Numerical Integration Filters for Maximum Likelihood Estimation of Asymmetric Stochastic Volatility Models
AbstractI consider two filtering algorithms (quadrature and mixture Gaussian) based on numerical integration for maximum likelihood estimation of stochastic volatility models with leverage. These algorithms extend straightforwardly to stochastic volatility models with non-Gaussian innovations. A small Monte Carlo simulation experiment shows that the mixture Gaussian filter performs remarkably well both in terms of accuracy and computation time. As an empirical application, I fit the asymmetric stochastic volatility model to the S&P 500 index daily returns with a Gaussian and skew-t innovation. The estimates from the two filtering algorithms are remarkably similar, suggesting the usefulness of the mixture Gaussian filter for practical use
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2005 with number 154.
Date of creation: 11 Nov 2005
Date of revision:
stochastic volatility; nonlinear filtering; mixture Gaussian; numerical integration;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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