A BRIEF COMMENT OF INCREMENTAL INFORMATION AND FORECAST HORIZON: PLATINUM VERSUS GOLD BY PROF. MICHAEL CHNG Min-Hsien Chiang Institute of International Business National Cheng Kung University Tainan, TAIWAN

THE MERITS OF THIS PAPER This paper is well written and well motivated. This paper provides a thorough examination of cross- market prediction comparisons between two commodities, platinum and gold. The different prediction structures of platinum and gold give us another thought over the information transmission mechanism between two closely related commodities, especially in terms of the lag structure (i.e., information leading role in each commodity). This paper did an excellent job in analyzing the profit evaluation scheme, which makes us understand more about economic value behind the forecast models.

CONCERNS It seems that the number of lags becomes a very important factor determining the model predictability power. Therefore, would it be possible that there exists an endogenous relationship between forecast models and the number of lags. In Table 6, in general, the MSE values of OLS models are relatively small in comparison with those of VECM models but it reverses in terms of prediction power and profit evaluation. Does it mean that in-sample precision cannot guarantee the out-of-sample forecast power? If this is true, do we have any economic rationale expounding this empirical observation? Could the leading role of platinum’s lagged volume in gold prediction be due to the different price patterns between platinum and gold in the test sample from March 2008 to July 2009? In Figure 1c, the platinum experienced a plunge during the early period of test sample. Are there any particular reasons using January 2003 to February 2008 as the estimation period and March 2008 to July 2009 as the test period? Why not extend the examined period to the end of 2009? The VIX dropped below 25 by September Therefore, is it possible that there exists a data snooping problem? It is very interesting to know how noise and incremental information effects contribute to the forecast power which leads to profit evaluation?

SUGGESTIONS The simplest VAR models incorporating lagged variables of the other commodity but without considering the cointegration relationship between platinum and gold prices might serve as another benchmark models to do comparisons with the VECM and simple OLS models. It is interesting to see that the empirical observation found in this paper still survives under the downward and upward trends or during different subperiods.