The hierarchical-likelihood approach to autoregressive stochastic volatility models

Woojoo Lee, Johan Lim, Youngjo Lee, Joan Del Castillo

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

Many volatility models used in financial research belong to a class of hierarchical generalized linear models with random effects in the dispersion. Therefore, the hierarchical-likelihood (h-likelihood) approach can be used. However, the dimension of the Hessian matrix is often large, so techniques of sparse matrix computation are useful to speed up the procedure of computing the inverse matrix. Using numerical studies we show that the h-likelihood approach gives better long-term prediction for volatility than the existing MCMC method, while the MCMC method gives better short-term prediction. We show that the h-likelihood approach gives comparable estimations of fixed parameters to those of existing methods. © 2010 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)248-260
JournalComputational Statistics and Data Analysis
Volume55
DOIs
Publication statusPublished - 1 Jan 2011

Keywords

  • Autoregressive stochastic volatility model
  • Hierarchical generalized linear model
  • Hierarchical likelihood
  • Prediction
  • Sparse matrix computation

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