Likelihood inference in non-linear term structure models: the importance of the lower bound
AbstractThis paper shows how to use adaptive particle filtering and Markov chain Monte Carlo methods to estimate quadratic term structure models (QTSMs) by likelihood inference. The procedure is applied to a quadratic model for the United States during the recent financial crisis. We find that this model provides a better statistical description of the data than a Gaussian affine term structure model. In addition, QTSMs account perfectly for the lower bound whereas Gaussian affine models frequently imply forecast distributions with negative interest rates. Such predictions appear during the recent financial crisis but also prior to the crisis.
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Bibliographic InfoPaper provided by Bank of England in its series Bank of England working papers with number 481.
Length: 35 pages
Date of creation: 20 Dec 2013
Date of revision:
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Adaptive particle filtering; Bayesian inference; Higher-order moments; PMCMC; Quadratic term structure models;
Find related papers by JEL classification:
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-12-29 (All new papers)
- NEP-ECM-2013-12-29 (Econometrics)
- NEP-FOR-2013-12-29 (Forecasting)
- NEP-ORE-2013-12-29 (Operations Research)
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