A fractionally cointegrated VAR model with deterministic trends and application to commodity futures markets

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. (aluminium, copper, lead, nickel, and zinc). To this end, we first extend the FCVAR model to accommodate deterministic trends in the levels of the processes. The methodological contribution is to provide representation theory for the FCVAR model with deterministic trends, where we show that the presence of the deterministic trend in the process induces both restricted and unrestricted constant terms in the vector error correction model. The consequences for the cointegration rank test are also briefly discussed. In our empirical application we use the data from FiguerolaFerretti and Gonzalo (2010), who conduct a similar analysis using the usual (non-fractional) cointegrated VAR model. The main conclusion from the empirical analysis is that, when using the FCVAR model, there is more support for the cointegration vector (1, −1) in the long-run equilibrium relationship between spot and futures prices, and hence less evidence of long-run backwardation, compared to the results from the non-fractional model. Specifically, we reject the hypothesis that the cointegration vector is (1, −1) using standard likelihood ratio tests only for the lead and nickel markets. Terms of use: Documents inJEL codes: C32, G14.

Futures hedging with Markov switching vector error correction FIEGARCH and FIAPARCH

“…Even though there is evidence of long memory in the basis or forward premiums in currency markets (Baillie and Bollerslev, 1994), equity markets (Lien and Tse, 1999) and commodity markets (Dolatabadi et al, 2014), fractional cointegration in prices (which are typically I(1)) is not considered. 10 Lien and Tse (1999) show that the relative size between a VECM MVHR and a Fractional-VECM MVHR is indeterminable in practice, and that a Fractional-VECM GARCH hedge performs no better than a VECM-GARCH hedge over 1 to 40 day horizons.…”

A Fractionally Cointegrated VAR Analysis of Price Discovery in Commodity Futures Markets

“…This fractionally cointegrated equilibrium model is suggested by Dolatabadi, Nielsen, and Xu (2014) (henceforth DNX), and is briefly reviewed in Section 2.3 below. DNX apply this model to analyze the fractional cointegration relationship empirically using the FG data set within the FCVAR model.…”

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

“…Our empirical work applies the version of the FCVAR model given in (5) and we provide comparisons with the CVAR model in (6). The asymptotic analysis of the FCVAR model is provided in Nielsen (2010, 2012), where it is shown that the maximum likelihood estimator of (b; ; 1 ; : : : ; k ) is asymptotically normal, while the maximum likelihood estimator of ( ; ) is asymptotically mixed normal when b > 1=2 and asymptotically normal when b < 1=2.…”

“…The forecastability of commodity market returns is a very active area of research in financial economics. In particular, recent research has shown that commodity spot and futures prices are fractionally cointegrated: see, inter alia, Baillie and Bollerslev (1994), Lien and Tse (1999), Maynard and Phillips (2001), Coakley, Dollery, and Kellard (2011), and Dolatabadi, Nielsen, and Xu (2016. An implication is that a fractionally cointegrated model may provide a better statistical fit for commodity prices and returns.…”