基于条件极值分布的金融高频数据VaR动态估计模型

高频数据由于自身数量大、周期短、信息丰富的特点而受到关注。基于高频数据。对金融时间序列的厚尾特征进行条件极值分布下的VaR估计。在对条件均值和条件波动率估计时,以往采用一阶自回归模型和GARCH模型,但基于高频数据的估计较为繁复。为了充分利用日内信息,基于高频样本观测值,建立已实现均值RM模型,在考虑市场异质性的基础上,对条件均值进行估计。通过对TCL股票价格进行实证分析,估计出VaR风险值,验证模型是合理的。     

基于高频数据的波动率预测研究

基于高频数据的金融分析与建模研究目前已成为金融工程研究领域的一大热点。在金融资产价格波动率的刻画上,金融高频波动率有着低频波动率无法比拟的信息优势,能够较为准确地刻画金融市场波动率的相关特征,并对金融市场波动率的变化做出较为精确的预测。本文选择基于高频数据的沪深300指数为样本,通过构建已实现波动率和已实现极差的长记忆性模型去研究高频数据建模预测中的方法,以对比研究的形式分析了已实现波动率和已实现极差在波动率预测中的能力大小,为高频数据波动率预测研究提供了参考和借鉴。     

基于高频数据的波动率预测研究

基于高频数据的金融分析与建模研究目前已成为金融工程研究领域的一大热点.在金融资产价格波动率的刻画上,金融高频波动率有着低频波动率无法比拟的信息优势,能够较为准确地刻画金融市场波动率的相关特征,并对金融市场波动率的变化做出较为精确的预测.本文选择基于高频数据的沪深300指数为样本,通过构建已实现波动率和已实现极差的长记忆性模型去研究高频数据建模预测中的方法,以对比研究的形式分析了已实现波动率和已实现极差在波动率预测中的能力大小,为高频数据波动率预测研究提供了参考和借鉴.     

高频价格数据信息对资产组合动态风险测度的影响研究——基于实现协方差矩阵模型的分析

组合风险的估计和预测一直都是风险管理中非常重要的一个方面。本文使用了利用高频数据信息的实现协方差矩阵、DCC-MVGARCH多元波动率模型、Risk Metrics模型和多元正交GARCH模型对沪深两市的指数资产组合风险在险价值的预测失败率进行了对比,并利用动态分位数检验方法对各模型的组合风险测度稳健性进行了对比研究。研究结果证明,基于高频数据的实现协方差矩阵模型能够显著提高组合风险测度的预测精度,且严格符合Va R置信区间所要求的失败率,能够很好地在提高资金使用效率与管理资产组合风险敞口间取得平衡。     

基于GARCH模型VaR方法的银行板块风险研究

本文对GARCH模型和VaR方法在风险管理方面的应用先做了介绍,然后通过选取2008年1月2日至2012年11月21日银行类板块指数日收盘价作为数据样本,运用GARCH模型VaR方法对我国银行板块的风险进行了实证分析.实证结果表明我国银行股股指对数收益率序列存在集聚效应,GARCH模型能够很好的描述我国银行板块的波动情况,因而测量的VaR值能较好的反映我国银行板块面临的风险程度.     

基于R-GARCH模型与HAR模型预测能力比较研究

本文采用沪深300日间隔为5分钟的高频数据,构建了日间收益序列和日已实现极差波动(RRV)序列,然后分别建立R-GARCH模型与HAR模型,并采用M-Z回归及损失函数作为判别准则对两类模型的波动预测能力进行了测度。结果表明,无论从M-Z回归结果还是损失函数值来看,R-GARCH模型都要优于HAR模型。     

基于GARCH模型的VaR方法在证券投资决策中的应用

由于股票市场的收益率具有尖峰厚尾及波动聚集的特性,使得对收益率的分析不能采用一般的方法,可以利用GARCH模型中的条件方差来度量其VaR,以此来消除股票日收益率的ARCH效应。将GARCH模型应用于我国证券市场中股票日收益率风险波动的计量,以民生银行和华夏银行为例,根据预测到的VaR值来计量股票风险波动的大小,从而得到同一行业中的哪只股票更有投资价值,为投资者提供正确的投资决策。     

