Forecasting Global Equity Indices Using Large Bayesian VARs

Florian Huber, Tamás Krisztin and Philipp Piribauer ()

No 184, Department of Economics Working Paper Series from WU Vienna University of Economics and Business

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)

Keywords: BVAR; stochastic volatility; log-scores; equity indices; forecasting; Aktenindex; Prognose; Bayes-Verfahren (search for similar items in EconPapers)
Date: 2014-10
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Related works:
Journal Article: FORECASTING GLOBAL EQUITY INDICES USING LARGE BAYESIAN VARS (2017)
Working Paper: Forecasting Global Equity Indices using Large Bayesian VARs (2014)
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