Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S. |
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| Institution: | 1. Business School, Zhengzhou University, School of Economics, Zhejiang University, China;2. School of Economics, Zhejiang University, China;3. School of Economics, Academy of Financial Research, Zhejiang University, China;4. University of Sydney Business School, University of Sydney, Australia;1. Department of Management, Università Politecnica delle Marche, Ancona, Italy;2. Department of Economics and Social Science, Università Politecnica delle Marche, Ancona, Italy;1. Department of Financial Engineering, Ajou University, Suwon, 16499, Republic of Korea;2. Department of Applied Mathematics & Institute of Natural Science, Kyung Hee University, Yongin, 17104, Republic of Korea;3. Department of Mathematical Sciences, Seoul National University, Seoul, 08826, Republic of Korea;1. Graduate School of Economics, Kobe University, 2-1 Rokko-dai, Nada, Kobe, 657-8501, Japan;2. Institute of Social and Economic Research, Osaka University, 6-1, Mihogaoka, Ibaraki, Osaka, 567-0047, Japan;1. CREM UMR 6211, Université de Caen Normandie, France;2. ICN Business School-CEREFIGE, Nancy, France;1. Texas A&M University, Department of Finance, Mays Business School, College Station, TX, 77843, USA;2. University of Valladolid (Spain), NRU Higher School of Economics (Russia), School of Business and Economics, Avda. Valle Del Esgueva 6, 47011, Valladolid, Spain;3. University of Valladolid, School of Business and Economics, Avda. Valle Del Esgueva 6, 47011, Valladolid, Spain |
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| Abstract: | We propose three Realized-GARCH-Kernel-type models which do not make the distribution assumptions on the return disturbance terms. We use this type of model to predict the return volatilities of the 50ETF in China and the S&P500 index in the U.S. The semiparametric kernel density estimator of our models, which captures the skewness, asymmetry and fat-tail of financial assets, performs well both statistically and economically. Our models have more predictive power than other eight comparable volatility models that need to pre-specify the distribution of the disturbance terms. Our results are robust to eight measures of realized volatility. Using option straddle strategies, we show that our models generate larger trading profits and greater Sharpe ratios than the other competing models. |
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| Keywords: | Realized-GARCH-Kernel-type models Semiparametric kernel density estimator Realized volatility G17 C14 |
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