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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Aït-Sahalia, Y., P. A. Mykland, and L. Zhang, 2005, “How often to sample a continuous-time process in the presence of market microstructure noise.” Review of Financial Studies 18, 351–416.
[2]	Andersen, T. G., and T. Bollerslev, 1997, “Intraday periodicity and volatility persistence in financial markets.” Journal of Empirical Finance 4, 115-158.
[3]	Andersen, T. G., T. Bollerslev, and F. X. Diebold, 2007, “Roughing it up: Disentangling continuous and Jump components in measuring, modeling and forecasting asset return volatility.” Review of Economics and Statistics, 89, 701-720.
[4]	Andersen, T. G., T. Bollerslev, F. X. Diebold, and P. Labys, 2001, “The distribution of realized exchange rate volatility.” Journal of the American Statistical Association 96, 42-55.
[5]	Andersen, T. G., T. Bollerslev, F. X. Diebold, and P. Labys, 2003, “Modeling and forecasting realized volatility.” Econometrica, 71, 579-625.
[6]	Andersen, T., T. Bollerslev, F. Diebold, and H. Ebens, 2001, The distribution of realized stock return volatility. Journal of Financial Economics, 61, 43-76.
[7]	Asai, M., R. Gupta, and M. McAleer, 2020, “Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks.” International Journal of Forecasting 36, 933-948.
[8]	Bakshi, G., C. Cao, and Z. Chen, 1997, “Empirical performance of alternative option pricing models.” Journal of Finance 52, 2003-2049.
[9]	Black, F., 1976, “The pricing of commodity contracts.” Journal of Financial Economics 3, 167-179.
[10]	Black, F., and M. Scholes, 1973, “The pricing of options and corporate liabilities.” Journal of Political Economy 81, 637-659.
[11]	Blair, B. J., S. Poon, and S. J. Taylor, 2001, “Forecasting S&P 100 volatility: The incremental information content of implied volatilities and high-frequency index returns.” Journal of Econometrics 105, 5-26.
[12]	Campa, J. M. and P. H. K. Chang, 1998, “The forecasting ability of correlations implied in foreign exchange options.” Journal of International Money and Finance 17, 855-880.
[13]	Canina, L., and S. Figlewski, 1993, “The informational content of implied volatility.” Review of Financial Studies 6, 659-681.
[14]	Chan, K., 1992, “A further analysis of the lead-lag relationship between the cash market and stock index futures market.” Review of Financial Studies 5, 23-152.
[15]	Chan, W. H., R. Jha, and M. Kalimipalli, 2009, “The economic value of using realized volatility in forecasting future implied volatility.” Journal of Financial Research 32, 231-259.
[16]	Chiras, D. P., and S. Manaster, 1978, “The information content of option prices and a test of market efficiency.” Journal of Financial Economics 10, 213-234.
[17]	Christie, A. A., 1982, “The stochastic behavior of common stock variances: Value, leverage and interest rate effects.” Journal of Financial Economics 10, 407-432.
[18]	Cheung, Y., and L. Ng, 1992, “Stock Price Dynamics and Firm Size: An Empirical Investigation.” Journal of Finance 47, 1985-1997.
[19]	Christensen, B. J., and N. R. Prabhala, 1998, “The relation between implied and realized volatility.” Journal of Financial Economics 50, 125-150.
[20]	Clemen, R. T., 1989, “Combining forecasts: A review and an annotated bibliography.” International Journal of Forecasting 5, 559-583.
[21]	Dumas, B., J. Fleming, and R. E. Whaley, 1998, “Implied volatility functions: Empirical tests.” Journal of Finance 53, 2059-2106.
[22]	Fair, R. C., and R. J. Shiller, 1990, “Comparing information in forecasts from econometric models.” American Economic Review 80, 375-389.
[23]	Fleming, J., 1998, “The quality of market volatility forecasts implied by S&P 100 index option prices.” Journal of Empirical Finance 5, 317-345.
[24]	Fleming, J., B. Ostdiek, and R. E. Whaley, 1996, “Trading costs and the relative rate of price discovery in stock, futures, and options markets.” Journal of Futures Markets 16, 353-387.
[25]	Gemmill, G., 1986, “The forecasting performance of stock options on the London traded options marker.” Journal of Business Finance and Accounting 13, 535-546.
[26]	Geweke, J., and S. Porter-Hudak, 1983, “The estimation and application of long memory time series models.” Journal of Time Series Analysis 4, 221-238.
