Forecasting Global Equity Indices Using Large Bayesian VARs

Florian Huber, Tamas Krisztin, Philipp Piribauer

Publication: Working/Discussion PaperWU Working Paper

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Abstract

This paper proposes a large Bayesian Vector Autoregressive (BVAR) model with common stochastic volatility to forecast global equity indices. Using a dataset consisting of monthly data on global stock indices the BVAR model inherently incorporates co-movements in the stock markets. The time-varying specification of the covariance structure moreover accounts for sudden shifts in the level of volatility. In an out-of-sample forecasting application we show that the BVAR model with stochastic volatility significantly outperforms the random walk both in terms of root mean squared errors as well as Bayesian log
predictive scores. The BVAR model without stochastic volatility, on the other hand, underperforms relative to the random walk. In a portfolio allocation exercise we moreover show that it is possible to use the forecasts obtained from our BVAR model with common stochastic volatility to set up simple investment strategies. Our results indicate that these simple investment schemes outperform a naive buy-and-hold strategy. (authors' abstract)
Original languageEnglish
DOIs
Publication statusPublished - 1 Oct 2014

Publication series

SeriesDepartment of Economics Working Paper Series
Number184

WU Working Paper Series

  • Department of Economics Working Paper Series

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