Journal Description
Risks
Risks
is an international, scholarly, peer-reviewed, open access journal for research and studies on insurance and financial risk management. Risks is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High visibility: indexed within Scopus, ESCI (Web of Science), EconLit, EconBiz, RePEc, and other databases.
- Journal Rank: CiteScore - Q1 (Economics, Econometrics and Finance (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20.4 days after submission; acceptance to publication is undertaken in 4.3 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers for a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done
Impact Factor:
2.2 (2022);
5-Year Impact Factor:
1.9 (2022)
Latest Articles
Optimising Portfolio Risk by Involving Crypto Assets in a Volatile Macroeconomic Environment
Risks 2024, 12(4), 68; https://doi.org/10.3390/risks12040068 - 17 Apr 2024
Abstract
Portfolio diversification is an accepted principle of risk management. When constructing an efficient portfolio, there are a number of asset classes to choose from. Financial innovation is expanding the range of instruments. In addition to traditional commodities and securities, other instruments have been
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Portfolio diversification is an accepted principle of risk management. When constructing an efficient portfolio, there are a number of asset classes to choose from. Financial innovation is expanding the range of instruments. In addition to traditional commodities and securities, other instruments have been added. These include cryptocurrencies. In our study, we seek to answer the question of what proportion of cryptocurrencies should be included alongside traditional instruments to optimise portfolio risk. We use VaR risk measures to optimise the process. Diversification opportunities are evaluated under normal return distributions, thick-tailed distributions, and asymmetric distributions. To answer our research questions, we have created a quantitative model in which we analysed the VaR of different portfolios, including crypto-diversified assets, using Monte Carlo simulations. The study database includes exchange rate data for two consecutive years. When selecting the periods under examination, it was important to compare favourable and less favourable periods from a macroeconomic point of view so that the study results can be interpreted as a stress test in addition to observing the diversification effect. The first period under examination is from 1 September 2020 to 31 August 2021, and the second from 1 September 2021 to 31 August 2022. Our research results ultimately confirm that including cryptoassets can reduce the risk of an investment portfolio. The two time periods examined in the simulation produced very different results. An analysis of the second period suggests that Bitcoin’s diversification ability has become significant in the unfolding market situation due to the Russian-Ukrainian war.
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(This article belongs to the Special Issue Risk Analysis in Financial Crisis and Stock Market)
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Risk Management in the Area of Bitcoin Market Development: Example from the USA
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Laeeq Razzak Janjua, Iza Gigauri, Agnieszka Wójcik-Czerniawska and Elżbieta Pohulak-Żołędowska
Risks 2024, 12(4), 67; https://doi.org/10.3390/risks12040067 - 15 Apr 2024
Abstract
This paper explores the relationship between Bitcoin returns, the consumer price index, and economic policy uncertainty. Employing the QARDL method, this study examines both short- and long-term dynamics between macroeconomic factors and Bitcoin returns. Our analysis of monthly time series data from January
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This paper explores the relationship between Bitcoin returns, the consumer price index, and economic policy uncertainty. Employing the QARDL method, this study examines both short- and long-term dynamics between macroeconomic factors and Bitcoin returns. Our analysis of monthly time series data from January 2011 to November 2023 reveals that volatile US economic policy indicators, such as high economic policy uncertainty, volatile inflation, and rising interest rates, have recently exerted a negative impact on Bitcoin returns. This study shows that these results are true not only for traditional money but also for cryptocurrencies such as Bitcoin, despite their cardinal features. Its decentralized nature, indicating that it has no physical representation, is not tied to any authority or national economy and relies on a complex algorithm to track transactions. Further, it yields volatile returns that depend on macroeconomic indicators.