[27]	Granger, C. W. J., 1980, “Long memory relationships and the aggregation of dynamic models.” Journal of Econometrics 14, 227-238.
[28]	Granger, C. W. J., and R. Joyeux, 1980, “An introduction to long-memory time series models and fractional differencing.” Journal of Time Series Analysis 1, 15–30.
[29]	Hsieh, G., 2002, “Market integration, price discovery, and information transmission in Taiwan index futures market.” Journal of Financial Studies 10, 1-31.
[30]	Hosking, J. R. M., 1981, “Fractional differencing.” Biometrika 68, 165-176.
[31]	Janacek, G., 1982, “Determining the degree of differencing for time series via the long spectrum.” Journal of Time Series Analysis 3, 177–183.
[32]	Jang, W. Y., and Y. F. He, 2004, “The temporal price relationships between spot, futures and options values on the TSE: early evidence.” Journal of Emerging Markets 9, 10-21.
[33]	Jiang, G. J., and Y. S. Tian, 2005, “The Model-Free implied volatility and its information content.” Review of Financial Studies 18, 1305-1342.
[34]	Jorion, P., 1995, “Predicting volatility in the foreign exchange market.” Journal of Finance 50, 507-528.
[35]	Kawaller, I. G., P. D. Koch, and T. W. Koch, 1987, “The temporal price relationship between S&P 500 futures and the S&P 500 index.” Journal of Finance 42, 1309-1329.
[36]	Koop, G., E. Ley, J. Osiewalski, and M. F. J. Steel, 1997, “Bayesian analysis of long memory and persistence using ARFIMA models.” Journal of Econometrics 76, 149-169.
[37]	Koopman, S. J., B. Jungbacker, and U. E. Hol, 2005, “Forecasting daily variability of the S&P 100 stock index using historical, realized and implied volatility measurements.” Structural Breaks and Nonlinearity. Tinbergen Institute Discussion Paper.
[38]	Lamoureux, C. G., and W. D. Lastrapes, 1993, “Forecasting stock-return variance: Toward an understanding of stochastic implied volatilities.” Review of Financial Studies 6, 293-326.
[39]	Lin, C. C., S. Y. Chen, D. Y. Hwang, and C. F. Lin, 2002, “Does index futures dominate index spot? Evidence from Taiwan market.” Review of Pacific Basin Financial Markets and Policies 5, 255 - 275.
[40]	Ma, F., Y. Wei, L. Liu, and D. Huang, 2018, “Forecasting realized volatility of oil futures market: A new insight.” Journal of Forecasting 37, 419-436.
[41]	Mayhew, S., 1995, “Implied volatility.” Financial Analysts Journal 51, 8-20.
[42]	Mincer, J., and V. Zarnowitz, 1969, “The evaluation of economic forecasts” in J. Mincer, ed., ‘Economic Forecasts and Expectations’, NBER, New York.
[43]	Neely, C. J., 2009, “Forecasting foreign exchange volatility: Why is implied volatility biased and inefficient? And does it matter?” Journal of International Financial Markets, Institutions and Money 19, 188–205.
[44]	Parkinson, M., 1980, “The Extreme value method for estimating the variance of the rate of return.” Journal of Business 53, 61-65.
[45]	Poon, S., and C. W. J. Granger, 2003, “Forecasting volatility in financial markets: A review.” Journal of Economic Literature 41, 478-539.
[46]	Pong, S., M. B. Shackleton, S. J. Taylor, and X. Xu, 2004, “Forecasting currency volatility: A comparison of implied volatilities and AR(FI)MA Models.” Journal of Banking and Finance 28, 2541-2563.
[47]	Rubinstein, M., 1994, “Implied Binomial Trees.” Journal of Finance 49, 771-818
[48]	Schwert, G. W., and P. J. Seguin, 1990, “Heteroskedasticity in stock returns.” Journal of Finance 45, 1129-1155.
[49]	Stoll, H. R., and R. E. Whaley, 1990, “The dynamics of stock index and stock index futures returns.” Journal of Financial and Quantitative Analysis 25, 441-468.
[50]	Tan, S., K. Ng, J. Chan, and I. Mohamed, 2019, “Quantile range-based volatility measure for modelling and forecasting volatility using high frequency data.” North American Journal of Economics and Finance 47, 537-551.
[51]	Whaley, R. E., 1982, “Valuation of American call options on dividend-paying stocks.” Journal of Financial Economics 10, 29–58.

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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