Estimating Stochastic Volatility Models Through Indirect Inference

Posted: 7 Apr 1999

See all articles by Chiara Monfardini

Chiara Monfardini

University of Bologna - Department of Economics; IZA Institute of Labor Economics

Abstract

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, G12

Suggested Citation

Monfardini, Chiara, Estimating Stochastic Volatility Models Through Indirect Inference. Available at SSRN: https://ssrn.com/abstract=156712

Chiara Monfardini (Contact Author)

University of Bologna - Department of Economics ( email )

Piazza Scaravilli 2
Bologna, 40126
Italy
0039 51 2098148 (Phone)
0039 51 221968 (Fax)

IZA Institute of Labor Economics

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Bonn, D-53072
Germany

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