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Stochastic Volatility in Peruvian Stock Market and Exchange Rate Returns: a Bayesian Approximation

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This study is one of the first to utilize the SV model to model Peruvian financial series, as well as estimating and comparing with GARCH models with normal and t-student errors. The analysis in this study corresponds to Peru’s stock market and exchange rate returns. The importance of this methodology is that the adjustment of the data is better than the GARCH models using the assumptions of normality in both models. In the case of the SV model, three Bayesian algorithms have been employed where we evaluate their respective inefficiencies in the estimation of the model’s parameters being the most efficient the Integration sampler. The estimated parameters in the SV model under the various algorithms are consistent, as they display little inefficiency. The Figures of the correlations of the iterations suggest that there are no problems at the time of Markov chaining in all estimations. We find that the volatilities in exchange rate and stock market volatilities follow similar patterns over time. That is, when economic turbulence caused by the economic circumstances occurs, for example, the Asian crisis and the recent crisis in the United States, considerable volatility was generated in both markets.


Stochastic Volatility Model, Bayesian Estimation, Gibbs Sampler, Mixture Sampler, Integration, Stock Market, Forex Market, GARCH Models, Peru

JEL Classification