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Relative performance of VIXC vs. GARCH in predicting realised volatility changes

Relative performance of VIXC vs. GARCH in predicting realised volatility changes This paper examines the forecasting power of the volatility index of Canada (VIXC) and GARCH-family volatility. Specifically, this paper is motivated by an enquiry into how well volatility estimators predict volatility changes. To this end, we use a daily series of VIXC from 1 October 2009 through 30 April 2015. To estimate out-of-sample parameters for GARCH volatilities, a series of daily returns of the TSX60 since 29 November 2002 is used roll-forwardly. Then we run the forecasting regressions for a full-sample and five subsamples grouped by the daily percentage change in realised volatility, and compare and the loss functions to assess the forecasting power of volatility estimators. Additionally, this paper proposes a new measurement called mean-directional-error (MDE), which can comprehensively evaluate the forecast error in both direction and size of movement. The key findings of this paper can be summarised as follows: even though the information content of VIXC is highest on average and intensifies when volatility falls high, the directional accuracy measured by MDE is worst for VIXC in all subsamples. GJR-GARCH (1,1) achieves the highest accuracy judged by the traditional loss functions. Focusing on the directional accuracy, GARCH (1,1) demonstrates the best predictability. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Investment Analysts Journal Taylor & Francis

Relative performance of VIXC vs. GARCH in predicting realised volatility changes

Jung, Young Cheol
Investment Analysts Journal , Volume 45 (sup1): 16 – Oct 28, 2016
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Relative performance of VIXC vs. GARCH in predicting realised volatility changes

Jung, Young Cheol
Investment Analysts Journal , Volume 45 (sup1): 16 – Oct 28, 2016

Abstract

This paper examines the forecasting power of the volatility index of Canada (VIXC) and GARCH-family volatility. Specifically, this paper is motivated by an enquiry into how well volatility estimators predict volatility changes. To this end, we use a daily series of VIXC from 1 October 2009 through 30 April 2015. To estimate out-of-sample parameters for GARCH volatilities, a series of daily returns of the TSX60 since 29 November 2002 is used roll-forwardly. Then we run the forecasting regressions for a full-sample and five subsamples grouped by the daily percentage change in realised volatility, and compare and the loss functions to assess the forecasting power of volatility estimators. Additionally, this paper proposes a new measurement called mean-directional-error (MDE), which can comprehensively evaluate the forecast error in both direction and size of movement. The key findings of this paper can be summarised as follows: even though the information content of VIXC is highest on average and intensifies when volatility falls high, the directional accuracy measured by MDE is worst for VIXC in all subsamples. GJR-GARCH (1,1) achieves the highest accuracy judged by the traditional loss functions. Focusing on the directional accuracy, GARCH (1,1) demonstrates the best predictability.

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Publisher
Taylor & Francis
Copyright
© 2016 Investment Analysts Society of South Africa
ISSN
2077-0227
eISSN
1029-3523
DOI
10.1080/10293523.2016.1151986
Publisher site
See Article on Publisher Site

Abstract

This paper examines the forecasting power of the volatility index of Canada (VIXC) and GARCH-family volatility. Specifically, this paper is motivated by an enquiry into how well volatility estimators predict volatility changes. To this end, we use a daily series of VIXC from 1 October 2009 through 30 April 2015. To estimate out-of-sample parameters for GARCH volatilities, a series of daily returns of the TSX60 since 29 November 2002 is used roll-forwardly. Then we run the forecasting regressions for a full-sample and five subsamples grouped by the daily percentage change in realised volatility, and compare and the loss functions to assess the forecasting power of volatility estimators. Additionally, this paper proposes a new measurement called mean-directional-error (MDE), which can comprehensively evaluate the forecast error in both direction and size of movement. The key findings of this paper can be summarised as follows: even though the information content of VIXC is highest on average and intensifies when volatility falls high, the directional accuracy measured by MDE is worst for VIXC in all subsamples. GJR-GARCH (1,1) achieves the highest accuracy judged by the traditional loss functions. Focusing on the directional accuracy, GARCH (1,1) demonstrates the best predictability.

Journal

Investment Analysts Journal – Taylor & Francis

Published: Oct 28, 2016

Keywords: Volatility; VIXC; GARCH; GJR-GARCH; mean-directional-error

References

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