Beyond distance: The spatial relationships of European regional economic growth
In: Journal of economic dynamics & control, Band 155, S. 104735
ISSN: 0165-1889
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In: Journal of economic dynamics & control, Band 155, S. 104735
ISSN: 0165-1889
In: Bulletin of economic research, Band 69, Heft 3, S. 288-308
ISSN: 1467-8586
ABSTRACTThis 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.
In: JEDC-D-23-00109
SSRN
In: The journal of development studies, Band 59, Heft 4, S. 570-592
ISSN: 1743-9140
World Affairs Online
In: Journal of Industrial Ecology, Band 24, Heft 5, S. 1059-1073
SSRN
In: Journal of Industrial Ecology, Band 24, Heft 5, S. 1059-1073
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In: JEMA-D-23-04043
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In: IFAD Research Series No. 32 (2018); ISBN 978-92-9072-856-6
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Working paper