Item Details
Skip Navigation Links
   ActiveUsers:4056Hits:20975790Skip Navigation Links
Show My Basket
Contact Us
IDSA Web Site
Ask Us
Today's News
HelpExpand Help
Advanced search

In Basket
  Journal Article   Journal Article
 

ID151195
Title ProperLong-memory modelling and forecasting of the returns and volatility of exchange-traded notes (ETNs)
LanguageENG
AuthorArgel S. Masa, John Francis T. Diaz ;  Masa, Argel S ;  Diaz, John Francis T
Summary / Abstract (Note)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.
`In' analytical NoteMargin Vol.11, No.1; Feb 2017: p.23-53
Journal SourceMargin 2017-03 11, 1
Key WordsExchange - Trated Notes ;  Long - memory Models ;  Out - Of - Sample Forcasting Analysis ;  FIGARCH and HyGARCH Models