Estimation and Evaluation of Conditional Asset Pricing Models

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 Generalized Method of Moments (GMM) estimator for models in which the stochastic discount factor (SDF) is a conditionally affine function of a set of priced risk factors, and we show that there is an optimal choice of managed portfolios to use in testing a null model against a proposed alternative generalized SDF.     

Examination of Conditional Asset Pricing in UK Stock Returns

I examine the empirical performance of various specifications of the capital asset pricing model (CAPM) in UK stock returns, using the stochastic discount framework. When the proxy for the market portfolio includes a proxy for labor income growth in addition to the stock market index, the performance of the CAPM improves. The improvement in performance shows in the magnitude and significance of the pricing errors and in the reduced impact of asset characteristics and other factors in the pricing of assets. There is further improvement when I use conditional versions of the models.     

Conditional Asset Pricing and Stock Market Anomalies in Europe

This study provides European evidence on the ability of static and dynamic specifications of the Fama‐French (1993) three‐factor model to price 25 size‐B/M portfolios. In contrast to US evidence, we detect a small‐growth premium and find that the size effect is still present in Europe. Furthermore, we document strong time variation in factor risk loadings. Incorporating these risk fluctuations in conditional specifications of the three‐factor model clearly improves its ability to explain time variation in expected returns. However, the model still fails to completely capture cross‐sectional variation in returns as it is unable to explain the momentum effect.     

Nonnormalities and Tests of Asset Pricing Theories

The robustness of the multivariate test of Gibbons, Ross, and Shanken (1986) to nonnormalities in the residual covariance matrix is examined. After considering the relative performance of various tests of normality, simulation techniques are used to determine the effects of nonnormalities on the multivariate test. It is found that, where the sample nonnormalities are severe, the size and/or power of the test can be seriously misstated. However, it is also shown that these extreme sample values may overestimate the population parameters. Hence, we conclude that the multivariate test is reasonably robust with respect to typical levels of nonnormality.     

Cross‐sectional Tests of Conditional Asset Pricing Models: Evidence from the German Stock Market

We study the performance of conditional asset pricing models and multifactor models in explaining the German cross‐section of stock returns. We focus on several variables, which (according to previous research) are associated with market expectations on future market excess returns or business cycle conditions. Our results suggest that the empirical performance of the Capital Asset Pricing Model (CAPM) can be improved when allowing for time‐varying parameters of the stochastic discount factor. A conditional CAPM using the term spread explains the returns on our size and book‐to‐market sorted portfolios about as well as the Fama‐French three‐factor model and performs best in terms of the Hansen‐Jagannathan distance. Structural break tests do not necessarily indicate parameter instability of conditional model specifications. Another major finding of the paper is that the Fama‐French model – despite its generally good cross‐sectional performance – is subject to model instability. Unconditional models, however, do a better job than conditional ones at capturing time‐series predictability of the test portfolio returns.     

Portfolio Construction for Tests of Asset Pricing Models

Portfolios are commonly used in finance literature to study asset‐pricing models. In business practice portfolios are used to detect abnormal performance in certain asset groups or to construct reference assets. However, analyses on practical issues related to portfolio construction are surprisingly few. This paper presents and discusses issues related to portfolio return calculation from theoretical and practical perspectives. Special attention is given both to smaller and emerging stock markets. These stock markets often share common features like low liquidity, multiple stock series, and changes in foreign ownership restrictions that greatly affect portfolio construction.     

Two-Pass Tests of Asset Pricing Models with Useless Factors

In this paper we investigate the properties of the standard two-pass methodology of testing beta pricing models with misspecified factors. In a setting where a factor is useless, defined as being independent of all the asset returns, we provide theoretical results and simulation evidence that the second-pass cross-sectional regression tends to find the beta risk of the useless factor priced more often than it should. More surprisingly, this misspecification bias exacerbates when the number of time series observations increases. Possible ways of detecting useless factors are also examined.     

Return Distributions and Improved Tests of Asset Pricing Models

We compare and contrast some existing ordinary least squares(OLS)- and generalized method of moments (GMM)-based tests ofasset pricing models with a new more general test. This newtest is valid under the assumption that returns are ellipticallydistributed, a necessary and sufficient assumption of the linearcapital asset pricing model (CAPM). This new test fails to rejectthe CAPM on a dataset of stocks sorted by market valuations,whereas similar tests constructed from OLS and GMM estimationmethods reject the linear CAPM. We also find that outliers reducethe OLS-estimated mispricing of the linear CAPM on monthly returnssorted by previous performance, that is, momentum. Monte Carloevidence supports superior size and power properties of thenew test relative to OLS- and GMM-based tests.     

Modeling Conditional Skewness in Stock Returns

Abstract

In this paper, we propose a new GARCH-in-Mean (GARCH-M) model allowing for conditional skewness. The model is based on the so-called z distribution capable of modeling skewness and kurtosis of the size typically encountered in stock return series. The need to allow for skewness can also be readily tested. The model is consistent with the volatility feedback effect in that conditional skewness is dependent on conditional variance. Compared to previously presented GARCH models allowing for conditional skewness, the model is analytically tractable, parsimonious and facilitates straightforward interpretation.Our empirical results indicate the presence of conditional skewness in the monthly postwar US stock returns. Small positive news is also found to have a smaller impact on conditional variance than no news at all. Moreover, the symmetric GARCH-M model not allowing for conditional skewness is found to systematically overpredict conditional variance and average excess returns.     

