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Forecasting and trading on the VIX futures market: A neural network approach based on open to close returns and coincident indicators

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  • Ballestra, Luca Vincenzo
  • Guizzardi, Andrea
  • Palladini, Fabio

Abstract

Previous work has highlighted the difficulty of obtaining accurate and economically significant predictions of VIX futures prices. We show that both low prediction errors and a significant amount of profitability can be obtained by using a neural network model to predict VIX futures returns. In particular, we focus on open-to-close returns (OTCRs) and consider intraday trading strategies, taking into account non-lagged exogenous variables that closely reflect the information possessed by traders at the time when they decide to invest. The neural network model with only the most recent exogenous variables (namely, the return on the Indian BSESN index) is superior to an unconstrained specification with ten lagged and coincident regressors, which is actually a form of weak efficiency involving markets of different countries. Moreover, the neural network turns out to be more profitable than either a logistic specification or heterogeneous autoregressive models.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:intfor:v:35:y:2019:i:4:p:1250-1262
    DOI: 10.1016/j.ijforecast.2019.03.022
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    4. 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.
    5. Yun‐Huan Lee & Tzu‐Hsiang Liao & Hsiu‐Chuan Lee, 2022. "Overnight returns of industry exchange‐traded funds, investor sentiment, and futures market returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(6), pages 1114-1134, June.
    6. Huang, Wenyang & Gao, Tianxiao & Hao, Yun & Wang, Xiuqing, 2023. "Transformer-based forecasting for intraday trading in the Shanghai crude oil market: Analyzing open-high-low-close prices," Energy Economics, Elsevier, vol. 127(PA).
    7. Li, Jianping & Li, Guowen & Liu, Mingxi & Zhu, Xiaoqian & Wei, Lu, 2022. "A novel text-based framework for forecasting agricultural futures using massive online news headlines," International Journal of Forecasting, Elsevier, vol. 38(1), pages 35-50.
    8. Xu Gong & Mengjie Li & Keqin Guan & Chuanwang Sun, 2023. "Climate change attention and carbon futures return prediction," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(9), pages 1261-1288, September.
    9. Xu Gong & Keqin Guan & Qiyang Chen, 2022. "The role of textual analysis in oil futures price forecasting based on machine learning approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1987-2017, October.

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