Estimation of Dynamic Term Structure Models

IF 0.9 Q3 BUSINESS, FINANCE
G. Duffee, Richard Stanton
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引用次数: 149

Abstract

We study the finite-sample properties of some of the standard techniques used to estimate modern term structure models. For sample sizes and models similar to those used in most empirical work, we reach three surprising conclusions. First, while maximum likelihood works well for simple models, it produces strongly biased parameter estimates when the model includes a flexible specification of the dynamics of interest rate risk. Second, despite having the same asymptotic efficiency as maximum likelihood, the small-sample performance of Efficient Method of Moments (a commonly used method for estimating complicated models) is unacceptable even in the simplest term structure settings. Third, the linearized Kalman filter is a tractable and reasonably accurate estimation technique, which we recommend in settings where maximum likelihood is impractical.
动态期限结构模型的估计
我们研究了一些用于估计现代期限结构模型的标准技术的有限样本性质。对于与大多数实证工作中使用的相似的样本量和模型,我们得出了三个惊人的结论。首先,虽然最大似然对简单模型很有效,但当模型包含利率风险动态的灵活规范时,它会产生强烈的偏差参数估计。其次,尽管具有与最大似然相同的渐近效率,但即使在最简单的期限结构设置中,有效矩量法(一种用于估计复杂模型的常用方法)的小样本性能也是不可接受的。第三,线性化卡尔曼滤波是一种易于处理且相当准确的估计技术,我们推荐在最大似然不切实际的情况下使用。
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来源期刊
Quarterly Journal of Finance
Quarterly Journal of Finance BUSINESS, FINANCE-
CiteScore
1.10
自引率
0.00%
发文量
0
期刊介绍: The Quarterly Journal of Finance publishes high-quality papers in all areas of finance, including corporate finance, asset pricing, financial econometrics, international finance, macro-finance, behavioral finance, banking and financial intermediation, capital markets, risk management and insurance, derivatives, quantitative finance, corporate governance and compensation, investments and entrepreneurial finance.
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