Tests of the Relations Among Marketwide Factors,Firm-Specific Variables,and Stock Returns Using a Conditional Asset Pricing Model

In this article we generalize Harvey's (1989) empirical specification of conditional asset pricing models to allow for both time-varying covariances between stock returns and marketwide factors and time-varying reward-to-covariabilities. The model is then applied to examine the effects of firm size and book-to-market equity ratios. We find that the traditional asset pricing model with commonly used factors can only explain a small portion of the stock returns predicted by firm size and book-to-market equity ratios. The results indicate that allowing time-varying covariances and time-varying reward-to-covariabilities does little to salvage the traditional asset pricing models.     

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

We use Markov Chain Monte Carlo (MCMC) methods for the parameter estimation and the testing of conditional asset pricing models. In contrast to traditional 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. Using S&P 500 panel data, we analyse 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. 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.     

Variance Spillover and Skewness in Financial Asset Returns

Bond and stock returns have been observed in the literature to exhibit unconditional skewness and temporal persistence in conditional skewness. We demonstrate that observed persistence in conditional third central moments can be due to the spillover of conditional variance dynamics. The confounding of true skewness and a variance spillover effect is problematic for financial modeling. Using market data, we empirically demonstrate that a simple standardization approach removes the variance‐induced skewness persistence. An important implication is that more parsimonious return and asset pricing models result if skewness persistence need not be modeled.     

Tests of Asset Pricing with Time-Varying Expected Risk Premiums and Market Betas

Tests of asset-pricing models are developed that allow expected risk premiums and market betas to vary over time. These tests exploit the relation between expected excess returns and current market values. Using weekly data for 1963 through 1982 on ten common stock portfolios formed according to equity capitalization, a single-risk-premium model is not rejected if the expected premium is time varying and is not constrained to correspond to a market factor. Conditional mean-variance efficiency of a value-weighted stock index is rejected, and the rejection is insensitive to how much variability of expected risk premiums is assumed.     

Unconditional Tests of Linear Asset Pricing Models with Time‐Varying Betas

In conditional affine factor models, estimated risk prices should satisfy certain unconditional constraints. Specifically, a cross‐sectional estimate of the unconditional slope associated with a risk factor should equal the average price of risk of the factor. The estimated slope associated with the product of a risk factor and an instrument should be equal to the covariance of the factor risk premium with the instrument. We show that the constraints only apply to the conditional models with time‐varying betas. We identify an unconditional constraint on unconditional betas for time‐varying beta models and incorporate it into model tests. We show that imposing this unconditional constraint changes estimates of unconditional betas and risk prices significantly.     

A best choice among asset pricing models? The Conditional Capital Asset Pricing Model in Australia

We use Australian data to test the Conditional Capital Asset Pricing Model (Jagannathan and Wang, 1996). Our results are generally supportive: the model performs well compared with a number of competing asset pricing models. In contrast to the study by Jagannathan and Wang, however, we find that the inclusion of the market for human capital does not save the concept of the time‐independent market beta (it remains insignificant). We find support for the role of a small‐minus‐big factor in pricing the cross‐section of returns and find grounds to disagree with Jagannathan and Wang's argument that this factor proxies for misspecified market risk.     

Attention Spillover in Asset Pricing

Exploiting a screen display feature whereby the order of stock display is determined by the stock's listing code, we lever a novel identification strategy and study how the interaction between overconfidence and limited attention affect asset pricing. We find that stocks displayed next to those with higher returns in the past two weeks are associated with higher returns in the future week, which are reverted in the long run. This is consistent with our conjectures that investors tend to trade more after positive investment experience and are more likely to pay attention to neighboring stocks, both confirmed using trading data.     

Multifactor Asset Pricing Models

Since the early 1960s, the mean-variance Capital Asset Pricing Model (CAPM) has been a dominant paradigm in modern finance. Recently, the accumulation of anomalous evidence, and a realisation that empirical tests of the model are tautologically related to the efficiency of the market index, have pushed that paradigm to a point of crisis. This paper reviews alternative asset pricing models which coexisted with the CAPM and may provide plausible substitutes. The major distinguishing feature of these models is that they predict multiple risk factors and, with the exception of the Arbitrage Pricing Theory (APT), are extensions of the CAPM.     

Assessing Asset Pricing Anomalies

The optimal portfolio strategy is developed for an investorwho has detected an asset pricing anomaly but is not certainthat the anomaly is genuine rather than merely apparent. Theanalysis takes account of the fact that the parameters of boththe underlying asset pricing model and the anomalous returnsare estimated rather than known. The value that an investorwould place on the ability to invest to exploit the apparentanomaly is also derived and illustrative calculations are presentedfor the Fama and French SMB and HML portfolios, whose returnsare anomalous relative to the CAPM.     

Asset Pricing without Garbage

This paper provides an explanation for why garbage implies a much lower relative risk aversion in the consumption‐based asset pricing model than National Income and Product Accounts (NIPA) consumption expenditure: Unlike garbage, NIPA consumption is filtered to mitigate measurement error. I apply a simple model of the filtering process that allows one to undo the filtering inherent in NIPA consumption. “Unfiltered NIPA consumption” well explains the equity premium and is priced in the cross‐section of stock returns. I discuss the likely properties of true consumption (i.e., without measurement error and filtering) and quantify implications for habit and long‐run risk models.