A Hybrid Data Cloning Maximum Likelihood Estimator for Stochastic Volatility Models

Márcio Laurini

Journal of Time Series Econometrics, 2013, vol. 5, issue 2, 193-229

Abstract: Abstract: In this article, 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 such as Markov Chain Monte Carlo. We analyze the performance of this estimator compared to methods based on 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, comparable to results obtained by the Simulated Maximum Likelihood estimator. The methodology is extended to models with leverage effects, continuous time formulations, multifactor and multivariate stochastic volatility.

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: 2013
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Citations: View citations in EconPapers (1)

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Working Paper: A Hybrid Data Cloning Maximum Likelihood Estimator for Stochastic Volatility Models (2012)
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DOI: 10.1515/jtse-2012-0025

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