What Causes Different Results for Forecasting Realized Volatility?
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This paper studies realized volatility forecasting performances. It is mainly based on performance comparisons between by using historical volatility and by using volatility indexes. The volatility indexes are introduced by implied option volatility and option price respectively. Many of the previous papers insist on their own result derived from their unique basic asset, time period and data interval. Especially, their forecasting performances on realized volatility are quite different with respect to implied volatility and historical volatility. What causes these different results for forecasting realized volatility? This paper tries to find one of the answers of this question. First, if they test regression analysis on using daily data with level, unit root could be made non-stationary problem. I explored unit root disappeared when the data period expanded 7 years. Second, if they get rid of non-stationary problem using differentiation method. That method causes serious problem. The differentiation method has an effect on R square of the equation. Namely, if who analyze level data or 1st differentiated data, the result would be opposite. In summary, I tested those issues using daily level data during 1990 to 2004 and I also exhibited volatility indexes had better forecast performance than historical volatility to forecast realized volatility. But, the unit root problem happened. Therefore I expanded data period to 7 years for eliminating non-stationary problem. At that time the unit root disappeared. In conclusion, volatility forecasting performance is sensitive in data interval and period. Therefore, realized volatility forecasting should be used by a level data after testing and eliminating on unit root. Finally, I test volatility forecasting performance by different economical situation. The forecasting performance shows same patterns. The Volatility Indexes explain much better performance than historical volatility when the bull market.
Abstract
This paper studies realized volatility forecasting performances. It is mainly based on performance comparisons between by using historical volatility and by using volatility indexes. The volatility indexes are introduced by implied option volatility and option price respectively. Many of the previous papers insist on their own result derived from their unique basic asset, time period and data interval. Especially, their forecasting performances on realized volatility are quite different with respect to implied volatility and historical volatility. What causes these different results for forecasting realized volatility? This paper tries to find one of the answers of this question. First, if they test regression analysis on using daily data with level, unit root could be made non-stationary problem. I explored unit root disappeared when the data period expanded 7 years. Second, if they get rid of non-stationary problem using differentiation method. That method causes serious problem. The differentiation method has an effect on R square of the equation. Namely, if who analyze level data or 1st differentiated data, the result would be opposite. In summary, I tested those issues using daily level data during 1990 to 2004 and I also exhibited volatility indexes had better forecast performance than historical volatility to forecast realized volatility. But, the unit root problem happened. Therefore I expanded data period to 7 years for eliminating non-stationary problem. At that time the unit root disappeared. In conclusion, volatility forecasting performance is sensitive in data interval and period. Therefore, realized volatility forecasting should be used by a level data after testing and eliminating on unit root. Finally, I test volatility forecasting performance by different economical situation. The forecasting performance shows same patterns. The Volatility Indexes explain much better performance than historical volatility when the bull market.
참고문헌 (22)
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