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(This article belongs to the Special Issue Risk Management in Economics and Finance for Sustainable Development in the Digital Ecosystem)
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Quantum Computing Approach to Realistic ESG-Friendly Stock Portfolios
by
Francesco Catalano, Laura Nasello and Daniel Guterding
Risks 2024, 12(4), 66; https://doi.org/10.3390/risks12040066 - 12 Apr 2024
Abstract
Finding an optimal balance between risk and returns in investment portfolios is a central challenge in quantitative finance, often addressed through Markowitz portfolio theory (MPT). While traditional portfolio optimization is carried out in a continuous fashion, as if stocks could be bought in
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Finding an optimal balance between risk and returns in investment portfolios is a central challenge in quantitative finance, often addressed through Markowitz portfolio theory (MPT). While traditional portfolio optimization is carried out in a continuous fashion, as if stocks could be bought in fractional increments, practical implementations often resort to approximations, as fractional stocks are typically not tradeable. While these approximations are effective for large investment budgets, they deteriorate as budgets decrease. To alleviate this issue, a discrete Markowitz portfolio theory (DMPT) with finite budgets and integer stock weights can be formulated, but results in a non-polynomial (NP)-hard problem. Recent progress in quantum processing units (QPUs), including quantum annealers, makes solving DMPT problems feasible. Our study explores portfolio optimization on quantum annealers, establishing a mapping between continuous and discrete Markowitz portfolio theories. We find that correctly normalized discrete portfolios converge to continuous solutions as budgets increase. Our DMPT implementation provides efficient frontier solutions, outperforming traditional rounding methods, even for moderate budgets. Responding to the demand for environmentally and socially responsible investments, we enhance our discrete portfolio optimization with ESG (environmental, social, governance) ratings for EURO STOXX 50 index stocks. We introduce a utility function incorporating ESG ratings to balance risk, return and ESG friendliness, and discuss implications for ESG-aware investors.
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(This article belongs to the Special Issue Portfolio Theory, Financial Risk Analysis and Applications)
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Relationship between Occupational Pension, Corporate Social Responsibility (CSR), and Organizational Resilience: A Study on Listed Chinese Companies
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Hao Wang, Tao Zhang, Xi Wang and Jiansong Zheng
Risks 2024, 12(4), 65; https://doi.org/10.3390/risks12040065 - 09 Apr 2024
Abstract
Numerous researchers acknowledge that the occupational pension protects employees. However, in China, the total cost of occupational pensions is shared between employees and employers, representing a significant financial commitment. This study aimed to explore the effect of the occupational pension on corporate social
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Numerous researchers acknowledge that the occupational pension protects employees. However, in China, the total cost of occupational pensions is shared between employees and employers, representing a significant financial commitment. This study aimed to explore the effect of the occupational pension on corporate social responsibility (CSR) and organizational resilience. Drawing on insights from cost-stickiness and resource-based theories, we developed a model that elucidated the influence of occupational pensions on firms’ approaches to CSR within the context of COVID-19 and how this, in turn, impacted organizational resilience. This study categorized CSR into strategic and responsive activities, employing the concept of cost stickiness as a framework. We analyzed a sample of 34,145 observations from Chinese A-share listed companies spanning the period 2010–2023 to examine the influence of occupational pension adjustments on CSR strategies. The findings of this study revealed that the cost pressure associated with contributions to occupational pensions prompted firms to decrease their engagement in responsive CSR activities while enhancing their strategic CSR initiatives. Furthermore, it was observed that strategic CSR contributed to improved organizational resilience, whereas responsive CSR did not exhibit the same effect. The relationship between occupational pension contributions and CSR was found to be significantly and negatively moderated by factors such as the minimum wage and population aging. Conversely, the relationship between CSR and organizational resilience was significantly and positively moderated by digital transformation and marketing capabilities.
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(This article belongs to the Special Issue Life Insurance and Pensions: Latest Advances and Prospects)
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Asymptotic Methods for Transaction Costs
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Eberhard Mayerhofer
Risks 2024, 12(4), 64; https://doi.org/10.3390/risks12040064 - 04 Apr 2024
Abstract
We propose a general approximation method for the determination of optimal trading strategies in markets with proportional transaction costs, with a polynomial approximation of the residual value function. The method is exemplified by several problems, from optimally tracking benchmarks and hedging the log
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We propose a general approximation method for the determination of optimal trading strategies in markets with proportional transaction costs, with a polynomial approximation of the residual value function. The method is exemplified by several problems, from optimally tracking benchmarks and hedging the log contract to maximizing utility from terminal wealth. Strategies are also approximated by practically executable, discrete trades. We identify the necessary trade-off between the trading frequency and trade size to ensure satisfactory agreement with the theoretically optimal, continuous strategies of infinite activity.
