基于实现极差和实现波动率的中国金融市场风险测度研究

目前比较流行的金融市场风险价值研究一般采用日收益数据,并基于GARCH类模型进行估计和预测。本文利用沪深股指日内高频数据,分别通过ARFIMA模型和CARR模型对实现波动率和较新的实现极差建模,计算风险价值。通过对VaR的似然比和动态分位数等回测检验,实证分析了各种模型的VaR预测能力。结果显示,使用日内高频数据的实现波动率和实现极差模型的预测能力强于采用日数据的各种GARCH类模型。     

基于VaR与多尺度分析的保险业风险值估计

风险值(Value-at-Risk,VaR)是现今衡量风险的标准。本文利用风险值(VaR)方法来为风险基础资本估计风险,使其能够准确地呈现保险人本身所面临风险的状况,并利于监督机关建立适当的监督预警措施,来保障全体保险人权益并维持金融秩序的稳定。考虑到多尺度变换对估计报酬率风险型态模型无需作假设的优点,且小波变换是一种重要的多尺度分析工具,本文引入小波变换来对非线性的保险数据序列中提取频率域的高频信息,利用多尺度分解的系数得到模型参数,从而实现更加准确的风险值估计。     

基于动态半参数法的我国证券市场风险研究

本文用动态半参数法估计了我国证券市场的风险值VaR。由于金融市场存在利好与利空信息所带来的非对称性,因此本文利用所选数据,建立EGARCH波动模型,同时用参数法、历史模拟法计算了VaR,并用Kupiec(1995)的Back-test对每种方法的有效性进行了评价。突出了动态半参数法对数据拟合的优越性。     

基于GARCH-VAR模型的股指期货市场风险度量研究

本文以沪深300股指期货的日收盘价格数据为实证载体,基于GARCH族模型中残差的正态分布、t分布和广义误差分布（GED）三种不同情形,分别采用GARCH、TGARCH和PARCH模型计算不同置信水平下沪深300股指期货收益波动序列的VaR值,结果表明：分布假定和置信水平对VaR的计算结果有显著影响,GARCH模型的选择对估计结果影响较小。但综合比较来看,基于广义误差分布的GARCH模型的估计效果最优。     

基于GARCH模型的VaR方法在上证市场中的应用

VaR(Value at Risk,在险价值)作为市场风险的度量方法之一,能够有效地度量金融市场的风险。本文在介绍了VaR的概念、特点以及计算方法的基础之上,利用GARCH模型估计、预测股市的VaR值,并对上证指数进行风险度量的实证研究。分析结果表明基于GARCH模型的VaR方法能够较好地反映出股市的风险,适合在上证市场进行风险管理。     

我国交易所上市公司债宏观市场风险度量

以我国交易所上市公司债为研究对象,选取2008年6月至2014年6月中证公司债指数的每日收盘数据,基于ARMA（1,1）-GARCH（1,1）和ARMA（1,1）-TGARCH（1,1）模型分别估计了正态分布、t分布和GED分布假设下模型的参数和条件方差,并运用TGARCH对三种分布下的VaR值进行了估计。研究结果表明：在95%的置信水平下,正态分布和GED分布高估了风险值,而t分布低估了风险值,但正态分布给出的估计结果明显优于其他两种分布。在99%的置信水平下,三种分布均低估了风险值,但t分布和GED分布下的结果明显好于正态分布。     

VaR：非对称成分GARCH模型的应用

采用ACGARCH模型在正态分布和广义误差分布下对上证综合指数的VaR值进行估计,然后把它与应用GARCH模型的估计结果进行比较分析,并进行了Kupiec失败率检验。     

我国商业银行利率风险模型及实证分析

银行间同业拆借利率是商业银行利率风险测度的重要指标。我国银行间同业拆借利率中的隔夜拆借利率具有明显的尖峰、厚尾特征,可以利用ARCH族模型估计得到的条件标准差来度量其VaR值。我们对2007年我国商业银行数据进行了实证分析,结果表明,只有ARCH(1)和EARCH(1,1)模型能较好的拟合隔夜拆借利率,且从VAR值可以看出我国商业银行利率的日风险巨大。     

