Regression Based Estimation of Dynamic Asset Pricing Models

Regression Based Estimation of Dynamic Asset Pricing Models
Author: Tobias Adrian
Publisher:
Total Pages: 53
Release: 2015
Genre: Assets (Accounting)
ISBN:

We propose regression based estimators for beta representations of dynamic asset pricing models with an affine pricing kernel specification. We allow for state variables that are cross sectional pricing factors, forecasting variables for the price of risk, and factors that are both. The estimators explicitly allow for time varying prices of risk, time varying betas and serially dependent pricing factors. Our approach nests the Fama-MacBeth two-pass estimator as a special case. We provide asymptotic multistage standard errors necessary to conduct inference for asset pricing tests. We illustrate our new estimators in an application to the joint pricing of stocks and bonds. The application features strongly time varying, highly significant prices of risk which are found to be quantitatively more important than time varying betas in reducing pricing errors.

Asset Pricing Models with Conditional Betas and Alphas

Asset Pricing Models with Conditional Betas and Alphas
Author: Wayne E. Ferson
Publisher:
Total Pages: 38
Release: 2010
Genre:
ISBN:

This paper studies the estimation of asset pricing model regressions with conditional alphas and betas, focusing on the joint effects of data snooping and spurious regression. We find that the regressions are reasonably well specified for conditional betas, even in settings where simple predictive regressions are severely biased. However, there are biases in estimates of the conditional alphas. When time-varying alphas are suppressed and only time-varying betas are considered, the betas become baised. Previous studies overstate the significance of time-varying alphas.

Empirical Dynamic Asset Pricing

Empirical Dynamic Asset Pricing
Author: Kenneth J. Singleton
Publisher: Princeton University Press
Total Pages: 497
Release: 2009-12-13
Genre: Business & Economics
ISBN: 1400829232

Written by one of the leading experts in the field, this book focuses on the interplay between model specification, data collection, and econometric testing of dynamic asset pricing models. The first several chapters provide an in-depth treatment of the econometric methods used in analyzing financial time-series models. The remainder explores the goodness-of-fit of preference-based and no-arbitrage models of equity returns and the term structure of interest rates; equity and fixed-income derivatives prices; and the prices of defaultable securities. Singleton addresses the restrictions on the joint distributions of asset returns and other economic variables implied by dynamic asset pricing models, as well as the interplay between model formulation and the choice of econometric estimation strategy. For each pricing problem, he provides a comprehensive overview of the empirical evidence on goodness-of-fit, with tables and graphs that facilitate critical assessment of the current state of the relevant literatures. As an added feature, Singleton includes throughout the book interesting tidbits of new research. These range from empirical results (not reported elsewhere, or updated from Singleton's previous papers) to new observations about model specification and new econometric methods for testing models. Clear and comprehensive, the book will appeal to researchers at financial institutions as well as advanced students of economics and finance, mathematics, and science.

Estimation and evaluation of conditional asset pricing models

Estimation and evaluation of conditional asset pricing models
Author: Stefan Nagel
Publisher:
Total Pages: 61
Release: 2010
Genre: Assets (Accounting)
ISBN:

We find that several recently proposed consumption-based models of stock returns, when evaluated using an optimal set of managed portfolios and the associated model-implied conditional moment restrictions, fail to capture key features of risk premiums in equity markets. To arrive at these conclusions, we construct an optimal GMM estimator for models in which the stochastic discount factor (SDF) is a conditionally affine function of a set of priced risk factors. Further, for the (often relevant) case where a researcher is proposing a generalized SDF relative to some null model, we show that there is an optimal choice of managed portfolios to use in testing the null against the proposed alternative.

