Forecasting Volatility in Taiwan with Encompassing Regression Models

Changwen Duan; Ken Hung; Shinhua Liu

doi:doi:10.11648/j.ijefm.20210902.12

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Forecasting Volatility in Taiwan with Encompassing Regression Models

Changwen Duan, Ken Hung, Shinhua Liu

Published in International Journal of Economics, Finance and Management Sciences (Volume 9, Issue 2)

Received: 24 December 2020 Accepted: 8 January 2021 Published: 23 April 2021

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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.

Published in	International Journal of Economics, Finance and Management Sciences (Volume 9, Issue 2)
DOI	10.11648/j.ijefm.20210902.12
Page(s)	62-76
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Bayesian ARFIMA, Encompassing Regression, Forecasting, Implied Volatility, Realized Volatility, Taiwan

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APA Style

Changwen Duan, Ken Hung, Shinhua Liu. (2021). Forecasting Volatility in Taiwan with Encompassing Regression Models. International Journal of Economics, Finance and Management Sciences, 9(2), 62-76. https://doi.org/10.11648/j.ijefm.20210902.12

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ACS Style

Changwen Duan; Ken Hung; Shinhua Liu. Forecasting Volatility in Taiwan with Encompassing Regression Models. Int. J. Econ. Finance Manag. Sci. 2021, 9(2), 62-76. doi: 10.11648/j.ijefm.20210902.12

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AMA Style

Changwen Duan, Ken Hung, Shinhua Liu. Forecasting Volatility in Taiwan with Encompassing Regression Models. Int J Econ Finance Manag Sci. 2021;9(2):62-76. doi: 10.11648/j.ijefm.20210902.12

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@article{10.11648/j.ijefm.20210902.12,
  author = {Changwen Duan and Ken Hung and Shinhua Liu},
  title = {Forecasting Volatility in Taiwan with Encompassing Regression Models},
  journal = {International Journal of Economics, Finance and Management Sciences},
  volume = {9},
  number = {2},
  pages = {62-76},
  doi = {10.11648/j.ijefm.20210902.12},
  url = {https://doi.org/10.11648/j.ijefm.20210902.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20210902.12},
  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.},
 year = {2021}
}

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TY  - JOUR
T1  - Forecasting Volatility in Taiwan with Encompassing Regression Models
AU  - Changwen Duan
AU  - Ken Hung
AU  - Shinhua Liu
Y1  - 2021/04/23
PY  - 2021
N1  - https://doi.org/10.11648/j.ijefm.20210902.12
DO  - 10.11648/j.ijefm.20210902.12
T2  - International Journal of Economics, Finance and Management Sciences
JF  - International Journal of Economics, Finance and Management Sciences
JO  - International Journal of Economics, Finance and Management Sciences
SP  - 62
EP  - 76
PB  - Science Publishing Group
SN  - 2326-9561
UR  - https://doi.org/10.11648/j.ijefm.20210902.12
AB  - 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.
VL  - 9
IS  - 2
ER  -

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Author Information

Changwen Duan

Department of Banking and Finance, Tamkang University, New Taipei City, Republic of China
Ken Hung

Sanchez School of Business, Texas A&M International University, Laredo, United States
Shinhua Liu

College of Business, University of Southern Mississippi, Hattiesburg, United States

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Plain Text BibTeX RIS

APA Style

Changwen Duan, Ken Hung, Shinhua Liu. (2021). Forecasting Volatility in Taiwan with Encompassing Regression Models. International Journal of Economics, Finance and Management Sciences, 9(2), 62-76. https://doi.org/10.11648/j.ijefm.20210902.12

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ACS Style

Changwen Duan; Ken Hung; Shinhua Liu. Forecasting Volatility in Taiwan with Encompassing Regression Models. Int. J. Econ. Finance Manag. Sci. 2021, 9(2), 62-76. doi: 10.11648/j.ijefm.20210902.12

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AMA Style

Changwen Duan, Ken Hung, Shinhua Liu. Forecasting Volatility in Taiwan with Encompassing Regression Models. Int J Econ Finance Manag Sci. 2021;9(2):62-76. doi: 10.11648/j.ijefm.20210902.12

Copy | Download

@article{10.11648/j.ijefm.20210902.12,
  author = {Changwen Duan and Ken Hung and Shinhua Liu},
  title = {Forecasting Volatility in Taiwan with Encompassing Regression Models},
  journal = {International Journal of Economics, Finance and Management Sciences},
  volume = {9},
  number = {2},
  pages = {62-76},
  doi = {10.11648/j.ijefm.20210902.12},
  url = {https://doi.org/10.11648/j.ijefm.20210902.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20210902.12},
  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.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Forecasting Volatility in Taiwan with Encompassing Regression Models
AU  - Changwen Duan
AU  - Ken Hung
AU  - Shinhua Liu
Y1  - 2021/04/23
PY  - 2021
N1  - https://doi.org/10.11648/j.ijefm.20210902.12
DO  - 10.11648/j.ijefm.20210902.12
T2  - International Journal of Economics, Finance and Management Sciences
JF  - International Journal of Economics, Finance and Management Sciences
JO  - International Journal of Economics, Finance and Management Sciences
SP  - 62
EP  - 76
PB  - Science Publishing Group
SN  - 2326-9561
UR  - https://doi.org/10.11648/j.ijefm.20210902.12
AB  - 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.
VL  - 9
IS  - 2
ER  -

Copy | Download