由GARCH 模型探讨深圳股市风险价值的应用--基于1997～2013样本数据实证分析

中国自改革开放经济快速成长,人们在追逐高额回报率的背后,高风险也伴随而来。近年来投资者对风险的意识逐渐抬头,如何采用适当模型与方法对风险进行预测,是当前金融研究领域的热门话题。本文采用 GARCH（1,1）模型对深证综指收益率序列进行研究,以VaR方法作为计算风险值的依据,进行波动率探讨。从实证的结果可知,GARCH（1,1）模型虽能预测深证综指的波动情况,但存在低估风险的情况。     

基于lnRV-VaR模型的沪深300股指期货风险测度与管理研究

鉴于低频数据信息采集的缺失和参数法VaR正态假设的局限,文章首先引入了高频数据模型,将参数法VaR应用于对数已实现波动率分布序列;然后建立lnRV-VaR模型对沪深300股指期货进行风险测度,并验证了这一风险测度模型的有效性和可操作性;最后提出可利用lnRV-VaR模型来改进市场风险β系数、设立动态保证金水平,将其应用于程序化交易等风险管理的对策建议。     

基于“已实现”波动率的ARFIMA模型预测实证研究

本文采用二次移动平均方法平衡影响"已实现"波动率预测精度的测量误差和市场微观结构误差,利用沪深300指数高频数据实证研究,结果表明"已实现"波动率序列的分布是非正态分布且具有长记忆性,对数"已实现"波动率序列接近于正态分布;最后建立ARFIMA模型,并对波动率进行了预测研究。     

Forecasting Value-at-Risk using high frequency data: The realized range model

Current studies on financial market risk measures usually use daily returns based on GARCH type models. This paper models realized range using intraday high frequency data based on CARR framework and apply it to VaR forecasting. Kupiec LR test and dynamic quantile test are used to compare the performance of VaR forecasting of realized range model with another intraday realized volatility model and daily GARCH type models. Empirical results of Chinese Stock Indices show that realized range model performs the same with realized volatility model, which performs much better than daily models.     

A Mixture Multiplicative Error Model for Realized Volatility

A multiplicative error model with time-varying parameters andan error term following a mixture of gamma distributions isintroduced. The model is fitted to the daily realized volatilityseries of deutschemark/dollar and yen/dollar returns and isshown to capture the conditional distribution of these variablesbetter than the commonly used autoregressive fractionally integratedmoving average model. The forecasting performance of the newmodel is found to be, in general, superior to that of the setof volatility models recently considered by Andersen et al.(2003, Econometrica 71, 579–625) for the same data.     

A Bayesian encompassing test using combined value-at-risk estimates

The Value at Risk (VaR) is a risk measure that is widely used by financial institutions in allocating risk. VaR forecast estimation involves the conditional evaluation of quantiles based on the currently available information. Recent advances in VaR evaluation incorporate conditional variance into the quantile estimation, yielding the Conditional Autoregressive VaR (CAViaR) models. However, the large number of alternative CAViaR models raises the issue of identifying the optimal quantile predictor. To resolve this uncertainty, we propose a Bayesian encompassing test that evaluates various CAViaR models predictions against a combined CAViaR model based on the encompassing principle. This test provides a basis for forecasting combined conditional VaR estimates when there are evidences against the encompassing principle. We illustrate this test using simulated and financial daily return data series. The results demonstrate that there are evidences for using combined conditional VaR estimates when forecasting quantile risk.     

Predicting VaR for China's stock market: A score-driven model based on normal inverse Gaussian distribution

Under the framework of dynamic conditional score, we propose a parametric forecasting model for Value-at-Risk based on the normal inverse Gaussian distribution (Hereinafter NIG-DCS-VaR), which creatively incorporates intraday information into daily VaR forecast. NIG specifies an appropriate distribution to return and the semi-additivity of the NIG parameters makes it feasible to improve the estimation of daily return in light of intraday return, and thus the VaR can be explicitly obtained by calculating the quantile of the re-estimated distribution of daily return. We conducted an empirical analysis using two main indexes of the Chinese stock market, and a variety of backtesting approaches as well as the model confidence set approach prove that the VaR forecasts of NIG-DCS model generally gain an advantage over those of realized GARCH (RGARCH) models. Especially when the risk level is relatively high, NIG-DCS-VaR beats RGARCH-VaR in terms of coverage ability and independence.     

Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective

This article applies realized volatility forecasting to Extreme Value Theory (EVT). We propose a two-step approach where returns are first pre-whitened with a high-frequency based volatility model, and then an EVT based model is fitted to the tails of the standardized residuals. This realized EVT approach is compared to the conditional EVT of McNeil & Frey (2000). We assess both approaches' ability to filter the dependence in the extremes and to produce stable out-of-sample VaR and ES estimates for one-day and ten-day time horizons. The main finding is that GARCH-type models perform well in filtering the dependence, while the realized EVT approach seems preferable in forecasting, especially at longer time horizons.     

A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data

We consider the problem of deriving an empirical measure ofdaily integrated variance (IV) in the situation where high-frequencyprice data are unavailable for part of the day. We study threeestimators in this context and characterize the assumptionsthat justify their use. We show that the optimal combinationof the realized variance and squared overnight return can bedetermined, despite the latent nature of IV, and we discussthis result in relation to the problem of combining forecasts.Finally, we apply our theoretical results and construct fouryears of daily volatility estimates for the 30 stocks of theDow Jones Industrial Average.     

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。     

典型事实、极值理论与金融市场动态风险测度研究

本文在金融市场典型事实约束下,运用ARFIMA模型对金融市场条件收益率建模,运用GARCH、GJR、FIGARCH、APARCH、FIAPARCH等5种模型对金融波动率进行建模,进而运用极值理论(EVT)对标准收益的极端尾部风险建模来测度各股市的动态风险,并用返回测试(Back-testing)方法检验模型的适应性。实证结果表明,总的来说,FIAPARCH-EVT模型对各个市场具有较强的适应性,风险测度能力较为优越。进一步,本文在ARFIMA-FIAPARCH模型下,假定标准收益分别服从正态分布(N)、学生t分布(st)、有偏学生t分布(skst)、广义误差分布(GED)共4种分布,对各股市的动态风险测度的准确性进行检验,并和EVT方法的测度结果进行对比分析。结果表明,EVT方法风险测度能力优于其他方法,有偏学生t分布假设下的风险测度模型虽然略逊于EVT方法,但也不失为一种较好的方法;ARFIMA-FI-APARCH-EVT不仅在中国大陆沪深股市表现最为可靠,而且在其他市场也表现出同样的可靠性。     

Properties of Bias-Corrected Realized Variance Under Alternative Sampling Schemes

In this article I study the statistical properties of a bias-correctedrealized variance measure when high-frequency asset prices arecontaminated with market microstructure noise. The analysisis based on a pure jump process for asset prices and explicitlydistinguishes among different sampling schemes, including calendartime, business time, and transaction time sampling. Two mainfindings emerge from the theoretical and empirical analysis.First, based on the mean-squared error (MSE) criterion, a biascorrection to realized variance (RV) allows for the more efficientuse of higher frequency data than the conventional RV estimator.Second, sampling in business time or transaction time is generallysuperior to the common practice of calendar time sampling inthat it leads to a further reduction in MSE. Using IBM transactiondata, I estimate a 2.5-minute optimal sampling frequency forRV in calendar time, which drops to about 12 seconds when afirst-order bias correction is applied. This results in a morethan 65% reduction in MSE. If, in addition, prices are sampledin transaction time, a further reduction of about 20% can beachieved.     

VaR模型比较技术及其评价——理论、实证回顾及其应用初探

随着VaR模型的不断创新,各类VaR模型的比较技术也由采用单一的回顾测试转变为设计一套较完整的比较评价体系来进行比较研究。近十年来,通过将指标评价工具、假设检验工具和比较评价工具三类VaR模型的比较工具进行不同的组合,学术界涌现出了大量相关的理论和实证文献。本文尝试对其进行较全面的回顾,并结合中国实际进行初步探索。