Conditional Asset Pricing - Predicting Time Varying Beta-Factors with Group Method of Data Handling Methods

Conditional Asset Pricing - Predicting Time Varying Beta-Factors with Group Method of Data Handling Methods
Author: Sebastian Schneider
Publisher:
Total Pages: 27
Release: 2005
Genre:
ISBN:

Allowing for time-varying risk premia yields sophisticated asset pricing models, but the search for adequate model specifications is more challenging. We introduce, to our knowledge, previously in conditional asset pricing not used Group Method of Data Handling (GMDH) that rests on sorting out requiring statsitical models for complex problems of unknown structure but does not require a model to predict conditional variation in betas. We find that lagged instruments used to proxy for expected returns in conditional asset pricing provide a challenge not only for the unconditional CAPM but also the Fama-French-model. Thereby non-linear GMDH-algorithms challenge traditional models of conditional asset pricing as we find a highly non-linear influence of lagged instruments on both conditional alphas and betas. Therefore, predetermining a structure for functional relationships between conditional alphas as well as betas and lagged instruments may lead to a significant misspecification of asset pricing models.

Empirical Asset Pricing Models

Empirical Asset Pricing Models
Author: Jau-Lian Jeng
Publisher: Springer
Total Pages: 277
Release: 2018-03-19
Genre: Business & Economics
ISBN: 3319741926

This book analyzes the verification of empirical asset pricing models when returns of securities are projected onto a set of presumed (or observed) factors. Particular emphasis is placed on the verification of essential factors and features for asset returns through model search approaches, in which non-diversifiability and statistical inferences are considered. The discussion reemphasizes the necessity of maintaining a dichotomy between the nondiversifiable pricing kernels and the individual components of stock returns when empirical asset pricing models are of interest. In particular, the model search approach (with this dichotomy emphasized) for empirical model selection of asset pricing is applied to discover the pricing kernels of asset returns.

Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo Approach

Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo Approach
Author: Manuel Ammann
Publisher:
Total Pages: 41
Release: 2014
Genre:
ISBN:

We propose a new approach for the estimation of conditional asset pricing models based on a Markov Chain Monte Carlo (MCMC) approach. In contrast to existing approaches, it is truly conditional because the assumption that time variation in betas is driven by a set of conditioning variables is not necessary. Moreover, the approach has exact finite sample properties and accounts for errors-in-variables in a one-step estimation procedure. Using Samp;P 500 panel data, we analyze the empirical performance of the CAPM and the Fama and French (1993) three-factor model. We find that time-variation of betas in the CAPM and the time variation of the coefficients for the size factor (SMB) and the distress factor (HML) in the three-factor model improve the empirical performance by a similar amount. Therefore, our findings are consistent with time variation of firm-specific exposure to market risk, systematic credit risk and systematic size effects. However, a Bayesian model comparison trading off goodness of fit and model complexity indicates that the conditional CAPM performs best, followed by the conditional three-factor model, the unconditional CAPM, and the unconditional three-factor model.

A Note on the Estimation of Asset Pricing Models Using Simple Regression Betas

A Note on the Estimation of Asset Pricing Models Using Simple Regression Betas
Author: Raymond Kan
Publisher:
Total Pages: 25
Release: 2015
Genre:
ISBN:

Since Black, Jensen, and Scholes (1972) and Fama and MacBeth (1973), the two-pass cross-sectional regression (CSR) methodology has become the most popular tool for estimating and testing beta asset pricing models. In this paper, we focus on the case in which simple regression betas are used as regressors in the second-pass CSR. Under general distributional assumptions, we derive asymptotic standard errors of the risk premia estimates that are robust to model misspecification. When testing whether the beta risk of a given factor is priced, our misspecification robust standard error and the Jagannathan and Wang (1998) standard error (which is derived under the correctly specified model) can lead to different conclusions.

Asset Pricing Models with Conditional Betas and Alphas

Asset Pricing Models with Conditional Betas and Alphas
Author: Wayne E. Ferson
Publisher:
Total Pages: 31
Release: 2006
Genre: Assets (Accounting)
ISBN:

This paper studies the estimation of asset pricing model regressions with conditional alphas and betas, focusing on the joint effects of data snooping and spurious regression. We find that the regressions are reasonably well specified for conditional betas, even in settings where simple predictive regressions are severely biased. However, there are biases in estimates of the conditional alphas. When time-varying alphas are suppressed and only time-varying betas are considered, the betas become baised. Previous studies overstate the significance of time-varying alphas.