Improved Quasi-Maximum Likelihood Estimation for Stochastic Volatility Models

F. Jay Breidt and Alicia L. Carriquiry
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F. Jay Breidt: Iowa State University, Department of Statistics
Alicia L. Carriquiry: Iowa State University, Department of Statistics

A chapter in Modelling and Prediction Honoring Seymour Geisser, 1996, pp 228-247 from Springer

Abstract: Abstract Jacquier, Poison and Rossi (1994, Journal of Business and Economic Statistics) have proposed a Bayesian hierarchical model and Markov Chain Monte Carlo methodology for parameter estimation and smoothing in a stochastic volatility model, where the logarithm of the conditional variance follows an autoregressive process. In sampling experiments, their estimators perform particularly well relative to a quasi-maximum likelihood approach, in which the nonlinear stochastic volatility model is linearized via a logarithmic transformation and the resulting linear state-space model is treated as Gaussian. In this paper, we explore a simple modification to the treatment of inlier observations which reduces the excess kurtosis in the distribution of the observation disturbances and improves the performance of the quasi-maximum likelihood procedure. The method we propose can be carried out with commercial software.

Keywords: Inliers; excess kurtosis; transformations (search for similar items in EconPapers)
Date: 1996
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4612-2414-3_14

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DOI: 10.1007/978-1-4612-2414-3_14

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