Estimating affine multifactor term structure models using closed-form likelihood expansions

Yacine Ait-Sahalia, Robert L. Kimmel

Research output: Contribution to journalArticle

52 Scopus citations

Abstract

We develop and implement a technique for closed-form maximum likelihood estimation (MLE) of multifactor affine yield models. We derive closed-form approximations to likelihoods for nine Dai and Singleton (2000) affine models. Simulations show our technique very accurately approximates true (but infeasible) MLE. Using US Treasury data, we estimate nine affine yield models with different market price of risk specifications. MLE allows non-nested model comparison using likelihood ratio tests; the preferred model depends on the market price of risk. Estimation with simulated and real data suggests our technique is much closer to true MLE than Euler and quasi-maximum likelihood (QML) methods.

Original languageEnglish (US)
Pages (from-to)113-144
Number of pages32
JournalJournal of Financial Economics
Volume98
Issue number1
DOIs
StatePublished - Oct 1 2010

All Science Journal Classification (ASJC) codes

  • Accounting
  • Finance
  • Economics and Econometrics
  • Strategy and Management

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