Risk Neutral Forecasting
AbstractThis paper develops statistical and computational tools for modelling returns forecasts to be used by a risk neutral investor. Any forecast with the same sign as the conditional mean optimises the loss function derived from this agents' decision problem, so the class of optimal predictors is rather broad.
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Bibliographic InfoPaper provided by European University Institute in its series Economics Working Papers with number eco98/40.
Length: 61 pages
Date of creation: 1998
Date of revision:
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ECONOMIC MODELS ; MATHEMATICAL ANALYSIS ; FINANCIAL MARKET;
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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- 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.
- 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.
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