基于已实现SV模型的动态VaR测度研究  

Study on dynamic VaR measures based on realized SV model

作　　者：吴鑫育[1] 周海林[1] WU Xin-yu;ZHOU Hai-lin(School of Finance,Anhui University of Finance and Economics,Bengbu 233030,China)

机构地区：[1]安徽财经大学金融学院,安徽蚌埠233030

出　　处：《管理工程学报》2018年第2期144-150,共7页Journal of Industrial Engineering and Engineering Management

基　　金：国家自然科学基金资助项目(71101001);教育部人文社会科学研究资助项目(14YJC790133);安徽省自然科学基金资助项目(1408085QG139);安徽省高等学校省级优秀青年人才基金重点资助项目(2013SQRW025ZD);安徽财经大学"资产价格与金融稳定"学科特区重点资助项目

摘　　要：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。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 highlighted 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 kernel,and many others.These meas

关 键 词：已实现SV模型 VAR 有偏广义误差分布 有效重要性抽样 极大似然 

分 类 号：F830.9[经济管理—金融学]