QRFE Webinar: Dr. Liang Chen (Peking University HSBC Business School)
Title: Quantile Factor Models
Quantitative Research in Financial Economics webinar.
Quantile Factor Models (QFM) represent a new class of factor models for high-dimensional panel data. Unlike Approximate Factor Models (AFM), where only location-shifting factors can be extracted, QFM also capture unobserved factors shifting other relevant parts of the distributions of observables. We propose a quantile regression approach, labeled Quantile Factor Analysis (QFA), to consistently estimate all the quantile-dependent factors and loadings. Their asymptotic distribution is derived using a kernel-smoothed version of the QFA estimators. Two consistent model-selection criteria, based on information criteria and rank minimization, are developed to determine the number of factors at each quantile. Moreover, in contrast to the conditions required by Principal Components Analysis in AFM, QFA estimation remains valid even when the idiosyncratic errors have heavy-tailed distributions. Three empirical applications (regarding climate, macroeconomic and finance panel data) illustrate that extra factors shifting quantiles other than the means could be relevant for causality analysis, prediction and economic interpretation of common factors.
Seminar Place: Zoom. To attend the seminar, please register by clicking on the link below:
Our PhD students in economics and finance have to attend the seminars. This is part of their training, which should help with their thesis.