scholarly journals Jumps in cross-sectional rank and expected returns: a mixture model

2008 ◽  
Vol 23 (5) ◽  
pp. 585-606 ◽  
Author(s):  
Gloria González-Rivera ◽  
Tae-Hwy Lee ◽  
Santosh Mishra
Keyword(s):  
Mixture Model ◽  
Expected Returns ◽  
Cross Sectional ◽  
Sectional Rank

The Cross Section of Expected Returns with MIDAS Betas

2011 ◽  
Vol 47 (1) ◽  
pp. 115-135 ◽  
Author(s):  
Mariano González ◽  
Juan Nave ◽  
Gonzalo Rubio
Keyword(s):  
Cross Section ◽  
Risk Premium ◽  
Market Risk ◽  
Ordinary Least Squares ◽  
Mixed Data ◽  
Expected Returns ◽  
Data Sampling ◽  
Cross Sectional ◽  
Market Risk Premium ◽  
The Cross

AbstractThis paper explores the cross-sectional variation of expected returns for a large cross section of industry and size/book-to-market portfolios. We employ mixed data sampling (MIDAS) to estimate a portfolio’s conditional beta with the market and with alternative risk factors and innovations to well-known macroeconomic variables. The market risk premium is positive and significant, and the result is robust to alternative asset pricing specifications and model misspecification. However, the traditional 2-pass ordinary least squares (OLS) cross-sectional regressions produce an estimate of the market risk premium that is negative, and significantly different from 0. Using alternative procedures, we compare both beta estimators. We conclude that beta estimates under MIDAS present lower mean absolute forecasting errors and generate better out-of-sample performance of the optimized portfolios relative to OLS betas.

scholarly journals Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters

2020 ◽  
Vol 13 (4) ◽  
pp. 64 ◽  
Author(s):  
Pietro Coretto ◽  
Michele La Rocca ◽  
Giuseppe Storti
Keyword(s):  
Mixture Model ◽  
Likelihood Estimation ◽  
Risk Groups ◽  
Gaussian Mixture ◽  
Cross Sectional ◽  
The Arts ◽  
State Dependent ◽  
Empirical Performance ◽  
Dependent Model ◽  
The Cross

The inhomogeneity of the cross-sectional distribution of realized assets’ volatility is explored and used to build a novel class of GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models. The inhomogeneity of the cross-sectional distribution of realized volatility is captured by a finite Gaussian mixture model plus a uniform component that represents abnormal variations in volatility. Based on the cross-sectional mixture model, at each time point, memberships of assets to risk groups are retrieved via maximum likelihood estimation, as well as the probability that an asset belongs to a specific risk group. The latter is profitably used for specifying a state-dependent model for volatility forecasting. We propose novel GARCH-type specifications the parameters of which act “clusterwise” conditional on past information on the volatility clusters. The empirical performance of the proposed models is assessed by means of an application to a panel of U.S. stocks traded on the NYSE. An extensive forecasting experiment shows that, when the main goal is to improve overall many univariate volatility forecasts, the method proposed in this paper has some advantages bover the state-of-the-arts methods.

scholarly journals Testing Beta-Pricing Models Using Large Cross-Sections

2019 ◽  
Vol 33 (6) ◽  
pp. 2796-2842 ◽  
Author(s):  
Valentina Raponi ◽  
Cesare Robotti ◽  
Paolo Zaffaroni
Keyword(s):  
Cross Sections ◽  
Risk Premia ◽  
Expected Returns ◽  
Firm Characteristics ◽  
Pricing Models ◽  
Cross Sectional ◽  
Misspecified Models ◽  
Oxford University ◽  
Fama French Factors ◽  
Published Paper

Abstract We propose a methodology for estimating and testing beta-pricing models when a large number of assets is available for investment but the number of time-series observations is fixed. We first consider the case of correctly specified models with constant risk premia, and then extend our framework to deal with time-varying risk premia, potentially misspecified models, firm characteristics, and unbalanced panels. We show that our large cross-sectional framework poses a serious challenge to common empirical findings regarding the validity of beta-pricing models. In the context of pricing models with Fama-French factors, firm characteristics are found to explain a much larger proportion of variation in estimated expected returns than betas. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Momentum Effect as Part of a Market Equilibrium

2014 ◽  
Vol 49 (1) ◽  
pp. 107-130 ◽  
Author(s):  
Seung Mo Choi ◽  
Hwagyun Kim
Keyword(s):  
Market Equilibrium ◽  
Expected Returns ◽  
Momentum Effect ◽  
Expected Return ◽  
Cross Sectional ◽  
Recursive Preferences ◽  
Momentum Profits ◽  
Multiple Assets ◽  
Positive Return

AbstractDoes the momentum effect arise naturally from the determination of asset prices in market equilibrium? We calibrate a standard endowment model of multiple assets under recursive preferences. The momentum effect partly comes from investors’ aversion to consumption risks. An unexpected dividend increase generates a positive return and increases the asset’s proportion of consumption, raising the correlation between its future dividend growth and consumption growth. This is compensated by a higher expected return, generating the momentum effect. The cross-sectional difference in expected returns is also a key contributor. The quantified model produces sizable momentum profits, often close to the observed profits.

scholarly journals The Cross Section of Foreign Currency Risk Premia and Consumption Growth Risk: Comment

2011 ◽  
Vol 101 (7) ◽  
pp. 3456-3476 ◽  
Author(s):  
Craig Burnside
Keyword(s):  
Foreign Currency ◽  
Risk Premia ◽  
Expected Returns ◽  
Stochastic Discount Factor ◽  
Forward Premium Puzzle ◽  
Cross Sectional ◽  
Consumption Growth ◽  
Forward Premium ◽  
The Cross ◽  
Premium Puzzle

Lustig and Verdelhan (2007) argue that the excess returns to borrowing US dollars and lending in foreign currency “compensate US investors for taking on more US consumption growth risk,” yet the stochastic discount factor corresponding to their benchmark model is approximately uncorrelated with the returns they study. Hence, one cannot reject the null hypothesis that their model explains none of the cross sectional variation of the expected returns. Given this finding, and other evidence, I argue that the forward premium puzzle remains a puzzle. JEL: C58, E21, F31, G11, G12

scholarly journals On the Cross-sectional Relation between Expected Returns and Betas

1994 ◽  
Vol 49 (1) ◽  
pp. 101-121 ◽  
Author(s):  
RICHARD ROLL ◽  
STEPHEN A. ROSS
Keyword(s):  
Expected Returns ◽  
Cross Sectional ◽  
The Cross
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