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Risks
Special Issues
Quantitative Risk Measurement and Management
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Quantitative Risk Measurement and Management

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 22564

Special Issue Editors

Dr. Daniël Linders

E-Mail Website
Guest Editor
The Amsterdam School of Economics, University of Amsterdam, 1012 WX Amsterdam, The Netherlands
Interests: actuarial science; risk measures; herd behavior; index options; implied correlation; systematic risk
Special Issues, Collections and Topics in MDPI journals
Dr. Wing Fung Chong

E-Mail Website
Guest Editor
Maxwell Institute for Mathematical Sciences and Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh, Scotland, UK
Interests: risk sharing; forward preferences and control strategies; life and retirement products; risk aggregation and resources allocation; cyber risk management; reinforcement learning; data science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jan Dhaene

E-Mail Website
Guest Editor
AFI Department, KU Leuven, 3000 Leuven, Belgium
Interests: actuarial science; risk measures; herd behavior; fair valuation; comonotonicity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Quantifying risks has become a major task for banks, financial institutions, governments, etc. Several recent developments require new risk measurement and risk management frameworks. Firstly, a series of forgotten and even new risks are threatening our society again. The World Economic Forum is listing risks such as a pandemic, climate change, natural hazards, cyber risk, asset price bubbles among the most important dangers for the near future. Moreover, longevity risk and the increasing costs for health care are posing challenges for governments to keep social security adequate and affordable. All these risks have in common that they are at least partly systematic in nature. Managing these newly emerged risks requires a new quantitative toolbox. Secondly, insurance and financial products are becoming increasingly complex and interconnected. Therefore, new valuation and risk management methods have to be designed to cope with these new hybrid products. Lastly, nowadays there is an abundance of data and computational power available, which allows for modern and sophisticated data analytics and machine learning algorithms and possibly groundbreaking solutions to existing risk management problems.

This Special Issue aims to compile high-quality papers that offer a discussion of state-of-the-art developments or introduce new theoretical or practical advances in the area of quantitative risk measurement and management. We welcome papers related but not limited to the following topics:

  • Risk measures and systemic risk
  • Solvency for financial institutions and risk aggregation
  • Extreme value theory in risk management
  • Pricing and valuation of unit-linked insurance
  • Hedging and control strategies
  • Longecity modelling and risk management
  • Cyber insurance and risk management
  • Catastrophe risk management
  • Machine learning applications in risk management
  • Stochastic orders

Dr. Daniël Linders
Dr. Wing Fung Chong
Prof. Dr. Jan Dhaene
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

  • Actuarial science
  • Quantitative risk management
  • Risk sharing
  • Risk measures
  • Aggregation and allocation
  • Machine learning
  • Stochastic orders
  • Dependence
  • Cyber risk
  • Longevity risk
  • Unit-linked insurance

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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Research

