SPECTRAL PROPERTIES OF ASSET PRICING MODELS: A GENERAL EQUILIBRIUM PERSPECTIVE

2006 ◽  
Vol 10 (2) ◽  
pp. 183-205
Author(s):  
KEVIN E. BEAUBRUN-DIANT
Keyword(s):  
Asset Pricing ◽  
Goodness Of Fit ◽  
Growth Models ◽  
High Frequencies ◽  
Asset Pricing Models ◽  
Specific Objective ◽  
Approximation Errors ◽  
Pricing Research ◽  
Stochastic Growth Models ◽  
New Evidence

This paper studies asset returns adopting an alternative strategy to assess a model's goodness of fit. Based on spectral analysis, this approach considers a model as an approximation to the process generating the observed data, and characterizes the dimensions for which the model provides a good approximation and those for which it does not. Our aim is to offer new evidence regarding the size and the location of approximation errors of a set of stochastic growth models considered to be decisive steps in the progress of the asset pricing research program. Our specific objective is to reevaluate the results of Jermann's (1998) model extending the calculations to the spectral domain. Spectral results are relatively satisfactory: the benchmark model needs very few contributions of approximation errors to account for the empirical equity premium. Second, the location of the approximation errors, when they are substantial, seems to be essentially concentrated at high frequencies.

Assessing Goodness-of-Fit of Asset Pricing Models: The Distribution of the Maximal R2

1997 ◽  
Vol 52 (2) ◽  
pp. 591-607 ◽  
Author(s):  
F. DOUGLAS FOSTER ◽  
TOM SMITH ◽  
ROBERT E. WHALEY
Keyword(s):  
Asset Pricing ◽  
Goodness Of Fit ◽  
Pricing Models ◽  
Asset Pricing Models

Assessing Goodness-Of-Fit of Asset Pricing Models: The Distribution of the Maximal R 2

1997 ◽  
Vol 52 (2) ◽  
pp. 591 ◽  
Author(s):  
F. Douglas Foster ◽  
Tom Smith ◽  
Robert E. Whaley
Keyword(s):  
Asset Pricing ◽  
Goodness Of Fit ◽  
Pricing Models ◽  
Asset Pricing Models

scholarly journals Avaliando Modelos de Precificação de Ativos via Abordagem de Fatores Simulados

2012 ◽  
Vol 10 (4) ◽  
pp. 425
Author(s):  
Carlos Enrique Carrasco-Gutierrez ◽  
Wagner Piazza Gaglianone
Keyword(s):  
Asset Pricing ◽  
Goodness Of Fit ◽  
Factor Models ◽  
Stochastic Discount Factor ◽  
Pricing Models ◽  
Fit Statistics ◽  
Asset Pricing Models ◽  
Starting Point ◽  
Pricing Error ◽  
The U.S

In this paper a methodology to compare the performance of different stochastic discount factor (SDF) models is suggested. The starting point is the estimation of several factor models in which the choice of the fundamental factors comes from different procedures. Then, a Monte Carlo simulation is designed in order to simulate a set of gross returns with the objective of mimicking the temporal dependency and the observed covariance across gross returns. Finally, the artificial returns are used to investigate the performance of the competing asset pricing models through the Hansen and Jagannathan (1997) distance and some goodness-of-fit statistics of the pricing error. An empirical application is provided for the U.S. stock market.

scholarly journals The impact of time-varying risk on stock returns: an experiment of cubic piecewise polynomial function model and the Fourier Flexible Form model

2021 ◽  
Vol 1 (2) ◽  
pp. 141-164
Author(s):  
Fangzhou Huang ◽  
◽  
Jiao Song ◽  
Nick J. Taylor ◽  
◽  
...  
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Goodness Of Fit ◽  
Time Varying ◽  
Pricing Models ◽  
Piecewise Polynomial Function ◽  
Data Smoothing ◽  
Asset Pricing Models ◽  
Flexible Form ◽  
The Impact

<abstract> <p>With fast evolving econometric techniques being adopted in asset pricing, traditional linear asset pricing models have been criticized by their limited function on capturing the time-varying nature of data and risk, especially the absence of data smoothing is of concern. In this paper, the impact of data smoothing is explored by applying two asset pricing models with non-linear feature: cubic piecewise polynomial function (CPPF) model and the Fourier Flexible Form (FFF) model are performed on US stock returns as an experiment. The traditional beta coefficient is treated asymmetrically as downside beta and upside beta in order to capture corresponding risk, and further, to explore the risk premia attached in a cross-sectional context. It is found that both models show better goodness of fit comparing to classic linear asset pricing model cross-sectionally. When appropriate knots and orders are determined by Akaike Information Criteria (AIC), the goodness of fit is further improved, and the model with both CPPF and FFF betas employed showed the best fit among other models. The findings fill the gap in literature, specifically on both investigating and pricing the time variation and asymmetric nature of systematic risk. The methods and models proposed in this paper embed advanced mathematical techniques of data smoothing and widen the options of asset pricing models. The application of proposed models is proven to superiorly provide high degree of explanatory power to capture and price time-varying risk in stock market.</p> </abstract>

Implications of Predictability Across Horizons for Asset Pricing Models

2012 ◽  
Author(s):  
Carlo A. Favero ◽  
Fulvio Ortu ◽  
Andrea Tamoni ◽  
Haoxi Yang
Keyword(s):  
Asset Pricing ◽  
Pricing Models ◽  
Asset Pricing Models
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