scholarly journals Avaliando Modelos de Precificação de Ativos via Abordagem de Fatores Simulados

2012 ◽  
Vol 10 (4) ◽  
pp. 425
Author(s):  
Carlos Enrique Carrasco-Gutierrez ◽  
Wagner Piazza Gaglianone
Keyword(s):  
Asset Pricing ◽  
Goodness Of Fit ◽  
Factor Models ◽  
Stochastic Discount Factor ◽  
Pricing Models ◽  
Fit Statistics ◽  
Asset Pricing Models ◽  
Starting Point ◽  
Pricing Error ◽  
The U.S

In this paper a methodology to compare the performance of different stochastic discount factor (SDF) models is suggested. The starting point is the estimation of several factor models in which the choice of the fundamental factors comes from different procedures. Then, a Monte Carlo simulation is designed in order to simulate a set of gross returns with the objective of mimicking the temporal dependency and the observed covariance across gross returns. Finally, the artificial returns are used to investigate the performance of the competing asset pricing models through the Hansen and Jagannathan (1997) distance and some goodness-of-fit statistics of the pricing error. An empirical application is provided for the U.S. stock market.

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.

Models or Stars: The Role of Asset Pricing Models and Heuristics in Investor Risk Adjustment

2020 ◽  
Vol 34 (1) ◽  
pp. 67-107 ◽  
Author(s):  
Richard B Evans ◽  
Yang Sun
Keyword(s):  
Asset Pricing ◽  
Risk Adjustment ◽  
Factor Models ◽  
Strongly Correlated ◽  
Pricing Models ◽  
Asset Pricing Models ◽  
Retail Investors ◽  
Before And After ◽  
Sector Funds

Abstract We examine the role of factor models and simple performance heuristics in investor decision-making using Morningstar’s 2002 rating methodology change. Before the change, flows strongly correlated with CAPM alphas. After, when funds are ranked by size and book-to-market groups, flows become more sensitive to 3-factor alphas (FF3). Flows to a matched institutional sample (same managers/strategies) follow FF3 before and after the change but are unrelated to the CAPM. Placebo tests with sector funds and other factor loadings show no effects. Our results imply that improvements in simple performance heuristics can result in more sophisticated risk adjustment by retail investors.

Assessing Goodness-of-Fit of Asset Pricing Models: The Distribution of the Maximal R2

1997 ◽  
Vol 52 (2) ◽  
pp. 591-607 ◽  
Author(s):  
F. DOUGLAS FOSTER ◽  
TOM SMITH ◽  
ROBERT E. WHALEY
Keyword(s):  
Asset Pricing ◽  
Goodness Of Fit ◽  
Pricing Models ◽  
Asset Pricing Models

Assessing Goodness-Of-Fit of Asset Pricing Models: The Distribution of the Maximal R 2

1997 ◽  
Vol 52 (2) ◽  
pp. 591 ◽  
Author(s):  
F. Douglas Foster ◽  
Tom Smith ◽  
Robert E. Whaley
Keyword(s):  
Asset Pricing ◽  
Goodness Of Fit ◽  
Pricing Models ◽  
Asset Pricing Models

New Entropy Restrictions and the Quest for Better-Specified Asset-Pricing Models

2018 ◽  
Vol 54 (6) ◽  
pp. 2517-2541 ◽  
Author(s):  
Gurdip Bakshi ◽  
Fousseni Chabi-Yo
Keyword(s):  
Asset Pricing ◽  
Lower Bound ◽  
Finite Number ◽  
Upper Bound ◽  
Discount Factor ◽  
Stochastic Discount Factor ◽  
Pricing Models ◽  
Asset Pricing Models

This article proposes the entropy of m2 (m is the stochastic discount factor) as a metric to evaluate asset-pricing models. We develop a bound on the entropy of m2 when m correctly prices a finite number of returns and consider models that pass the lower bound on m, yet fail the lower bound on m2. Interpreting our results, we elaborate on the distinction between the entropy of m2 versus the entropy of m. We further show that the entropy of m2 represents an upper bound on the expected excess (log) return of the security with the payoff of m.

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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