Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models

Geert Mesters, Siem Jan Koopman and Marius Ooms

No 11-090/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: An exact maximum likelihood method is developed for the estimation of parameters in a non-Gaussian nonlinear log-density function that depends on a latent Gaussian dynamic process with long-memory properties. Our method relies on the method of importance sampling and on a linear Gaussian approximating model from which the latent process can be simulated. Given the presence of a latent long-memory process, we require a modification of the importance sampling technique. In particular, the long-memory process needs to be approximated by a finite dynamic linear process. Two possible approximations are discussed and are compared with each other. We show that an auto-regression obtained from minimizing mean squared prediction errors leads to an effective and feasible method. In our empirical study we analyze ten log-return series from the S&P 500 stock index by univariate and multivariate long-memory stochastic volatility models.

Keywords: Fractional Integration; Importance Sampling; Kalman Filter; Latent Factors; Stochastic Volatility (search for similar items in EconPapers)
JEL-codes: C33 C43 (search for similar items in EconPapers)
Date: 2011-06-27
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Citations: View citations in EconPapers (4)

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Journal Article: Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20110090

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