Dynamics of Equity Factor Returns and Asset Pricing

Author(s):  
Stoyan V Stoyanov ◽  
Francesco A Fabozzi

Abstract In empirical equity asset pricing, the stochastic discount factor (SDF) is implicitly modeled as a linear function of equity factors and is influenced by the empirical properties of the factor returns. We investigate the pricing error introduced by a misspecified SDF which ignores each of the following established empirical phenomena: autocorrelation, dynamics of covariances, dynamics of correlations, and heavy tails for the conditional factor return distribution. We consider near-linear SDFs and nonlinear specifications characterized by a high degree of risk aversion. We find that assuming constant covariances or constant correlations can significantly overprice certain equity portfolios at all risk-aversion levels and that ignoring fat tails can lead to large pricing errors for some derivative assets for highly nonlinear SDFs.

2017 ◽  
Vol 32 (6) ◽  
pp. 1156-1177 ◽  
Author(s):  
Alexis Akira Toda ◽  
Kieran James Walsh

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Shiyuan Wang ◽  
Yali Feng ◽  
Shukai Duan ◽  
Lidan Wang

Conventional low degree spherical simplex-radial cubature Kalman filters often generate low filtering accuracy or even diverge for handling highly nonlinear systems. The high-degree Kalman filters can improve filtering accuracy at the cost of increasing computational complexity; nevertheless their stability will be influenced by the negative weights existing in the high-dimensional systems. To efficiently improve filtering accuracy and stability, a novel mixed-degree spherical simplex-radial cubature Kalman filter (MSSRCKF) is proposed in this paper. The accuracy analysis shows that the true posterior mean and covariance calculated by the proposed MSSRCKF can agree accurately with the third-order moment and the second-order moment, respectively. Simulation results show that, in comparison with the conventional spherical simplex-radial cubature Kalman filters that are based on the same degrees, the proposed MSSRCKF can perform superior results from the aspects of filtering accuracy and computational complexity.


2009 ◽  
Vol 33 (6) ◽  
pp. 1314-1331 ◽  
Author(s):  
Prasad V. Bidarkota ◽  
Brice V. Dupoyet ◽  
J. Huston McCulloch

2017 ◽  
Vol 16 (2) ◽  
pp. 169-187 ◽  
Author(s):  
Rajesh Pathak ◽  
Thanos Verousis ◽  
Yogesh Chauhan

This study examines the information content of pricing error, measured by the difference between the implied price computed using the cost of carry model and the spot price of Single Stock Futures (SSFs), traded on National Stock Exchange (NSE), India. The returns of portfolios, based on ranking of such pricing errors, are investigated. The consistency of results is verified by controlling for established risk factors, that is, market, size, value and momentum premium, and idiosyncratic factors such as firm’s liquidity and size. Our study reveals that the pricing error is a priced risk factor that contains incremental information about stock returns of day t, and not beyond. We conclude that implied spot prices from stock futures market are useful for traders to profit in the spot market. JEL Classification: G120, G130


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