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Multi-Step-Ahead Forecasting of the CBOE Volatility Index in a Data-Rich Environment: Application of Random Forest with Boruta Algorithm

Author

Listed:
  • Byung Yeon Kim

    (Sungkyunkwan University)

  • Heejoon Han

    (Sungkyunkwan University)

Abstract

The CBOE volatility index (VIX) is a representative barometer of the overall sentiment and volatility of the financial market. This paper seeks to apply random forest and its variable importance measure to forecasting the VIX index. Compared to the previous literature which has found it difficult to outperform the pure HAR process in terms of forecasting the VIX index due to its persistent nature, random forest can produce forecasts that are significantly more accurate than the HAR and augmented HAR models for multidays forecasting horizons. This paper shows that the forecasting accuracy of random forest could be further improved by systematically selecting the optimal number of the most important covariates from a dataset of 298 macro-finance variables, while using the Boruta algorithm which ranks the variables based on random forest’s variable importance measure. The superior predictability of this method is more evident with longer forecasting horizons.

Suggested Citation

  • Byung Yeon Kim & Heejoon Han, 2022. "Multi-Step-Ahead Forecasting of the CBOE Volatility Index in a Data-Rich Environment: Application of Random Forest with Boruta Algorithm," Korean Economic Review, Korean Economic Association, vol. 38, pages 541-569.
    • Handle: RePEc:kea:keappr:ker-20220701-38-3-07
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    References listed on IDEAS

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    1. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    2. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2015. "Complete subset regressions with large-dimensional sets of predictors," Journal of Economic Dynamics and Control, Elsevier, vol. 54(C), pages 86-110.
    3. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    4. Konstantinidi, Eirini & Skiadopoulos, George & Tzagkaraki, Emilia, 2008. "Can the evolution of implied volatility be forecasted? Evidence from European and US implied volatility indices," Journal of Banking & Finance, Elsevier, vol. 32(11), pages 2401-2411, November.
    5. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    6. Ballestra, Luca Vincenzo & Guizzardi, Andrea & Palladini, Fabio, 2019. "Forecasting and trading on the VIX futures market: A neural network approach based on open to close returns and coincident indicators," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1250-1262.
    7. Psaradellis, Ioannis & Sermpinis, Georgios, 2016. "Modelling and trading the U.S. implied volatility indices. Evidence from the VIX, VXN and VXD indices," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1268-1283.
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    More about this item

    Keywords

    Random Forest; Boruta Algorithm; Machine Learning; VIX Index; Volatility Forecasting;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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