中小板证券市场VaR估计精度实证研究

本文首先针对深市综合指数收益进行了基本统计,其次在二值损失函数标准和平方损失函数双重检验标准下,对三类VaR模型的估计精度进行了实证检验。结果表明：非参数类和半参数类度量模型对于我国中小板证券市场风险估计的精度较高,而参数类VaR模型的估计精度最差。由于参数类VaR模型主要采用了正态假定且忽略了波动率的聚集性,因此,在一定程度上也说明了我国中小板证券市场收益不符合正态性假定且存在波动率聚集现象。     

关于风险度量方法VaR的研究

VaR作为金融市场风险测量的主流工具,目前己被全球各主要银行、投资公司、证券公司及金融监管机构广泛采用.因此,本文试图通过对VaR的估计方法、VaR的估计模型以及在金融市场风险中的应用进行系统阐述,在此基础上探求与中国金融市场特点相适应的度量风险的有效VaR方法,这有利于准确预测和控制中国金融市场风险,对我国的金融市场建设具有重要的现实意义.     

关于风险度量方法VaR的研究

VaR作为金融市场风险测量的主流工具,目前己被全球各主要银行、投资公司、证券公司及金融监管机构广泛采用。因此,本文试图通过对wRN估计方法、VaR的估计模型以及在金融市场风险中的应用进行系统阐述,在此基础上探求与中国金融市场特点相适应的度量风险的有效VaR方法,这有利于准确预测和控制中国金融市场风险,对我国的金融市场建设具有重要的现实意义。     

新巴塞尔协议框架与VaR方法的运用

作为当前最重要的风险管理方法之一,VaR被运用于金融风险管理的各个方面,商业银行的风险管理也是其应用的重要领域.在新巴塞尔协议的框架下,基于VaR的风险度量模型已被应用于商业银行面临的全部三类风险:信用风险、市场风险和操作风险.该模型运用先进的数量技术,定量地分析了商业银行的风险程度,为商业银行度量风险并相应地配置风险资本金给出了明确的依据.     

商业银行操作风险计量的极值模型实证研究

本文利用极值理论中的BMM模型、POT模型分别对中国商业银行2004-2011年度的225起操作风险损失事件采用广义极值分布、广义帕累托分布构建VaR模型,运用极大似然估计法估计有关参数,进而计算操作风险损失VaR.运用基本指标法对BMM模型和POT模型的计量结果进行验证以确定优劣.实证研究显示,在数据较少条件下,用POT模型度量操作风险损失VaR较BMM模型更为合理,这为我国商业银行操作风险计量提供一个切实可行的量化方法.     

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。     

上证综指收益波动性及VaR度量研究

基于对上证综指日回报序列分布分别作正态分布、t分布和广义误差分布(GED)的假设基础上,采用(E)GARCH模型和方差-协方差法,度量了上海股票市场的潜在风险和波动性。在验证了三个模型对VaR估计的有效性之后,得出AR(1)-EGARCH(1,1)-M-GED模型对上海股票市场的拟合最优,并得出了有效的VaR估值。     

浅析VaR测量模型对金融市场的度量

VaR风险度量是目前国内外金融界度量风险普遍使用的评估方法.本文简要阐述了VaR测量模型度量金融市场风险的优缺点及今后我国风险管理理论研究和金融界领域的研究方向.     

基于VaR的投资组合综合风险研究

在VaR(Value at Risk)模型基础上,融入了内生流动性风险和外生流动性风险,建立了一个较新的综合度量市场风险和流动性风险的LaVaRen(liquidity adjusted VaR endogenous)模型。最后利用一个中等规模的开放式基金投资组合进行实证研究。对于机构投资者特别是开放式基金,考虑内生流动性风险的综合风险度量模型准确性更高。     

碳排放权交易价格的VaR风险度量研究

为研究广义自回归条件异方差(GARCH)模型是否适用于测量碳排放权交易价格的风险,以2013年11月28日—2018年7月31日的北京市碳排放权交易价格为对象展开研究,通过建立ARMA-GARCH族模型估计和预测碳排放权交易价格的VaR,并比较各模型度量碳排放权交易价格风险的准确性。结果显示:标准化残差服从重新参数化的Johnson Su分布的ARMA(2, 3)-EGARCH(1, 1)和ARMA(2, 3)-NGARCH(1, 1)模型在95%、99%置信水平下通过了Christoffersen联合检验,适用于度量碳排放权交易价格的风险。     

