Finance & Financial Econometrics : 
Time: 10.00 am
Date: 19th of December 2024
Room 3001

Luca TRAPIN (University of Bologna) “Quasi Maximum Likelihood Estimation of High-Dimensional Approximate Dynamic Matrix Factor Models via the EM Algorithm”

Abstract : This paper considers an approximate dynamic matrix factor model that accounts for the time series nature of the data by explicitly modelling the time evolution of the factors. We study Quasi Maximum Likelihood estimation of the model parameters based on the Expectation Maximization (EM) algorithm, implemented jointly with the Kalman smoother which gives estimates of the factors. This approach allows to easily handle arbitrary patterns of missing data. We establish the consistency of the estimated loadings and factor matrices as the sample size T and the matrix dimensions p1 and p2 diverge to infinity. The finite sample properties of the estimators are assessed through a large simulation study and an application to a financial dataset of volatility proxies.

Organizers: 

Jean-Michel ZAKOIAN (CREST)

Sponsors:
CREST