### Table 5. Volatility Forecasts

### Table 7: Diebold and Mariano Test for VolatilityForecast Performance

"... In PAGE 37: ... Since we are inter- ested in detecting whether ONI manages to reduce persistence in predicted volatility,we will suggest, in the spirit of Granger and Pesaran #281996#29 or of Christo#0Bersen and Diebold #281996#29, a simple nonlinear function g#28#01#29 which penalizes overprediction more than underprediction, namely g#28e i;t #29= 8 #3E #3E #3E #3C #3E #3E #3E : je i;t j if e i;t #15 0 e 2 i;t if e i;t #3C 0 #28recall that forecast errors are smaller than 1 in modulus in our case#29. 6 The results are arranged in Table7 where the sign of the test statistics signals a better forecast of the model in the column relative to GARCH if 6 Note that this function is di#0Berent from what was considered in the illustrative example of Diebold and Mariano, who use in their paper symmetric functions such as the absolute value and the square of forecast errors, therefore testing for the signi#0Ccance of the di#0Berence between Mean Absolute Errors and between Mean Squared Errors.... ..."

### Table 5: In-sample volatility forecasting statistics for the British pound.

### Table 6: In-sample volatility forecasting statistics for the German mark.

### Table 7: In-sample volatility forecasting statistics for the Japanese yen.

### Table 14. Results for the basic methodology (depend on both volatility and correlation forecasts)

1998

"... In PAGE 29: ... In this subsection, we present results for the four rainbow options defined in Table 2. Table14 shows the mean daily profit of the five forecasting agents when they trade and price the four different rainbow options alone, along with robust standard errors. The rankings of the five forecasting models are consistent across all four rainbow options: D-GARCH, C-GARCH, EWMA, MARKOV, NAIVE.... In PAGE 30: ... The ranking of forecasting models is D-GARCH, NAIVE, C-GARCH, MARKOV, EWMA. Compared with the results in Table14 , the ordering of forecasting models is again quite... ..."

Cited by 1

### Table 2: Out-of-sample volatility forecasting perfor- mance based on the Median Squared Error1 Model

"... In PAGE 12: ... As a measure of the true variance, we use the squared residuals ^ quot;2 t from (8) (obtained in each estimation round) when no GARCH model is tted to the data. - insert Table2 about here - The forecasting results are summarized in Table 2. The linear GARCH model appears to be the best for 1986.... In PAGE 12: ... As a measure of the true variance, we use the squared residuals ^ quot;2 t from (8) (obtained in each estimation round) when no GARCH model is tted to the data. - insert Table 2 about here - The forecasting results are summarized in Table2 . The linear GARCH model appears to be the best for 1986.... ..."