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Risks Journal: A Decade of Advancing Knowledge and Shaping the...
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Risks Journal: A Decade of Advancing Knowledge and Shaping the Future

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

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 22677

Special Issue Editor

Prof. Dr. Mogens Steffensen

E-Mail Website
Guest Editor
Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, Copenhagen Ø, DK-2100 Copenhagen, Denmark
Interests: life insurance mathematics; asset-liability management; optimal asset allocation; personal finance and insurance; stochastic control theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce the upcoming Special Issue of Risks that will commemorate its remarkable journey over the past ten years. Risks, an open access publication, was founded in 2013 under the visionary leadership of Prof. Dr. Mogens Steffensen from the University of Copenhagen (Denmark). Since its inception, the journal has been dedicated to advancing the field of insurance and financial risk management, providing a platform for cutting-edge research and knowledge exchange.

Since its first publication in 2013, Risks has demonstrated its commitment to open access principles, offering unrestricted access to high-quality research. The journal's efforts were soon recognized when it was indexed by the Emerging Sources Citation Index (ESCI), Web of Science (Clarivate Analytics), in 2015, followed by its indexing by Scopus, Elsevier, in 2018, further amplifying its global reach and impact.

We are thrilled to share that Risks achieved a significant milestone in 2022 with the publication of its 1000th paper, a testament to the growing significance of the journal within the research community. The continuous dedication to excellence resulted in Risks receiving its CiteScore (2022) of 3.1 and its inaugural Impact Factor of 2.2 in 2023.

We are calling for contributions to our Special Issue, “Risks Journal: A Decade of Advancing Knowledge and Shaping the Future,” to celebrate this momentous occasion. We invite researchers, practitioners, and scholars worldwide to submit their original research articles, reviews, and case studies that align with the aims and scope of the Risks journal (https://www.mdpi.com/journal/risks/about).

This Special Issue aims to reflect on the diverse and innovative research that has shaped the field of insurance and financial risk management over the past ten years. By participating in this Special Issue, you can showcase your work to a broad audience of researchers, practitioners, and policymakers worldwide.

We look forward to your valuable contributions and joining us in celebrating a decade of the Risks journal.

Prof. Dr. Mogens Steffensen
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

  • insurance
  • financial risk management
  • asset pricing
  • statistical modeling
  • insurance finance
  • insurance markets
  • insurance institutions
  • insurance regulation
  • actuarial sciences

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Published Papers (12 papers)

