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Modeling the U.S. Short-Term Interest Rate by Mixture Autoregressive Processes

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  • Markku Lanne
  • Pentti Saikkonen

Abstract

A new kind of mixture autoregressive model with GARCH errors is introduced and applied to the U.S. short-term interest rate. According to the diagnostic tests developed in the article and further informal checks, the model is capable of capturing both of the typical characteristics of the short-term interest rate: volatility persistence and the dependence of volatility on the level of the interest rate. The model also allows for regime switches whose presence has been a third central result emerging from the recent empirical literature on the U.S. short-term interest rate. Realizations generated from the estimated model seem stable and their properties resemble those of the observed series closely. The drift and diffusion functions implied by the new model are in accordance with the results in much of the literature on continuous-time diffusion models for the short-term interest rate, and the term structure implications agree with historically observed patterns. , .

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Bibliographic Info

Article provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.

Volume (Year): 1 (2003)
Issue (Month): 1 ()
Pages: 96-125

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Handle: RePEc:oup:jfinec:v:1:y:2003:i:1:p:96-125

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Cited by:
  1. Lanne , Markku, 2002. "Nonlinear dynamics of interest rate and inflation," Research Discussion Papers, Bank of Finland 21/2002, Bank of Finland.
  2. Carvalho, Alexandre & Skoulakis, Georgios, 2005. "Ergodicity and existence of moments for local mixtures of linear autoregressions," Statistics & Probability Letters, Elsevier, Elsevier, vol. 71(4), pages 313-322, March.
  3. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Working Papers, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University 415, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  4. Lanne, Markku & Ahoniemi, Katja, 2008. "Implied Volatility with Time-Varying Regime Probabilities," MPRA Paper 23721, University Library of Munich, Germany.
  5. Tom Pak-wing Fong & Chun-shan Wong, 2008. "Stress Testing Banks' Credit Risk Using Mixture Vector Autoregressive Models," Working Papers, Hong Kong Monetary Authority 0813, Hong Kong Monetary Authority.
  6. Saikkonen, Pentti, 2005. "Stability results for nonlinear error correction models," Journal of Econometrics, Elsevier, Elsevier, vol. 127(1), pages 69-81, July.
  7. Luc, BAUWENS & Arie, PREMINGER & Jeroen, ROMBOUTS, 2006. "Regime switching GARCH models," Discussion Papers (ECON - Département des Sciences Economiques), Université catholique de Louvain, Département des Sciences Economiques 2006006, Université catholique de Louvain, Département des Sciences Economiques.
  8. Haas, Markus & Mittnik, Stefan & Mizrach, Bruce, 2005. "Assessing central bank credibility during the EMS crises: Comparing option and spot market-based forecasts," CFS Working Paper Series, Center for Financial Studies (CFS) 2005/09, Center for Financial Studies (CFS).
  9. Mandler, Martin, 2007. "The Taylor rule and interest rate uncertainty in the U.S. 1955-2006," MPRA Paper 2340, University Library of Munich, Germany.
  10. Mohamed Saidane & Christian Lavergne, 2009. "Optimal Prediction with Conditionally Heteroskedastic Factor Analysed Hidden Markov Models," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 34(4), pages 323-364, November.
  11. Nyberg, Henri, 2010. "QR-GARCH-M Model for Risk-Return Tradeoff in U.S. Stock Returns and Business Cycles," MPRA Paper 23724, University Library of Munich, Germany.
  12. Giannikis, D. & Vrontos, I.D. & Dellaportas, P., 2008. "Modelling nonlinearities and heavy tails via threshold normal mixture GARCH models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 52(3), pages 1549-1571, January.

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