DYNAMIC QUANTILE MODELS

Abstract

This paper introduces new dynamic quantile models called the Dynamic Additive Quantile (DAQ) model and Quantile Factor Model (QFM) for univariate time series and panel data, respectively. The Dynamic Additive Quantile (DAQ) model is suitable for applications to financial data such as univariate returns, and can be used for computation and updating of the Value-at-Risk. The Quantile Factor Mode (QFM) is a multivariate model that can represent the dynamics of cross-sectional distributions of returns, individual incomes, and corporate ratings. The estimation method proposed in the paper relies on an optimization criterion based on the inverse KLIC measure. Goodness of fit tests and diagnostic tools for fit assessment are also provided. For illustration, the models are estimated on stock return data form the Toronto Stock Exchange (TSX).Value-at-Risk, Factor Model, Information Criterion, Income Inequality, Panel Data, Loss-Given-Default