| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w10914 |
| 来源ID | Working Paper 10914 |
| Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies | |
| Eric Ghysels; Pedro Santa-Clara; Rossen Valkanov | |
| 发表日期 | 2004-11-22 |
| 出版年 | 2004 |
| 语种 | 英语 |
| 摘要 | We consider various MIDAS (Mixed Data Sampling) regression models to predict volatility. The models differ in the specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use of daily or intra-daily (5-minute) data, and in the length of the past history included in the forecasts. The MIDAS framework allows us to compare models across all these dimensions in a very tightly parameterized fashion. Using equity return data, we find that daily realized power (involving 5-minute absolute returns) is the best predictor of future volatility (measured by increments in quadratic variation) and outperforms model based on realized volatility (i.e. past increments in quadratic variation). Surprisingly, the direct use of high-frequency (5-minute) data does not improve volatility predictions. Finally, daily lags of one to two months are sucient to capture the persistence in volatility. These findings hold both in- and out-of-sample. |
| 主题 | Financial Economics ; Financial Markets |
| URL | https://www.nber.org/papers/w10914 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/568549 |
| 推荐引用方式 GB/T 7714 | Eric Ghysels,Pedro Santa-Clara,Rossen Valkanov. Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies. 2004. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w10914.pdf（369KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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