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

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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.

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File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2007/B07056.pdf
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Paper provided by Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne in its series Documents de travail du Centre d'Economie de la Sorbonne with number b07056.

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Length: 27 pages
Date of creation: Oct 2007
Date of revision:
Handle: RePEc:mse:cesdoc:b07056

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Keywords: Subjective distribution; autoregressive conditional density; generalized hyperbolic distribution; Fed Funds futures contracts.;

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  1. 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.
  2. 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.
  3. Rosenberg, Joshua V. & Engle, Robert F., 2002. "Empirical pricing kernels," Journal of Financial Economics, Elsevier, vol. 64(3), pages 341-372, June.
  4. 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 705-30, August.
  5. Jens Carsten Jackwerth, 1998. "Recovering Risk Aversion from Option Prices and Realized Returns," Finance 9803002, EconWPA.
  6. Marie Briere, 2006. "Market Reactions to Central Bank Communication Policies :Reading Interest Rate Options Smiles," Working Papers CEB 38, ULB -- Universite Libre de Bruxelles.
  7. 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.
  8. 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.
  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 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.
  11. 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.
  12. 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 10.
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Cited by:
  1. Chevallier, Julien & Ielpo, Florian & Mercier, Ludovic, 2009. "Risk aversion and institutional information disclosure on the European carbon market : a case-study of the 2006 compliance event," Economics Papers from University Paris Dauphine 123456789/4221, Paris Dauphine University.

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