Estimating stochastic volatility models through indirect inference
Year: 1998
Type: article
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.
Source: Econometrics Journal
Author: Chiara Monfardini
Institution: European University Institute
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2091056387