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Estimation of Asymmetric Box-Cox Stochastic Volatility Models Using MCMC Simulation

Listed author(s):
  • Xibin Zhang


  • Maxwell L. King


The stochastic volatility model enjoys great success in modeling the time-varying volatility of asset returns. There are several specifications for volatility including the most popular one which allows logarithmic volatility to follow an autoregressive Gaussian process, known as log-normal stochastic volatility. However, from an econometric viewpoint, we lack a procedure to choose an appropriate functional form for volatility. Instead of the log-normal specification, Yu, Yang and Zhang (2002) assumed Box-Cox transformed volatility follows an autoregressive Gaussian process. However, the empirical evidence they found from currency markets is not strong enough to support the Box-Cox transformation against the alternatives, and it is necessary to seek further empirical evidence from the equity market. This paper develops a sampling algorithm for the Box-Cox stochastic volatility model with a leverage effect incorporated. When the model and the sampling algorithm are applied to the equity market, we find strong empirical evidence to support the Box-Cox transformation of volatility. In addition, the empirical study shows that it is important to incorporate the leverage effect into stochastic volatility models when the volatility of returns on a stock index is under investigation.

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Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 10/03.

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Length: 29 pages
Date of creation: Apr 2003
Handle: RePEc:msh:ebswps:2003-10
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