Are low-frequency data really uninformative? A forecasting combination perspective |
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| Institution: | 1. School of Economics & Management, Southwest Jiaotong University, Chengdu, China;2. School of Finance, Nanjing Audit University, Nanjing, China;1. Department of Mathematics, University of Macau, Macau SAR, China;2. UMacau Zhuhai Research Institute, Zhuhai, China;1. Sveriges Riksbank, Sweden;2. Swiss Life, Switzerland;1. School of Economics and Management, Southwest Jiaotong University, Chengdu, China;2. School of Finance, Nanjing University of Finance and Economics, Nanjing, China;3. Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, Ontario, Canada |
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| Abstract: | In this study, we investigate whether low-frequency data improve volatility forecasting when high-frequency data are available. To answer this question, we utilize four forecast combination strategies that combine low-frequency and high-frequency volatility models and employ a rolling window and a range of loss functions in the framework of the novel Model Confidence Set test. Out-of-sample results show that combination forecasts with GARCH-class models can achieve high forecast accuracy. However, the combination forecast methods appear not to significantly outperform individual high-frequency volatility models. Furthermore, we find that models that combine low-frequency and high-frequency volatility yield significantly better performance than other models and combination forecast strategies in both a statistical and economic sense. |
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| Keywords: | Volatility forecasting Realized volatility Combine forecasts Forecasting evaluation |
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