scholarly journals Hierarchical Time-Varying Estimation of Asset Pricing Models

2022 ◽  
Vol 15 (1) ◽  
pp. 14
Author(s):  
Richard T. Baillie ◽  
Fabio Calonaci ◽  
George Kapetanios
Keyword(s):  
Asset Pricing ◽  
Bandwidth Selection ◽  
Risk Premia ◽  
Pricing Models ◽  
Factor Loadings ◽  
Asset Pricing Models ◽  
Regression Framework ◽  
Out Of Sample ◽  
Validation Procedure ◽  
Dynamic Asset Pricing

This paper presents a new hierarchical methodology for estimating multi factor dynamic asset pricing models. The approach is loosely based on the sequential Fama–MacBeth approach and developed in a kernel regression framework. However, the methodology uses a very flexible bandwidth selection method which is able to emphasize recent data and information to derive the most appropriate estimates of risk premia and factor loadings at each point in time. The choice of bandwidths and weighting schemes are achieved by a cross-validation procedure; this leads to consistent estimators of the risk premia and factor loadings. Additionally, an out-of-sample forecasting exercise indicates that the hierarchical method leads to a statistically significant improvement in forecast loss function measures, independently of the type of factor considered.

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.

scholarly journals Regression-based estimation of dynamic asset pricing models

2015 ◽  
Vol 118 (2) ◽  
pp. 211-244 ◽  
Author(s):  
Tobias Adrian ◽  
Richard K. Crump ◽  
Emanuel Moench
Keyword(s):  
Asset Pricing ◽  
Pricing Models ◽  
Asset Pricing Models ◽  
Dynamic Asset Pricing

scholarly journals THE SEARCH FOR TIME-SERIES PREDICTABILITY-BASED ANOMALIES

2021 ◽  
Vol 0 (0) ◽  
pp. 1-19
Author(s):  
Javier Humberto Ospina-Holguín ◽  
Ana Milena Padilla-Ospina
Keyword(s):  
Time Series ◽  
Asset Pricing ◽  
Market Timing ◽  
The United States ◽  
Trading Rule ◽  
Optimal Parameters ◽  
Pricing Models ◽  
Asset Pricing Model ◽  
Asset Pricing Models ◽  
Out Of Sample

This paper introduces a new algorithm for exploiting time-series predictability-based patterns to obtain an abnormal return, or alpha, with respect to a given benchmark asset pricing model. The algorithm proposes a deterministic daily market timing strategy that decides between being fully invested in a risky asset or in a risk-free asset, with the trading rule represented by a parametric perceptron. The optimal parameters are sought in-sample via differential evolution to directly maximize the alpha. Successively using two modern asset pricing models and two different portfolio weighting schemes, the algorithm was able to discover an undocumented anomaly in the United States stock market cross-section, both out-of-sample and using small transaction costs. The new algorithm represents a simple and flexible alternative to technical analysis and forecast-based trading rules, neither of which necessarily maximizes the alpha. This new algorithm was inspired by recent insights into representing reinforcement learning as evolutionary computation.

scholarly journals NONPARAMETRIC IDENTIFICATION OF POSITIVE EIGENFUNCTIONS

2014 ◽  
Vol 31 (6) ◽  
pp. 1310-1330 ◽  
Author(s):  
Timothy M. Christensen
Keyword(s):  
Asset Pricing ◽  
Habit Formation ◽  
Linear Operators ◽  
Nondegeneracy Condition ◽  
Pricing Models ◽  
Nonparametric Models ◽  
Asset Pricing Models ◽  
Long Run ◽  
Dynamic Asset Pricing ◽  
Long Run Risk

Important features of certain economic models may be revealed by studying positive eigenfunctions of appropriately chosen linear operators. Examples include long-run risk–return relationships in dynamic asset pricing models and components of marginal utility in external habit formation models. This paper provides identification conditions for positive eigenfunctions in nonparametric models. Identification is achieved if the operator satisfies two mild positivity conditions and a power compactness condition. Both existence and identification are achieved under a further nondegeneracy condition. The general results are applied to obtain new identification conditions for external habit formation models and for positive eigenfunctions of pricing operators in dynamic asset pricing models.

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