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A flexible observed factor model with separate dynamics for the factor volatilities and their correlation matrix

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In this article, we consider a novel regression model with observed factors. To allow for
the prediction of future observations, we model the observed factors using a flexible multivariate
stochastic volatility (MSV) structure with separate dynamics for the volatilities and the correlation
matrix. The correlation matrix of the factors is time varying, and its evolution is described by an
inverse Wishart process. We develop an estimation procedure based on Bayesian Markov chain Monte
Carlo methods, which has two major advantages compared to existing methods for similar models
in the literature. First, the procedure is computationally more efficient. Second, it can be applied to
calculate the predictive distributions for future observations. We compare the proposed model with
other multivariate volatility models using Fama-French factors and portfolio weighted return data.
The result shows that our model has better predictive performance.

Key words: correlated factors; time-varying covariance; inverse Wishart; Markov chain Monte Carlo;
stochastic volatility

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Ketersediaan

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Informasi Detail

Judul Seri

Statistical Modelling

No. Panggil

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Penerbit

London : SAGE., 2014

Deskripsi Fisik

Statistical Modelling 2014; 14(1): 1–20

Bahasa

English

ISBN/ISSN

DOI: 10.1177/1471082

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

2014; 14(1)

Subjek

STATISTIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

agus

Versi lain/terkait

Lampiran Berkas

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