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Multivariate Time Series Models for Asset Prices

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Handbook of Computational Finance

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Abstract

The modelling of multivariate financial time series has attracted an enormous interest recently, both from a theoretical and practical perspective. Focusing on factor type models that reduce the dimensionality and other models that are tractable in high dimensions, we review models for volatility, correlation and dependence, and show their application to quantities of interest such as value-at-risk or minimum-variance portfolio. In an application to a 69-dimensional asset price time series, we compare the performance of factor-based multivariate GARCH, stochastic volatility and dynamic copula models.

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Authors and Affiliations

  1. Institut de statistique and CORE, Université catholique de Louvain, Voie du Roman Pays 20, 1348, Louvain-la-Neuve, Belgium

    Christian M. Hafner

  2. Department of Social and Economic Statistics, University of Cologne, Cologne, Germany

    Hans Manner

Authors
  1. Christian M. Hafner
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  2. Hans Manner
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Correspondence to Christian M. Hafner .

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Editors and Affiliations

  1. , Risk Management Institute, National University of Singapore, 21 Heng Mui Keng Terrace, Level 4, Singapore, 119613, Singapore

    Jin-Chuan Duan

  2. L.v.Bortkiewicz Chair of Statistics, C.A.S.E. Centre f. Appl. Stat. & Econ., Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany

    Wolfgang Karl Härdle

  3. , Department of Computational and Data Sci, George Mason University, University Drive 4400, Fairfax, 22030, Virginia, USA

    James E. Gentle

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Hafner, C.M., Manner, H. (2012). Multivariate Time Series Models for Asset Prices. In: Duan, JC., Härdle, W., Gentle, J. (eds) Handbook of Computational Finance. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17254-0_5

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