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Fat-tailed stochastic volatility model and the stock market returns in China

Fat-tailed stochastic volatility model and the stock market returns in China The purpose of this paper is to investigate how the selection of return distribution impacts estimated volatility in China’s stock market.Design/methodology/approachThe authors use a Bayesian analysis of fat-tailed stochastic volatility (SV) model with Student’s t-distribution, and conduct an out-of-sample test with realized volatility.FindingsEmpirical analysis results indicate that fat-tailed SV model performs better in capturing the dynamics of daily returns. The authors find that asymmetry, holiday and day of the week effects are detected in estimated volatility. However, the out-of-sample comparison shows that fat-tailed SV models fail to outperform SV models with normal distribution in fitting and predicting realized volatility.Originality/valueThe contribution of this paper to existing literature is twofold. First, it proves that fat-tailed SV models with Student’s t-distribution perform better than normally distributed SV models in fitting daily returns of China’s stock market. Second, this paper takes asymmetry, holiday and day of the week effects into consideration at the same time in the fat-tailed SV model. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png China Finance Review International Emerald Publishing

Fat-tailed stochastic volatility model and the stock market returns in China

China Finance Review International , Volume 11 (2): 15 – Apr 27, 2021

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2044-1398
DOI
10.1108/cfri-03-2018-0028
Publisher site
See Article on Publisher Site

Abstract

The purpose of this paper is to investigate how the selection of return distribution impacts estimated volatility in China’s stock market.Design/methodology/approachThe authors use a Bayesian analysis of fat-tailed stochastic volatility (SV) model with Student’s t-distribution, and conduct an out-of-sample test with realized volatility.FindingsEmpirical analysis results indicate that fat-tailed SV model performs better in capturing the dynamics of daily returns. The authors find that asymmetry, holiday and day of the week effects are detected in estimated volatility. However, the out-of-sample comparison shows that fat-tailed SV models fail to outperform SV models with normal distribution in fitting and predicting realized volatility.Originality/valueThe contribution of this paper to existing literature is twofold. First, it proves that fat-tailed SV models with Student’s t-distribution perform better than normally distributed SV models in fitting daily returns of China’s stock market. Second, this paper takes asymmetry, holiday and day of the week effects into consideration at the same time in the fat-tailed SV model.

Journal

China Finance Review InternationalEmerald Publishing

Published: Apr 27, 2021

Keywords: China; Bayesian; Realized volatility; Fat-tailed; Stochastic volatility; C58; C11

References