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(This article belongs to the Special Issue Optimal Investment and Risk Management)
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Intangible Assets and Analysts’ Overreaction and Underreaction to Earnings Information: Empirical Evidence from Saudi Arabia
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Taoufik Elkemali
Risks 2024, 12(4), 63; https://doi.org/10.3390/risks12040063 - 02 Apr 2024
Abstract
Several prior studies indicate that financial analysts exhibit systematic underreaction to information; others illustrate systematic overreaction. We assume that cognitive biases influence analysts’ behavior and that these misreactions are not systematic, but they depend on the nature of news. As cognitive biases intensify
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Several prior studies indicate that financial analysts exhibit systematic underreaction to information; others illustrate systematic overreaction. We assume that cognitive biases influence analysts’ behavior and that these misreactions are not systematic, but they depend on the nature of news. As cognitive biases intensify in situations of high ambiguity, we distinguish between bad and good news and investigate the impact of intangible assets—synonymous with high uncertainty and risk—on financial analysts’ reactions. We explore the effect of information conveyed by prior-year earnings announcements on the current-year forecast error. Our findings in the Saudi financial market reveal a tendency for overreaction to positive prior-year earnings change (good performance) and positive prior-year forecast errors (good surprise). Conversely, there is an underreaction to the negative prior-year earnings change (bad performance) and negative prior-year forecast error (bad surprise). Notably, analysts exhibit systematic optimism rather than systematic underreaction or overreaction. The results also highlight that the simultaneous phenomena of overreaction and underreaction is more pronounced in high intangible asset firms compared to low intangible asset firms.
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(This article belongs to the Special Issue Optimal Investment and Risk Management)
Open AccessArticle
A Comparison of Generalised Linear Modelling with Machine Learning Approaches for Predicting Loss Cost in Motor Insurance
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Alinta Ann Wilson, Antonio Nehme, Alisha Dhyani and Khaled Mahbub
Risks 2024, 12(4), 62; https://doi.org/10.3390/risks12040062 - 31 Mar 2024
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This study explores the insurance pricing domain in the motor insurance industry, focusing on the creation of “technical models” which are essentially obtained after combining the frequency model (the expected number of claims per unit of exposure) and the severity model (the expected
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This study explores the insurance pricing domain in the motor insurance industry, focusing on the creation of “technical models” which are essentially obtained after combining the frequency model (the expected number of claims per unit of exposure) and the severity model (the expected amount per claim). Technical models are designed to predict the loss costs (the product of frequency and severity, i.e., the expected claim amount per unit of exposure) and this is a main factor that is taken into account for pricing insurance policies. Other factors for pricing include the company expenses, investments, reinsurance, underwriting, and other regulatory restrictions. Different machine learning methodologies, including the Generalised Linear Model (GLM), Gradient Boosting Machine (GBM), Artificial Neural Networks (ANN), and a unique hybrid model that combines GLM and ANN, were explored for creating the technical models. This study was conducted on the French Motor Third Party Liability datasets, “freMTPL2freq” and “freMTPL2sev” included in the R package CASdatasets. After building the aforementioned models, they were evaluated and it was observed that the hybrid model which combines GLM and ANN outperformed all other models. ANN also demonstrated better predictions closely aligning with the performance of the hybrid model. The better performance of neural network models points to the need for actuarial science and the insurance industry to look beyond traditional modelling methodologies like GLM.
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COVID-19 and Excess Mortality: An Actuarial Study
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Camille Delbrouck and Jennifer Alonso-García
Risks 2024, 12(4), 61; https://doi.org/10.3390/risks12040061 - 30 Mar 2024
Abstract
The study of mortality is an ever-active field of research, and new methods or combinations of methods are constantly being developed. In the actuarial domain, the study of phenomena disrupting mortality and leading to excess mortality, as in the case of COVID-19, is
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The study of mortality is an ever-active field of research, and new methods or combinations of methods are constantly being developed. In the actuarial domain, the study of phenomena disrupting mortality and leading to excess mortality, as in the case of COVID-19, is of great interest. Therefore, it is relevant to investigate the extent to which an epidemiological model can be integrated into an actuarial approach in the context of mortality. The aim of this project is to establish a method for the study of excess mortality due to an epidemic and to quantify these effects in the context of the insurance world to anticipate certain possible financial instabilities. We consider a case study caused by SARS-CoV-2 in Belgium during the year 2020. We propose an approach that develops an epidemiological model simulating excess mortality, and we incorporate this model into a classical approach to pricing life insurance products.
