Speculation and the Bond Market: An Empirical No-Arbitrage Framework

2019 ◽  
Vol 65 (9) ◽  
pp. 4179-4203 ◽  
Author(s):  
Francisco Barillas ◽  
Kristoffer Nimark
Keyword(s):  
Asset Pricing ◽  
Risk Premia ◽  
Bond Yields ◽  
Heterogeneous Expectations ◽  
Heterogeneous Information ◽  
No Arbitrage ◽  
Common Information ◽  
Affine Model ◽  
Asset Pricing Models ◽  
The Common

An affine no-arbitrage asset pricing framework is developed that allows for agents to have rational but heterogeneous expectations. The framework can match both bond yields and the observed dispersion of yield expectations in survey data. Heterogeneous information introduces a speculative component in bond prices that is (i) statistically distinct from classical components such as risk premia and expectations about future short rates and (ii) quantitatively important, at times accounting for up to 125 basis points of U.S. yields. Allowing for heterogeneous expectations also changes the estimated relative importance of risk premia and expectations about future short rates in historical bond yields compared to a standard affine model. The framework imposes weaker restrictions than existing heterogeneous information asset pricing models and is thus well suited to empirically quantify the importance of relaxing the common information assumption. This paper was accepted by Tomasz Piskorski, finance.

scholarly journals A Synthesis of Two Factor Estimation Methods

2015 ◽  
Vol 50 (4) ◽  
pp. 825-842 ◽  
Author(s):  
Gregory Connor ◽  
Robert A. Korajczyk ◽  
Robert T. Uhlaner
Keyword(s):  
Fixed Point ◽  
Asset Pricing ◽  
Risk Premia ◽  
Estimation Methods ◽  
Errors In Variables ◽  
Pricing Models ◽  
Cross Sectional ◽  
Asset Pricing Models ◽  
The Common ◽  
Factor Estimation

AbstractTwo-pass cross-sectional regression (TPCSR) is frequently used in estimating factor risk premia. Recent papers argue that the common practice of grouping assets into portfolios to reduce the errors-in-variables (EIV) problem leads to loss of efficiency and masks potential deviations from asset pricing models. One solution that allows the use of individual assets while overcoming the EIV problem is iterated TPCSR (ITPCSR). ITPCSR converges to a fixed point regardless of the initial factors chosen. ITPCSR is intimately linked to the asymptotic principal components (APC) method of estimating factors since the ITPCSR estimates are the APC estimates, up to a rotation.

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 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.

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