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Multivariate time-varying parameter modelling for stock markets

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Abstract

This paper evaluates the appropriateness of a Linear Market Model (LMM) which allows for systematic covariance (beta) risk. The performance of LMM will be compared against two extensions, a comparison having yet to be undertaken in the literature. The first extension is the Time-varying Linear Market Model (Tv-LMM) which allows for time-varying systematic covariance risk in the form of a mean reverting state space model via the Kalman filter. The second extension is the multivariate Time-varying Linear Market Model (MTv-LMM) which allows for the time-varying systematic covariance risk of country stock market correlation structure via the multivariate KFMR. The comparison between LMM, Tv-LMM and MTv-LMM, is implemented utilising weekly data collected from several developed and emerging markets for the periods; before and after financial crisis in October 2008, and forecasting 2 years forwards. The empirical findings of that process overwhelmingly support the use of the Multivariate Time-varying Linear Market Model (MTv-LMM) when modelling and forecasting stock market returns, especially for developed stock markets.

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Authors and Affiliations

  1. Department of Statistics, Faculty of Science and Letters, Eskisehir Osmangazi University, Eskisehir, Turkey

    Serdar Neslihanoglu

  2. Department of Economics, European University Institute, Florence, Italy

    Stelios Bekiros

  3. Rimini Centre for Economic Analysis, Wilfrid Laurier University, Waterloo, Canada

    Stelios Bekiros

  4. School of Mathematics and Statistics, University of Glasgow, Glasgow, UK

    John McColl & Duncan Lee

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  1. Serdar Neslihanoglu
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  2. Stelios Bekiros
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  3. John McColl
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  4. Duncan Lee
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Correspondence to Serdar Neslihanoglu.

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Neslihanoglu, S., Bekiros, S., McColl, J. et al. Multivariate time-varying parameter modelling for stock markets. Empir Econ 61, 947–972 (2021). https://doi.org/10.1007/s00181-020-01896-2

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  • DOI: https://doi.org/10.1007/s00181-020-01896-2

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