26 pages, 11125 KiB  
Article
Portfolio Optimization for Extreme Risks with Maximum Diversification: An Empirical Analysis
by Navya Jayesh Mehta and Fan Yang
Risks 2022, 10(5), 101; https://doi.org/10.3390/risks10050101 - 11 May 2022
Cited by 6 | Viewed by 2961
Abstract
Heavy tailedness and interconnectedness widely exist in stock returns and large insurance claims, which contributes to huge losses for financial institutions. Diversification ratio (DR) measures the degree of diversification using the Value-at-Risk, which is known to capture extreme risks better than variance. The [...] Read more.
Heavy tailedness and interconnectedness widely exist in stock returns and large insurance claims, which contributes to huge losses for financial institutions. Diversification ratio (DR) measures the degree of diversification using the Value-at-Risk, which is known to capture extreme risks better than variance. The portfolio optimization strategy based on DR maximizes the effect of diversification for extreme risks. In this paper, we empirically examine the DR strategy by using more than 350 S&P 500 stocks under the assumption that the stock losses are modeled with a flexible multivariate heavy-tailed model. This assumption is verified empirically. The performance of DR strategy is compared with four benchmark strategies: equally weighted portfolio, minimum-variance portfolio, extreme risk index portfolio, and most diversified portfolio. The performance of comparison includes annualized portfolio return, modified Sharpe ratio, maximum drawdown, portfolio concentration, portfolio turnover, and the degree of diversification. DR outperforms other strategies. In particular, DR shows the highest return and maintains the highest level of diversification during the global financial crisis of 2007–2009. Full article
(This article belongs to the Special Issue Quantitative Risk Measurement and Management)
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22 pages, 2117 KiB  
Article
The Risky-Opportunity Analysis Method (ROAM) to Support Risk-Based Decisions in a Case-Study of Critical Infrastructure Digitization
by Ali Aghazadeh Ardebili, Elio Padoano, Antonella Longo and Antonio Ficarella
Risks 2022, 10(3), 48; https://doi.org/10.3390/risks10030048 - 23 Feb 2022
Cited by 4 | Viewed by 4006
Abstract
Socio-ecologic, socio-economic, and socio-technical transitions are opportunities that require fundamental changes in the system. These will encounter matters associated with security, service adoption by end-users, infrastructure and availability. The purpose of this study is to examine and overcome the risks to take advantage [...] Read more.
Socio-ecologic, socio-economic, and socio-technical transitions are opportunities that require fundamental changes in the system. These will encounter matters associated with security, service adoption by end-users, infrastructure and availability. The purpose of this study is to examine and overcome the risks to take advantage of opportunities through the novel Risky-Opportunity Analysis Method (ROAM). A novel quantitative method is designed to determine when, after making some changes, the risks become acceptable so that the opportunity does not deviate from the objectives. The approach provided a quantitative evaluation of the possible changes in parallel with digitization, towards providing a green Service Supply Chain (SSC). The result of ROAM shows that the most cost-effective change to increase the resilience of the system is a solution (SMS) which is different from that identified by a TOPSIS multi-criteria method. Real-word decisions in change management should tackle the complexity of systems and uncertainty of events during and after transition through a careful analysis of the alternatives. A case-study was carried out to evaluate the alternatives of an ancillary service in the Payment Service Providers (PSP). The comparison of the ROAM results with the traditional TOPSIS of the case-study unveils the priority of the ROAM in practice when the alternatives are Risky-Opportunities. The existing risk assessment tools do not take advantage of risky opportunities. To this aim, the current article introduces the term Risky-Opportunity, and two indexes—Stress and Strain—of the alternatives that are designed to be employed in the new quantitative ROAM approach. Full article
(This article belongs to the Special Issue Quantitative Risk Measurement and Management)
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30 pages, 722 KiB  
Article
Explaining Aggregated Recovery Rates
by Stephan Höcht, Aleksey Min, Jakub Wieczorek and Rudi Zagst
Risks 2022, 10(1), 18; https://doi.org/10.3390/risks10010018 - 11 Jan 2022
Cited by 4 | Viewed by 2478
Abstract
This study on explaining aggregated recovery rates (ARR) is based on the largest existing loss and recovery database for commercial loans provided by Global Credit Data, which includes defaults from 5 continents and over 120 countries. The dependence of monthly ARR from bank [...] Read more.
This study on explaining aggregated recovery rates (ARR) is based on the largest existing loss and recovery database for commercial loans provided by Global Credit Data, which includes defaults from 5 continents and over 120 countries. The dependence of monthly ARR from bank loans on various macroeconomic factors is examined and sources of their variability are stated. For the first time, an influence of stochastically estimated monthly growth of GDP USA and Europe is quantified. To extract monthly signals of GDP USA and Europe, dynamic factor models for panel data of different frequency information are employed. Then, the behavior of the ARR is investigated using several regression models with unshifted and shifted explanatory variables in time to improve their forecasting power by taking into account the economic situation after the default. An application of a Markov switching model shows that the distribution of the ARR differs between crisis and prosperity times. The best fit among the compared models is reached by the Markov switching model. Moreover, a significant influence of the estimated monthly growth of GDP in Europe is observed for both crises and prosperity times. Full article
(This article belongs to the Special Issue Quantitative Risk Measurement and Management)
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28 pages, 745 KiB  
Article
Optimal Asset Allocation Subject to Withdrawal Risk and Solvency Constraints
