Quantile Regression for Risk Assessment
A special issue of Risks (ISSN 2227-9091).
Deadline for manuscript submissions: closed (31 January 2018) | Viewed by 9435
Special Issue Editor
Interests: bayesian inference; quantile regression; tail risk measures and models; time series
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Quantile regression has become a very popular approach to provide a wide description of the distribution of a response variable conditionally on a set of regressors. While linear regression analysis aims at estimating the conditional mean of a variable of interest, in the quantile regression we may estimate any conditional quantile of any level in (0,1). Starting from the seminal work of Koenker and Basset, several papers in the literature consider quantile regression analysis both from a frequentist and a Bayesian points of view. Since, in general, quantile regression proves to be useful whenever one is interested in focusing on particular segments of the distribution also on extremes, particular attention has been given on the relation between quantile regression and risk assessment and modelling. Recently, several papers have been developed concerning the use of the quantile regression to evaluate the Value at Risk in financial research.
Generalization of quantiles, such as expectiles, M-quantiles and Lp-quantiles, have also been considered in a regression framework by means of the minimization of suitable asymmetric loss functions. Those quantities have been recently linked with risk measures and studied from the point of view of the axiomatic theory of risk.
The Special Issue aims at highlighting quality papers that propose advances in modeling and application in the risk framework by using quantile, generalized quantile regression and its dynamic version in the frequentist and Bayesian contest.
We welcome research papers related, but not limited to the following risk framework:
- Actuarial
- Insurance
- Finance
- Environmental
- Climate
- Hydrologic
- Economics
- Medical Malpractice
Prof. Lea Petrella
Guest Editor
Manuscript Submission Information
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