Tests Of Asset Pricing Models Research Articles

Estimation and inference in low frequency factor model regressions with overlapping observations

A low frequency factor model regression uses changes or returns computed at a lower frequency than data available. Using overlapping observations to estimate low frequency factor model regressions results in more efficient estimates of OLS coefficients and standard errors, relative to using independent observations or high frequency estimates. I derive the relevant inference and propose a new method to correct for the induced autocorrelation. I present a series of simulations and empirical examples to support the theoretical results. In tests of asset pricing models, using overlapping observations results in lower pricing errors, compared to existing alternatives.

Testing asset pricing models with individual stocks: An instrumental variables approach

This study empirically tests time-varying asset pricing models in an emerging market with individual stocks. We employ a recently proposed instrumental variables (IV) technique that uses individual stocks as test assets while consistently estimating ex-post risk premiums. This method differs from constructing test portfolios, a common practice employed to mitigate errors-in-variables bias, and, instead, uses factor sensitivity estimates from alternating even and odd months as IVs. Applying this approach, we observe statistically insignificant factor risk premiums under various multifactor models in asset pricing tests at Borsa Istanbul, after accounting for asset characteristics. Our method facilitates the inclusion of essential risk or return-related characteristics of individual stocks in tests, raising insights usually obscured by conventional test portfolios. The results contribute to empirical asset pricing by highlighting the failure of classical models to explain risk premiums at Borsa Istanbul, a significant emerging stock market, when tested with individual stocks using an IV approach.

Corporate fraud, inertia and market conditions in explaining the differences in the exchange characteristics of bonds in the Russian market

Subject. This article deals with the empirical tests of asset pricing models with Russian bonds. Objectives. The article aims to analyze risk factor sensitivities for bond portfolios using asset pricing models with prominent factors and newly proposed risk factors, based on bond market fundamental anomalies, and identify term structure of factor loadings through hidden bond market states corresponding to business cycle stages. Methods. We used the Hidden Markov Model to recover the sequence of bond market states based on spread of ten-year minus three-month government bond yields. Results. The article provides evidence of the significant contribution of the newly proposed risk factors, corresponding with corporate fraud, and momentum factors to the explanatory power of asset pricing models for bond portfolios excess returns. Specifically, we find a strong idiosyncratic momentum effect in the cross-section of Russian bond returns and hence introduce a bond idiosyncratic return momentum factor. We introduce hidden bond market states based on spread of government bond yields and show that proposed market states are statistically and economically significant. We examine the state-dependent explanatory power of the risk factors for test portfolios. Conclusions. The study contributes to the risk estimation accuracy, and presents theoretical framework and empirical evidence of factor-based investing in fixed income instruments. Investors in the Russian bond market should account for bond exposure to the newly proposed risk factors in the risk-adjusted performance analysis of bond portfolios.

The empirical test of Asset Pricing Models in US Machinery market: Comparison of CAPM, FF3 and FF5 Models

As a sample, this study looks at data relating to the machinery sector of the US stock market from July 1926 to July 2022. This study's overarching objective is to investigate how three different financial models—the CAPM, the Fama-French 3 Factor Model, and the Fama-French 5 Factor Model, which have been implemented in the machinery sector over the course of time. This will allow the researchers to determine whether or not these models are applicable to the US stock market. In general, the three-factor model has better explanatory power for excess returns than the five-factor model, even with the addition of the investment and profitability factors for a single sector in the United States, the machinery sector and it explains almost all of the excess returns. This maybe because the three-factor model takes into account fewer variables than the multifactor model. Furthermore, the new profitability and investment factors do not have a single-factor effect that is as strong as that of the size and valuation factors.

