Novo Working Paper - A Hybrid Data Cloning Maximum Likelihood Estimator for Stochastic Volatility Models

A Hybrid Data Cloning Maximum Likelihood Estimator for Stochastic Volatility Models

Márcio Laurini

(IBMEC Business School)

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.

É a primeira versão do artigo de HDC-MLE para a estimação de modelos SV. É o desenvolvimento de uma idéia que apresentei durante o mini-curso de modelos de volatilidade que apresentei na Escola de Séries Temporais no ano passado.