We proposed a method to estimate extreme conditional quantiles by combining quantile GARCH model of Xiao and Koenker (2009) and extreme value theory (EVT) approach. We first estimate the latent volatility process using the information of intermediate quantiles. We then apply EVT to the tail...

This chapter presents a unified set of estimation methods for fitting a rich array of models describing dynamic relationships within a longitudinal data setting. The discussion surveys approaches for characterizing the micro dynamics of continuous dependent variables both over time and across...

We consider a model which allows data-driven threshold selection in extreme value analysis. A mixture exponential distribution is employed as the thin-tailed distribution in view of the special structure of insurance claims, where individuals are often grouped into categories. An EM...

We develop new methods for the estimation of time-varying risk-neutral jump tails in asset returns. In contrast to existing procedures based on tightly parameterized models, our approach imposes much fewer structural assumptions, relying on extreme-value theory approximations together with...

We modify a two-step approach by McNeil and Frey (2000) for forecasting Value-at-Risk (VaR). Our approach combines the asymmetric GARCH (GJR) model that allows the high-order moments (i.e., skewness and kurtosis) of the skewed generalized t (SGT) distribution to rely on the past information set...

We investigate the impact of monetary conditions on stock market returns at different points on the return distributions. Our results reveal no association between stock returns and monetary environments at the lower quantiles. At the upper quantiles, however, we find that expansive monetary...

This paper investigates the impact of the local and the US monetary policy environments on stock returns at the different locations on the return distributions. Using data for stock returns and interest rates of 30 countries, the quantile regression technique is employed to estimate the...

This note proposes a computationally simple estimator for quantile regression in a linear model context, as an alternative to Koenker and Bassett’s (1978) algorithm. The new estimator can remedy several drawbacks associated with Powell’s (1986) censored quantile regression estimator.

This paper studies panel quantile regression models with individual fixed effects. We formally establish sufficient conditions for consistency and asymptotic normality of the quantile regression estimator when the number of individuals, n, and the number of time periods, T, jointly go to...

This analysis assesses the role of social capital in generating heterogeneity in growth processes across U.S. counties by estimating growth regressions, using the novel semiparametric smooth coefficient quantile regression method in which parameters are unspecified functions of a measure of...