Time-Varying Conditional Covariances in Tests of Asset Pricing Models

1989 ◽  
Author(s):  
Campbell R. Harvey
Keyword(s):  
Asset Pricing ◽  
Time Varying ◽  
Pricing Models ◽  
Asset Pricing Models

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

2008 ◽  
Vol 43 (2) ◽  
pp. 331-353 ◽  
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.

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

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>

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