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Risks
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Quantile Regression for Risk Assessment
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Quantile Regression for Risk Assessment

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (31 January 2018) | Viewed by 10582

Special Issue Editor

Prof. Dr. Lea Petrella

E-Mail Website
Guest Editor
Department of Methods and Models for Economics, Territory and Finance, Sapienza University of Rome, 00184 Rome, Italy
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (2 papers)

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Research

1111 KiB  
Article
Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression
by Matthias Fischer, Daniel Kraus, Marius Pfeuffer and Claudia Czado
Risks 2017, 5(3), 38; https://doi.org/10.3390/risks5030038 - 19 Jul 2017
Cited by 6 | Viewed by 5425
Abstract
Measuring interdependence between probabilities of default (PDs) in different industry sectors of an economy plays a crucial role in financial stress testing. Thereby, regression approaches may be employed to model the impact of stressed industry sectors as covariates on other response sectors. We [...] Read more.
Measuring interdependence between probabilities of default (PDs) in different industry sectors of an economy plays a crucial role in financial stress testing. Thereby, regression approaches may be employed to model the impact of stressed industry sectors as covariates on other response sectors. We identify vine copula based quantile regression as an eligible tool for conducting such stress tests as this method has good robustness properties, takes into account potential nonlinearities of conditional quantile functions and ensures that no quantile crossing effects occur. We illustrate its performance by a data set of sector specific PDs for the German economy. Empirical results are provided for a rough and a fine-grained industry sector classification scheme. Amongst others, we confirm that a stressed automobile industry has a severe impact on the German economy as a whole at different quantile levels whereas, e.g., for a stressed financial sector the impact is rather moderate. Moreover, the vine copula based quantile regression approach is benchmarked against both classical linear quantile regression and expectile regression in order to illustrate its methodological effectiveness in the scenarios evaluated. Full article
(This article belongs to the Special Issue Quantile Regression for Risk Assessment)
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405 KiB  
Article
A Robust Approach to Hedging and Pricing in Imperfect Markets
by Hirbod Assa and Nikolay Gospodinov
Risks 2017, 5(3), 36; https://doi.org/10.3390/risks5030036 - 18 Jul 2017
Viewed by 3790
Abstract
This paper proposes a model-free approach to hedging and pricing in the presence of market imperfections such as market incompleteness and frictions. The generality of this framework allows us to conduct an in-depth theoretical analysis of hedging strategies with a wide family of [...] Read more.
This paper proposes a model-free approach to hedging and pricing in the presence of market imperfections such as market incompleteness and frictions. The generality of this framework allows us to conduct an in-depth theoretical analysis of hedging strategies with a wide family of risk measures and pricing rules, and study the conditions under which the hedging problem admits a solution and pricing is possible. The practical implications of our proposed theoretical approach are illustrated with an application on hedging economic risk. Full article
(This article belongs to the Special Issue Quantile Regression for Risk Assessment)
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