Tradable Factor Risk Premia and Oracle Tests of Asset Pricing Models

Tradable Factor Risk Premia and Oracle Tests of Asset Pricing Models

Principal Portfolios

ABSTRACTWe propose a new asset pricing framework in which all securities' signals predict each individual return. While the literature focuses on securities' own‐signal predictability, assuming equal strength across securities, our framework includes cross‐predictability—leading to three main results. First, we derive the optimal strategy in closed form. It consists of eigenvectors of a “prediction matrix,” which we call “principal portfolios.” Second, we decompose the problem into alpha and beta, yielding optimal strategies with, respectively, zero and positive factor exposure. Third, we provide a new test of asset pricing models. Empirically, principal portfolios deliver significant out‐of‐sample alphas to standard factors in several data sets.

Nonparametric Specification Testing of Conditional Asset Pricing Models

This article presents an adaptive omnibus specification test of asset pricing models where the stochastic discount factor is conditionally affine in the pricing factors. These models provide constraints that conditional moments of returns and pricing factors must satisfy, but most of them do not provide information on the functional form of those moments. Our test is robust to functional form misspecification, and also detects any relationship between pricing errors and conditioning variables. We give special emphasis to the test implementation and calibration, and extensive simulation studies prove the functioning in practice. Our empirical applications show a conditional counterpart of a well-known problem of unconditional models. The lack of rejection of consumption based conditional models seems to be due to a poor conditional correlation between consumption and stock returns.

Cross-sectional tests of asset pricing models with full-rank mimicking portfolios

In the standard tests of asset pricing models, factor risk premia are estimated on a test asset span so that models are tested with degrees of freedom reduced by the number of factors. Risk premia of traded factors can be further restricted to be equal to their expected returns, but such restrictions cannot be imposed on models with nontraded factors, which may create a problem of testing without full restrictions or on unequal asset spans across models. We propose a full-rank mimicking portfolio approach by projecting nontraded factors onto a combined span of test assets and benchmark traded factors. Under the Hansen-Jagannathan distance framework, we demonstrate that full-rank mimicking portfolios can provide improved power and fair performance comparison against a benchmark model in both specification and model comparison tests.

Conditional Dynamics and the Multihorizon Risk-Return Trade-Off

Abstract We propose testing asset pricing models using multihorizon returns (MHRs). MHRs effectively generate a new set of test assets that is endogenous to the model and that identifies a broad set of possible conditional misspecifications. We apply MHR-based testing to prominent linear factor models and show that these models typically do a poor job of pricing longer-horizon returns, with pricing errors that are similar in magnitude to the risk premiums they were designed to explain. We trace the errors to the conditional factor dynamics. Explicitly incorporating factor timing into the models often makes mispricing worse, thereby posing a challenge for future research.

The Risk and Return Relations: New Evidence from Pakistani Stock Market

In this study, we try to answer several empirical questions related to testing of asset pricing models in Pakistan. First, we test the assumptions of capital asset pricing model (CAPM) using cross-sectional regression methodology of Fama and MacBeth (FMB) (1973). Second, we test the conditional relationship between beta and expected returns using FMB cross-sectional regressions. Third, we test and compare the explanatory power of CAPM and Fama and French (1993) three factor models using time-series regressions. For all of the above empirical tests, we use sufficiently large data set of weekly data from January 2006 to December 2018 of non-financial firms listed at the Pakistan Stock Exchange. Results of the cross-sectional regressions suggest that beta cannot explain expected returns. However, there is weak evidence that a conditional relation exits between beta and expected returns. Results of the time-series regression suggest that both CAPM and three factor model do well in explaining expected returns. However, GRS-based test of regression intercepts and regressions R2 indicate that Fama and French model better captures variations in observed stock returns than the CAPM.

Testing Asset Pricing Models on Individual Stocks

This paper tests asset pricing models using individual stocks as test assets, rather than sorted portfolios. Sorted portfolios have the severe limitation that the researcher must know, in advance, reliable predictors of expected returns. We show how to generate appropriately sized tests and verify that our tests have considerable test power. In simulations when the CAPM describes the population, our tests (correctly) reject the Fama and French (2015) six factor model 97.5% of the time, while our tests (incorrectly) reject the CAPM less than 5%. We apply our tests to several leading factor models and reject nine of the eleven models tested. The instrumented factor model of Kelly, Pruitt and Su stands out as the most successful.

