Extreme value estimators: Their long memory feature and forecasting performances in the U.S. stock indexes.

详细信息   

• 作者：Kwon ; Yongjae.
• 学历：Doctor
• 年：2009
• 导师：Savickas, Robert,eadvisorSoyer, Refikecommittee memberJoutz, Frederick L.ecommittee memberHwang, Minecommittee memberJostova, Gerganaecommittee member
• 毕业院校：The George Washington University
• Department：Finance
• ISBN：9781109102178
• CBH：3352498
• Country：USA
• 语种：English
• FileSize：2978481
• Pages：195

文摘

This dissertation studies long memory and forecasting performances of extreme value volatility estimators which are constructed with the highest and lowest intraday prices. First, I estimate long memory in the extreme value estimators. I conduct the long memory tests to break-eliminated series. From the examination, I continue to find significant long memory in the new series, but the degrees of long memory become smaller. This evidence demonstrates that the long memory processes can be suitable to model the extreme value estimators. Second, I examine if the long memory feature in the extreme value estimators enhance prediction performance. From forecast comparisons, I find that long memory ARFIMA forecasts underperform short memory ARMA forecasts. Examining the performances of the long and short memory forecasts further, I find that the poor performance of the ARFIMA models are related to the presence of structural breaks in the forecast evaluation periods. Since 1) ARFIMA models are slow to react to structural breaks in the series because they attribute weights to distant lags when forming forecasts Gabriel and Martins, 2004)； and 2) the efficiencies of the extreme value estimators deteriorate in high volatility regimes Brandt and Kinlay, n.d.), the ARFIMA models perform poorly. My result suggests some caution be given to Bollerslev 2001) who recommend long memory models for prediction. Third, I test whether the extreme value estimator based forecasts are competitive compared to the forecasts with information on structural breaks in volatility. Motivated by Choi et al. 2006), I construct the break-adjusted forecasts which contain information on multiple breaks in volatility. Then, I compare them with the extreme value estimator based forecasts. From the comparisons, I find that the ARFIMA forecasts underperform the break-adjusted forecasts while the ARMA forecasts perform as good as or often better than the break-adjusted forecasts. Fourth, the extreme value estimator based forecasts are compared with several conventional forecasts. From the pairwise comparison, I find that the forecasts outperform the RiskMetricsTM) and GARCH 1,1) forecasts although their superiority is not always statistically meaningful. Examining the performances of the two forecasts across volatility regimes, I find that the performances of the asymmetric GARCH forecasts are close to or superior to those of the extreme value estimator based forecasts when the markets are highly volatile. This is because the volatility asymmetry is more helpful to prediction in the high volatility regimes. Fifth, I apply forecast combination techniques, which have been popular in the fields of macroeconomic forecasting and decision science, to the field of volatility forecasting. The performances of the extreme value estimator based forecasts were somewhat disappointing since the forecasts did not perform significantly better than some of the benchmark forecasts. The combining techniques may enable us to exploit information in those forecasts selectively to predict future volatility. In an effort to examine this possibility, I combine the extreme value estimator based forecasts with a few GARCH type forecasts. Employing several combining methods proposed by past studies Bates and Granger, 1969； Newbold and Granger, 1974； Bunn, 1975； Makridakis and Winkler, 1983； Clemen and Winkler, 1986), I combine the volatility forecasts. To evaluate the performances of the combined forecasts, they are compared with 1) their own component forecasts； and 2) the realized volatility based forecasts proposed by Andersen et al. 2003). Conducting the Diebold-Mariano equal accuracy test, I find that the combination techniques are generally effective at improving prediction accuracy as long as the performances of two component forecasts are not far from each other. Abstract shortened by UMI.)