by Areski Cousin, Ying Jiao, Christian Yann Robert and Olivier David Zerbib
Risks 2022, 10(1), 15; https://doi.org/10.3390/risks10010015 - 6 Jan 2022
Cited by 2 | Viewed by 3227
Abstract
This paper investigates the optimal asset allocation of a financial institution whose customers are free to withdraw their capital-guaranteed financial contracts at any time. In accounting for the asset-liability mismatch risk of the institution, we present a general utility optimization problem in a [...] Read more.
This paper investigates the optimal asset allocation of a financial institution whose customers are free to withdraw their capital-guaranteed financial contracts at any time. In accounting for the asset-liability mismatch risk of the institution, we present a general utility optimization problem in a discrete-time setting and provide a dynamic programming principle for the optimal investment strategies. Furthermore, we consider an explicit context, including liquidity risk, interest rate, and credit intensity fluctuations, and show by numerical results that the optimal strategy improves both the solvency and asset returns of the institution compared to a standard institutional investor’s asset allocation. Full article
(This article belongs to the Special Issue Quantitative Risk Measurement and Management)
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19 pages, 19621 KiB  
Article
Decomposition of Natural Catastrophe Risks: Insurability Using Parametric CAT Bonds
by Morteza Tavanaie Marvi and Daniël Linders
Risks 2021, 9(12), 215; https://doi.org/10.3390/risks9120215 - 1 Dec 2021
Cited by 5 | Viewed by 3260
Abstract
Nat Cat risks are not insurable by traditional insurance mainly because of producing highly correlated losses. The source of such correlation among buildings of a region subject to a natural hazard is discussed. A decomposition method is proposed to split Nat Cat risk [...] Read more.
Nat Cat risks are not insurable by traditional insurance mainly because of producing highly correlated losses. The source of such correlation among buildings of a region subject to a natural hazard is discussed. A decomposition method is proposed to split Nat Cat risk into idiosyncratic (and hence insurable) risk and systematic risk (carrying the correlated part). It is explained that the systematic risk can be transferred to capital markets using a set of parametric CAT bonds. Premium calculation is presented for insuring the decomposed risk. Portfolio risk-return trade-off measures for investing on the parametric CAT bond are derived. Multi-regional and multi-hazard parametric CAT bonds are introduced to reduce the risk of the investment. The methodology is applied on a region with about 3000 residential buildings subject to flood hazards. Full article
(This article belongs to the Special Issue Quantitative Risk Measurement and Management)
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19 pages, 937 KiB  
Article
It Takes Two to Tango: Estimation of the Zero-Risk Premium Strike of a Call Option via Joint Physical and Pricing Density Modeling
by Stephan Höcht, Dilip B. Madan, Wim Schoutens and Eva Verschueren
Risks 2021, 9(11), 196; https://doi.org/10.3390/risks9110196 - 4 Nov 2021
Viewed by 1924
Abstract
It is generally said that out-of-the-money call options are expensive and one can ask the question from which moneyness level this is the case. Expensive actually means that the price one pays for the option is more than the discounted average payoff one [...] Read more.
It is generally said that out-of-the-money call options are expensive and one can ask the question from which moneyness level this is the case. Expensive actually means that the price one pays for the option is more than the discounted average payoff one receives. If so, the option bears a negative risk premium. The objective of this paper is to investigate the zero-risk premium moneyness level of a European call option, i.e., the strike where expectations on the option’s payoff in both the P- and Q-world are equal. To fully exploit the insights of the option market we deploy the Tilted Bilateral Gamma pricing model to jointly estimate the physical and pricing measure from option prices. We illustrate the proposed pricing strategy on the option surface of stock indices, assessing the stability and position of the zero-risk premium strike of a European call option. With small fluctuations around a slightly in-the-money level, on average, the zero-risk premium strike appears to follow a rather stable pattern over time. Full article
(This article belongs to the Special Issue Quantitative Risk Measurement and Management)
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29 pages, 6456 KiB  
Article
One-Year and Ultimate Reserve Risk in Mack Chain Ladder Model
by Marcin Szatkowski and Łukasz Delong
Risks 2021, 9(9), 152; https://doi.org/10.3390/risks9090152 - 25 Aug 2021
Cited by 1 | Viewed by 3016
Abstract
We investigate the relation between one-year reserve risk and ultimate reserve risk in Mack Chain Ladder model in a simulation study. The first goal is to validate the so-called linear emergence pattern formula, which maps the ultimate loss to the one-year loss, in [...] Read more.
We investigate the relation between one-year reserve risk and ultimate reserve risk in Mack Chain Ladder model in a simulation study. The first goal is to validate the so-called linear emergence pattern formula, which maps the ultimate loss to the one-year loss, in case when we measure the risks with Value-at-Risk. The second goal is to estimate the true emergence pattern of the ultimate loss, i.e., the conditional distribution of the one-year loss given the ultimate loss, from which we can properly derive a risk measure for the one-year horizon from the simulations of ultimate losses. Finally, our third goal is to test if classical actuarial distributions can be used for modelling of the outstanding loss from the ultimate and the one-year perspective. In our simulation study, we investigate several synthetic loss triangles with various duration of the claims development process, volatility, skewness, and distributional assumptions of the individual development factors. We quantify the reserve risks without and with the estimation error of the claims development factors. Full article
(This article belongs to the Special Issue Quantitative Risk Measurement and Management)
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