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The Volatility Structure of the Fixed Income Market under the HJM Framework: A Nonlinear Filtering Approach

Carl Chiarella (), Hing Hung and Thuy-Duong To
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Thuy-Duong To: School of Banking and Finance, University of NSW

No 151, Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney

Abstract: This paper considers the dynamics for interest rate processes within a multi-factor Heath, Jarrow and Morton (1992) specification. Despite the flexibility of and the notable advances in theoretical research about the HJM models, the number of empirical studies is still inadequate. This paucity is principally because of the difficulties in estimating models in this class, which are not only high-dimensional, but also nonlinear and involve latent state variables. This paper treats the estimation of a fairly broad class of HJM models as a nonlinear filtering problem, and adopts the local linearization filter of Jimenez and Ozaki (2003), which is known to have some desirable statistical and numerical features, to estimate the model via the maximum likelihood method. The estimator is then applied to the interbank offered-rates of the U.S, U.K, Australian and Japanese markets. The two-factor model, with the factors being the level and the slope effect, is found to be a reasonable choice for all of the markets. However, the contribution of each factor towards overall variability of the interest rates and the financial reward each factor claims differ considerably from one market to another.

Keywords: term structure; Heath-Jarrow-Morton; local linearization; filtering (search for similar items in EconPapers)
JEL-codes: C51 E43 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-fmk and nep-mac
Date: 2005-01-01
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Related works:
Working Paper: The Volatility Structure of the Fixed Income Market under the HJM Framework: A Nonlinear Filtering Approach (2005) Downloads
Journal Article: The volatility structure of the fixed income market under the HJM framework: A nonlinear filtering approach (2009) Downloads
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