RT Journal Article
SR 00
ID 10.1111/boer.12094
A1 Huber, F.
A1 Krisztin, T.
A1 Piribauer, P.
T1 Forecasting Global Equity Indices using Large Bayesian VARs
JF Bulletin of Economic Research
YR 2017
FD 2017-07-10
VO 69
IS 3
SP 288
OP 308
K1 BVAR; equity indices; forecasting; log-scores; stochastic volatility; C11; C22; C53; E17; G11
AB 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.
PB Wiley-Blackwell
SN 03073378
LK https://pure.iiasa.ac.at/id/eprint/13713/