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• W2395679582 abstract "Time varying model parameters offer tremendous flexibility while requiring more sophisticated learning methods. We discuss on-line estimation of time varying DLM parameters by means of a dynamic mixture model composed of constant parameter DLMs. For time series with low signal-to-noise ratios, we propose a novel method of constructing model priors. We calculate model likelihoods by comparing forecast distributions with observed values. We utilize computationally efficient moment matching Gaussians to approximate exact mixtures of path dependent posterior densities. The effectiveness of our approach is illustrated by extracting insightful time varying parameters for an ETF returns model in a period spanning the 2008 financial crisis. We conclude by demonstrating the superior performance of time varying mixture models against constant parameter DLMs in a statistical arbitrage application." @default.
• W2395679582 created "2016-06-24" @default.
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• W2395679582 date "2012-01-01" @default.
• W2395679582 modified "2023-10-04" @default.
• W2395679582 title "DYNAMICALLY MIXING DYNAMIC LINEAR MODELS WITH APPLICATIONS IN FINANCE" @default.
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• W2395679582 doi "https://doi.org/10.5220/0003712602950302" @default.
• W2395679582 hasPublicationYear "2012" @default.
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