基于跳跃厚尾随机波动模型的股市波动研究

通过对4个不同SV模型对比分析,试图了解股市收益序列中具有较大波动幅度的极端实现值能够被解释为一个非高斯分布的尾部行为,还是高斯扩散中一个跳跃组分的叠加,抑或是这两种设定同时起作用.采用两种具有不同波动程度的上证综指日收益数据进行的实证研究发现,我国股市日收益序列不仅存在显著的尖峰(厚尾)特征,而且波动持续性较低,以及受政府政策影响较多.此外,两组收益数据所对应模型的实证比较发现,跳跃设定有助于SV模型描述波动剧烈的收益序列,但却不适合波动平缓的收益序列.

Analysis on Stock Market Volatility by Using Stochastic Volatility Models with Fat-tailed Distributions and Jumps

By comparing four different stochastic volatility models, this paper attempted to find out whether the extreme realizations out of a stock market return time series can be interpreted as a tail behavior of a non-Gaussian distribution, or as the superimposition of a jump component on a Gaussian diffusion, or as a phenomenon in which both forces are at work within the same data generating process. An empirical study of Shanghai stock composition index daily returns data shows that China''s Shanghai stock market volatility is characterized by obvious leptokurtic (fat-tail) and lower persistence. In addition, the jump parameters can be estimated precisely due to enough excess returns, which means the stock market is heavily affected by various related government policies. And model comparisons between the two return series show that SV models with a jump component can sufficiently describe a return time series with dramatic moves, not one with mild volatility.