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Estimation of Risk Measures from Data -- Estimators, Computation, Robustness...
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Estimation of Risk Measures from Data -- Estimators, Computation, Robustness and Elicitability

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

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 12974

Special Issue Editor

Prof. Dr. Peter Ruckdeschel

E-Mail Website
Guest Editor
Institute for Mathematics, School of Mathematics and Science, Carl von Ossietzky University Oldenburg, PO box 2503, 26111 Oldenburg (Oldb), Germany
Interests: robust statistics; time series models; financial statistics; financial risk; fraud detection

Special Issue Information

Dear Colleagues,

Computation of risk measures for both regulatory and internal use has become daily use in financial services, banking and insurances, but also beyond. This touches several statistical aspects with many open questions and issues and is reflected by the following (non-exhaustive) list of topics:

+ statistical models (parametric, non-parametric, semi-parametric)

+ stability and efficiency of estimates (in terms of precision and computional time)

+ acceptable model assumptions (e.g., stationarity, ergodicity)

+ statistical models for dependence in the underlyings (and their stability)

+ regime switching models

+ recovery times of risk measure estimates after shocks

+ weighting schemes for observations

+ decisions on rolling/growing/disjoint windows for estimation and validation

+ estimators derived from extreme value statistics

+ behaviour as to outliers, missing values and/or with violated model assumptions

+ elicitability and fair ranking of procedures

+ accounting for time dependence in validity tests

+ bootstrap strategies to assess precision

+ bias considerations

+ influence functions/sensitivity curves

The aim of this special issue is to address some of these topics and related ones in high quality papers which may cover both theoretical, simulational and empirical results. A particular focus in these papers should lie on the impact of these results on the use of risk measures in daily business.

Prof. Dr. Peter Ruckdeschel
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.

Keywords

  • Risk Measures
  • Statistics in Finance
  • Robust Statistics
  • Elicitability

Published Papers (4 papers)

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Research

16 pages, 557 KiB  
Article
Model Efficiency and Uncertainty in Quantile Estimation of Loss Severity Distributions
by Vytaras Brazauskas and Sahadeb Upretee
Risks 2019, 7(2), 55; https://doi.org/10.3390/risks7020055 - 15 May 2019
Cited by 1 | Viewed by 2555
Abstract
Quantiles of probability distributions play a central role in the definition of risk measures (e.g., value-at-risk, conditional tail expectation) which in turn are used to capture the riskiness of the distribution tail. Estimates of risk measures are needed in many practical situations such [...] Read more.
Quantiles of probability distributions play a central role in the definition of risk measures (e.g., value-at-risk, conditional tail expectation) which in turn are used to capture the riskiness of the distribution tail. Estimates of risk measures are needed in many practical situations such as in pricing of extreme events, developing reserve estimates, designing risk transfer strategies, and allocating capital. In this paper, we present the empirical nonparametric and two types of parametric estimators of quantiles at various levels. For parametric estimation, we employ the maximum likelihood and percentile-matching approaches. Asymptotic distributions of all the estimators under consideration are derived when data are left-truncated and right-censored, which is a typical loss variable modification in insurance. Then, we construct relative efficiency curves (REC) for all the parametric estimators. Specific examples of such curves are provided for exponential and single-parameter Pareto distributions for a few data truncation and censoring cases. Additionally, using simulated data we examine how wrong quantile estimates can be when one makes incorrect modeling assumptions. The numerical analysis is also supplemented with standard model diagnostics and validation (e.g., quantile-quantile plots, goodness-of-fit tests, information criteria) and presents an example of when those methods can mislead the decision maker. These findings pave the way for further work on RECs with potential for them being developed into an effective diagnostic tool in this context. Full article
(This article belongs to the Special Issue Estimation of Risk Measures from Data -- Estimators, Computation, Robustness and Elicitability)
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27 pages, 1622 KiB  
Article
Macroeconomic News Sentiment: Enhanced Risk Assessment for Sovereign Bonds
by Christina Erlwein-Sayer
Risks 2018, 6(4), 141; https://doi.org/10.3390/risks6040141 - 07 Dec 2018
Cited by 6 | Viewed by 3924
Abstract
We enhance the modelling and risk assessment of sovereign bond spreads by taking into account quantitative information gained from macro-economic news sentiment. We investigate sovereign bonds spreads of five European countries and improve the prediction of spread changes by incorporating news sentiment from [...] Read more.
We enhance the modelling and risk assessment of sovereign bond spreads by taking into account quantitative information gained from macro-economic news sentiment. We investigate sovereign bonds spreads of five European countries and improve the prediction of spread changes by incorporating news sentiment from relevant entities and macro-economic topics. In particular, we create daily news sentiment series from sentiment scores as well as positive and negative news volume and investigate their effects on yield spreads and spread volatility. We conduct a correlation and rolling correlation analysis between sovereign bond spreads and accumulated sentiment series and analyse changing correlation patterns over time. Market regimes are detected through correlation series and the impact of news sentiment on sovereign bonds in different market circumstances is investigated. We find best-suited external variables for forecasts in an ARIMAX model set-up. Error measures for forecasts of spread changes and volatility proxies are improved when sentiment is considered. These findings are then utilised to monitor sovereign bonds from European countries and detect changing risks through time. Full article
(This article belongs to the Special Issue Estimation of Risk Measures from Data -- Estimators, Computation, Robustness and Elicitability)
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15 pages, 3367 KiB  
Article
Generating VaR Scenarios under Solvency II with Product Beta Distributions
by Dietmar Pfeifer and Olena Ragulina
Risks 2018, 6(4), 122; https://doi.org/10.3390/risks6040122 - 18 Oct 2018
Cited by 5 | Viewed by 3227
Abstract
We propose a Monte Carlo simulation method to generate stress tests by VaR scenarios under Solvency II for dependent risks on the basis of observed data. This is of particular interest for the construction of Internal Models. The approach is based on former [...] Read more.
We propose a Monte Carlo simulation method to generate stress tests by VaR scenarios under Solvency II for dependent risks on the basis of observed data. This is of particular interest for the construction of Internal Models. The approach is based on former work on partition-of-unity copulas, however with a direct scenario estimation of the joint density by product beta distributions after a suitable transformation of the original data. Full article
(This article belongs to the Special Issue Estimation of Risk Measures from Data -- Estimators, Computation, Robustness and Elicitability)
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30 pages, 1107 KiB  
Article
Bootstrapping Average Value at Risk of Single and Collective Risks
by Eric Beutner and Henryk Zähle
Risks 2018, 6(3), 96; https://doi.org/10.3390/risks6030096 - 12 Sep 2018
Cited by 1 | Viewed by 2834
Abstract
Almost sure bootstrap consistency of the blockwise bootstrap for the Average Value at Risk of single risks is established for strictly stationary β -mixing observations. Moreover, almost sure bootstrap consistency of a multiplier bootstrap for the Average Value at Risk of collective risks [...] Read more.
Almost sure bootstrap consistency of the blockwise bootstrap for the Average Value at Risk of single risks is established for strictly stationary β -mixing observations. Moreover, almost sure bootstrap consistency of a multiplier bootstrap for the Average Value at Risk of collective risks is established for independent observations. The main results rely on a new functional delta-method for the almost sure bootstrap of uniformly quasi-Hadamard differentiable statistical functionals, to be presented here. The latter seems to be interesting in its own right. Full article
(This article belongs to the Special Issue Estimation of Risk Measures from Data -- Estimators, Computation, Robustness and Elicitability)
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