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Multivariate stochastic volatility modelling using Wishart autoregressive processes

Kostas Triantafyllopoulos ()

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Abstract: A new multivariate stochastic volatility estimation procedure for financial time series is proposed. A Wishart autoregressive process is considered for the volatility precision covariance matrix, for the estimation of which a two step procedure is adopted. The first step is the conditional inference on the autoregressive parameters and the second step is the unconditional inference, based on a Newton-Raphson iterative algorithm. The proposed methodology, which is mostly Bayesian, is suitable for medium dimensional data and it bridges the gap between closed-form estimation and simulation-based estimation algorithms. An example, consisting of foreign exchange rates data, illustrates the proposed methodology.

Date: 2013-11
New Economics Papers: this item is included in nep-ecm and nep-ets
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Published in Journal of Time Series Analysis, 2012, 33, 48-60

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Journal Article: Multi‐variate stochastic volatility modelling using Wishart autoregressive processes (2012) Downloads
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