Asymptotic Variances for Tests of Portfolio Efficiency and Factor Model Comparisons with Conditioning Information

2019 ◽  
Author(s):  
Wayne E. Ferson ◽  
Andrew F. Siegel ◽  
Junbo L. Wang
Keyword(s):  
Factor Model ◽  
Model Comparisons ◽  
Portfolio Efficiency ◽  
Conditioning Information ◽  
Asymptotic Variances

Factor Model Comparisons with Conditioning Information

2021 ◽  
Author(s):  
Junbo L. Wang ◽  
Wayne Ferson ◽  
Andrew F. Siegel
Keyword(s):  
Factor Model ◽  
Model Comparisons ◽  
Conditioning Information

scholarly journals Understanding Portfolio Efficiency with Conditioning Information

2016 ◽  
Vol 51 (3) ◽  
pp. 985-1011 ◽  
Author(s):  
Francisco Peñaranda
Keyword(s):  
Sharpe Ratio ◽  
Pricing Models ◽  
Asset Return ◽  
Conditional Mean ◽  
Portfolio Strategies ◽  
Asset Pricing Models ◽  
Fixed Weight ◽  
Portfolio Efficiency ◽  
Conditioning Information ◽  
Mean Variance

AbstractI develop two new types of portfolio efficiency when returns are predictable. The first type maximizes the unconditional Sharpe ratio of excess returns and differs from unconditional efficiency unless the safe asset return is constant over time. The second type maximizes conditional mean-variance preferences and differs from unconditional efficiency unless, additionally, the maximum conditional Sharpe ratio is constant. Using stock data, I quantify and test their performance differences with respect to unconditionally and fixed-weight efficient returns. I also show the relevance of the two new portfolio strategies to test conditional asset pricing models.

scholarly journals Bayes Factor Model Comparisons Across Parameter Values for Mixed Models

2021 ◽  
Author(s):  
Maximilian Linde ◽  
Don van Ravenzwaaij
Keyword(s):  
Repeated Measures ◽  
Model Comparison ◽  
Bayes Factor ◽  
Factor Model ◽  
Simulated Data ◽  
Experimental Manipulation ◽  
Data Set ◽  
Model Comparisons ◽  
Multiple Data ◽  
Multiple Data Sets

Nested data structures, in which conditions include multiple trials, are often analyzed using repeated-measures analysis of variance or mixed effects models. Typically, researchers are interested in determining whether there is an effect of the experimental manipulation. Unfortunately, these kinds of analyses have different appropriate specifications for the null and alternative models, and a discussion on which is to be preferred and when is sorely lacking. van Doorn et al. (2021) performed three types of Bayes factor model comparisons on a simulated data set in order to examine which model comparison is most suitable for quantifying evidence for or against the presence of an effect of the experimental manipulation. Here we extend their results by simulating multiple data sets for various scenarios and by using different prior specifications. We demonstrate how three different Bayes factor model comparison types behave under changes in different parameters, and we make concrete recommendations on which model comparison is most appropriate for different scenarios.

scholarly journals Testing Portfolio Efficiency with Conditioning Information

2009 ◽  
Vol 22 (7) ◽  
pp. 2735-2758 ◽  
Author(s):  
Wayne E. Ferson ◽  
Andrew F. Siegel
Keyword(s):  
Portfolio Efficiency ◽  
Conditioning Information
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