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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/122079

    Title: Forecasting Volatility in Taiwan with Encompassing Regression Models
    Authors: Duan, Chang-Wen;Hung, Ken;Liu, Shinhua
    Keywords: Bayesian ARFIMA;Encompassing Regression;Forecasting;Implied Volatility;Realized Volatility;Taiwan
    Date: 2021-04-23
    Issue Date: 2022-01-25 12:10:40 (UTC+8)
    Publisher: SciencePG
    Abstract: 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.
    Relation: International Journal of Economics, Finance and Management Sciences 9(2), P.62-76
    Appears in Collections:[Graduate Institute & Department of Banking and Finance] Journal Article

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