我国证券投资基金、股票市场和债券市场的溢出风险测度——来自上海证券市场的证据

本文基于VAR-DCC-MGARCH模型分析了沪市基金指数与股票指数和国债指数的波动相关性和溢出效应,估计了三个市场的VaR,并通过失败检验法进行了验证,研究发现:基金市场与股票市场的条件相关系数一直呈现正向相关关系;股票市场与国债市场以及基金市场与国债市场的动态条件相关系数具有很强的时变特征,而且走势呈现相似性,但是统计检验显示基金市场与国债市场的相关性不明显。基金市场对自身和股票市场存在显著波动溢出效应,股票市场对国债市场和基金市场存在一定显著的波动溢出效应。在给定期望损失概率下,发现基金指数收益率的VaR波动最为剧烈;股票指数收益率的VaR变动风险与均值的比值是最高的;结合剔除其他市场波动影响的VaR发现,三个市场的风险承受度更高,可以接受更大的损失收益率。     

极值理论在股指期货市场高频数据中的VaR研究

本文运用我国沪深300股指期货合约IF1106Q自2010年11月24日至2011年2月18日每5分钟的收盘价高频数据，引入广义帕雷托分布（GPD）代替传统的正态分布，精确描述金融高频数据损失序列的厚尾特征。进而估算不同置信水平下的VaR值，并进行返回检验，结果表明，极值理论方法可以比较精确地度量高频数据的VaR值。     

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.     

Asian Pacific Stock Market Volatility Modeling and Value at Risk Analysis

The potential for stock market growth in Asian Pacific countries has attracted foreign investors. However, higher growth rates come with higher risk. We apply value at risk (VaR) analysis to measure and analyze stock market index risks in Asian Pacific countries, exposing and detailing both the unique risks and system risks embedded in those markets. To implement the VaR measure, it is necessary to perform "volatility modeling" by mixture switch, exponentially weighted moving average (EWMA), or generalized autoregressive conditional heteroskedasticity (GARCH) models. After estimating the volatility parameters, we can calibrate the VaR values of individual and system risks. Empirically, we find that, on average, Indonesia and Korea exhibit the highest VaRs and VaR sensitivity, and currently, Australia exhibits relatively low values. Taiwan is liable to be in high-state volatility. In addition, the Kupiec test indicates that the mixture switch VaR is superior to delta normal VaR; the quadratic probability score (QPS) shows that the EWMA is inclined to underestimate the VaR for a single series, and GARCH shows no difference from GARCH t and GARCH generalized error distribution (GED) for a multivariate VaR estimate with more assets.     

High-frequency financial data modeling using Hawkes processes

Intraday Value-at-Risk (VaR) is one of the risk measures used by market participants involved in high-frequency trading. High-frequency log-returns feature important kurtosis (fat tails) and volatility clustering (extreme log-returns appear in clusters) that VaR models should take into account. We propose a marked point process model for the excesses of the time series over a high threshold that combines Hawkes processes for the exceedances with a generalized Pareto distribution model for the marks (exceedance sizes). The conditional approach features intraday clustering of extremes and is used to calculate instantaneous conditional VaR. The models are backtested on real data and compared to a competitor approach that proposes a nonparametric extension of the classical peaks-over-threshold method. Maximum likelihood estimation is computationally intensive; we use a differential evolution genetic algorithm to find adequate starting values for the optimization process.     

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.     

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.     

Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence

The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period value-at-risk (VaR) and expected shortfall (ES) across 20 stock indices worldwide. The dataset is composed of daily data covering the period from 1989 to 2009. The research addresses the question of whether or not accounting for long memory in the conditional variance specification improves the accuracy of the VaR and ES forecasts produced, particularly for longer time horizons. Accounting for fractional integration in the conditional variance model does not appear to improve the accuracy of the VaR forecasts for the 1-day-ahead, 10-day-ahead and 20-day-ahead forecasting horizons relative to the short memory GARCH specification. Additionally, the results suggest that underestimation of the true VaR figure becomes less prevalent as the forecasting horizon increases. Furthermore, the GARCH model has a lower quadratic loss between actual returns and ES forecasts, for the majority of the indices considered for the 10-day and 20-day forecasting horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve the VaR and ES forecasting accuracy, even for longer forecasting horizons. Finally, the rolling-sampled estimated FIGARCH parameters change less smoothly over time compared to the GARCH models. Hence, the parameters' time-variant characteristic cannot be entirely due to the news information arrival process of the market; a portion must be due to the FIGARCH modelling process itself.     

Mean and volatility dynamics of Indian rupee/US dollar exchange rate series: an empirical investigation

The paper is concerned with time series modelling of foreign exchange rate of an important emerging economy, viz., India, with due consideration to possible sources of misspecification of the conditional mean like serial correlation, parameter instability, omitted time series variables and nonlinear dependences. Since structural change is pervasive in economic time series relationships, the paper first studies this aspect of the exchange rate series in detail and finds the existence of four structural breaks. Accordingly, the entire sample period is divided into five sub-periods of stable parameters each, and then the appropriate mean specification for each of these sub-periods is determined by incorporating functions of recursive residuals. Thereafter, the GARCH and EGARCH models are considered to capture the volatility contained in the data. The estimated models thus obtained suggest that return on Indian exchange rate series is marked by instabilities and that the appropriate volatility model is EGARCH. Further, out-of-sample forecasting performance of the model has been studied by standard forecasting criteria, and then compared with that of an AR model only to find that the findings are quite favorable for the former.      

Time Variations and Covariations in the Expectation and Volatility of Stock Market Returns

This article investigates empirically the comovements of the conditional mean and volatility of stock returns. It extends the results in the literature by demonstrating the role of the commercial paper—Treasury yield spread in predicting time variation in volatility. The conditional mean and volatility exhibit an asymmetric relation, which contrasts with the contemporaneous relation that has been tested previously. The volatility leads the expected return, and this time series relation is documented using offset correlations, short-horizon contemporaneous correlations, and a vector autoregression. These results bring into question the value of modeling expected returns as a constant function of conditional volatility.     

A note on the importance of overnight information in risk management models

This paper examines the economic value of overnight information to users of risk management models. In addition to the information revealed by overseas markets that trade during the (domestic) overnight period, this paper exploits information generated via recent innovations in the structure of financial markets. In particular, certain securities (and associated derivative products) can now be traded at any time over a 24-h period. As such, it is now possible to make use of information generated by trading, in (almost) identical securities, during the overnight period. Of the securities that are available over such time periods, S&P 500 related products are by far the most actively traded and are, therefore, the subject of this paper. Using a variety of conditional volatility models that allow time-dependent information flow within (and across) three different S&P 500 markets, the results show that overnight information flow has a significant impact on the conditional volatility of daytime traded S&P 500 securities. Moreover (time-consistent) forecasts from models that incorporate overnight information are shown to have economic value to risk managers. In particular, Value-at-Risk (VaR) models based on these conditional volatility models are shown to be more accurate than VaR models that ignore overnight information.     

Value at risk forecasts by extreme value models in a conditional duration framework

The analysis of extremes in financial return series is often based on the assumption of independent and identically distributed observations. However, stylized facts such as clustered extremes and serial dependence typically violate the assumption of independence. This has been the main motivation to propose an approach that is able to overcome these difficulties by considering the time between extreme events as a stochastic process. One of the advantages of the method consists in its capability to capture the short-term behavior of extremes without involving an arbitrary stochastic volatility model or a prefiltration of the data, which would certainly affect the estimate. We make use of the proposed model to obtain an improved estimate for the value at risk (VaR). The model is then compared to various competing approaches such as Engle and Marianelli's CAViaR and the GARCH-EVT model. Finally, we present a comparative empirical illustration with transaction data from Bayer AG, a typical blue chip stock from the German stock market index DAX, the DAX index itself and a hypothetical portfolio of international equity indexes already used by other authors.