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Monte Carlo Likelihood Estimation for Three Multivariate Stochastic Volatility Models

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  • Borus Jungbacker
  • Siem Jan Koopman

Abstract

Estimating parameters in a stochastic volatility (SV) model is a challenging task. Among other estimation methods and approaches, efficient simulation methods based on importance sampling have been developed for the Monte Carlo maximum likelihood estimation of univariate SV models. This paper shows that importance sampling methods can be used in a general multivariate SV setting. The sampling methods are computationally efficient. To illustrate the versatility of this approach, three different multivariate stochastic volatility models are estimated for a standard data set. The empirical results are compared to those from earlier studies in the literature. Monte Carlo simulation experiments, based on parameter estimates from the standard data set, are used to show the effectiveness of the importance sampling methods.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 25 (2006)
Issue (Month): 2-3 ()
Pages: 385-408

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Handle: RePEc:taf:emetrv:v:25:y:2006:i:2-3:p:385-408

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Related research

Keywords: Importance sampling; Monte Carlo likelihood; Stochastic volatility;

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Cited by:
  1. Tsunehiro Ishihara & Yasuhiro Omori & Manabu Asai, 2013. "Matrix Exponential Stochastic Volatility with Cross Leverage," CIRJE F-Series CIRJE-F-904, CIRJE, Faculty of Economics, University of Tokyo.
  2. Jun Yu, 2007. "Automated Likelihood Based Inference for Stochastic Volatility Models," Working Papers 01-2007, Sim Kee Boon Institute for Financial Economics.
  3. Geert Mesters & Bernd Schwaab & Siem Jan Koopman, 2014. "A Dynamic Yield Curve Model with Stochastic Volatility and Non-Gaussian Interactions: An Empirical Study of Non-standard Monetary Policy in the Euro Area," Tinbergen Institute Discussion Papers 14-071/III, Tinbergen Institute.
  4. Siddhartha Chib & Yasuhiro Omori & Manabu Asai, 2007. "Multivariate stochastic volatility (Revised in May 2007, Handbook of Financial Time Series (Published in "Handbook of Financial Time Series" (eds T.G. Andersen, R.A. Davis, Jens-Peter Kreiss," CARF F-Series CARF-F-094, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  5. Christian N. Brinch, 2008. "Simulated Maximum Likelihood using Tilted Importance Sampling," Discussion Papers 540, Research Department of Statistics Norway.
  6. Siddhartha Chib & Yasuhiro Omori & Manabu Asai, 2007. "Multivariate stochastic volatility," CIRJE F-Series CIRJE-F-488, CIRJE, Faculty of Economics, University of Tokyo.

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