Beta Matrix and Common Factors in Stock Returns

2018 ◽  
Vol 53 (3) ◽  
pp. 1417-1440 ◽  
Author(s):  
Seung C. Ahn ◽  
Alex R. Horenstein ◽  
Na Wang
Keyword(s):  
Stock Returns ◽  
Common Factors ◽  
Information Criterion ◽  
Full Column Rank ◽  
Estimation Methods ◽  
Pricing Models ◽  
Risk Premiums ◽  
Multifactor Model ◽  
Asset Pricing Models ◽  
Beta Matrix

We consider the estimation methods for the rank of a beta matrix corresponding to a multifactor model and study which method would be appropriate for data with a large number of assets. Our simulation results indicate that a restricted version of Cragg and Donald’s (1997) Bayesian information criterion estimator is quite reliable for such data. We use this estimator to analyze some selected asset pricing models with U.S. stock returns. Our results indicate that the beta matrix from many models fails to have full column rank, suggesting that risk premiums in these models are underidentified.

A Transaction-Cost Perspective on the Multitude of Firm Characteristics

2020 ◽  
Vol 33 (5) ◽  
pp. 2180-2222 ◽  
Author(s):  
Victor DeMiguel ◽  
Alberto Martín-Utrera ◽  
Francisco J Nogales ◽  
Raman Uppal
Keyword(s):  
Transaction Costs ◽  
Stock Returns ◽  
Firm Characteristics ◽  
Pricing Models ◽  
Oxford University ◽  
Asset Pricing Models ◽  
Economic Rationale ◽  
Underlying Stocks ◽  
Different Characteristics ◽  
Published Paper

Abstract We investigate how transaction costs change the number of characteristics that are jointly significant for an investor’s optimal portfolio and, hence, how they change the dimension of the cross-section of stock returns. We find that transaction costs increase the number of significant characteristics from 6 to 15. The explanation is that, as we show theoretically and empirically, combining characteristics reduces transaction costs because the trades in the underlying stocks required to rebalance different characteristics often cancel out. Thus, transaction costs provide an economic rationale for considering a larger number of characteristics than that in prominent asset-pricing models. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

scholarly journals Optimisation of Time-Varying Asset Pricing Models with Penetration of Value at Risk and Expected Shortfall

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 394
Author(s):  
Adeel Nasir ◽  
Kanwal Iqbal Khan ◽  
Mário Nuno Mata ◽  
Pedro Neves Mata ◽  
Jéssica Nunes Martins
Keyword(s):  
At Risk ◽  
Asset Pricing ◽  
Stock Returns ◽  
Value At Risk ◽  
Expected Shortfall ◽  
Time Varying ◽  
Pricing Models ◽  
Asset Pricing Model ◽  
Asset Pricing Models ◽  
Market Beta

This study aims to apply value at risk (VaR) and expected shortfall (ES) as time-varying systematic and idiosyncratic risk factors to address the downside risk anomaly of various asset pricing models currently existing in the Pakistan stock exchange. The study analyses the significance of high minus low VaR and ES portfolios as a systematic risk factor in one factor, three-factor, and five-factor asset pricing model. Furthermore, the study introduced the six-factor model, deploying VaR and ES as the idiosyncratic risk factor. The theoretical and empirical alteration of traditional asset pricing models is the study’s contributions. This study reported a strong positive relationship of traditional market beta, value at risk, and expected shortfall. Market beta pertains its superiority in estimating the time-varying stock returns. Furthermore, value at risk and expected shortfall strengthen the effects of traditional beta impact on stock returns, signifying the proposed six-factor asset pricing model. Investment and profitability factors are redundant in conventional asset pricing models.

Linear Beta Pricing with Inefficient Benchmarks

2013 ◽  
Vol 03 (01) ◽  
pp. 1350004 ◽  
Author(s):  
George Diacogiannis ◽  
David Feldman
Keyword(s):  
Asset Pricing ◽  
Market Risk ◽  
Expected Returns ◽  
Pricing Model ◽  
Pricing Models ◽  
Expected Return ◽  
Risk Premiums ◽  
Asset Pricing Models ◽  
Current Asset ◽  
Mean Variance

