This paper generalizes the stochastic volatility model to allow for the double exponential jumps. To derive the jumps and time-varying volatility in returns, we implement an efficient Markov chain Monte Carlo approach based on the band and sparse matrix algorithms used in Chan and Hsiao (SSRN Electron J., 2013, https://doi.org/10.2139/ssrn.2359838) to estimate this model. We illustrate the the methodology using the daily data for the Shanghai Composite Index, Hangseng Index, Nikkei 225 Index and Kospi Index. We find that the stochastic volatility model with double exponential jumps provide better fitness in sample period.

本文对随机波动率模型进行了概括，以考虑双指数跳跃。为了得出收益的跳跃和时变波动率，我们基于Chan和Hsiao（SSRN Electron J.，2013，https：//doi.org/中的带和稀疏矩阵算法）实现了有效的马尔可夫链蒙特卡罗方法10.2139 / ssrn.2359838）来估算此模型。我们使用上证综合指数，恒生指数，日经225指数和Kospi指数的每日数据来说明该方法。我们发现具有双指数跳跃的随机波动率模型在样本期间提供了更好的适应性。