Forecasting Volatility in Taiwan with Encompassing Regression Models

Changwen Duan,Shinhua Liu,Ken Hung

doi:10.11648/j.ijefm.20210902.12

Changwen Duan, Shinhua Liu + Show 1 more

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https://doi.org/10.11648/j.ijefm.20210902.12

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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.

Highlights

There has been sustained interest in volatility forecasting in the finance literature, as illustrated by some recent studies [7, 40, 50]
The majority of the prior studies on volatility forecasting concludes that the forecasting ability of historical volatility (HV) is inferior to that of implied volatility (IV)
It seems that DVF implied volatility (DVFIV) does not have superior forecasting power for realized volatility (RV)

Summary

Introduction

There has been sustained interest in volatility forecasting in the finance literature, as illustrated by some recent studies [7, 40, 50]. The literature on volatility forecasting can be roughly grouped into two branches, one about realized volatility (RV) and another about implied volatility (IV). Realized volatility forecasting is conducted through historical volatility (HV) and implied volatility, such as those studies carried out by Canina and Figlewski, Christensen and Prabhala, and Jiang and Tian [13, 19, 33]. These studies maintain that IV or perhaps HV could be an unbiased estimator of RV under the efficient-market hypothesis and can be used as the predictor variable for RV. Jiang and Tian [33] provide support for the informational efficiency of the option markets

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