基于已实现SV模型的动态VaR测度研究

摘要

Accurate measurement of financial market risks plays an important role for the survival and development of financial institutions,and the stability of the whole financial system.The recent 2007-2009 global financial crisis caused a broad impact on the real economy,and higMighted the necessity of financial market risk management.During the turbulent period of high volatility,accurate risk measurement and assessment are even more critical because there is a widespread risk of global financial instability.The most widely used market risk management tool is the so-called Value-at-Risk (VaR),which is widely used to assess the risk exposure of investments.It measures the worst expected loss over a given time horizon within a given confidence level.Numerous financial institutions,risk managers,and Bank for International Settlements (BIS) have adopted VaR as the first line of defense against market risk.VaR has become a standard risk measure used in financial risk management because of its conceptual simplicity,ease of computation,and applicability.It is well-known that the latent volatility of asset returns is a crucial factor in accurately estimating VaR.There is a remarkable amount of empirical evidence that financial market volatility is not a constant but in fact changes over time.In addition,volatility clustering has been observed in financial return data.The most popular models used to capture these empirical stylized facts of volatility are GARCH-type models and stochastic volatility (SV) models.Traditionally,VaR is computed based on these volatility models.However,traditional GARCH-type models and SV models use only daily returns for modelling volatility dynamics.Clearly,an individual return observed on a given day can provide only limited information about the volatility of asset return.High-frequency financial data are now widely available and many authors have recently introduced a large number of realized volatility measures,such as realized volatility,bipower variation,realized kemel,and many others.These measures are far more informative about the current level of volatility than the daily returns,which would provide a consistent estimator of the latent volatility in the ideal market condition.This motivates us to extend traditional volatility models that use only daily returns.In addition,this study takes advantage of additional volatility information provided by high-frequency intra-day data to measure financial market risks.It is now well-documented that the volatility of asset returns responds asymmetrically to market news (good news and bad news),which is known as the volatility asymmetric effect.Moreover,the distribution of financial asset returns generally exhibit skewness,leptokurtosis and heavy-tails.It is important to specify these empirical stylized facts of financial asset returns as failure to do so can result in a substantial bias in VaR estimates.Several studies have shown that the skewed generalized error distribution (sged) can successfully capture skewness,leptokurtosis and heavy-mils of financial asset returns,and yield more accurate VaR estimates than the alternatives.Based on the above analysis,this paper proposes the realized SV model with sged distribution (RSV-sged model),which incorporates the standard SV model that only uses daily returns with realized volatility which is constructed by using the intra-daily high-frequency data,empirically stylized facts of financial asset returns (skewness,leptokurtosis,heavy-tails and volatility asymmetry effect),and dynamic VaR measures.The efficient sampling technique is proposed to implement the maximum likelihood method for our proposed RSV-sged model.Empirical results for intra-day data of Shanghai Stock Exchange composite index and Shenzhen Stock Exchange component index demonstrate that our proposed RSV-sged model can describe efficiently the volatility dynamics of Chinese stock markets and produce more accurate VaR estimates than other models.%基于日内高频数据构建的已实现波动率测度在金融计量经济学文献中引起了学者们的广泛关注.将已实现波动率引入传统的SV模型(基于日度收益率),同时考虑金融资产收益率与波动率的“有偏”、“尖峰厚尾”以及“非对称效应”等典型特征事实,构建融合高频与低频数据信息的已实现SV(RSV)模型,与有偏广义误差分布(sged)相结合来测度动态风险值(VaR).为了估计RSV-sged模型的参数,提出基于有效重要性抽样技巧的极大似然方法.采用上证综合指数和深证成份指数日内高频数据进行的实证研究表明,RSV-sged模型能够有效地刻画中国股票市场的波动性特征,并且展现出优越的风险测度能力.