Estimation of stochastic volatility models: An empirical study of China's stock market

WU Xin-yu, MA Chao-qun, WANG Shou-yang

Systems Engineering - Theory & Practice ›› 2014, Vol. 34 ›› Issue (1) : 35-44.

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Systems Engineering - Theory & Practice ›› 2014, Vol. 34 ›› Issue (1) : 35-44. DOI: 10.12011/1000-6788(2014)1-35

Estimation of stochastic volatility models: An empirical study of China's stock market

  • WU Xin-yu1, MA Chao-qun2, WANG Shou-yang3
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Abstract

Based on efficient importance sampling (EIS) technique, we propose the maximum likelihood (ML) method to estimate the stochastic volatility (SV) models based on four different return distributions. Taking Shanghai Stock Exchange composite and Shenzhen Stock Exchange component indices as an example, we empirically test the performance of the SV models based on different return distributions, and aim to find the appropriate return distribution for China's stock market. Empirical results demonstrate that the SV model based on the skew student's t-distribution (SVSKt model) which can account for skewed and peaked and heavy-tailed returns provides significant improvement in model fit over the SV models based on the normal, the student's t and the generalized error distributions (GED).

Key words

stochastic volatility model / skew / peak and heavy tails / efficient importance sampling / maximum likelihood method

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WU Xin-yu , MA Chao-qun , WANG Shou-yang. Estimation of stochastic volatility models: An empirical study of China's stock market. Systems Engineering - Theory & Practice, 2014, 34(1): 35-44 https://doi.org/10.12011/1000-6788(2014)1-35

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