scholarly journals A NOTE ON THE EXACT SOLUTION OF ASSET PRICING MODELS WITH HABIT PERSISTENCE

2006 ◽  
Vol 10 (2) ◽  
pp. 273-283 ◽  
Author(s):  
FABRICE COLLARD ◽  
PATRICK FÈVE ◽  
IMEN GHATTASSI
Keyword(s):  
Asset Pricing ◽  
Rational Expectation ◽  
Closed Form Solution ◽  
Autoregressive Process ◽  
Form Solution ◽  
Estimation Methods ◽  
Finite Sample ◽  
Habit Persistence ◽  
Asset Pricing Models ◽  
Finite Sample Properties

This paper provides a closed-form solution to a standard asset pricing model with habit formation when the growth rate of endowment follows a first-order Gaussian autoregressive process. We determine conditions that guarantee the existence of a stationary bounded equilibrium. The findings are useful because they allow to evaluate the accuracy of various approximation methods to nonlinear rational expectation models. Furthermore, they can be used to perform simulation experiments to study the finite sample properties of various estimation methods.

scholarly journals Identifying outliers in asset pricing data with a new weighted forward search estimator

2020 ◽  
Vol 31 (84) ◽  
pp. 458-472
Author(s):  
Alexandre Aronne ◽  
Luigi Grossi ◽  
Aureliano Angel Bressan
Keyword(s):  
Asset Pricing ◽  
Factor Model ◽  
The United States ◽  
Estimation Methods ◽  
Annual Data ◽  
Forward Search ◽  
Pricing Models ◽  
Asset Pricing Models ◽  
The Impact ◽  
Three Factor

ABSTRACT The purpose of this work is to present the Weighted Forward Search (FSW) method for the detection of outliers in asset pricing data. This new estimator, which is based on an algorithm that downweights the most anomalous observations of the dataset, is tested using both simulated and empirical asset pricing data. The impact of outliers on the estimation of asset pricing models is assessed under different scenarios, and the results are evaluated with associated statistical tests based on this new approach. Our proposal generates an alternative procedure for robust estimation of portfolio betas, allowing for the comparison between concurrent asset pricing models. The algorithm, which is both efficient and robust to outliers, is used to provide robust estimates of the models’ parameters in a comparison with traditional econometric estimation methods usually used in the literature. In particular, the precision of the alphas is highly increased when the Forward Search (FS) method is used. We use Monte Carlo simulations, and also the well-known dataset of equity factor returns provided by Prof. Kenneth French, consisting of the 25 Fama-French portfolios on the United States of America equity market using single and three-factor models, on monthly and annual basis. Our results indicate that the marginal rejection of the Fama-French three-factor model is influenced by the presence of outliers in the portfolios, when using monthly returns. In annual data, the use of robust methods increases the rejection level of null alphas in the Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model, with more efficient estimates in the absence of outliers and consistent alphas when outliers are present.

scholarly journals Nonparametric Instrumental-Variable Estimation

2018 ◽  
Vol 18 (4) ◽  
pp. 937-950 ◽  
Author(s):  
Denis Chetverikov ◽  
Dongwoo Kim ◽  
Daniel Wilhelm
Keyword(s):  
Instrumental Variable ◽  
Estimation Methods ◽  
Monte Carlo Experiment ◽  
Finite Sample ◽  
Gasoline Demand ◽  
Tuning Parameters ◽  
Demand Functions ◽  
Instrumental Variable Estimation ◽  
Finite Sample Properties ◽  
Shape Restriction

In this article, we introduce the commands npiv and npivcv, which implement nonparametric instrumental-variable (NPIV) estimation methods without and with a cross-validated choice of tuning parameters, respectively. Both commands can impose the constraint that the resulting estimated function is monotone. Using such a shape restriction may significantly improve the performance of the NPIV estimator (Chetverikov and Wilhelm, 2017, Econometrica 85: 1303–1320) because the ill-posedness of the NPIV estimation problem leads to unconstrained estimators that suffer from particularly poor statistical properties such as high variance. However, the constrained estimator that imposes the monotonicity significantly reduces variance by removing nonmonotone oscillations of the estimator. We provide a small Monte Carlo experiment to study the estimators’ finite-sample properties and an application to the estimation of gasoline demand functions.

Analytical and Simple Form of Shrinkage Functions for Non-Convex Penalty Functions in Fused Lasso Algorithm

2020 ◽  
Vol 29 (06) ◽  
pp. 2050020
Author(s):  
Pichid Kittisuwan
Keyword(s):  
Penalty Function ◽  
Additive White Gaussian Noise ◽  
Closed Form Solution ◽  
Form Solution ◽  
Penalty Functions ◽  
Estimation Methods ◽  
Fused Lasso ◽  
Fixed Point Algorithm ◽  
Shrinkage Function ◽  
Shrinkage Functions

In some circumstances, the performance of machine learning (ML) tasks are based on the quality of signal (data) that is processed in these tasks. Therefore, the pre-processing techniques, such as reconstruction and denoising methods, are important techniques in ML tasks. In reconstructed (estimated) method, the fused lasso algorithm with non-convex penalty function is an efficient method when the signal corrupted by additive white Gaussian noise (AWGN) is considered. Therefore, this paper proposes new shrinkage functions for non-convex penalty functions, modified arctangent and exponential models, in fused lasso formulation. A lot of works present the shrinkage function for arctangent penalty function. Unfortunately, there is no closed-form solution. The numerical solution is required for shrinkage function of this penalty function. However, the analytical solution is derived in this paper. Moreover, the shrinkage function of modified exponential penalty function is proposed. This shrinkage function obtains from simple iterative method, fixed-point algorithm. We demonstrate the proposed methods through simulations with standard one-dimensional signals contaminated by AWGN. The proposed techniques are compared with traditional estimation methods, such as total variation (TV) and wavelet denoising methods. In experimental results, our proposed methods outperform several exiting methods both visual quality and in terms of root mean square error (RMSE). In fact, the proposed methods can better preserve the feature of noise-free signal than the compared methods. The denoised signals produced by the proposed methods are less smooth than the denoised signals produced by the compared methods.

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.

Finite sample multivariate tests of asset pricing models with coskewness

2009 ◽  
Vol 53 (6) ◽  
pp. 2008-2021 ◽  
Author(s):  
Marie-Claude Beaulieu ◽  
Jean-Marie Dufour ◽  
Lynda Khalaf
Keyword(s):  
Asset Pricing ◽  
Finite Sample ◽  
Pricing Models ◽  
Multivariate Tests ◽  
Asset Pricing Models
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