台灣證券市場權益風險溢酬之估測_比較GARCH-M、移動視窗和MIDAS模型

This paper investigates risk premiums of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) by using Ghysel’s mixed data sampling (MIDAS) model which is a new regression regarding volatility estimation. We study the intertemporal relation between conditional mean and conditional variance of the aggregate stock market return. Compared with various approaches such as GARCH-in-mean, rolling window and MIDAS models, we find that: (i) We support for a negative relation between risk and equity risk premium in TSEC weighted index during the period 2006 - 2010. (ii) MIDAS is more convincing in predicting regression for sampled time-series data. (iii) Empirical results show out-sample forecasting ability of MIDAS model also performs well. Specifically, it has smaller forecasting error. (iv) Under MIDAS model of different volatility predictors and different sampling frequencies, a squared premium polynomial with daily frequency data has better estimation.