In this paper we compared two distinct volatility forecasting approaches. GARCH models were contrasted to the models which modelled proxies of volatility directly. More precisely, focus was put on the economic valuation of forecasting accuracy of one-day-ahead volatility forecasts. Profits from trading of one-day at-the-money straddles on the hypothetical (artificial) market were used for assessing the relative volatility forecasting accuracy. Our contribution lies in developing a novel approach to the economic valuation of the volatility forecasts - the artificial option market with a single market price - and its comparison with the established approaches. Further on, we compared the relative intra- and inter-group volatility forecasting accuracy of the competing model families. Finally, we measured the economic value of richer information provided by high-frequency data. To preview the results, we show that the economic valuation of volatility forecasts can bring a meaningful and robust ranking. Additionally, we show that this ranking is similar to the ranking implied by established statistical methods. Moreover, it was shown that modelling of volatility directly is strongly dependent on the volatility proxy in place. It was also shown, as a corollary, that the use of high frequency data to predict a future volatility is of considerable economic value.
GARCH realized volatility economic loss function volatility forecasting