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Research

20 pages, 4025 KiB  
Article
A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction
by Farhat Iqbal, Dimitrios Koutmos, Eman A. Ahmed and Lulwah M. Al-Essa
Risks 2024, 12(9), 139; https://doi.org/10.3390/risks12090139 - 30 Aug 2024
Viewed by 942
Abstract
The global foreign exchange (FX) market represents a critical and sizeable component of our financial system. It is a market where firms and investors engage in both speculative trading and hedging. Over the years, there has been a growing interest in FX modeling [...] Read more.
The global foreign exchange (FX) market represents a critical and sizeable component of our financial system. It is a market where firms and investors engage in both speculative trading and hedging. Over the years, there has been a growing interest in FX modeling and prediction. Recently, machine learning (ML) and deep learning (DL) techniques have shown promising results in enhancing predictive accuracy. Motivated by the growing size of the FX market, as well as advancements in ML, we propose a novel forecasting framework, the MVO-BiGRU model, which integrates variational mode decomposition (VMD), data augmentation, Optuna-optimized hyperparameters, and bidirectional GRU algorithms for monthly FX rate forecasting. The data augmentation in the Prevention module significantly increases the variety of data combinations, effectively reducing overfitting issues, while the Optuna optimization ensures optimal model configuration for enhanced performance. Our study’s contributions include the development of the MVO-BiGRU model, as well as the insights gained from its application in FX markets. Our findings demonstrate that the MVO-BiGRU model can successfully avoid overfitting and achieve the highest accuracy in out-of-sample forecasting, while outperforming benchmark models across multiple assessment criteria. These findings offer valuable insights for implementing ML and DL models on low-frequency time series data, where artificial data augmentation can be challenging. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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12 pages, 948 KiB  
Article
Fair and Sustainable Pension System: Market Equilibrium Using Implied Options
by Ishay Wolf and Lorena Caridad López del Río
Risks 2024, 12(8), 127; https://doi.org/10.3390/risks12080127 - 8 Aug 2024
Viewed by 923
Abstract
This study contributes to the discussion about a fair and balanced pension system with a collectively funded pension scheme or social security and a defined contribution pillar. With an invigorated risk approach using financial option positions, it considers the variance of socioeconomic interests [...] Read more.
This study contributes to the discussion about a fair and balanced pension system with a collectively funded pension scheme or social security and a defined contribution pillar. With an invigorated risk approach using financial option positions, it considers the variance of socioeconomic interests of different society-earning cohorts. By that, it enables the assumption of un-uniformity in interests about the fair and sustainable pension design. Specifically, we claim that the alternative cost of hedging the ideal position to the counterparty position studies the implied risks and returns that participants are willing to absorb and hence may lead to a fair compromise when there are different interests. The novelty of the introduced method is mainly based on the variety of participants’ risks and not on the utility function. Accordingly, we spare the discussion about the right shape of the utility function and the proper calibrations. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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15 pages, 656 KiB  
Article
Forecasting Age- and Sex-Specific Survival Functions: Application to Annuity Pricing
by Shaokang Wang, Han Lin Shang, Leonie Tickle and Han Li
Risks 2024, 12(7), 117; https://doi.org/10.3390/risks12070117 - 22 Jul 2024
Viewed by 1076
Abstract
We introduce the function principal component regression (FPCR) forecasting method to model and forecast age-specific survival functions observed over time. The age distribution of survival functions is an example of constrained data whose values lie within a unit interval. Because of the constraint, [...] Read more.
We introduce the function principal component regression (FPCR) forecasting method to model and forecast age-specific survival functions observed over time. The age distribution of survival functions is an example of constrained data whose values lie within a unit interval. Because of the constraint, such data do not reside in a linear vector space. A natural way to deal with such a constraint is through an invertible logit transformation that maps constrained onto unconstrained data in a linear space. With a time series of unconstrained data, we apply a functional time-series forecasting method to produce point and interval forecasts. The forecasts are then converted back to the original scale via the inverse logit transformation. Using the age- and sex-specific survival functions for Australia, we investigate the point and interval forecast accuracies for various horizons. We conclude that the functional principal component regression (FPCR) provides better forecast accuracy than the Lee–Carter (LC) method. Therefore, we apply FPCR to calculate annuity pricing and compare it with the market annuity price. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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14 pages, 420 KiB  
