Forecasting Taiwan Stock Return Using VIX and Volatility

This study investigates whether volatility-related variables and traditional financial predictors can explain and forecast Taiwan stock returns. Using the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and eight major Taiwan industry indices, we examine the predictive ability of long-term adjusted volatility (LVadj), short-term volatility (SV), the volatility index (VIX), idiosyncratic volatility (IVOL), the earnings-to-price ratio (EP), the book-to-market ratio (BM), and the turnover ratio (TURN). The sample period spans from January 2007 to December 2025. Univariate and bivariate predictive regression models are estimated using ordinary least squares (OLS) and exponentially weighted least squares (EWLS). The empirical results show that SV exhibits the strongest in-sample explanatory power for TAIEX returns, whereas TURN plays a more important role in explaining industry-level returns. Out-of-sample forecasting performance varies considerably across industries. For TAIEX returns, the combination of LVadj and TURN provides the strongest forecasting performance, while models incorporating SV and VIX perform relatively well in several industry sectors. Overall, the results suggest that volatility-related variables provide useful predictive information under certain model specifications and industry sectors, with EWLS generally outperforming conventional OLS estimation.