Current asset pricing models require mean-variance efficient benchmarks, which are generally unavailable because of partial securitization and free float restrictions. We provide a pricing model that uses inefficient benchmarks, a two-beta model, one induced by the benchmark and one adjusting for its inefficiency. While efficient benchmarks induce zero-beta portfolios of the same expected return, any inefficient benchmark induces infinitely many zero-beta portfolios at all expected returns. These make market risk premiums empirically unidentifiable and explain empirically found dead betas and negative market risk premiums. We characterize other misspecifications that arise when using inefficient benchmarks with models that require efficient ones. We provide a space geometry description and analysis of the specifications and misspecifications. We enhance Roll (1980), Roll and Ross's (1994), and Kandel and Stambaugh's (1995) results by offering a "Two Fund Theorem," and by showing the existence of strict theoretical "zero relations" everywhere inside the portfolio frontier.

scholarly journals Identifying outliers in asset pricing data with a new weighted forward search estimator

2020 ◽  
Vol 31 (84) ◽  
pp. 458-472
Author(s):  
Alexandre Aronne ◽  
Luigi Grossi ◽  
Aureliano Angel Bressan
Keyword(s):  
Asset Pricing ◽  
Factor Model ◽  
The United States ◽  
Estimation Methods ◽  
Annual Data ◽  
Forward Search ◽  
Pricing Models ◽  
Asset Pricing Models ◽  
The Impact ◽  
Three Factor

ABSTRACT The purpose of this work is to present the Weighted Forward Search (FSW) method for the detection of outliers in asset pricing data. This new estimator, which is based on an algorithm that downweights the most anomalous observations of the dataset, is tested using both simulated and empirical asset pricing data. The impact of outliers on the estimation of asset pricing models is assessed under different scenarios, and the results are evaluated with associated statistical tests based on this new approach. Our proposal generates an alternative procedure for robust estimation of portfolio betas, allowing for the comparison between concurrent asset pricing models. The algorithm, which is both efficient and robust to outliers, is used to provide robust estimates of the models’ parameters in a comparison with traditional econometric estimation methods usually used in the literature. In particular, the precision of the alphas is highly increased when the Forward Search (FS) method is used. We use Monte Carlo simulations, and also the well-known dataset of equity factor returns provided by Prof. Kenneth French, consisting of the 25 Fama-French portfolios on the United States of America equity market using single and three-factor models, on monthly and annual basis. Our results indicate that the marginal rejection of the Fama-French three-factor model is influenced by the presence of outliers in the portfolios, when using monthly returns. In annual data, the use of robust methods increases the rejection level of null alphas in the Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model, with more efficient estimates in the absence of outliers and consistent alphas when outliers are present.

Firm Characteristics, Cross-Sectional Regression Estimates, and Asset Pricing Tests

2019 ◽  
Vol 10 (2) ◽  
pp. 290-334 ◽  
Author(s):  
Chris Kirby
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Nonlinear Effects ◽  
Firm Characteristics ◽  
Pricing Models ◽  
Cross Sectional ◽  
Portfolio Returns ◽  
Asset Pricing Models ◽  
Formidable Challenge ◽  
Expected Stock Returns

Abstract I test a number of well-known asset pricing models using regression-based managed portfolios that capture nonlinearity in the cross-sectional relation between firm characteristics and expected stock returns. Although the average portfolio returns point to substantial nonlinearity in the data, none of the asset pricing models successfully explain the estimated nonlinear effects. Indeed, the estimated expected returns produced by the models display almost no variation across portfolios. Because the tests soundly reject every model considered, it is apparent that nonlinearity in the relation between firm characteristics and expected stock returns poses a formidable challenge to asset pricing theory. (JEL G12, C58)

Arbitrage Portfolios

2020 ◽  
Author(s):  
Soohun Kim ◽  
Robert A Korajczyk ◽  
Andreas Neuhierl
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Predictive Power ◽  
Abnormal Returns ◽  
Firm Characteristics ◽  
Pricing Models ◽  
Asset Pricing Models ◽  
Maximal Weight ◽  
Large Panel ◽  
Sharpe Ratios

Abstract We propose a new methodology for forming arbitrage portfolios that utilizes the information contained in firm characteristics for both abnormal returns and factor loadings. The methodology gives maximal weight to risk-based interpretations of characteristics’ predictive power before any attribution is made to abnormal returns. We apply the methodology to simulated economies and to a large panel of U.S. stock returns. The methodology works well in our simulation and when applied to stocks. Empirically, we find the arbitrage portfolio has (statistically and economically) significant alphas relative to several popular asset pricing models and annualized Sharpe ratios ranging from 1.31 to 1.66.

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