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(This article belongs to the Special Issue Extreme Events: Mortality Modelling and Insurance)
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Two-Population Mortality Forecasting: An Approach Based on Model Averaging
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Luca De Mori, Pietro Millossovich, Rui Zhu and Steven Haberman
Risks 2024, 12(4), 60; https://doi.org/10.3390/risks12040060 - 27 Mar 2024
Abstract
The analysis of residual life expectancy evolution at retirement age holds great importance for life insurers and pension schemes. Over the last 30 years, numerous models for forecasting mortality have been introduced, and those that allow us to predict the mortality of two
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The analysis of residual life expectancy evolution at retirement age holds great importance for life insurers and pension schemes. Over the last 30 years, numerous models for forecasting mortality have been introduced, and those that allow us to predict the mortality of two or more related populations simultaneously are particularly important. Indeed, these models, in addition to improving the forecasting accuracy overall, enable evaluation of the basis risk in index-based longevity risk transfer deals. This paper implements and compares several model-averaging approaches in a two-population context. These approaches generate predictions for life expectancy and the Gini index by averaging the forecasts obtained using a set of two-population models. In order to evaluate the eventual gain of model-averaging approaches for mortality forecasting, we quantitatively compare their performance to that of the individual two-population models using a large sample of different countries and periods. The results show that, overall, model-averaging approaches are superior both in terms of mean absolute forecasting error and interval forecast accuracy.
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(This article belongs to the Special Issue Advancement in Mortality Forecasting and Mortality/Longevity Risk Management)
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The Effect of Corporate Governance on the Degree of Agency Cost in the Korean Market
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Younghwan Lee and Ana Belén Tulcanaza-Prieto
Risks 2024, 12(4), 59; https://doi.org/10.3390/risks12040059 - 27 Mar 2024
Abstract
This study examines the relationship between corporate governance (CG) and agency costs using Korean market data, particularly for chaebol firms. The final sample includes 660 firm-year observations between 2016 and 2020 for Korean non-financial firms listed on the Korean Composite Stock Price Index
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This study examines the relationship between corporate governance (CG) and agency costs using Korean market data, particularly for chaebol firms. The final sample includes 660 firm-year observations between 2016 and 2020 for Korean non-financial firms listed on the Korean Composite Stock Price Index (KOSPI). This study employs an ordinary least-squares panel data regression model using two proxies for agency costs, namely, asset utilization ratio and operating expense ratio, and six CG individual metrics as independent variables (CG score, protection of shareholder rights, board structure, disclosure, audit organization, and managerial discretion and error management). We find that firms with high CG experience lower agency costs than those with low CG. Moreover, our evidence suggests that firms can decrease agency costs by improving the quality of CG. The results of our regression model also support the idea that CG is effective in reducing agency costs for chaebol firms but not for non-chaebol firms. Finally, our findings suggest that the implementation of effective CG mechanisms in firms might improve managerial behavior through better decision-making to maximize the value of firms.
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The Impact of Village Savings and Loan Associations as a Financial and Climate Resilience Strategy for Mitigating Food Insecurity in Northern Ghana
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Cornelius K. A. Pienaah and Isaac Luginaah
Risks 2024, 12(4), 58; https://doi.org/10.3390/risks12040058 - 25 Mar 2024
Abstract
In semi-arid Northern Ghana, smallholder farmers face food insecurity and financial risk due to climate change. In response, the Village Savings and Loan Association (VSLA) model, a community-led microfinance model, has emerged as a promising finance and climate resilience strategy. VSLAs offer savings,
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In semi-arid Northern Ghana, smallholder farmers face food insecurity and financial risk due to climate change. In response, the Village Savings and Loan Association (VSLA) model, a community-led microfinance model, has emerged as a promising finance and climate resilience strategy. VSLAs offer savings, loans, and other financial services to help smallholder farmers cope with climate risks. In northern Ghana, where formal financial banking is limited, VSLAs serve as vital financial resources for smallholder farmers. Nevertheless, it remains to be seen how VSLAs can bridge financial inclusion and climate resilience strategies to address food insecurity. From a sustainable livelihoods framework (SLF) perspective, we utilized data from a cross-sectional survey of 517 smallholder farmers in northern Ghana’s Upper West Region to investigate how VSLAs relate to food insecurity. Results from an ordered logistic regression show that households with membership in a VSLA were less likely to experience severe food insecurity (OR = 0.437, p < 0.01). In addition, households that reported good resilience, owned land, had higher wealth, were female-headed, and made financial decisions jointly were less likely to experience severe food insecurity. Also, spending time accessing the market increases the risk of severe food insecurity. Despite the challenges of the VSLA model, these findings highlight VSLAs’ potential to mitigate food insecurity and serve as a financially resilient and climate-resilient strategy in resource-poor contexts like the UWR and similar areas in Sub-Saharan Africa. VSLAs could contribute to achieving SDG2, zero hunger, and SDG13, climate action. However, policy interventions are necessary to support and scale VSLAs as a sustainable development and food security strategy in vulnerable regions.
