IEEE Trans Pattern Anal Mach Intell - Gaussian Process-Mixture Conditional Heteroscedasticity.

Tópicos

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Resumo

Generalized autoregressive conditional heteroscedasticity (GARCH) models have long been considered as one of the most successful families of approaches for volatility modeling in financial return series. In this paper, we propose an alternative approach based on methodologies widely used in the field of statistical machine learning. Specifically, we propose a novel nonparametric Bayesian mixture of Gaussian process regression models, each component of which models the noise variance process that contaminates the observed data as a separate latent Gaussian process driven by the observed data. This way, we essentially obtain a Gaussian process-mixture conditional heteroscedasticity (GPMCH) model for volatility modeling in financial return series. We impose a nonparametric prior with power-law nature over the distribution of the model mixture components, namely the Pitman-Yor process prior, to allow for better capturing modeled data distributions with heavy tails and skewness. Finally, we provide a copula-based approach for obtaining a predictive posterior for the covariances over the asset returns modeled by means of a postulated GPMCH model. We evaluate the efficacy of our approach in a number of benchmark scenarios, and compare its performance to state-of-the-art methodologies.

Resumo Limpo

general autoregress condit heteroscedast garch model long consid one success famili approach volatil model financi return seri paper propos altern approach base methodolog wide use field statist machin learn specif propos novel nonparametr bayesian mixtur gaussian process regress model compon model nois varianc process contamin observ data separ latent gaussian process driven observ data way essenti obtain gaussian processmixtur condit heteroscedast gpmch model volatil model financi return seri impos nonparametr prior powerlaw natur distribut model mixtur compon name pitmanyor process prior allow better captur model data distribut heavi tail skew final provid copulabas approach obtain predict posterior covari asset return model mean postul gpmch model evalu efficaci approach number benchmark scenario compar perform stateoftheart methodolog

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