摘要
经济物理学(econophysics)的大量研究表明,金融市场的波动具有复杂的多分形(multifractal)特征,因此准确地测度和预测市场波动,对金融风险管理工作的意义重大。在已有多分形波动率(multifractal volatility)测度及其模型应用基础上,以上证综指10年的高频数据为对象,提出了基于多分形波动率的样本外动态风险价值(out-of-sample dynamic VaR)预测法。通过两种规范的后验分析(backtesting)结果表明,与8种主流的线性和非线性GARCH族模型相比,在高风险水平上,基于多分形波动率测度的VaR模型明显具有更高的样本外动态风险预测精度。
Much literature in Econophysics reveals that the volatility in financial markets presents multi- fractal features. Thus, measuring and forecasting the market volatility accurately is very important for fi- nancial risk management. Based on the earlier research of multifractal volatility and its model, an out-of-sample dynamic VaR forecasting method is proposed in this paper. The empirical results on two backtesting techniques show that, on high-risk levels, VaR fnodel based on multifractal volatility produces much better out-of-sample VaR forecasts than eight popular linear and nonlinear GARCH models.
出处
《中国管理科学》
CSSCI
北大核心
2012年第5期7-15,共9页
Chinese Journal of Management Science
基金
国家自然科学基金资助项目(70771097
71071131
71090402)
教育部创新团队发展计划(PCSIRT0860)
中央高校基本科研业务费专项资金资助项目(SWJTU11ZT30
SWJTU11CX137)
