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Frontiers of Economics in China

ISSN 1673-3444

ISSN 1673-3568(Online)

CN 11-5744/F

Postal Subscription Code 80-978

Front Econ Chin    2012, Vol. 7 Issue (1) : 22-43    https://doi.org/10.3868/s060-001-012-0002-6
research-article
Empirical Evidence of the Leverage Effect in a Stochastic Volatility Model: A Realized Volatility Approach
Dinghai Xu1(), Yuying Li2()
1. Department of Economics, University of Waterloo, Waterloo, ON N2L 3G1, Canada; 2. School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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Abstract

Increasing attention has been focused on the analysis of the realized volatility, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a proxy in a stochastic volatility model estimation. We estimate the leveraged stochastic volatility model using the realized volatility computed from five popular methods across six sampling-frequency transaction data (from 1-min to 60- min) based on the trust region method. Availability of the realized volatility allows us to estimate the model parameters via the MLE and thus avoids computational challenge in the high dimensional integration. Six stock indices are considered in the empirical investigation. We discover some consistent findings and interesting patterns from the empirical results. In general, the significant leverage effect is consistently detected at each sampling frequency and the volatility persistence becomes weaker at the lower sampling frequency.

Keywords realized volatility      stochastic volatility model      leverage effect      high frequency data      MLE      trust-region method     
Corresponding Author(s): Dinghai Xu,Email:dhxu@uwaterloo.ca; Yuying Li,Email:yuying@uwaterloo.ca   
Issue Date: 05 March 2012
 Cite this article:   
Dinghai Xu,Yuying Li. Empirical Evidence of the Leverage Effect in a Stochastic Volatility Model: A Realized Volatility Approach[J]. Front Econ Chin, 2012, 7(1): 22-43.
 URL:  
https://academic.hep.com.cn/fec/EN/10.3868/s060-001-012-0002-6
https://academic.hep.com.cn/fec/EN/Y2012/V7/I1/22
[1] George J. Jiang, Guanzhong Pan. Analysis of High Frequency Data in Finance: A Survey[J]. Front. Econ. China, 2020, 15(2): 141-166.
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