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Economic significance of commodity return forecasts from the fractionally cointegrated VAR model
Author(s) -
Dolatabadi Sepideh,
Narayan Paresh Kumar,
Nielsen Morten Ørregaard,
Xu Ke
Publication year - 2018
Publication title -
journal of futures markets
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.88
H-Index - 55
eISSN - 1096-9934
pISSN - 0270-7314
DOI - 10.1002/fut.21866
Subject(s) - futures contract , economics , econometrics , vector autoregression , autoregressive model , commodity , cvar , sample (material) , cointegration , variance (accounting) , financial economics , expected shortfall , finance , portfolio , chemistry , accounting , chromatography
We model and forecast commodity spot and futures prices using fractionally cointegrated vector autoregressive (FCVAR) models generalizing the well‐known (non‐fractional) CVAR model to accommodate fractional integration. In our empirical analysis to daily data on 17 commodity markets, the fractional model is statistically superior in terms of in‐sample fit and out‐of‐sample forecasting. We analyze economic significance of the forecasts through dynamic (mean‐variance) trading strategies, leading to statistically significant and economically meaningful profits in most markets. We generally find that the fractional model generates higher profits on average, especially in the futures markets.