Abstract: The volatility risk premia contain important information about investor risk aversion, which has been the focus of much attention in financial econometrics literature. In this paper, we consider estimation of the volatility risk premia for the Hong Kong warrant market based on the non-affine GARCH diffusion stochastic volatility (SV) model. Using the Hong Kong Hang Seng Index and index warrant data, we develop the efficient importance sampling-based maximum likelihood (EIS-ML) method for estimating the model parameters (objective and risk-neutral parameters). Consequently, it allows us to infer the volatility risk premia. Empirical results demonstrate that, in the Hong Kong stock market, the volatility risk is priced by the market investors. In other words, there exists volatility risk premia in the market, and the volatility risk premia are positive in most cases, which implies that investors generally act risk seeking in the Hong Kong stock market.

Key words: volatility risk premia, GARCH diffusion model, stochastic volatility, maximum likelihood estimation

摘要： 波动率风险溢价包含了关于投资者风险厌恶的重要信息,它的估计是金融计量学文献关注的一个核心问题。本文基于香港权证市场数据和GARCH扩散随机波动率(SV)模型,对香港证券市场的波动率风险溢价进行了估计研究。采用香港恒生指数和指数权证数据,通过建立基于有效重要性抽样的极大似然(EIS-ML)方法联合估计了GARCH扩散模型的客观与风险中性测度,进而得到了香港证券市场的波动率风险溢价。研究结果发现,在香港证券市场上,市场投资者对波动率风险进行了定价,即存在波动率风险溢价,且波动率风险溢价在绝大多数情形下为正,说明市场投资者总体表现为风险爱好。

关键词: 波动率风险溢价, GARCH扩散模型, 随机波动率, 极大似然估计