John Francis T. Diaz

This study determines which index has the strongest influence on the exchange-trade note (ETN) returns using the grey relational analysis. Results show that the volatility index is the strongest, followed by the S&P 500 stock index, the US dollar index, the CRB index, the Trade index, and the Brent crude oil index. However, the US dollar index has the most significant effect of using the index values of currency ETNs, followed by the S&P 500 stock index, volatility index, Brent crude oil index, the CRB index, and Trade index. This study applies four types of the artificial neural network model, namely, back-propagation neural network (BPN), recurrent neural network (RNN), time-delay recurrent neural network (TDRNN), and radial basis function neural network (RBFNN) to capture the nonlinear tendencies of ETNs for better forecasting accuracy. The paper finds that the RNN and RBFNN models have stronger predictive power among the models, and provides the highest forecasting accuracy for the majority of the currency ETNs. However, the RNN model consistently shows that the low grey relational grades (GRG) variables have the strongest influence on the ETN returns, compared with combining all and high GRG variables. These findings suggest that fund managers and traders can potentially rely on both RNN and RBFNN models, particularly the former, in their applications in financial time-series modeling.

This study used three multivariate general autoregressive conditional heteroskedasticity models to analyze the volatility dynamics in the ASEAN–China Free Trade Agreement. Results indicated the presence of long-run persistence, wherein shocks in China’s stock market affect other ASEAN stock indices in the long term. Further tests revealed the presence of time-varying correlations, suggesting dynamic models, such as the dynamic conditional correlations model, are appropriate. The Baba, Engle, Kraft, and Kroner model determined that the conditional covariances of the Chinese and ASEAN indices are functions of their lagged covariances, further proving that China’s stock volatilities impact the volatilities of ASEAN counterparts. JEL Classification: C58, G15

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

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.