Forecasting Global Equity Indices Using Large Bayesian VARs

Huber, Florian ORCID: and Krisztin, Tamás and Piribauer, Philipp (2014) Forecasting Global Equity Indices Using Large Bayesian VARs. Department of Economics Working Paper Series, 184. WU Vienna University of Economics and Business, Vienna.


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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)

Item Type: Paper
Keywords: BVAR / stochastic volatility / log-scores / equity indices / forecasting / Aktenindex / Prognose / Bayes-Verfahren
Classification Codes: RVK QH 237, SK 830 ; JEL C11, C22, C53, E17, G11
Divisions: Departments > Volkswirtschaft
Depositing User: Claudia Tering-Raunig
Date Deposited: 15 Oct 2014 09:19
Last Modified: 02 Sep 2020 15:19


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