杆效应SV模型更能刻画金融市场的实际特征。将杠杆效应的随机波动SV模型应用于VaR的计算,并作实证研究。通过与EGARCH模型下的结果对比,得到基于杠杆效应SV模型计算的VaR更具有动态性和准确性,VaR更贴切地反映了金融市场的风险水平。     

基于EVT-POT-SV模型的极值风险度量

金融市场常受各种因素的影响造成剧烈波动,资产收益也会因此产生异常变化。针对金融资产收益的厚尾性、波动的异方差性等特征,采用基于Markov链的Monte Carlo模拟积分方法,对随机波动模型进行参数估计并取得标准残差序列,应用极值理论与随机波动模型相结合,建立了基于EVT-POT-SV的动态VaR模型。通过对上证综指收益做实证分析,结果表明：该模型能很好地刻画收益序列的波动性及尾部分布特征,在度量上证综指收益的风险方面更加合理而有效。     

Markov chain importance sampling with applications to rare event probability estimation

We present a versatile Monte Carlo method for estimating multidimensional integrals, with applications to rare-event probability estimation. The method fuses two distinct and popular Monte Carlo simulation methods—Markov chain Monte Carlo and importance sampling—into a single algorithm. We show that for some applied numerical examples the proposed Markov Chain importance sampling algorithm performs better than methods based solely on importance sampling or MCMC.     

Value at risk estimation under stochastic volatility models using adaptive PMCMC methods

In this paper, we propose a value-at-risk (VaR) estimation technique based on a new stochastic volatility model with leverage effect, nonconstant conditional mean and jump. In order to estimate the model parameters and latent state variables, we integrate the particle filter and adaptive Markov Chain Monte Carlo (MCMC) algorithms to develop a novel adaptive particle MCMC (A-PMCMC) algorithm. Comprehensive simulation experiments based on three stock indices and two foreign exchange time series show effectiveness of the proposed A-PMCMC algorithm and the VaR estimation technique.     

Simulation from a Target Distribution Based on Discretization and Weighting

We present a simulation method which is based on discretization of the state space of the target distribution (or some of its components) followed by proper weighting of the simulated output. The method can be used in order to simplify certain Monte Carlo and Markov chain Monte Carlo algorithms. Its main advantage is that the autocorrelations of the weighted output almost vanish and therefore standard methods for iid samples can be used for estimating the Monte Carlo standard errors. We illustrate the method via toy examples as well as the well-known dugongs and Challenger datasets.     

On the performance of some new Liu parameters for the gamma regression model

The maximum likelihood (ML) method is used to estimate the unknown Gamma regression (GR) coefficients. In the presence of multicollinearity, the variance of the ML method becomes overstated and the inference based on the ML method may not be trustworthy. To combat multicollinearity, the Liu estimator has been used. In this estimator, estimation of the Liu parameter d is an important problem. A few estimation methods are available in the literature for estimating such a parameter. This study has considered some of these methods and also proposed some new methods for estimation of the d. The Monte Carlo simulation study has been conducted to assess the performance of the proposed methods where the mean squared error (MSE) is considered as a performance criterion. Based on the Monte Carlo simulation and application results, it is shown that the Liu estimator is always superior to the ML and recommendation about which best Liu parameter should be used in the Liu estimator for the GR model is given.     

Pricing arithmetic Asian option under a two-factor stochastic volatility model with jumps

This paper presents an efficient Monte Carlo simulation scheme based on the variance reduction methods to evaluate arithmetic average Asian options in the context of the double Heston's stochastic volatility model with jumps. This paper consists of two essential parts. The first part presents a new flexible stochastic volatility model, namely, the double Heston model with jumps. In the second part, by combining two variance reduction procedures via Monte Carlo simulation, we propose an efficient Monte Carlo simulation scheme for pricing arithmetic average Asian options under the double Heston model with jumps. Numerical results illustrate the efficiency of our method.     

Bayesian covariance matrix estimation using a mixture of decomposable graphical models

We present a Bayesian approach to estimating a covariance matrix by using a prior that is a mixture over all decomposable graphs, with the probability of each graph size specified by the user and graphs of equal size assigned equal probability. Most previous approaches assume that all graphs are equally probable. We show empirically that the prior that assigns equal probability over graph sizes outperforms the prior that assigns equal probability over all graphs in more efficiently estimating the covariance matrix. The prior requires knowing the number of decomposable graphs for each graph size and we give a simulation method for estimating these counts. We also present a Markov chain Monte Carlo method for estimating the posterior distribution of the covariance matrix that is much more efficient than current methods. Both the prior and the simulation method to evaluate the prior apply generally to any decomposable graphical model.     

贷款组合信用风险VaR仿真计算的一种优化方法

This paper presents the principle of Monte Carlo optimize calculation of credit risk VaR for loanportfolio using Importance Sampling technique. Based on Matlab language, simulation experiments arecarried out and the result shows this approach can effectively reduce the numher of simulation runs andimprove the precision of parameter estimation.     

Value-at-risk estimation by LS-SVR and FS-LS-SVR based on GAS model

ABSTRACT

Conditional risk measuring plays an important role in financial regulation and depends on volatility estimation. A new class of parameter models called Generalized Autoregressive Score (GAS) model has been successfully applied for different error's densities and for different problems of time series prediction in particular for volatility modeling and VaR estimation. To improve the estimating accuracy of the GAS model, this study proposed a semi-parametric method, LS-SVR and FS-LS-SVR applied to the GAS model to estimate the conditional VaR. In particular, we fit the GAS(1,1) model to the return series using three different distributions. Then, LS-SVR and FS-LS-SVR approximate the GAS(1,1) model. An empirical research was performed to illustrate the effectiveness of the proposed method. More precisely, the experimental results from four stock indexes returns suggest that using hybrid models, GAS-LS-SVR and GAS-FS-LS-SVR provides improved performances in the VaR estimation.     

Assessing and comparing the accuracy of various bootstrap methods

We evaluate the performance of various bootstrap methods for constructing confidence intervals for mean and median of several common distributions. Using Monte Carlo simulation, we assessed performance by looking at coverage percentages and average confidence interval lengths. Poor performance is characterized by coverage deviating from 0.95 and large confidence interval lengths. Undercoverage is of greater concern than overcoverage. We also assess the performance of bootstrap methods in estimating the parameters of the Cox Proportional Hazard model and Accelerated Failure Time model.