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Land of Addicts? An Empirical Investigation of Habit-Based Asset Pricing Models

机译:成瘾者的土地?基于习惯的资产定价模型的实证研究

摘要

A popular explanation of aggregate stock market behavior suggests that assets are priced as if there were a representative investor whose utility is a power function of the difference between aggregate consumption and a “habit” level, where the habit is some function of lagged and (possibly) contemporaneous consumption. But theory does not provide precise guidelines about the parametric functional relationship between the habit and aggregate consumption. This makes for- mal estimation and testing challenging; at the same time, it raises an empirical question about the functional form of the habit that best explains asset pricing data. This paper studies the ability of a general class of habit-based asset pricing models to match the conditional moment restrictions implied by asset pricing theory. Our approach is to treat the functional form of the habit as unknown, and to estimate it along with the rest of the model’s finite dimensional parameters. This semiparametric approach allows us to empirically evaluate a number of interesting hypotheses about the specification of habit-based asset pricing models. Using stationary quarterly data on consumption growth, assets returns and instruments, our empirical results indicate that the estimated habit function is nonlinear, the habit formation is internal, and the estimated time-preference parameter and the power utility parameter are sensible. In addition, our estimated habit function generates a positive stochastic discount factor (SDF) proxy and performs well in explaining cross-sectional stock return data. We find that an internal habit SDF proxy can explain a cross-section of size and book-market sorted portfolio equity returns better than (i) the Fama and French (1993) three-factor model, (ii) the Lettau and Ludvigson (2001b) scaled consumption CAPM model, (iii) an external habit SDF proxy, (iv) the classic CAPM, and (v) the classic consumption CAPM.
机译:关于股票市场总行为的一种流行解释是,资产的定价就好像有一个具有代表性的投资者,其效用是总消费和“习惯”水平之间的差异的幂函数,而习惯是某种滞后和(可能是)函数。 )同时消费。但是,理论并未就习惯与总消费之间的参数函数关系提供精确的指导。这使得正式的评估和测试具有挑战性。同时,这引发了一个关于习惯功能形式的实证问题,该习惯能最好地解释资产定价数据。本文研究了一般类别的基于习惯的资产定价模型与资产定价理论所隐含的条件矩约束相匹配的能力。我们的方法是将习惯的功能形式视为未知形式,并将其与模型的其余有限维度参数一起进行估算。这种半参数方法使我们能够根据经验评估有关基于习惯的资产定价模型规范的许多有趣假设。使用关于消费增长,资产回报和工具的固定季度数据,我们的经验结果表明,估计的习惯函数是非线性的,习惯形成是内部的,估计的时间偏好参数和电力效用参数是明智的。此外,我们的估计惯性函数会生成正随机折现因子(SDF)代理,并且在解释横截面股票收益数据方面表现出色。我们发现内部习惯SDF代理可以解释规模的横截面和按书市分类的投资组合股权收益要比(i)Fama and French(1993)三因素模型,(ii)Lettau and Ludvigson(2001b) )规模化消费CAPM模型,(iii)外部习惯SDF代理,(iv)经典CAPM,以及(v)经典消费CAPM。

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