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(This article belongs to the Special Issue Climate Risks: Business Scenarios and Financial Implications)
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Adding Shocks to a Prospective Mortality Model
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Frédéric Planchet and Guillaume Gautier de La Plaine
Risks 2024, 12(3), 57; https://doi.org/10.3390/risks12030057 - 20 Mar 2024
Abstract
This work proposes a simple model to take into account the annual volatility of the mortality level observed on the scale of a country like France in the construction of prospective mortality tables. By assigning a frailty factor to a basic hazard function,
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This work proposes a simple model to take into account the annual volatility of the mortality level observed on the scale of a country like France in the construction of prospective mortality tables. By assigning a frailty factor to a basic hazard function, we generalise the Lee–Carter model. The impact on prospective life expectancies and capital requirements in the context of a life annuity scheme is analysed in detail.
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(This article belongs to the Special Issue Advancement in Mortality Forecasting and Mortality/Longevity Risk Management)
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Exploring Systemic Risk Dynamics in the Chinese Stock Market: A Network Analysis with Risk Transmission Index
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Xiaowei Zeng, Yifan Hu, Chengjun Pan and Yanxi Hou
Risks 2024, 12(3), 56; https://doi.org/10.3390/risks12030056 - 20 Mar 2024
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Systemic risk refers to the potential for a disruption in one part of a financial system to trigger a cascade of adverse effects, impacting the functioning of the system. Despite the progress on novel systemic risk measures, research on dynamics of systemic risk
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Systemic risk refers to the potential for a disruption in one part of a financial system to trigger a cascade of adverse effects, impacting the functioning of the system. Despite the progress on novel systemic risk measures, research on dynamics of systemic risk network structure and its community effect is still in its initial state. In this study, we utilize price data from 107 representative Chinese stocks spanning the period from 2017 to 2022. A systemic risk network is derived from the Risk Transmission Index based on TENET and the QR–Lasso model. By utilizing DBSCAN, HITS and community detection algorithms on the network, we aim to propose a more suitable definition of systemically important companies, explore the interrelationships between companies, and discuss its plausible reasons for dynamics structural changes. The empirical findings demonstrate a substantial involvement of insurance companies in both contributing to and receiving systemic risk within the analyzed context. We identify prominent risk output and input centers, and emphasize the profound impact of the COVID-19 pandemic on the dynamics of systemic risk.
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(This article belongs to the Special Issue Risk Analysis in Financial Crisis and Stock Market)
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Capital Structure Models and Contingent Convertible Securities
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Di Meng, Adam Metzler and R. Mark Reesor
Risks 2024, 12(3), 55; https://doi.org/10.3390/risks12030055 - 18 Mar 2024
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We implemented a methodology to calibrate capital structure models for banks that have issued contingent convertible securities (CoCos). Typical studies involving capital structure model calibration focus on non-financial firms as they have lower leverage and no contingent convertible securities. From a theoretical perspective,
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We implemented a methodology to calibrate capital structure models for banks that have issued contingent convertible securities (CoCos). Typical studies involving capital structure model calibration focus on non-financial firms as they have lower leverage and no contingent convertible securities. From a theoretical perspective, we found that jumps in the asset value process were necessary to obtain a satisfactory fit to the market data. In practice, contingent capital conversion triggers are discretionary, and there is considerable uncertainty around when regulators are likely to enforce conversion. The market-implied conversion triggers we obtain indicate that the market expects regulators to enforce conversion while the issuing bank is a going concern, as opposed to a gone concern. This fact is presumably of interest to potential dealers, regulators, issuers, and investors.
