Short selling stock indices on signals from implied volatility index changes: evidence from quantile regression-based techniques

David E. Allen, Abhay K. Singh, Robert J. Powell, Akhmad Kramadibrata

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

Abstract

This chapter investigates the profitability of a strategy based on short selling indices. It is constructed on the principle of the importance of leverage effects, which relates to the fact that increases in volatility are linked with falls in stock prices and vice versa for decreases in volatility. Several new techniques based on the use of forward-looking implied volatilities on two indices, the FTSE100 and the S&P 500 are used to generate short and long trading strategies in the two indices. These strategies employ variants of quantile regression-based techniques, including linear quantile regression, kernel-based quantile regression, and quantile regression random forests, to predict quantile intervals and employ changes in these to generate a trading strategy. Out of these, Kernel-based quantile regression methods appear to generate the greatest returns in the hold-out sample periods and dominate buy and hold returns. Kernel quantile regression is an evolving quantile regression technique in the field of nonlinear quantile regressions. As it is capable of modeling the nonlinear behavior of time series data, it proves to be more efficient in forecasting risk than other methods, including linear quantile regression.

LanguageEnglish
Title of host publicationHandbook of short selling
EditorsGreg N. Gregoriou
Place of PublicationWaltham
PublisherElsevier
Chapter33
Pages479-492
Number of pages14
ISBN (Electronic)9780123877253
ISBN (Print)9780123877246
DOIs
Publication statusPublished - 2012
Externally publishedYes

Fingerprint

Volatility index
Short selling
Implied volatility
Stock index
Quantile regression
Kernel
Trading strategies
Leverage effect
Profitability
Quantile
Modeling
Stock prices
Regression method
Time series data

Keywords

  • implied volatility
  • Kernel-based quantile regression
  • linear quantile regression
  • machine learning-based methods
  • ordinary least squares method
  • quantile regression random forests

Cite this

Allen, D. E., Singh, A. K., Powell, R. J., & Kramadibrata, A. (2012). Short selling stock indices on signals from implied volatility index changes: evidence from quantile regression-based techniques. In G. N. Gregoriou (Ed.), Handbook of short selling (pp. 479-492). Waltham: Elsevier. https://doi.org/10.1016/B978-0-12-387724-6.00033-7
Allen, David E. ; Singh, Abhay K. ; Powell, Robert J. ; Kramadibrata, Akhmad. / Short selling stock indices on signals from implied volatility index changes : evidence from quantile regression-based techniques. Handbook of short selling. editor / Greg N. Gregoriou. Waltham : Elsevier, 2012. pp. 479-492
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Allen, DE, Singh, AK, Powell, RJ & Kramadibrata, A 2012, Short selling stock indices on signals from implied volatility index changes: evidence from quantile regression-based techniques. in GN Gregoriou (ed.), Handbook of short selling. Elsevier, Waltham, pp. 479-492. https://doi.org/10.1016/B978-0-12-387724-6.00033-7

Short selling stock indices on signals from implied volatility index changes : evidence from quantile regression-based techniques. / Allen, David E.; Singh, Abhay K.; Powell, Robert J.; Kramadibrata, Akhmad.

Handbook of short selling. ed. / Greg N. Gregoriou. Waltham : Elsevier, 2012. p. 479-492.

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

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Allen DE, Singh AK, Powell RJ, Kramadibrata A. Short selling stock indices on signals from implied volatility index changes: evidence from quantile regression-based techniques. In Gregoriou GN, editor, Handbook of short selling. Waltham: Elsevier. 2012. p. 479-492 https://doi.org/10.1016/B978-0-12-387724-6.00033-7