A Hybrid Data Cloning Maximum Likelihood Estimator for Stochastic Volatility Models

Márcio Laurini

No 2012-02, IBMEC RJ Economics Discussion Papers from Economics Research Group, IBMEC Business School - Rio de Janeiro

Abstract: In this paper we analyze a maximum likelihood estimator using data cloning for stochastic volatility models.This estimator is constructed using a hybrid methodology based on Integrated Nested Laplace Approximations to calculate analytically the auxiliary Bayesian estimators with great accuracy and computational efficiency, without requiring the use of simulation methods as Markov Chain Monte Carlo. We analyze the performance of this estimator compared to methods based in Monte Carlo simulations (Simulated Maximum Likelihood, MCMC Maximum Likelihood) and approximate maximum likelihood estimators using Laplace Approximations. The results indicate that this data cloning methodology achieves superior results over methods based on MCMC, and comparable to results obtained by the Simulated Maximum Likelihood estimator.

Keywords: Stochastic Volatility: Data Cloning; Maximum Likelihood; MCMC; Laplace Approximations. (search for similar items in EconPapers)
JEL-codes: C53 E43 G17 (search for similar items in EconPapers)
Date: 2012-03-16
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Journal Article: A Hybrid Data Cloning Maximum Likelihood Estimator for Stochastic Volatility Models (2013)
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