Premium
FORECASTING GLOBAL EQUITY INDICES USING LARGE BAYESIAN VARS
Author(s) -
Huber Florian,
Krisztin Tamás,
Piribauer Philipp
Publication year - 2017
Publication title -
bulletin of economic research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.227
H-Index - 29
eISSN - 1467-8586
pISSN - 0307-3378
DOI - 10.1111/boer.12094
Subject(s) - volatility (finance) , econometrics , stochastic volatility , economics , bayesian probability , autoregressive model , equity (law) , random walk , bayesian vector autoregression , stock (firearms) , portfolio , covariance , implied volatility , financial economics , mathematics , statistics , mechanical engineering , engineering , political science , law
This paper proposes a large Bayesian Vector Autoregressive (BVAR) model with common stochastic volatility to forecast global equity indices. Using a monthly dataset on global stock indices, the BVAR model controls for co‐movement commonly observed in global stock markets. Moreover, the time‐varying specification of the covariance structure 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 point as well as density predictions. The BVAR model without stochastic volatility, on the other hand, shows some merits relative to the random walk for forecast horizons greater than six months ahead. In a portfolio allocation exercise we moreover provide evidence that it is possible to use the forecasts obtained from our 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.