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標題:Forecasting Volatility in Taiwan with Encompassing Regression Models
學年109
學期2
出版(發表)日期2021/04/23
作品名稱Forecasting Volatility in Taiwan with Encompassing Regression Models
作品名稱(其他語言)
著者Chang-Wen Duan; Ken Hung; Shinhua Liu
單位
出版者
著錄名稱、卷期、頁數International Journal of Economics, Finance and Management Sciences 9(2), P.62-76
摘要Volatility forecasting is important both theoretically and in practice, varying by forecasting methods and financial markets. In this article, we explore this topic in the Taiwanese markets, using the encompassing regression models. We use the volatility of the Taiwan Stock Index (TAIEX) and its futures in the encompassing regression model to respectively make asynchronous forecasts of realized volatility (RV) and implied volatility (IV). Besides trading frequency, we find that transaction matching time is a key factor for obtaining steady RV values. Also, we find that the TAIEX index RV has a long memory. Moreover, we discover that, to obtain a stationary RV with a stable, long memory parameter, the optimal sampling intervals for the intraday return were nine (9) and thirty (30) minutes. In addition, we uncover that the spot volatility is more predictive of RV than the futures volatility. In the forecasting of IV, the volatility of futures has more information content, which can help improve overall forecast performance, especially when employing the ARFIMA+Jump model in the non-bear market and the ARFIMA+Jump/Leverage model in the bear market. The empirical result implies that the underlying asset of the TAIEX options (TXO) is approximately the index futures rather than the spot index, owing mainly to the demands for hedging and arbitrage from the TXO holders.
關鍵字Bayesian ARFIMA;Encompassing Regression;Forecasting;Implied Volatility;Realized Volatility;Taiwan
語言英文(美國)
ISSN2326-9553
期刊性質國外
收錄於
產學合作
通訊作者Chang-Wen Duan
審稿制度
國別美國
公開徵稿
出版型式,電子版,紙本
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