Bayesian quantile regression and statistical modelling represent a growing paradigm in contemporary data analysis, extending conventional regression by estimating various conditional quantiles rather ...

Estimating the conditional quantiles of outcome variables of interest is frequent in many research areas, and quantile regression is foremost among the utilized methods. The coefficients of a quantile ...

Covariates may affect continuous responses differently at various points of the response distribution. For example, some exposure might have minimal impact on conditional means, whereas it might lower ...

The goal of a machine learning regression problem is to predict a single numeric value. Quantile regression is a variation where you are concerned with under-prediction or over-prediction. I'll phrase ...

In this paper we propose a semi-parametric, parsimonious value-at-risk forecasting model based on quantile regression and readily available market prices of option contracts from the over-the-counter ...

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This paper examines a set of value-at-risk (VaR) models and their ability to appropriately describe and capture price-change risk in the European energy market. We make in-sample, one-day-ahead VaR ...