Multivariate time-varying parameter modelling for stock markets
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Multivariate time-varying parameter modelling for stock markets Serdar Neslihanoglu1
· Stelios Bekiros2,3 · John McColl4 · Duncan Lee4
Received: 9 October 2019 / Accepted: 29 May 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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 (TvLMM) 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 MTvLMM, 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. Keywords CAPM · Multivariate model · State space model · Stock market returns · Systematic covariance (beta) risk · Time-varying beta JEL Classification C53 · G12 · G17
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Serdar Neslihanoglu [email protected]
1
Department of Statistics, Faculty of Science and Letters, Eskisehir Osmangazi University, Eskisehir, Turkey
2
Department of Economics, European University Institute, Florence, Italy
3
Rimini Centre for Economic Analysis, Wilfrid Laurier University, Waterloo, Canada
4
School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
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S. Neslihanoglu et al.
1 Introduction The Two-Moment Capital Asset Pricing Model (CAPM) which is consistent with the Linear Market Model (LMM) was introduced by Sharpe (1964), Lintner (1965) and Mossin (1966) independently. The CAPM will be used as benchmark model for this study since it is considered the most common and widely used asset pricing model in financial literature. The validity of this model depends on various restrictive conditions which will be discussed in this paper. It has been observed that there is a linear relationship between the anticipated return of a financial asset and the entire market in which this financial asset is being traded. The slope coefficient of CAPM is termed as systematic covariance (beta) risk, which is commonly assumed to be constant over time and is estimated via Ordinary Least Squares (OLS). At the moment, there is substantial empirical evidence which demonstrates that this model may be an insufficient tool for the characterision of financial time series (see, e.g. Mergner and Bulla 2008; Choudhry and Wu 2009; Neslihanoglu 2014; Neslihanoglu et al
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