Predicting international equity returns: Evidence from time-varying parameter vector autoregressive models

Rangan Gupta, Florian Huber and Philipp Piribauer ()

International Review of Financial Analysis, 2020, vol. 68, issue C

Abstract: In this paper, we forecast monthly stock returns of eight advanced economies using a time-varying parameter vector autoregressive model (TVP-VAR) with mixture innovations. Compared to standard TVP-VARs, our proposed model automatically detects whether time-variation in the parameters is needed through the introduction of a latent process. This framework is capable of dynamically detecting whether a given regression coefficient is constant or time-varying during distinct time periods. We moreover compare the performance of this model with a wide range of nested alternative time-varying and constant parameter VAR models. Our results indicate that our proposed framework outperforms its competitors in terms of point and density forecasts. A portfolio allocation exercise confirms the superiority of our proposed model. In addition, a copula-based analysis shows that it pays off to adopt a multivariate modeling framework during periods of stress, like the recent financial crisis.

Keywords: International equity markets; Time-varying vector autoregression; Point and density forecasts; Portfolio allocation (search for similar items in EconPapers)
JEL-codes: C32 G10 G17 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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Working Paper: Predicting International Equity Returns: Evidence from Time-Varying Parameter Vector Autoregressive Models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:68:y:2020:i:c:s1057521918307555

DOI: 10.1016/j.irfa.2020.101456

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