Long-memory and shifts in the unconditional variance in the exchange rate euro/US dollar returns

Nouira, Leïla; Ahamada, Ibrahim; Jouini, Jamel; Nurbel, Alain

doi:10.1080/1350485042000230733

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…Despite the above-mentioned limitations, the power of FI models over the conventional ARIMA and GARCH models has been demonstrated in several financial instruments, such as stock returns (e.g., Aloui & Mabrouk, 2010; Tan & Khan, 2010), exchange rates (e.g., Beine, Laurent & Lecourt, 2002; Nouira, Ahamada, Jouinid & Nurbel, 2004), commodities (Choi & Hammoudeh, 2009; Kyrtsou, Labys & Terraza, 2004) and exchange-traded funds (ETFs) (e.g., Rompotis, 2011; Yang, Cabrera & Wang, 2010). This research article is interested in extending the FI long-memory models to a relatively new investment instrument called exchange-traded note (ETN).…”

Section: Introductionmentioning

confidence: 99%

Long-memory Modelling and Forecasting of the Returns and Volatility of Exchange-traded Notes (ETNs)

Masa

Diaz

2017

Margin: The Journal of Applied Economic Research

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This research provides evidence in determining the predictability of exchange-traded notes (ETNs). It utilises commodity, currency and equity ETNs as data samples, and examines the performance of the three combinations of long-memory models, that is, autoregressive fractionally integrated moving average and generalised autoregressive conditional heteroskedasticity (ARFIMA-GARCH), autoregressive fractionally integrated moving average and fractionally integrated generalised autoregressive conditional heteroskedasticity (ARFIMA-FIGARCH) and autoregressive fractionally integrated moving average and hyperbolic generalised autoregressive conditional heteroskedasticity (ARFIMA-HYGARCH), and three forecasting horizons, that is, 1-, 5- and 20-step-ahead horizons, to model ETNs returns and volatilities. The article finds long-memory processes in ETNs; however, dual long-memory process in returns and volatilities is not verified. The research also poses a challenge to the weak-form efficiency hypothesis of Fama (1970) because lagged changes determine future values, especially in volatility. The findings also show that differences in the characteristics of commodity, currency and equity ETNs are not concluded because of similarities in ETN traits and several insignificant results. However, the presence of intermediate memory was identified, and should serve as a warning sign for investors not to keep these investments in the long run. Lastly, the ARFIMA-FIGARCH model has a slight edge over the ARFIMA-GARCH and ARFIMA-HYGARCH specifications using 1-, 5- and 20-forecast horizons. JEL Classification: G11, G17

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Section: Introductionmentioning

confidence: 99%

Long-memory Modelling and Forecasting of the Returns and Volatility of Exchange-traded Notes (ETNs)

Masa

Diaz

2017

Margin: The Journal of Applied Economic Research

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“…This property is commonly connected to the leverage effects phenomenon, because negative changes are often followed by future higher volatility than positive innovations. These data characteristics have been seen in stock returns (e.g., [2,3]), exchange rates (e.g., [4,5]), commodities [6,7], exchange-traded funds (ETFs) (e.g., [8,9]), and exchange-traded notes (ETNs) (e.g., [10]). However, the literature has yet to characterize the predictability and asymmetric volatility of platinum and palladium spot prices.…”

Section: Introductionmentioning

confidence: 99%

Do Scarce Precious Metals Equate to Safe Harbor Investments? The Case of Platinum and Palladium

Diaz

2016

Economics Research International

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This research establishes the predictability and safe harbor properties of two scarce precious metals, namely, platinum and palladium. Utilizing their spot prices, the study concludes intermediate memory in the return structures of both precious metals, which implies the instability of platinum and palladium returns’ persistency in the long run. However, both the ARFIMA-FIGARCH and the ARFIMA-FIAPARCH models confirm long-memory properties in the volatility of the two spot prices. The leverage effects phenomenon is not also present based on the ARFIMA-APARCH and ARFIMA-FIAPARCH models, which may possibly conclude the resilience of both precious metals against increased volatility. However, further tests proved that only platinum has a symmetric volatility response to shocks with the presence of negative gamma parameter, which proves that only platinum can be considered a safe harbor investment, because negative and positive shocks have equal effects on their returns and volatilities. Comparing the four models utilized in this study, the combined ARFIMA-FIAPARCH models are the best fitting model to characterize both precious metals’ spot prices.

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Price dynamics in agricultural commodity markets: a comparison of European and US markets

Statnik

Verstraete

2014

Empir Econ

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Long-memory and shifts in the unconditional variance in the exchange rate euro/US dollar returns

Cited by 6 publications

References 7 publications

Long-memory Modelling and Forecasting of the Returns and Volatility of Exchange-traded Notes (ETNs)

Long-memory Modelling and Forecasting of the Returns and Volatility of Exchange-traded Notes (ETNs)

Do Scarce Precious Metals Equate to Safe Harbor Investments? The Case of Platinum and Palladium

Price dynamics in agricultural commodity markets: a comparison of European and US markets

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