Article
Use of Prediction Bias in Active Learning and Its Application to Large Variable Annuity Portfolios
by Hyukjun Gweon, Shu Li and Yangxuan Xu
Risks 2024, 12(6), 85; https://doi.org/10.3390/risks12060085 - 22 May 2024
Viewed by 1204
Abstract
Given the computational challenges associated with valuing large variable annuity (VA) portfolios, a variety of data mining frameworks, including metamodeling and active learning, have been proposed in recent years. Active learning, a promising alternative to metamodeling, enhances the efficiency of VA portfolio assessments [...] Read more.
Given the computational challenges associated with valuing large variable annuity (VA) portfolios, a variety of data mining frameworks, including metamodeling and active learning, have been proposed in recent years. Active learning, a promising alternative to metamodeling, enhances the efficiency of VA portfolio assessments by adaptively improving a predictive regression model. This is achieved by augmenting data for model training with strategically selected informative samples. Successful application of active learning requires an effective metric in order to gauge the informativeness of data. Current sampling methods, which focus on prediction error-based informativeness, typically rely solely on prediction variance and assume an unbiased predictive model. In this paper, we address the fact that prediction bias can be nonnegligible in large VA portfolio valuation and investigate the impact of prediction bias in both the modeling and sampling stages of active learning. Our experimental results suggest that bias-based sampling can rival the efficacy of traditional ambiguity-based sampling, with its success contingent upon the extent of bias present in the predictive model. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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33 pages, 5115 KiB  
Article
The Regime-Switching Structural Default Risk Model
by Andreas Milidonis and Kevin Chisholm
Risks 2024, 12(3), 48; https://doi.org/10.3390/risks12030048 - 4 Mar 2024
Viewed by 1746
Abstract
We develop the regime-switching default risk (RSDR) model as a generalization of Merton’s default risk (MDR) model. The RSDR model supports an expanded range of asset probability density functions. First, we show using simulation that the RSDR model incorporates [...] Read more.
We develop the regime-switching default risk (RSDR) model as a generalization of Merton’s default risk (MDR) model. The RSDR model supports an expanded range of asset probability density functions. First, we show using simulation that the RSDR model incorporates sudden changes in asset values faster than the MDR model. Second, we empirically implement the RSDR, MDR and an extension of the MDR model with changes in drift parameters, using maximum likelihood estimation. Focusing on the period before and after corporate rating downgrades used primarily for investment advice, we find that the RSDR model uses changes in equity mean returns and volatility to produce higher estimated default probabilities, faster, than both benchmark models. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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28 pages, 10433 KiB  
Article
L1 Regularization for High-Dimensional Multivariate GARCH Models
by Sijie Yao, Hui Zou and Haipeng Xing
Risks 2024, 12(2), 34; https://doi.org/10.3390/risks12020034 - 4 Feb 2024
Cited by 1 | Viewed by 2039
Abstract
The complexity of estimating multivariate GARCH models increases significantly with the increase in the number of asset series. To address this issue, we propose a general regularization framework for high-dimensional GARCH models with BEKK representations, and obtain a penalized quasi-maximum likelihood (PQML) estimator. [...] Read more.
The complexity of estimating multivariate GARCH models increases significantly with the increase in the number of asset series. To address this issue, we propose a general regularization framework for high-dimensional GARCH models with BEKK representations, and obtain a penalized quasi-maximum likelihood (PQML) estimator. Under some regularity conditions, we establish some theoretical properties, such as the sparsity and the consistency, of the PQML estimator for the BEKK representations. We then carry out simulation studies to show the performance of the proposed inference framework and the procedure for selecting tuning parameters. In addition, we apply the proposed framework to analyze volatility spillover and portfolio optimization problems, using daily prices of 18 U.S. stocks from January 2016 to January 2018, and show that the proposed framework outperforms some benchmark models. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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17 pages, 414 KiB  
Article
Invariance of the Mathematical Expectation of a Random Quantity and Its Consequences
by Pierpaolo Angelini
Risks 2024, 12(1), 14; https://doi.org/10.3390/risks12010014 - 18 Jan 2024
Cited by 2 | Viewed by 2203
Abstract
Possibility and probability are the two aspects of uncertainty, where uncertainty represents the ignorance of a given individual. The notion of alternative (or event) belongs to the domain of possibility. An event is intrinsically subdivisible and a quadratic metric, whose value is intrinsic [...] Read more.
Possibility and probability are the two aspects of uncertainty, where uncertainty represents the ignorance of a given individual. The notion of alternative (or event) belongs to the domain of possibility. An event is intrinsically subdivisible and a quadratic metric, whose value is intrinsic or invariant, is used to study it. By subdividing the notion of alternative, a joint (bivariate) distribution of mass appears. The mathematical expectation of X is proved to be invariant using joint distributions of mass. The same is true for X12 and X12…m. This paper describes the notion of α-product, which refers to joint distributions of mass, as a way to connect the concept of probability with multilinear matters that can be treated through statistical inference. This multilinear approach is a meaningful innovation with regard to the current literature. Linear spaces over R with a different dimension can be used as elements of probability spaces. In this study, a more general expression for a measure of variability referred to a single random quantity is obtained. This multilinear measure is obtained using different joint distributions of mass, which are all considered together. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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17 pages, 446 KiB  
Article
The Applications of Generalized Poisson Regression Models to Insurance Claim Data
by Pouya Faroughi, Shu Li and Jiandong Ren
Risks 2023, 11(12), 213; https://doi.org/10.3390/risks11120213 - 7 Dec 2023
Viewed by 3083
Abstract
Predictive modeling has been widely used for insurance rate making. In this paper, we focus on insurance claim count data and address their common issues with more flexible modeling techniques. In particular, we study the zero-inflated and hurdle-generalized Poisson and negative binomial distributions [...] Read more.
Predictive modeling has been widely used for insurance rate making. In this paper, we focus on insurance claim count data and address their common issues with more flexible modeling techniques. In particular, we study the zero-inflated and hurdle-generalized Poisson and negative binomial distributions in a functional form for modeling insurance claim count data. It is shown that these models are useful in addressing the problem of excess zeros and over-dispersion of the claim count variable. In addition, we show that including the exposure as a covariate in both the zero and the count part of the model is an effective approach to incorporating exposure information in zero-inflated and hurdle models. We illustrate the effectiveness and versatility of the introduced models using three real datasets. The results suggest their promising applications in insurance risk classification and beyond. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
20 pages, 1528 KiB  
Article
Coupled Price–Volume Equity Models with Auto-Induced Regime Switching
by Manuel L. Esquível, Nadezhda P. Krasii, Pedro P. Mota and Victoria V. Shamraeva
Risks 2023, 11(11), 203; https://doi.org/10.3390/risks11110203 - 17 Nov 2023
Viewed by 1797
Abstract
In this work, we present a rigorous development of a model for the Price–Volume relationship of transactions introduced in 2009. For this development, we rely on the precise formulation of diffusion auto-induced regime-switching models presented in our previous work of 2020. The auto-induced [...] Read more.
In this work, we present a rigorous development of a model for the Price–Volume relationship of transactions introduced in 2009. For this development, we rely on the precise formulation of diffusion auto-induced regime-switching models presented in our previous work of 2020. The auto-induced regime-switching models referred to may be based on a finite set of stochastic differential equations (SDE)—all defined on the same bounded time interval—and a sequence of interlacing stopping times defined by the hitting threshold times of the trajectories of the solutions of the SDE. The coupling between price and volume—which we take as a proxy of liquidity—is assumed to be the following: the regime switching in the price variable occurs at the stopping times for which there is a change of region—in the product state space of price and liquidity—for the liquidity variable (and vice versa). The regimes may be defined parametrically—that is, the SDE coefficients keep the same functional form but with varying parameters—or the functional form of the SDE coefficients may change with each regime. By using the same noise source for both the price and the liquidity regime-switching models—volume (liquidity), which, in general, is not a tradable asset—we ensure that despite incorporating information on liquidity, the price part of the coupled model can be assumed to be arbitrage free and complete, allowing the pricing and hedging of derivatives in a simple way. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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26 pages, 5597 KiB  
Article
Macroeconomic Risks and Monetary Policy in Central European Countries: Parallels in the Czech Republic, Hungary, and Poland
by István Ábel and Pierre Siklos
Risks 2023, 11(11), 200; https://doi.org/10.3390/risks11110200 - 15 Nov 2023
Viewed by 2128
Abstract
