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

Abstract

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

Regime-Switching Fractionally Integrated Asymmetric Power Neural Network Modeling of Nonlinear Contagion for Chaotic Oil and Precious Metal Volatilities

“…The findings for platinum are in accordance with the fact that this metal has stable demand from industrial users, jewelers, and automotive manufacturers. Our findings for regime 1 are similar to the results Masa and Diaz [79] obtained with ARFIMA-FIGARCH models for oil. Batten [80] showed that macroeconomic factors such as financial market sentiments, monetary policy and business cycles have effects on the volatility of silver, gold, platinum and palladium.…”

Long Term Dynamics of Indian ADRs Market: The Case of Persistence and Irregular Cycles

“…The incomprehensiveness of literature in the area of persistence lies in the fact that the major focus of these studies is equity markets, and very few focus on other types of security viz. bonds, FX or DR markets (Malkei, 2003;Beine & Laurent, 2003;Oh, Kim & Eom, 2006;Kumar and Maheswaran, 2013;Madhavan, 2014;Anagnostidis and Emmanouilides, 2015;Ferreira & Dionisio, 2016;Sensoy & Tabak, 2016;and Masa and Diaz, 2017).…”