Multivariate time-varying parameter modelling for stock markets

NESLİHANOĞLU S. , Bekiros S., McColl J., Lee D.

Empirical Economics, vol.61, no.2, pp.947-972, 2021 (Journal Indexed in SCI Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 61 Issue: 2
  • Publication Date: 2021
  • Doi Number: 10.1007/s00181-020-01896-2
  • Title of Journal : Empirical Economics
  • Page Numbers: pp.947-972
  • Keywords: CAPM, Multivariate model, State space model, Stock market returns, Systematic covariance (beta) risk, Time-varying beta, RISK, EQUILIBRIUM, PORTFOLIOS, BETA


© 2020, Springer-Verlag GmbH Germany, part of Springer Nature.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.