Bayesian quantile regression and statistical modelling represent a growing paradigm in contemporary data analysis, extending conventional regression by estimating various conditional quantiles rather ...
We develop a Bayesian method for nonparametric model—based quantile regression. The approach involves flexible Dirichlet process mixture models for the joint distribution of the response and the ...
The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 49, No. 3 (September/septembre 2021), pp. 698-730 (33 pages) We propose a flexible Bayesian semiparametric quantile ...
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 ...
Commodity value-at-risk modeling: comparing RiskMetrics, historic simulation and quantile regression
Commodities constitute a nonhomogeneous asset class. Return distributions differ widely across different commodities, both in terms of tail fatness and skewness. These are features that we need to ...
Bitcoin researcher Smithson With has presented a new approach to predicting the crypto's cycle top price using a quantile regression model. This model, according to the researcher, suggests that ...
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