Markets liquidity risk under extremal dependence: Analysis with VaRs methods

Value-at-Risk (VaR) is a widely used tool for assessing financial market risk. In practice, the estimation of liquidity extreme risk by VaR generally uses models assuming independence of bid–ask spreads. However, bid–ask spreads tend to occur in clusters with time dependency, particularly during crisis period. Our paper attempts to fill this gap by studying the impact of negligence of dependency in liquidity extreme risk assessment of Tunisian stock market. The main methods which take into account returns dependency to assess market risk is Time series–Extreme Value Theory combination. Therefore we compare VaRs estimated under independency (Variance–Covariance Approach, Historical Simulation and the VaR adjusted to extreme values) relatively to the VaR when dependence is considered. The efficiency of those methods was tested and compared using the backtesting tests. The results confirm the adequacy of the recent extensions of liquidity risk in the VaR estimation. Therefore, we prove a performance improvement of VaR estimates under the assumption of dependency across a significant reduction of the estimation error, particularly with AR (1)-GARCH (1,1)-GPD model.     

VaR模型在套期保值比率计算中的实证分析

提出了考虑套期保值期内不同期限价格风险的最小平均VaR套期保值比率计算模型。基于我国外汇市场及股票市场数据,用最小平均VaR套期保值模型进行了实证分析,并同常用的最小方差及最小VaR套期保值模型进行了对比,得出了最小平均VaR模型在套期保值过程中的效果要优于其他两种模型,并能更有效地降低投资者提前终止套期保值可能面临额外风险的结论。     

A novel nonlinear value-at-risk method for modeling risk of option portfolio with multivariate mixture of normal distributions

This paper proposes a novel nonlinear model for calculating Value-at-Risk (VaR) when the market risk factors of an option portfolio are heavy-tailed. A multivariate mixture of normal distributions is used to depict the heavy-tailed market risk factors and accordingly a closed form expression for the moment generating function that can reflect the change in option portfolio value can be derived. Moreover, in order to make use of the correlation between the characteristic function and the moment generating function, Fourier-Inversion method and adaptive Simpson rule with iterative algorithm of numerical integration into the nonlinear VaR model for option portfolio are applied for calculation of VaR values of option portfolio. VaR values of option portfolio obtained from different methods are compared. Numerical results of Fourier-Inversion method and Monte Carlo simulation method show that high accuracy VaR values can be obtained when risk factors have multivariate mixture of normal distributions than when they have normal distributions. Moreover, VaR values obtained by using the Fourier-Inversion method are not obviously different from VaR values obtained by using Monte Carlo simulation when market risk factors have normal distributions or multivariate mixture of normal distributions. However, the speed of computation is obviously faster when using Fourier-Inversion method, than when using Monte Carlo simulation method. Besides, Cornish Fisher method is faster and simpler than Monte Carlo simulation method or Fourier-Inversion method. However, this method does not offer high accuracy and cannot be used to calculate VaR values of option portfolio when market risk factors have heavy-tailed distributions.     

Estimating value-at-risk under a Heath–Jarrow–Morton framework with jump

This article proposes a new methodology for measuring Value-at-Risk (hereafter VaR) using a model that incorporates both volatility and jumps. Heath–Jarrow–Morton (HJM) model has been used for the valuation of interest rate derivatives. This study extends the use of HJM model to the estimation VaR. This article specifically uses a two-factor HJM jump-diffusion model for the computation. The study models the Eurodollar futures prices using its derivatives. In addition, this article uses a new volatility specification of Ze-To (2002) to construct the HJM dynamics. The result indicates that the VaR model using HJM jump-diffusion framework performs well in capturing the nonnormality and in providing accurate VaR forecasts in the in-sample and out-sample tests.     