Further tests of asset pricing models: Liquidity risk matters

The recent asset pricing literature has largely neglected liquidity risk since the price-impact-based factor shows limited pricing ability. Using different liquidity factors, this paper evaluates the liquidity-risk-based models together with the non-liquidity-based ones. With the new testing procedures and the different testing portfolios, we find that the liquidity-augmented capital asset pricing model (LCAPM) performs well. It yields a significant liquidity risk premium robust to all the other models. The success of the LCAPM lies in the fact that the trading-discontinuity-based factor captures the systematic nature of liquidity risk. It shows that liquidity risk is priced highly during the down and turmoil markets, whereas all the other factors examined exhibit insignificant risk prices when market volatility is high. Our evidence indicates that liquidity risk matters and the LCAPM is preferable to use for investment decision making, financial market research and regulation.

AH Premium: A Natural Experiment

A large portion of the Chinese twin stocks is traded both in the Shanghai (A-share) and Hong-Kong (H-share) markets. The A- and H-shares are different assets since they can not be exchanged one-to-one. A-shares have sold at a premium: the AH premium. This premium is large (20-50%) and persistent, it has been present since the two markets were connected in Nov 2014 until now. Since both shares pay the same dividends and traders can operate on both markets this provides a natural experiment to test asset pricing models. We show that various standard RE and Bayesian RE asset pricing models cannot explain the AH premium, but a model of internally rational learning where agents learn about stock prices provides a natural explanation. This emphasizes the importance of modeling investors who learn about equity prices. The premium survives the introduction of convergence traders: those who bet on the AH premium going to zero are highly likely to suffer big losses.

Relative Entropy and Minimum-Variance Pricing Kernel in Asset Pricing Model Evaluation.

Recent literature shows that many testing procedures used to evaluate asset pricing models result in spurious rejection probabilities. Model misspecification, the strong factor structure of test assets, or skewed test statistics largely explain this. In this paper we use the relative entropy of pricing kernels to provide an alternative framework for testing asset pricing models. Building on the fact that the law of one price guarantees the existence of a valid pricing kernel, we study the relationship between the mean-variance efficiency of a model’s factor-mimicking portfolio, as measured by the cross-sectional generalized least squares (GLS) statistic, and the relative entropy of the pricing kernel, as determined by the Kullback–Leibler divergence. In this regard, we suggest an entropy-based decomposition that accurately captures the divergence between the factor-mimicking portfolio and the minimum-variance pricing kernel resulting from the Hansen-Jagannathan bound. Our results show that, although GLS statistics and relative entropy are strongly correlated, the relative entropy approach allows us to explicitly decompose the explanatory power of the model into two components, namely, the relative entropy of the pricing kernel and that corresponding to its correlation with asset returns. This makes the relative entropy a versatile tool for designing robust tests in asset pricing.

Multi-factor asset pricing models in emerging and developed markets

PurposeThe purpose of this paper is to compare the performance of various multifactor asset pricing models across ten emerging and developed markets.Design/methodology/approachThe general methodology to test asset pricing models involves regressing test asset returns (left-hand side assets) on pricing factors (right-hand side assets). Then the performance of different models is evaluated based on how well they price multiple test assets together. The parameters used to compare relative performance of different models are their pricing errors (GRS statistic and average absolute intercepts) and explained variation (average adjusted R2).FindingsThe Fama-French five-factor model improves the pricing performance for stocks in Australia, Canada, China and the USA. The pricing in these countries appears to be more integrated. However, the superior performance in these four countries is not consistent across a variety of test assets and the magnitude of reduction in pricing errors vis-à-vis three- or four-factor models is often economically insignificant. For other markets, the parsimonious three-factor model or its four-factor variants appear to be more suitable.Originality/valueUnlike most asset pricing studies that use test assets based on variables that are already used to construct RHS factors, this study uses test assets that are generally different from RHS sorts. This makes the tests more robust and less biased to be in favour of any multifactor model. Also, most international studies of asset pricing tests use data for different markets and combine them into regions. This study provides the evidence from ten countries separately because prior research has shown that locally constructed factors are more suitable to explain asset prices. Further, this study also tests for the usefulness of adding a quality factor in the existing asset pricing models.

