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

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.

Download Full-text

Asset Pricing Models with Conditional Betas and Alphas: The Effects of Data Snooping and Spurious Regression

Journal of Financial and Quantitative Analysis ◽

10.1017/s0022109000003549 ◽

2008 ◽

Vol 43 (2) ◽

pp. 331-353 ◽

Cited By ~ 32

Author(s):

Wayne E. Ferson ◽

Sergei Sarkissian ◽

Timothy Simin

Keyword(s):

Asset Pricing ◽

Time Varying ◽

Pricing Model ◽

Data Snooping ◽

Pricing Models ◽

Asset Pricing Model ◽

Spurious Regression ◽

Predictive Regressions ◽

Asset Pricing Models ◽

Conditional Betas

AbstractThis 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 biased. Previous studies overstate the significance of time-varying alphas.

Download Full-text

The impact of time-varying risk on stock returns: an experiment of cubic piecewise polynomial function model and the Fourier Flexible Form model

Data Science in Finance and Economics ◽

10.3934/dsfe.2021008 ◽

2021 ◽

Vol 1 (2) ◽

pp. 141-164

Author(s):

Fangzhou Huang ◽

◽

Jiao Song ◽

Nick J. Taylor ◽

◽

...

Keyword(s):

Asset Pricing ◽

Stock Returns ◽

Goodness Of Fit ◽

Time Varying ◽

Pricing Models ◽

Piecewise Polynomial Function ◽

Data Smoothing ◽

Asset Pricing Models ◽

Flexible Form ◽

The Impact

<abstract> <p>With fast evolving econometric techniques being adopted in asset pricing, traditional linear asset pricing models have been criticized by their limited function on capturing the time-varying nature of data and risk, especially the absence of data smoothing is of concern. In this paper, the impact of data smoothing is explored by applying two asset pricing models with non-linear feature: cubic piecewise polynomial function (CPPF) model and the Fourier Flexible Form (FFF) model are performed on US stock returns as an experiment. The traditional beta coefficient is treated asymmetrically as downside beta and upside beta in order to capture corresponding risk, and further, to explore the risk premia attached in a cross-sectional context. It is found that both models show better goodness of fit comparing to classic linear asset pricing model cross-sectionally. When appropriate knots and orders are determined by Akaike Information Criteria (AIC), the goodness of fit is further improved, and the model with both CPPF and FFF betas employed showed the best fit among other models. The findings fill the gap in literature, specifically on both investigating and pricing the time variation and asymmetric nature of systematic risk. The methods and models proposed in this paper embed advanced mathematical techniques of data smoothing and widen the options of asset pricing models. The application of proposed models is proven to superiorly provide high degree of explanatory power to capture and price time-varying risk in stock market.</p> </abstract>

Download Full-text

Examining Asset Pricing Anomalies: Evidence from Europe

Business Perspectives and Research ◽

10.1177/22785337211025712 ◽

2021 ◽

pp. 227853372110257

Author(s):

Asheesh Pandey ◽

Rajni Joshi

Keyword(s):

Asset Pricing ◽

Pricing Model ◽

Pricing Models ◽

Investment Rate ◽

Asset Pricing Model ◽

Fund Managers ◽

Asset Pricing Models ◽

Asset Pricing Anomalies ◽

Pricing Anomalies ◽

Portfolio Managers

We examine five important asset pricing anomalies, namely, size, value, momentum, profitability, and investment rate to evaluate their efficacy in major West European economies, that is, France, Germany, Italy, and Spain. We employ four prominent asset pricing models, namely Capital Asset Pricing Model (CAPM), Fama–French three-factor (FF3) model, Carhart model and Fama–French five-factor (FF5) model to evaluate whether portfolio managers can create trading strategies to generate risk-adjusted extra normal returns for their investors. We also examine the prominent anomalies which pass the test of asset pricing in our sample countries and evaluate the best performing asset pricing model in explaining returns in each of these countries. We find that in spite of being matured markets, these countries provide portfolio managers with opportunities to exploit these strategies to generate extra normal returns for their investors. Momentum anomaly for Germany and profitability anomaly for Italy can be exploited by fund managers for generating risk-adjusted returns. For France, except for net investment rate anomaly, all the other anomalies remained unexplained by asset pricing models. We also find CAPM to be the better model in explaining returns of Italy and Spain. While FF3 factor and FF5 factor models explain returns in Germany, our sample asset pricing models failed to work for France. Our study has implications for portfolio managers, academia, and policymakers.

Download Full-text

The competitions of time-varying and constant loadings in asset pricing models: empirical evidence and agent-based simulations

Journal of Economic Interaction and Coordination ◽

10.1007/s11403-021-00337-2 ◽

2021 ◽

Author(s):

Hung-Wen Lin ◽

Jing-Bo Huang ◽

Kun-Ben Lin ◽

Shu-Heng Chen

Keyword(s):

Asset Pricing ◽

Empirical Evidence ◽

Time Varying ◽

Pricing Models ◽

Agent Based ◽

Asset Pricing Models ◽

Agent Based Simulations

Download Full-text

Can consumption-based asset pricing models using monetary conditioning variables explain the cross-section of German stock returns?

Applied Economics ◽

10.1080/00036846.2012.725935 ◽

2013 ◽

Vol 45 (25) ◽

pp. 3564-3573 ◽

Cited By ~ 1

Author(s):

Benjamin R. Auer

Keyword(s):

Asset Pricing ◽

Stock Returns ◽

Cross Section ◽

Pricing Models ◽

Asset Pricing Models ◽

German Stock ◽

The Cross

Download Full-text

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

The Review of Asset Pricing Studies ◽

10.1093/rapstu/raz005 ◽

2019 ◽

Vol 10 (2) ◽

pp. 290-334 ◽

Cited By ~ 3

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)

Download Full-text

Arbitrage Portfolios

Review of Financial Studies ◽

10.1093/rfs/hhaa102 ◽

2020 ◽

Cited By ~ 2

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.

Download Full-text