Comparative Study on Asset Pricing Models in Explaining Cross Sectional Variation of Stock Returns in the Colombo Stock Exchange

2016 ◽  
Author(s):  
M.I.M. Riyath
Keyword(s):  
Asset Pricing ◽  
Comparative Study ◽  
Stock Returns ◽  
Stock Exchange ◽  
Pricing Models ◽  
Cross Sectional ◽  
Asset Pricing Models

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)

scholarly journals Neoclassical Asset Pricing Tests in Sri Lanka: A Structural Empirical Review

2020 ◽  
Vol 12 (2) ◽  
pp. 39
Author(s):  
Neelangie Sulochana Nanayakkara ◽  
P. D. Nimal ◽  
Y. K. Weerakoon
Keyword(s):  
Asset Pricing ◽  
Sri Lanka ◽  
Stock Returns ◽  
Pricing Models ◽  
Cross Sectional ◽  
Holistic View ◽  
Asset Pricing Models ◽  
Fama And French ◽  
The Impact ◽  
Empirical Investigations

Neoclassical asset pricing models try to explain cross sectional variation in stock returns. This study critically reviews the findings of empirical investigations on neoclassical asset pricing models in the Colombo Stock Exchange (CSE), Sri Lanka. The study uses the structural empirical review (SER) methodology to capture a holistic view of empirical investigations carried out in the CSE from the year 1997 to 2017.The pioneering Capital Asset Pricing Model (CAPM) (Sharpe, 1964; Lintner, 1965: Black, 1972) (SLB) states that market betas of stocks are sufficient to explain the cross sectional variation of stock returns. Alternatively there are multifactor models (Ross, 1976; Chen, 1986; Fama and French, 1993, 2015; Cahart, 1997) that state stock returns are driven by multiple risk factors. Similar to other markets the findings on the SLB model are not consistent in the CSE. The Fama and French (1993) and the Cahart (1997) models are supported in the CSE which is consistent with other markets, but the explanatory powers of them are substantially low in the Sri Lankan context. Contrasting the findings of a significant impact of macroeconomic factors on stock returns in developed markets, the impact of them in the CSE are temporary.The overall findings of the applicability of neoclassical asset pricing models in the CSE are inconsistent and inconclusive and the study identifies two reasons that may have contributed to such results. Firstly, it recognises that the inherent limitations of neoclassical asset pricing models may have affected the findings in the CSE. Secondly, it supports the argument that neoclassical models, as they are may not be applicable in emerging or frontier markets, thus they may need to be augmented with characteristics of such markets to make them more applicable.

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.

scholarly journals Size, Value and Momentum in Pakistan Equity Market: Size and Liquidity Exposures

2018 ◽  
Vol III (I) ◽  
pp. 376-394 ◽  
Author(s):  
Romana Bangash ◽  
Faisal Khan ◽  
Zohra Jabeen
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Stock Exchange ◽  
Equity Market ◽  
Future Research ◽  
Momentum Strategy ◽  
Monthly Data ◽  
Average Value ◽  
Asset Pricing Models ◽  
Fama And French

The study inspects the size and liquidity pattern in Pakistan equity market. Sample size contains 278 non-financial firm's monthly data listed on Pakistan Stock Exchange (PSX) from 2001 to 2012. This study uses three asset pricing models (eq.5), (eq.6) and (eq.7). Four factors asset pricing model estimates that momentum factor is positively and negatively linked with winner and loser stocks, both in size and liquidity patterns. Although it is observed that the presence of size and liquidity does not affect the coefficient results but average value of momentum premium in larger in liquidity than size pattern. Further, the study reveals high average stock returns on momentum strategy in liquidity pattern than size that is 8.05% Vs 6.67%, respectively. Results of this study contradicts Fama and French (2012) who concluded that size pattern in momentum factor outperform the equity market. But this study conclude that liquidity pattern outperforms the size pattern in momentum factor. This study raises the question that should investors and academicians consider size or liquidity pattern in momentum factor for high returns and future research?

Using Stocks or Portfolios in Tests of Factor Models

2019 ◽  
Vol 55 (3) ◽  
pp. 709-750 ◽  
Author(s):  
Andrew Ang ◽  
Jun Liu ◽  
Krista Schwarz
Keyword(s):  
Asset Pricing ◽  
Risk Premia ◽  
Factor Models ◽  
Idiosyncratic Volatility ◽  
Standard Errors ◽  
Coefficient Estimates ◽  
Pricing Models ◽  
Cross Sectional ◽  
Factor Loadings ◽  
Asset Pricing 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.

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

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 721
Author(s):  
Javier Rojo-Suárez ◽  
Ana Belén Alonso-Conde
Keyword(s):  
Asset Pricing ◽  
Relative Entropy ◽  
Explanatory Power ◽  
Minimum Variance ◽  
Pricing Models ◽  
Pricing Kernel ◽  
Cross Sectional ◽  
Testing Procedures ◽  
Asset Pricing Models ◽  
Leibler Divergence

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) R 2 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 R 2 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.

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

2019 ◽  
Vol 22 (02) ◽  
pp. 1950012
Author(s):  
Thomas Gramespacher ◽  
Armin Bänziger
Keyword(s):  
Asset Pricing ◽  
Risk Premium ◽  
Measurement Errors ◽  
Factor Model ◽  
Pricing Models ◽  
Time Series Regression ◽  
Cross Sectional ◽  
Asset Pricing Models ◽  
Slope Estimator ◽  
Regression Tests

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.

scholarly journals CAPM or APT? A Comparison of Two Asset Pricing Models for Malaysia

2020 ◽  
Author(s):  
Cung Huck Khoon ◽  
Ahmadu Umaru Sanda ◽  
G.S Gupta
Keyword(s):  
Asset Pricing ◽  
Stock Exchange ◽  
Capital Asset Pricing ◽  
Arbitrage Pricing Theory ◽  
Kuala Lumpur ◽  
Pricing Models ◽  
Market Index ◽  
Asset Pricing Models ◽  
Monthly Return ◽  
Pricing Theory

This study uses monthly return data on 213 stocks listed on the main board of Kuala Lumpur Stock Exchange, Malaysia for the period September 1988 to June 1997 to compare two frequently cited asset pricing models: the capital asset pricing model, CAPM and the arbitrage pricing theory, APT. A comparison was performed along the lines of Chen (1983) and the results showed the APT to perform better than the CAP/ in explaining the variations in cross section of returns. The implication for investors is that the market index is but one of several sources of risk, which should be taken into account in any decision governing investment in the stock market.  

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