Volatility forecasting of exchange rate by quantile regression |
| |
| Authors: | Alex YiHou Huang Sheng-Pen Peng Fangjhy Li Ching-Jie Ke |
| |
| Affiliation: | 1. Quantitative Supervision and Research Unit, Federal Reserve Bank of Richmond, Baltimore, MD, USA;2. Department of Economics, University of Houston, Houston, TX,USA;1. School of Business, Macau University of Science and Technology, Macau 999078, China;2. School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China;3. School of Economics and Management, Beihang University, Beijing 100191, China |
| |
| Abstract: | Exchange rates are known to have irregular return patterns; not only their return volatilities but the distribution functions themselves vary with time. Quantile regression allows one to predict the volatility of time series without assuming an explicit form for the underlying distribution. This study presents an approach to exchange rate volatility forecasting by quantile regression utilizing a uniformly spaced series of estimated quantiles. Based on empirical evidence of nine exchange rate series, using 19 years of daily data, the adopted approach generally produces more reliable volatility forecasts than other key methods. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
