Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/87519 
Erscheinungsjahr: 
2011
Schriftenreihe/Nr.: 
Tinbergen Institute Discussion Paper No. 11-090/4
Verlag: 
Tinbergen Institute, Amsterdam and Rotterdam
Zusammenfassung: 
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.
Schlagwörter: 
Fractional Integration
Importance Sampling
Kalman Filter
Latent Factors
Stochastic Volatility
JEL: 
C33
C43
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Dokumentart: 
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