Estimating stochastic Volatility Models Through Indirect Inference
University of Bologna - Department of Economics; Institute for the Study of Labor (IZA)
The Econometrics Journal, Vol. 1, 1998
We propose as a tool for the estimation of stochastic volatility models two indirect inference estimators based on the choice of an autoregressive auxiliary model and an ARMA auxiliary model, respectively. These choices make the auxiliary parameter easy to estimate and at the same time allow the derivation of optimal indirect inference estimators. The results of some Monte Carlo experiments provide evidence that the indirect inference estimators perform well in finite sample, although less efficiently than Bayes and Simulated EM algorithms.
JEL Classification: C10, C15, G12Accepted Paper Series
Date posted: April 7, 1999
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