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>

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 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.

Nonrevealing Equilibria and Consumption-Based Asset Pricing Models

2006 ◽  
Vol 6 (1) ◽  
Author(s):  
James E Gunderson
Keyword(s):  
Asset Pricing ◽  
Private Information ◽  
Rational Expectations ◽  
Investment Risk ◽  
Conditional Distributions ◽  
Pricing Models ◽  
Asset Pricing Models ◽  
Empirical Tests ◽  
The Impact ◽  
Investment Choices

In the rational expectations equilibrium of this paper, agents have private information and differing information partitions and therefore assign differing conditional distributions to asset payoffs and other economic variables relevant to their investment choices. Standard asset pricing models typically do not recognize the impact of these differing information partitions, and empirical tests based on these models thus measure asset riskiness in a way that may not be relevant to any of the agents' decisions. I show how this can lead to distorted estimates of investment risk and how it can make the equity premium appear difficult to explain.

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.

Sign in / Sign up

Export Citation Format

Share Document