VOLATILIDADE ESTOCÁSTICA VIA VEROSSIMILHANÇA DE MONTE CARLO: UM ESTUDO COMPARATIVO / STOCHASTIC VOLATILITY VIA MONTE CARLO LIKELIHOOD: A COMPARATIVE STUDY

AUTOR(ES)

RAPHAEL PIMENTEL DE OLIVEIRA CRUZ

DATA DE PUBLICAÇÃO

2004

RESUMO

This dissertation discusses the estimation of the Stochastic Volatility (SV)model using a Durbin &Koopman methodology called Monte Carlo Like-lihood (MCL). The conditional coverage of value at risk (VaR) of SV via MCL model was compared to the GARCH (1,1) model and to the SV model via Quasi Maximum Likelihood (QML) estimation. The models were extended to Gaussian and Student-t isturbances in the mean equation. The performances of the models were evaluated out-of-sample for daily returns on the Ibovespa, S&P500, Nasdaq and Dow Jones indexes. Christoffersen test were applied for the evaluation criteria. In terms of the VaR conditional coverage, empirical evidences indicate that the SV model via MCL estimation is as efficient as the GARCH (1,1) model.

ASSUNTO(S)

importance sampling espaco de estado state space verossimilhanca de monte carlo amostragem por importancia monte carlo likelihood stochastic volatility volatilidade estocastica

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