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Authors: Kevin R. Keane and Jason J. Corso

Affiliation: University at Buffalo and The State University of New York, United States

ISBN: 978-989-8425-99-7

Keyword(s): Bayesian inference, Dynamic linear models, Multi-process models, Statistical arbitrage.

Related Ontology Subjects/Areas/Topics: Applications ; Economics, Business and Forecasting Applications ; Pattern Recognition

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.

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Paper citation in several formats:
Keane, K. and J. Corso, J. (2012). DYNAMICALLY MIXING DYNAMIC LINEAR MODELS WITH APPLICATIONS IN FINANCE.In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8425-99-7, pages 295-302. DOI: 10.5220/0003712602950302

@conference{icpram12,
author={Keane, K. and Jason J. Corso.},
title={DYNAMICALLY MIXING DYNAMIC LINEAR MODELS WITH APPLICATIONS IN FINANCE},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2012},
pages={295-302},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003712602950302},
isbn={978-989-8425-99-7},
}

TY - CONF

JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - DYNAMICALLY MIXING DYNAMIC LINEAR MODELS WITH APPLICATIONS IN FINANCE
SN - 978-989-8425-99-7
AU - Keane, K.
AU - J. Corso, J.
PY - 2012
SP - 295
EP - 302
DO - 10.5220/0003712602950302

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