Estimation of high dimensional factor model with multiple threshold-type regime shifts

2021 ◽  
Vol 157 ◽  
pp. 107153
Author(s):  
Jianhong Wu
Keyword(s):  
Factor Model ◽  
Regime Shifts ◽  
High Dimensional ◽  
Multiple Threshold

scholarly journals ON DETERMINING THE NUMBER OF SPIKES IN A HIGH-DIMENSIONAL SPIKED POPULATION MODEL

2012 ◽  
Vol 01 (01) ◽  
pp. 1150002 ◽  
Author(s):  
DAMIEN PASSEMIER ◽  
JIAN-FENG YAO
Keyword(s):  
Covariance Matrix ◽  
Population Model ◽  
Factor Model ◽  
Fundamental Problem ◽  
High Dimensional ◽  
Sample Covariance ◽  
Or Economics ◽  
Linear Mixture Model ◽  
The Difference ◽  
Scientific Fields

In a spiked population model, the population covariance matrix has all its eigenvalues equal to units except for a few fixed eigenvalues (spikes). Determining the number of spikes is a fundamental problem which appears in many scientific fields, including signal processing (linear mixture model) or economics (factor model). Several recent papers studied the asymptotic behavior of the eigenvalues of the sample covariance matrix (sample eigenvalues) when the dimension of the observations and the sample size both grow to infinity so that their ratio converges to a positive constant. Using these results, we propose a new estimator based on the difference between two consecutive sample eigenvalues.

scholarly journals High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model

2020 ◽  
Vol 10 (04) ◽  
pp. 2050017
Author(s):  
Liao Zhu ◽  
Sumanta Basu ◽  
Robert A. Jarrow ◽  
Martin T. Wells
Keyword(s):  
Risk Factors ◽  
Asset Pricing ◽  
Factor Model ◽  
Financial Data ◽  
High Dimensional ◽  
Arbitrage Pricing Theory ◽  
Pricing Model ◽  
Arbitrage Pricing ◽  
Asset Pricing Model ◽  
Pricing Theory

The paper proposes a new algorithm for the high-dimensional financial data — the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small. We first obtain an adaptive collection of basis assets and then simultaneously test which basis assets correspond to which securities, using high-dimensional methods. The AMF model, along with the GIBS algorithm, is shown to have a significantly better fitting and prediction power than the Fama–French 5-factor model.

scholarly journals Wavelet estimation for factor models with time-varying loadings

2021 ◽  
pp. 2150033
Author(s):  
Duván Humberto Cataño ◽  
C. Vladimir Rodríguez-Caballero ◽  
Daniel Peña ◽  
Chang Chiann
Keyword(s):  
Factor Model ◽  
Common Factors ◽  
Estimation Procedure ◽  
Generalized Least Squares ◽  
High Dimensional ◽  
Second Step ◽  
Time Varying ◽  
Finite Sample ◽  
Electricity Prices ◽  
Two Stages

We introduce a high-dimensional factor model with time-varying loadings. We cover both stationary and nonstationary factors to increase the possibilities of applications. We propose an estimation procedure based on two stages. First, we estimate common factors by principal components. In the second step, considering the estimated factors as observed, the time-varying loadings are estimated by an iterative generalized least squares procedure using wavelet functions. We investigate the finite sample features by some Monte Carlo simulations. Finally, we apply the model to study the Nord Pool power market’s electricity prices and loads.

scholarly journals High-Dimensional Conditional Covariance Matrices Estimation Using a Factor-GARCH Model

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 158
Author(s):  
Xiaoling Li ◽  
Xingfa Zhang ◽  
Yuan Li
Keyword(s):  
Covariance Matrix ◽  
Factor Model ◽  
Asymptotic Properties ◽  
Garch Model ◽  
Likelihood Estimation ◽  
Least Square ◽  
High Dimensional ◽  
Conditional Covariance ◽  
Financial Application ◽  
Quasi Maximum Likelihood

Estimation of a conditional covariance matrix is an interesting and important research topic in statistics and econometrics. However, modelling ultra-high dimensional dynamic (conditional) covariance structures is known to suffer from the curse of dimensionality or the problem of singularity. To partially solve this problem, this paper establishes a model by combining the ideas of a factor model and a symmetric GARCH model to describe the dynamics of a high-dimensional conditional covariance matrix. Quasi maximum likelihood estimation (QMLE) and least square estimation (LSE) methods are used to estimate the parameters in the model, and the plug-in method is introduced to obtain the estimation of conditional covariance matrix. Asymptotic properties are established for the proposed method, and simulation studies are given to demonstrate its performance. A financial application is presented to support the methodology.

Sign in / Sign up

Export Citation Format

Share Document