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Risk Neutral Forecasting

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  • Spyros Skouras

Abstract

A notion of forecast quality is defined that is appropriate when returns forecasts are used in a simple investment decision. The relation between the conditional distribution of returns and optimal point forecasts for a risk neutral investor is characterised and it is shown that the conditional mean is a small subset of optimal forecasts. Taking into account potential model misspecification and the structure of the set of optimal forecasts, methods for developing specifically `risk neutral forecasting' models are proposed. Estimation by Empirical Risk Minimisation is shown to converge to parameters associated with optimal decisions and simulations suggest that performance in small samples is acceptable even in unfavourable circumstances. Usefulness of the proposed methods is illustrated with an empirical application in which they dominate popular alternatives.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2001 with number 50.

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Date of creation: 01 Apr 2001
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Handle: RePEc:sce:scecf1:50

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Web page: http://www.econometricsociety.org/conference/SCE2001/SCE2001.html
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Keywords: financial decision-making; empirical risk minimisation;

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Cited by:
  1. Dewachter, H.D.R. & Lyrio, M., 2003. "The Cost of Technical Trading Rules in the Forex Market: A Utility-based Evaluation," ERIM Report Series Research in Management ERS-2003-052-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
  2. Skouras, Spyros, 2003. "An algorithm for computing estimators that optimize step functions," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 349-361, March.
  3. Fong, Wai Mun & Yong, Lawrence H. M., 2005. "Chasing trends: recursive moving average trading rules and internet stocks," Journal of Empirical Finance, Elsevier, vol. 12(1), pages 43-76, January.

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