Abstract

In this paper, we describe and compare two simulated Maximum Likelihood estimation methods for a basic stochastic volatility model. For both methods, the likelihood function is estimated using importance sampling techniques. Based on a Monte Carlo study, we assess which method is more effective. Further, we validate the two methods using diagnostic importance sampling test procedures. Stochastic volatility models with Gaussian and Student-t distributed disturbances are considered.

Recommended Citation

Kai Ming Lee and Siem Jan Koopman (2004) "Estimating Stochastic Volatility Models: A Comparison of Two Importance Samplers", Studies in Nonlinear Dynamics & Econometrics: Vol. 8: No. 2, Article 5.
http://www.bepress.com/snde/vol8/iss2/art5

Related Files

Lee_code.zip (235 kB)
Code