Department of Economics and Business Economics

Economic significance of commodity return forecasts from the fractionally cointegrated VAR model

Research output: Working paperResearch

Standard

Economic significance of commodity return forecasts from the fractionally cointegrated VAR model. / Dolatabadi, Sepideh; Narayan, Paresh Kumar; Nielsen, Morten Ørregaard; Xu, Ke.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2018.

Research output: Working paperResearch

Harvard

Dolatabadi, S, Narayan, PK, Nielsen, MØ & Xu, K 2018 'Economic significance of commodity return forecasts from the fractionally cointegrated VAR model' Institut for Økonomi, Aarhus Universitet, Aarhus.

APA

Dolatabadi, S., Narayan, P. K., Nielsen, M. Ø., & Xu, K. (2018). Economic significance of commodity return forecasts from the fractionally cointegrated VAR model. Institut for Økonomi, Aarhus Universitet. CREATES Research Papers No. 2018-35

CBE

Dolatabadi S, Narayan PK, Nielsen MØ, Xu K. 2018. Economic significance of commodity return forecasts from the fractionally cointegrated VAR model. Aarhus: Institut for Økonomi, Aarhus Universitet.

MLA

Dolatabadi, Sepideh et al. Economic significance of commodity return forecasts from the fractionally cointegrated VAR model. Aarhus: Institut for Økonomi, Aarhus Universitet. (CREATES Research Papers; Journal number 2018-35). 2018., 37 p.

Vancouver

Dolatabadi S, Narayan PK, Nielsen MØ, Xu K. Economic significance of commodity return forecasts from the fractionally cointegrated VAR model. Aarhus: Institut for Økonomi, Aarhus Universitet. 2018 Dec 18.

Author

Dolatabadi, Sepideh ; Narayan, Paresh Kumar ; Nielsen, Morten Ørregaard ; Xu, Ke. / Economic significance of commodity return forecasts from the fractionally cointegrated VAR model. Aarhus : Institut for Økonomi, Aarhus Universitet, 2018. (CREATES Research Papers; No. 2018-35).

Bibtex

@techreport{bf647d75ebf2444ba751dcb38b6c9ff1,
title = "Economic significance of commodity return forecasts from the fractionally cointegrated VAR model",
abstract = "Based on recent evidence of fractional cointegration in commodity spot and futures markets, we investigate whether a fractionally cointegrated model can provide statistically and/or economically significant forecasts of commodity returns. Specifically, we propose to model and forecast commodity spot and futures prices using a fractionally cointegrated vector autoregressive (FCVAR) model that generalizes the more well-known (non-fractional) CVAR model to allow fractional integration. We derive the best linear predictor for the FCVAR model and perform an out-of-sample forecast comparison with the non-fractional model. In our empirical analysis to daily data on 17 commodity markets, the fractional model is found to be superior in terms of in-sample fit and also out-of-sample forecasting based on statistical metrics of forecast comparison. We analyze the economic significance of the forecasts through a dynamic trading strategy based on a portfolio with weights derived from a mean-variance utility function. Although there is much heterogeneity across commodity markets, this analysis leads to statistically significant and economically meaningful profits in most markets, and shows that profits from both the fractional and non-fractional models are higher on average and statistically more significant than profits derived from a simple moving-average strategy. The analysis also shows that, in spite of the statistical advantage of the fractional model, the fractional and non-fractional models generate very similar profits with only a slight advantage to the fractional model onaverage.",
keywords = "commodity markets, economic significance, forecasting, fractional cointegration, futures markets, price discovery, trading rule, vector error correction model",
author = "Sepideh Dolatabadi and Narayan, {Paresh Kumar} and Nielsen, {Morten {\O}rregaard} and Ke Xu",
year = "2018",
month = dec,
day = "18",
language = "English",
series = "CREATES Research Papers",
publisher = "Institut for {\O}konomi, Aarhus Universitet",
number = "2018-35",
type = "WorkingPaper",
institution = "Institut for {\O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Economic significance of commodity return forecasts from the fractionally cointegrated VAR model

AU - Dolatabadi, Sepideh

AU - Narayan, Paresh Kumar

AU - Nielsen, Morten Ørregaard

AU - Xu, Ke

PY - 2018/12/18

Y1 - 2018/12/18

N2 - Based on recent evidence of fractional cointegration in commodity spot and futures markets, we investigate whether a fractionally cointegrated model can provide statistically and/or economically significant forecasts of commodity returns. Specifically, we propose to model and forecast commodity spot and futures prices using a fractionally cointegrated vector autoregressive (FCVAR) model that generalizes the more well-known (non-fractional) CVAR model to allow fractional integration. We derive the best linear predictor for the FCVAR model and perform an out-of-sample forecast comparison with the non-fractional model. In our empirical analysis to daily data on 17 commodity markets, the fractional model is found to be superior in terms of in-sample fit and also out-of-sample forecasting based on statistical metrics of forecast comparison. We analyze the economic significance of the forecasts through a dynamic trading strategy based on a portfolio with weights derived from a mean-variance utility function. Although there is much heterogeneity across commodity markets, this analysis leads to statistically significant and economically meaningful profits in most markets, and shows that profits from both the fractional and non-fractional models are higher on average and statistically more significant than profits derived from a simple moving-average strategy. The analysis also shows that, in spite of the statistical advantage of the fractional model, the fractional and non-fractional models generate very similar profits with only a slight advantage to the fractional model onaverage.

AB - Based on recent evidence of fractional cointegration in commodity spot and futures markets, we investigate whether a fractionally cointegrated model can provide statistically and/or economically significant forecasts of commodity returns. Specifically, we propose to model and forecast commodity spot and futures prices using a fractionally cointegrated vector autoregressive (FCVAR) model that generalizes the more well-known (non-fractional) CVAR model to allow fractional integration. We derive the best linear predictor for the FCVAR model and perform an out-of-sample forecast comparison with the non-fractional model. In our empirical analysis to daily data on 17 commodity markets, the fractional model is found to be superior in terms of in-sample fit and also out-of-sample forecasting based on statistical metrics of forecast comparison. We analyze the economic significance of the forecasts through a dynamic trading strategy based on a portfolio with weights derived from a mean-variance utility function. Although there is much heterogeneity across commodity markets, this analysis leads to statistically significant and economically meaningful profits in most markets, and shows that profits from both the fractional and non-fractional models are higher on average and statistically more significant than profits derived from a simple moving-average strategy. The analysis also shows that, in spite of the statistical advantage of the fractional model, the fractional and non-fractional models generate very similar profits with only a slight advantage to the fractional model onaverage.

KW - commodity markets, economic significance, forecasting, fractional cointegration, futures markets, price discovery, trading rule, vector error correction model

M3 - Working paper

T3 - CREATES Research Papers

BT - Economic significance of commodity return forecasts from the fractionally cointegrated VAR model

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -