Title

Short Selling Stock Indices On Signals From Implied Volatility Index Changes: Evidence From Quantile Regression Based Techniques

Document Type

Book Chapter

Faculty

Faculty of Business and Law

School

School of Accounting, Finance and Economics / Finance, Economics, Markets and Accounting Research Centre

RAS ID

12922

Comments

This chapter was originally published as: Allen, D. E., Powell, R. , Singh, A. , & Kramadibrata, A. R. (2011). Short Selling Stock Indices On Signals From Implied Volatility Index Changes: Evidence From Quantile Regression Based Techniques. In G.N. Gregoriou (Eds.). Handbook of Short Selling (pp. 479-492). Location: Elsevier.

Abstract

An investorr equires a prediction of the direction of the underlying asset price movements to devise a profitable trading strategy. In most of the techniques used for forecasting , a point estimate of the expected return or its volatility is calculated. The poin testimate, although useful, does not give the extremes of the estimate, which can be useful in evaluating th epossible losses or gains for the asset. This chapter uses machine learning-based methods for quantile regressions to calculate an extreme interval estimate for the expected volatility in index return and uses the inverse relationship between volatilities and index levels to generate a directional signal. We use the interval estimate to devise a trading strategy based on the expected direction of change in the interva to trade th eunderlyin gprice index and compare results obtained from linear quantile regressions to machine learning-based methods.

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