Changes in interest rates, inflation, and exchange rates are the main components of macroeconomic risks (financial risks) in projects evaluation. However, the conduct of monetary policy as well as its impact on the economic environment is seldom considered as an important component of [...] Read more.
Changes in interest rates, inflation, and exchange rates are the main components of macroeconomic risks (financial risks) in projects evaluation. However, the conduct of monetary policy as well as its impact on the economic environment is seldom considered as an important component of macroeconomic risks. In this paper, we offer a simple framework to analyze the conduct of monetary policy. We examine the stabilizing properties of monetary policy, its impact, and the parallels in the monetary policy approaches taken in the Czech Republic, Hungary, and Poland until the pandemic. We provide a simple theoretical background to motivate the main elements of the debate and the choice of policy strategy. We then rationalize the adoption of a form of flexible inflation targeting (FIT). It is characterized by an explicit concern over exchange rates. The empirical evidence, consisting of calibrated and extended Taylor rules, together with local projections estimates, suggests that monetary policy has been practiced with considerable flexibility by all three central banks and has contributed to business cycle stabilization in the region. Most notably, the exchange rate plays an important role in the conduct of monetary policy. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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17 pages, 2980 KiB  
Article
Discovering Intraday Tail Dependence Patterns via a Full-Range Tail Dependence Copula
by Lei Hua
Risks 2023, 11(11), 195; https://doi.org/10.3390/risks11110195 - 11 Nov 2023
Viewed by 1711
Abstract
In this research, we employ a full-range tail dependence copula to capture the intraday dynamic tail dependence patterns of 30 s log returns among stocks in the US market in the year of 2020, when the market experienced a significant sell-off and a [...] Read more.
In this research, we employ a full-range tail dependence copula to capture the intraday dynamic tail dependence patterns of 30 s log returns among stocks in the US market in the year of 2020, when the market experienced a significant sell-off and a rally thereafter. We also introduce a model-based unified tail dependence measure to directly model and compare various tail dependence patterns. Using regression analysis of the upper and lower tail dependence simultaneously, we have identified some interesting intraday tail dependence patterns, such as interactions between the upper and lower tail dependence over time among growth and value stocks and in different market regimes. Our results indicate that tail dependence tends to peak towards the end of the regular trading hours, and, counter-intuitively, upper tail dependence tends to be stronger than lower tail dependence for short-term returns during a market sell-off. Furthermore, we investigate how the Fama–French five factors affect the intraday tail dependence patterns and provide plausible explanations for the occurrence of these phenomena. Among these five factors, the market excess return plays the most important role, and our study suggests that when there is a moderate positive excess return, both the upper and lower tails tend to reach their lowest dependence levels. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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25 pages, 857 KiB  
Article
Assessing the Impact of Credit Risk on Equity Options via Information Contents and Compound Options
by Federico Maglione and Maria Elvira Mancino
Risks 2023, 11(10), 183; https://doi.org/10.3390/risks11100183 - 20 Oct 2023
Viewed by 2438
Abstract
This work aims to develop a measure of how much credit risk is priced into equity options. Such a measure appears particularly appealing when applied to a portfolio of equity options, as it allows for the factoring in of firm-specific default dynamics, thus [...] Read more.
This work aims to develop a measure of how much credit risk is priced into equity options. Such a measure appears particularly appealing when applied to a portfolio of equity options, as it allows for the factoring in of firm-specific default dynamics, thus producing a comparable statistic across different equities. As a matter of fact, comparing options written on different equities based on their moneyness does offer much guidance in understanding which option offers a better hedging against default. Our newly-introduced measure aims to fulfil this gap: it allows us to rank options written on different names based on the amount of default risk they carry, incorporating firm-specific characteristics such as leverage and asset risk. After having computed this measure using data from the US market, several empirical tests confirm the economic intuition of puts being more sensitive to changes in the default risk as well as a good integration of the CDS and option markets. We further document cross-sectional sectorial differences based on the industry the companies operate in. Moreover, we show that this newly-introduced measure displays forecasting power in explaining future changes in the skew of long-term maturity options. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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