Economic Value of Stock and Interest Rate Predictability in the UK
AbstractIn this paper, we evaluate the forecast performance of a range of atheoretic and theory informed models of bond and stock returns. The decision making environment is fully described for an investor who would like to optimally allocate his portfolio between bonds and stocks, over an investment horizon of up to two years. We use a weekly dataset on UK Treasury Bill rates and the FTSE All-Share Index over the period 1997 to 2007, to examine the impact parameter uncertainty and predictability in returns have on how the investor optimally allocates his portfolio. The methods by which the forecasts should be computed and used in this context are described. Both statistical and decision based criteria are used to evaluate the out-of-sample forecasting performance of the models. Our results suggest that in the context of investment decision making under an economic value criterion, the investor gains from not only assuming predictability but by modelling the bond and stock returns together.
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Bibliographic InfoPaper provided by Department of Economics, University of Leicester in its series Discussion Papers in Economics with number 10/13.
Date of creation: Apr 2010
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Postal: Department of Economics University of Leicester, University Road. Leicester. LE1 7RH. UK
Phone: +44 (0)116 252 2887
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Web page: http://www2.le.ac.uk/departments/economics
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Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
- E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
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- Pasquale Della Corte & Lucio Sarno & Daniel L. Thornton, 2007.
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