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Flexible time series models for subjective distribution estimation with monetary policy in view

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  • Dominique Guegan

    (Centre d'Economie de la Sorbonne)

  • Florian Ielpo

    (Centre d'Economie de la Sorbonne)

Abstract

In this paper, we introduce a new approach to estimate the subjective distribution of the future short rate from the historical dynamics of futures, based on a model generated by a Normal Inverse Gaussian distribution, with dynamical parameters. The model displays time varying conditional volatility, skewness and kurtosis and provides a flexible framework to recover the conditional distribution of the future rates. For the estimation, we use maximum likelihood method. Then, we apply the model to Fed Fund futures and discuss its performance

Suggested Citation

  • Dominique Guegan & Florian Ielpo, 2007. "Flexible time series models for subjective distribution estimation with monetary policy in view," Documents de travail du Centre d'Economie de la Sorbonne b07056, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    • Handle: RePEc:mse:cesdoc:b07056
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    References listed on IDEAS

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    1. Ait-Sahalia, Yacine & Lo, Andrew W., 2000. "Nonparametric risk management and implied risk aversion," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 9-51.
    2. Jackwerth, Jens Carsten, 2000. "Recovering Risk Aversion from Option Prices and Realized Returns," The Review of Financial Studies, Society for Financial Studies, vol. 13(2), pages 433-451.
    3. Piazzesi, Monika & Swanson, Eric T., 2008. "Futures prices as risk-adjusted forecasts of monetary policy," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 677-691, May.
    4. Rosenberg, Joshua V. & Engle, Robert F., 2002. "Empirical pricing kernels," Journal of Financial Economics, Elsevier, vol. 64(3), pages 341-372, June.
    5. Robert A. Jarrow & Arkadev Chatterjea, 2019. "Interest Rates," World Scientific Book Chapters, in: An Introduction to Derivative Securities, Financial Markets, and Risk Management, chapter 2, pages 22-52, World Scientific Publishing Co. Pte. Ltd..
    6. John C. Robertson & Daniel L. Thornton, 1997. "Using federal funds futures rates to predict Federal Reserve actions," Review, Federal Reserve Bank of St. Louis, issue Nov, pages 45-53.
    7. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    8. Morten B. Jensen & Asger Lunde, 2001. "The NIG-S&ARCH model: a fat-tailed, stochastic, and autoregressive conditional heteroskedastic volatility model," Econometrics Journal, Royal Economic Society, vol. 4(2), pages 1-10.
    9. Jondeau, Eric & Rockinger, Michael, 2003. "Conditional volatility, skewness, and kurtosis: existence, persistence, and comovements," Journal of Economic Dynamics and Control, Elsevier, vol. 27(10), pages 1699-1737, August.
    10. John B. Carlson & William R. Melick & Erkin Y. Sahinoz, 2003. "An option for anticipating Fed action," Economic Commentary, Federal Reserve Bank of Cleveland, issue Sep.
    11. Marie Briere, 2006. "Market Reactions to Central Bank Communication Policies :Reading Interest Rate Options Smiles," Working Papers CEB 38, ULB -- Universite Libre de Bruxelles.
    12. Ielpo, Florian & Guégan, Dominique, 2006. "An econometric specification of monetary policy dark art," MPRA Paper 1004, University Library of Munich, Germany, revised 07 Oct 2006.
    13. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    More about this item

    Keywords

    Subjective distribution; autoregressive conditional density; generalized hyperbolic distribution; Fed Funds futures contracts;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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