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Decision‐Based Forecast Evaluation of UK Interest Rate Predictability

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  • Kavita Sirichand
  • Stephen G. Hall

Abstract

This paper illustrates the importance of density forecasting in portfolio decision making involving bonds of different maturities. The forecast performance of an atheoretic and a theory informed model of bond returns is evaluated. The decision making environment is fully described for an investor seeking to optimally allocate his portfolio between long and short Treasury Bills, over investment horizons of up to two years. Using weekly data over 1997 to 2007 we examine the impact of parameter uncertainty and predictability in returns on the investor's allocation. We describe how the forecasts are computed and used in this context. Both statistical and decision-based criteria are used to assess the out-of-sample forecasting performance of the models. Our results show sensitivity to the evaluation criterion used. In the context of investment decision making under an economic value criterion, we find some potential gain for the investor from assuming predictability.
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Suggested Citation

  • Kavita Sirichand & Stephen G. Hall, 2016. "Decision‐Based Forecast Evaluation of UK Interest Rate Predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 93-112, March.
    • Handle: RePEc:wly:jforec:v:35:y:2016:i:2:p:93-112
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    References listed on IDEAS

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    1. Della Corte, Pasquale & Sarno, Lucio & Thornton, Daniel L., 2008. "The expectation hypothesis of the term structure of very short-term rates: Statistical tests and economic value," Journal of Financial Economics, Elsevier, vol. 89(1), pages 158-174, July.
    2. Sarno, Lucio & Thornton, Daniel L. & Valente, Giorgio, 2007. "The Empirical Failure of the Expectations Hypothesis of the Term Structure of Bond Yields," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 42(1), pages 81-100, March.
    3. Yihong Xia, 2001. "Learning about Predictability: The Effects of Parameter Uncertainty on Dynamic Asset Allocation," Journal of Finance, American Finance Association, vol. 56(1), pages 205-246, February.
    4. De Pooter, Michiel & Ravazzolo, Francesco & van Dijk, Dick, 2006. "Predicting the term structure of interest rates incorporating parameter uncertainty, model uncertainty and macroeconomic information," MPRA Paper 2512, University Library of Munich, Germany, revised 03 Mar 2007.
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    Cited by:

    1. Ana-Maria Fuertes & Elena Kalotychou & Natasa Todorovic, 2015. "Daily volume, intraday and overnight returns for volatility prediction: profitability or accuracy?," Review of Quantitative Finance and Accounting, Springer, vol. 45(2), pages 251-278, August.
    2. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2022. "Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals," International Journal of Forecasting, Elsevier, vol. 38(3), pages 944-969.

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    More about this item

    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; State Space Models
    • 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
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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