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Are VIX futures prices predictable? An empirical investigation

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  • Konstantinidi, Eirini
  • Skiadopoulos, George

Abstract

This paper investigates whether volatility futures prices per se can be forecasted by studying the fast-growing VIX futures market. To this end, alternative model specifications are employed. Point and interval out-of-sample forecasts are constructed and evaluated under various statistical metrics. Next, the economic significance of the forecasts obtained is also assessed by performing trading strategies. Only weak evidence of statistically predictable patterns in the evolution of volatility futures prices is found. No trading strategy yields economically significant profits. Hence, the hypothesis that the VIX volatility futures market is informationally efficient cannot be rejected.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 27 (2011)
Issue (Month): 2 ()
Pages: 543-560

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Handle: RePEc:eee:intfor:v:27:y:2011:i:2:p:543-560

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

Keywords: Bootstrapping; Interval forecasts; Market efficiency; Predictability; Performance measures; VIX; Volatility futures;

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References

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Citations

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Cited by:
  1. Dunis, Christian & Kellard, Neil M. & Snaith, Stuart, 2013. "Forecasting EUR–USD implied volatility: The case of intraday data," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4943-4957.
  2. Goulas, Lambros & Skiadopoulos, George, 2012. "Are freight futures markets efficient? Evidence from IMAREX," International Journal of Forecasting, Elsevier, vol. 28(3), pages 644-659.
  3. Cheng, Jun & Ibraimi, Meriton & Leippold, Markus & Zhang, Jin E., 2012. "A remark on Lin and Chang's paper ‘Consistent modeling of S&P 500 and VIX derivatives’," Journal of Economic Dynamics and Control, Elsevier, vol. 36(5), pages 708-715.
  4. Cathy Chen & Shu-Yu Chen & Sangyeol Lee, 2013. "Bayesian Unit Root Test in Double Threshold Heteroskedastic Models," Computational Economics, Society for Computational Economics, vol. 42(4), pages 471-490, December.

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