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Risks
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Applied Financial and Actuarial Risk Analytics
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Applied Financial and Actuarial Risk Analytics

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4330

Special Issue Editors

Prof. Dr. Tak Kuen Ken Siu

E-Mail Website
Guest Editor
Department of Actuarial Studies and Business Analytics, Macquarie Business School, Macquarie University, Sydney, NSW 2109, Australia
Interests: mathematical finance; actuarial science; quantitative risk management; applications of stochastic processes; filtering and control; applied statistics; quantitative analytics
Prof. Dr. Hailiang Yang

E-Mail Website
Guest Editor
Department of Financial and Actuarial Mathematics, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
Interests: actuarial science; equity linked insurance products; optimal insurance strategy; mathematical finance; applications of AI and data science in insurance and actuarial science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modeling financial and actuarial risks has long been a pressing issue, and is of high priority in the research agendas of actuarial science, finance, and risk management. The field is truly interdisciplinary and draws on concepts, methods, and techniques from diverse fields, including, but not limited to, probability theory, stochastic processes, statistics, econometrics, finance, actuarial mathematics, financial mathematics, economics, computing, optimization, and control theory. Recently, with the advancement of computing technologies as well as the availability of granular and big data, machine learning, data analytics, and artificial intelligence (AI) are becoming more and more important in modeling as well as predicting financial and actuarial risks. Techniques in machine learning, data analytics, and AI have transformed both the theories and practices of financial and insurance risk modeling. A new era of the field has emerged, and new and exciting research opportunities are waiting for further explorations.

In this Special Issue, we aim to provide a platform to explore the new and exciting research opportunities in financial and actuarial risk modeling via innovative techniques and/or applications of machine learning, data analytics, and AI. We believe that traditional techniques in modeling financial and actuarial risks are important ingredients to increase the proliferation of various important and innovative uses of machine learning, data analytics, and AI. We also subscribe to the view of the diversification of research ideas and approaches. We welcome and sincerely invite colleagues from both academia and industry to share their latest and cutting-edge research on financial and actuarial risks from both traditional and modern perspectives. All areas of financial and actuarial risk are welcome.

Prof. Dr. Tak Kuen Ken Siu
Prof. Dr. Hailiang Yang
Guest Editors

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

  • financial risk
  • actuarial risk
  • data analytics
  • machine learning
  • AI

Published Papers (3 papers)

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Research

15 pages, 446 KiB  
Article
Multivariate Spectral Backtests of Forecast Distributions under Unknown Dependencies
by Janine Balter and Alexander J. McNeil
Risks 2024, 12(1), 13; https://doi.org/10.3390/risks12010013 - 17 Jan 2024
Viewed by 1127
Abstract
Under the revised market risk framework of the Basel Committee on Banking Supervision, the model validation regime for internal models now requires that models capture the tail risk in profit-and-loss (P&L) distributions at the trading desk level. We develop multi-desk backtests, which simultaneously [...] Read more.
Under the revised market risk framework of the Basel Committee on Banking Supervision, the model validation regime for internal models now requires that models capture the tail risk in profit-and-loss (P&L) distributions at the trading desk level. We develop multi-desk backtests, which simultaneously test all trading desk models and which exploit all the information available in the presence of an unknown correlation structure between desks. We propose a multi-desk extension of the spectral test of Gordy and McNeil, which allows the evaluation of a model at more than one confidence level and contains a multi-desk value-at-risk (VaR) backtest as a special case. The spectral tests make use of realised probability integral transform values based on estimated P&L distributions for each desk and are more informative and more powerful than simpler tests based on VaR violation indicators. The new backtests are easy to implement with a reasonable running time; in a series of simulation studies, we show that they have good size and power properties. Full article
(This article belongs to the Special Issue Applied Financial and Actuarial Risk Analytics)
19 pages, 5357 KiB  
Article
Equity Price Dynamics under Shocks: In Distress or Short Squeeze
by Cho-Hoi Hui, Chi-Fai Lo and Chi-Hei Liu
Risks 2024, 12(1), 1; https://doi.org/10.3390/risks12010001 - 20 Dec 2023
Viewed by 1241
Abstract
This paper proposes a simple bounded stochastic motion to model equity price dynamics under shocks. The stochastic process has a quasi-bounded boundary which can be breached if the probability leakage condition is met. The quasi-boundedness of the process at the boundary can thus [...] Read more.
This paper proposes a simple bounded stochastic motion to model equity price dynamics under shocks. The stochastic process has a quasi-bounded boundary which can be breached if the probability leakage condition is met. The quasi-boundedness of the process at the boundary can thus provide an indicator of the possible risk of equities under price shocks or in distress. Empirical calibration of the model parameters of the proposed process for equities can be performed easily due to the availability of an analytically tractable probability density function which generates fat-tailed distributions consistent with empirical observations. The volatility and mean-reversion of the S&P500 dynamics calibrated by the process are positively and negatively co-integrated, respectively, with the VIX index representing the level of market distress. The process captures the high likelihood of Hertz’s default about two months earlier, using only information until that point, and before the firm filed for Chapter 11 bankruptcy in May 2020 as a result of the COVID-19 pandemic. Empirical calibration of the process for GameStop’s stock price shows that the short squeeze in the stock occurred when the condition for breaching the upper boundary was met on 14 January 2021, i.e., about two weeks before major short-sellers closed out their positions with significant losses. The trading volume of the stock was positively co-integrated with the probability leakage ratio. Full article
(This article belongs to the Special Issue Applied Financial and Actuarial Risk Analytics)
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37 pages, 582 KiB  
Article
Rank-Based Multivariate Sarmanov for Modeling Dependence between Loss Reserves
by Anas Abdallah and Lan Wang
Risks 2023, 11(11), 187; https://doi.org/10.3390/risks11110187 - 26 Oct 2023
Cited by 1 | Viewed by 1464
Abstract
The interdependence between multiple lines of business has an important impact on determining loss reserves and risk capital, which are crucial for the solvency of a property and casualty (P&C) insurance company. In this work, we introduce the two-stage inference method using the [...] Read more.
The interdependence between multiple lines of business has an important impact on determining loss reserves and risk capital, which are crucial for the solvency of a property and casualty (P&C) insurance company. In this work, we introduce the two-stage inference method using the Sarmanov family of multivariate distributions to the actuarial literature. In fact, we study rank-based methods using the Sarmanov distribution to adequately estimate the loss reserves and properly capture the dependence between lines of business. An inadequate choice of the dependence structure may negatively impact the estimation of the marginals and, hence, the reserve. Thus, we propose a two-stage inference strategy in this research to address this, while taking advantage of the flexibility of the Sarmanov distribution. We show that this strategy leads to a more robust estimation, and better captures the dependence between the risks. We also show that it generates smaller risk capital and a better diversification benefit. We extend the model to the multivariate case with more than two lines of business. To illustrate and validate our methods, we use three different sets of real data from both a major US property–casualty insurer and a large Canadian insurance company. Full article
(This article belongs to the Special Issue Applied Financial and Actuarial Risk Analytics)
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