The Bias in Two-Pass Regression Tests of Asset-Pricing Models in Presence of Idiosyncratic Errors with Cross-Sectional Dependence

In two-pass regression-tests of asset-pricing models, cross-sectional correlations in the errors of the first-pass time-series regression lead to correlated measurement errors in the betas used as explanatory variables in the second-pass cross-sectional regression. The slope estimator of the second-pass regression is an estimate for the factor risk-premium and its significance is decisive for the validity of the pricing model. While it is well known that the slope estimator is downward biased in presence of uncorrelated measurement errors, we show in this paper that the correlations seen in empirical return data substantially suppress this bias. For the case of a single-factor model, we calculate the bias of the OLS slope estimator in the presence of correlated measurement errors with a first-order Taylor-approximation in the size of the errors. We show that the bias increases with the size of the errors, but decreases the more the errors are correlated. We illustrate and validate our result using a simulation approach based on empirical data commonly used in asset-pricing tests.

Using Stocks or Portfolios in Tests of Factor Models

We examine the efficiency of using individual stocks or portfolios as base assets to test asset pricing models using cross-sectional data. The literature has argued that creating portfolios reduces idiosyncratic volatility and allows more precise estimates of factor loadings, and consequently risk premia. We show analytically and empirically that smaller standard errors of portfolio beta estimates do not lead to smaller standard errors of cross-sectional coefficient estimates. Factor risk premia standard errors are determined by the cross-sectional distributions of factor loadings and residual risk. Portfolios destroy information by shrinking the dispersion of betas, leading to larger standard errors.

Empirical tests of asset pricing models with individual assets: Resolving the errors-in-variables bias in risk premium estimation

To attenuate an inherent errors-in-variables bias, portfolios are widely employed to test asset pricing models; but portfolios might mask relevant risk- or return-related features of individual stocks. We propose an instrumental variables approach that allows the use of individual stocks as test assets, yet delivers consistent estimates of ex post risk premiums. This estimator also yields well-specified tests in small samples. The market risk premium under the capital asset pricing model (CAPM) and the liquidity-adjusted CAPM, premiums on risk factors under the Fama–French three- and five-factor models, and the Hou et al. (2015) four-factor model are all insignificant after controlling for asset characteristics.

Asset Pricing Test Using Alternative Sets of Portfolios: Evidence from India

Empirical test of asset-pricing models are typically performed on portfolios based on firm-characteristics such as size and book-to-market ratios etc. However, because of their strong factor structure, the characteristic sorted portfolios do not provide a sufficient test for asset pricing models. In recent, the appropriateness to use characteristics sorted portfolios has been debated. Literature suggests various alternative test portfolios sorted by other attributes to improve the empirical tests. To address this issue, we construct three sets of test portfolios sorted by firm beta, volatility, and clustering method to test various asset pricing models. We examine whether portfolios sorted by the above methods can improve the explanatory power of various alternative asset pricing models. Our test results suggest that for unconditional models, the statistical significance and estimated risk premiums depend on the choice of tests portfolios. The conditional model has more power to explain the variation of average returns than the unconditional model.

Testing asset pricing models on the Pakistan Stock Exchange

This study investigates the performance of CAPM, three-factor and five-factor asset pricing models on the Pakistan Stock Exchange using monthly data of 896 companies from November 2000 to December 2016. The results from the time-series approach show that the three-factor model performs relatively better than the CAPM and the five-factor model, whereas the cross-sectional approach establishes the superiority of the five-factor model. It can thus be concluded that it is important to incorporate factors, such as size, value, profitability and investment when predicting returns on securities in the Pakistan Stock Exchange.