基于事件研究法的“大小非”解禁关于股票市场风险研究

通过比较＂大小非＂解禁事件前后不同时期的风险价值VaR,来评价大小非解禁对证券市场风险的影响。首先针对股票收益率序列具有波动聚集以及尖峰、厚尾的分布形态,应用GARCH类模型计算解禁前后各一段时期内沪深两市不同解禁量股票的VaR;其次应用多种定性、定量统计方法对所计算的VaR值进行前后分析比较,分析结果表明,采用的方法能够很好地捕捉到＂大小非＂解禁事件增大股票市场风险趋势这一现象。     

Realized volatility models and alternative Value-at-Risk prediction strategies

We assess the Value-at-Risk (VaR) forecasting performance of recently proposed realized volatility (RV) models combined with alternative parametric and semi-parametric quantile estimation methods. A benchmark inter-daily GJR-GARCH model is also employed. Based on four asset classes, i.e. equity, FOREX, fixed income and commodity, and a turbulent six year out-of-sample period (2007–2013), we find that statistical accuracy and regulatory compliance is essentially improved when we use quantile methods which account for the fat tails and the asymmetry of the innovations distribution. In particular, empirical analysis gives evidence in favor of the skewed student distribution and the Extreme Value Theory (EVT) method. Nonetheless, efficiency of VaR estimates, as defined by the minimization of Basel II capital requirements and its opportunity costs, is reassured only with the use of realized volatility models. Overall, empirical evidence support the use of an asymmetric HAR realized volatility model coupled with the EVT method since it produces statistically accurate VaR forecasts which comply with Basel II accuracy mandates and allows for more efficient capital allocations.     

Fuzzy value-at-risk and expected shortfall for portfolios with heavy-tailed returns

This paper is concerned with linear portfolio value-at-risk (VaR) and expected shortfall (ES) computation when the portfolio risk factors are leptokurtic, imprecise and/or vague. Following Yoshida (2009), the risk factors are modeled as fuzzy random variables in order to handle both their random variability and their vagueness. We discuss and extend the Yoshida model to some non-Gaussian distributions and provide associated ES. Secondly, assuming that the risk factors' degree of imprecision changes over time, original fuzzy portfolio VaR and ES models are introduced. For a given subjectivity level fixed by the investor, these models allow the computation of a pessimistic and an optimistic estimation of the value-at-risk and of the expected shortfall. Finally, some empirical examples carried out on three portfolios constituted by some chosen French stocks, show the effectiveness of the proposed methods.     

Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model

In this study, we propose a non-linear random mapping model called GELM. The proposed model is based on a combination of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the Extreme Learning Machine (ELM), and can be used to calculate Value-at-Risk (VaR). Alternatively, the GELM model is a non-parametric GARCH-type model. Compared with conventional models, such as the GARCH models, ELM, and Support Vector Machine (SVM), the computational results confirm that the GELM model performs better in volatility forecasting and VaR calculation in terms of efficiency and accuracy. Thus, the GELM model can be an essential tool for risk management and stress testing.     

Modeling the fat tails in Asian stock markets

We test whether stock returns in the Asian markets are characterized by infinite variance or just large variance, which has an important implication for the applicability of many financial models in Asian market data. Employing the extreme value framework, we find that the Asian index return distributions are fat-tailed but have finite variance. However, the tails of the distributions behave similarly to those in the U.S. and the MSCI World index returns, suggesting that any financial model or risk management tool that incorporates the second moment would work equally well for the Asian market data as it does for developed market data. We apply the Value-at-Risk method using Asian and U.S. data and find no significant difference in performance.     

基于VaR的沪深300股指期货风险管理实证研究

我国以沪深300为标的指数的股指期货即将推出。股指期货在具有控制风险功能的同时,也与其他金融衍生产品一样,具有风险性,且其风险远远大于股票现货市场。因此,必须采用积极的风险管理技术,加强对股指期货的风险防范。在GARCH模型的基础上,采用VaR方法对我国的沪深300股指期货仿真交易进行定量研究,计算出它们的VaR值,并将其与期望值进行比较。经过对比分析可以得出:基于GARCH模型的VaR方法适合我国的股指期货风险管理。