Risk Neutral Forecasting
AbstractAny mapping that has the same sign as the conditional mean of returns is a risk neutral investor's best predictor so it may be difficult to estimate the conditional mean yet easy to estimate a `risk neutral best predictor'. An asymptotically consistent estimator for risk neutral best predictors is proposed and is characterised both analytically and using simulations. Our results suggest that there are broad circumstances in which an investor should prefer forecasts based on this estimator to those generated by maximum likelihood estimation of the conditional mean. To facilitate the estimator's computation, a tailor-made algorithm is proposed and its properties are investigated.The decision problem we choose to focus on leads to the development of statistical and computational methods which can be applied to the estimation of `investment rules' and of `economically valuable' forecasting models.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 117.
Date of creation: 05 Jul 2000
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Postal: CEF 2000, Departament d'Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas, 25,27, 08005, Barcelona, Spain
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Other versions of this item:
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- G19 - Financial Economics - - General Financial Markets - - - Other
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- Skouras, Spyros, 2003. "An algorithm for computing estimators that optimize step functions," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 349-361, March.
- Dewachter, Hans & Lyrio, Marco, 2006.
"The cost of technical trading rules in the Forex market: A utility-based evaluation,"
Journal of International Money and Finance,
Elsevier, vol. 25(7), pages 1072-1089, November.
- Dewachter, H.D.R. & Lyrio, M., 2003. "The Cost of Technical Trading Rules in the Forex Market: A Utility-based Evaluation," Research Paper 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.
- 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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