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Risks
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Statistical Methods for Quantitative Risk Management
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Statistical Methods for Quantitative Risk Management

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

Deadline for manuscript submissions: closed (30 August 2022) | Viewed by 12165

Special Issue Editor

Dr. Alexandra Dias

E-Mail Website
Guest Editor
University of York Management School, University of York, YO10 5DD York, UK
Interests: finance; insurance; quantitative risk management; dependence modelling; multivariate analysis; extreme-values
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Challenges in risk management keep appearing, either due to new developments, or because as a society we want to address long lasting problems previously ignored. We are especially interested in the development and use of quantitative methods for managing insurance and financial risk inherent to social change caused by environmental, demographic, cultural, economic, political, or technological factors.

We welcome submissions of articles in statistical methods for quantitative risk management in finance and insurance, addressing issues within dependence modelling, multivariate analysis or extreme-value modelling with applications related, but not limited, to sustainable finance and insurance, financial inclusion, and climate change.

Dr. Alexandra Dias
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 analysis
  • Finance
  • Insurance
  • Multivariate analysis
  • Extreme events

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Published Papers (4 papers)

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Research

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15 pages, 937 KiB  
Article
Forming a Risk Management System Based on the Process Approach in the Conditions of Economic Transformation
by Elena Sidorova, Yuri Kostyukhin, Lyudmila Korshunova, Svetlana Ulyanova, Alexey Shinkevich, Irina Ershova and Alena Dyrdonova
Risks 2022, 10(5), 95; https://doi.org/10.3390/risks10050095 - 4 May 2022
Cited by 5 | Viewed by 3057
Abstract
The economy is in a state of transformation into a new system, and it is quite realistic that economic entities will be under the influence of certain risky moments. The process of risk analysis and management should be considered by an enterprise as [...] Read more.
The economy is in a state of transformation into a new system, and it is quite realistic that economic entities will be under the influence of certain risky moments. The process of risk analysis and management should be considered by an enterprise as an integral part of enterprise management in extreme conditions of the economic development trajectory and be a guarantor of financial insurance. The goal of this study is the formation of a process approach in enterprise management—the creation of a universal risk management system based on the proposed risk management model to minimize the financial risks of an enterprise. We propose a risk management system that influences the forecasting of financial stability of an enterprise. To form the risk management system, we propose a model of an organisational system, the structural elements of which correspond to the principles of completeness, information capacity and consistency. We identified the diversity of direct and inverse relationships between factorial and productive characteristics, which indicates the complexity of the organisational system management process. The presence of “bottlenecks” in the implementation of expert systems tools was also noted. This was expressed in the difficulty of acquiring the knowledge necessary for the development of meaningful systems and structuring the knowledge gained in a form that is convenient for use. We used the method of qualitative modelling using the apparatus of weighted directed graphs. The study allowed us to formulate a list of factors that characterize the main activities of an enterprise in order to form a mathematical model of enterprise risk management. The developed predictive model is used to simulate extreme events and risks in the process of enterprise development, as one of the foundations of the enterprise management system in the conditions of economic transformation. Full article
(This article belongs to the Special Issue Statistical Methods for Quantitative Risk Management)
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18 pages, 929 KiB  
Article
Parameter Learning and Change Detection Using a Particle Filter with Accelerated Adaptation
by Karol Gellert and Erik Schlögl
Risks 2021, 9(12), 228; https://doi.org/10.3390/risks9120228 - 16 Dec 2021
Cited by 2 | Viewed by 2447
Abstract
This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent [...] Read more.
This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in online sequential filtering does not match the model posterior filtered based on data up to a current point in time. The examples considered encompass parameter regime shifts and stochastic volatility. The filter adapts to regime shifts extremely rapidly and delivers a clear heuristic for distinguishing between regime shifts and stochastic volatility, even though the model dynamics assumed by the filter exhibit neither of those features. Full article
(This article belongs to the Special Issue Statistical Methods for Quantitative Risk Management)
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Review

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55 pages, 1013 KiB  
Review
Stochastic Claims Reserving Methods with State Space Representations: A Review
by Nataliya Chukhrova and Arne Johannssen
Risks 2021, 9(11), 198; https://doi.org/10.3390/risks9110198 - 4 Nov 2021
Cited by 6 | Viewed by 2469
Abstract
Often, the claims reserves exceed the available equity of non-life insurance companies and a change in the claims reserves by a small percentage has a large impact on the annual accounts. Therefore, it is of vital importance for any non-life insurer to handle [...] Read more.
Often, the claims reserves exceed the available equity of non-life insurance companies and a change in the claims reserves by a small percentage has a large impact on the annual accounts. Therefore, it is of vital importance for any non-life insurer to handle claims reserving appropriately. Although claims data are time series data, the majority of the proposed (stochastic) claims reserving methods is not based on time series models. Among the time series models, state space models combined with Kalman filter learning algorithms have proven to be very advantageous as they provide high flexibility in modeling and an accurate detection of the temporal dynamics of a system. Against this backdrop, this paper aims to provide a comprehensive review of stochastic claims reserving methods that have been developed and analyzed in the context of state space representations. For this purpose, relevant articles are collected and categorized, and the contents are explained in detail and subjected to a conceptual comparison. Full article
(This article belongs to the Special Issue Statistical Methods for Quantitative Risk Management)
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Other

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5 pages, 326 KiB  
Commentary
Kalman Filter Learning Algorithms and State Space Representations for Stochastic Claims Reserving
by Nataliya Chukhrova and Arne Johannssen
Risks 2021, 9(6), 112; https://doi.org/10.3390/risks9060112 - 6 Jun 2021
Cited by 3 | Viewed by 2675
Abstract
In stochastic claims reserving, state space models have been used for almost 40 years to forecast loss reserves and to compute their mean squared error of prediction. Although state space models and the associated Kalman filter learning algorithms are very powerful and flexible [...] Read more.
In stochastic claims reserving, state space models have been used for almost 40 years to forecast loss reserves and to compute their mean squared error of prediction. Although state space models and the associated Kalman filter learning algorithms are very powerful and flexible tools, comparatively few articles on this topic were published during this period. Most recently, several articles have been published which highlight the benefits of state space models in stochastic claims reserving and may lead to a significant increase in its popularity for applications in actuarial practice. To further emphasize the merits of these papers, this commentary highlights various additional aspects that are useful for practical applications and offer some fruitful directions for future research. Full article
(This article belongs to the Special Issue Statistical Methods for Quantitative Risk Management)
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