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(This article belongs to the Special Issue Risk Analysis in Financial Crisis and Stock Market)
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Shareholders in the Driver’s Seat: Unraveling the Impact on Financial Performance in Latvian Fintech Companies
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Ramona Rupeika-Apoga, Stefan Wendt and Victoria Geyfman
Risks 2024, 12(3), 54; https://doi.org/10.3390/risks12030054 - 18 Mar 2024
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Fintech companies are relatively young and operate in a rapidly evolving and ever-changing industry, which makes it important to understand how different factors, including shareholder presence in management roles, affect their performance. This study investigates the impact of shareholder presence in director and
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Fintech companies are relatively young and operate in a rapidly evolving and ever-changing industry, which makes it important to understand how different factors, including shareholder presence in management roles, affect their performance. This study investigates the impact of shareholder presence in director and manager positions on the financial performance of Latvian fintechs. Our investigation centers on essential financial ratios, including Return on Assets, Return on Equity, Profit Margin, Liquidity Ratio, Current Ratio, and Solvency Ratio. Our findings suggest that the presence of shareholders in director and manager roles does not significantly affect the financial performance of fintech companies. Although the statistical analysis did not yield significant results, it is important to consider additional insights garnered from Cliff’s Delta effect sizes. Specifically, despite the lack of statistical significance, practical significance indicates that fintech companies in which directors and managers are shareholders show slightly better performance than other fintech companies. Beyond shedding light on the intricacies of corporate governance in the fintech sector, this research serves as a valuable resource for investors, stakeholders, and fellow researchers seeking to understand the impact of shareholder presence in director and manager roles on the financial performance of fintechs.
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(This article belongs to the Special Issue Financial Analysis, Corporate Finance and Risk Management)
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A Quantitative Comparison of Mortality Models with Jumps: Pre- and Post-COVID Insights on Insurance Pricing
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Şule Şahin and Selin Özen
Risks 2024, 12(3), 53; https://doi.org/10.3390/risks12030053 - 14 Mar 2024
Abstract
Population events such as natural disasters, pandemics, extreme weather, and wars might cause jumps that have an immediate impact on mortality rates. The recent COVID-19 pandemic has demonstrated that these events should not be treated as nonrepetitive exogenous interventions. Therefore, mortality models incorporating
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Population events such as natural disasters, pandemics, extreme weather, and wars might cause jumps that have an immediate impact on mortality rates. The recent COVID-19 pandemic has demonstrated that these events should not be treated as nonrepetitive exogenous interventions. Therefore, mortality models incorporating jump effects are particularly important to capture the adverse mortality shocks. The mortality models with jumps, which we consider in this study, differ in terms of the duration of the jumps–transitory or permanent–the frequency of the jumps, and the size of the jumps. To illustrate the effect of the jumps, we also consider benchmark mortality models without jump effects, such as the Lee-Carter model, Renshaw and Haberman model and Cairns-Blake-Dowd model. We discuss the performance of all the models by analysing their ability to capture the mortality deterioration caused by COVID-19. We use data from different countries to simulate the mortality rates for the pandemic and post-pandemic years and examine their accuracy in forecasting the mortality jumps due to the pandemic. Moreover, we also examine the jump-free and jump models in terms of their impact on insurance pricing, specifically term annuity and life insurance present values calibrated for both pre- and post-COVID data.
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(This article belongs to the Special Issue Extreme Events: Mortality Modelling and Insurance)
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Unveiling Outperformance: A Portfolio Analysis of Top AI-Related Stocks against IT Indices and Robotics ETFs
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Ali Trabelsi Karoui, Sonia Sayari, Wael Dammak and Ahmed Jeribi
Risks 2024, 12(3), 52; https://doi.org/10.3390/risks12030052 - 13 Mar 2024
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In this study, we delve into the financial market to compare the performance of prominent AI and robotics-related stocks against traditional IT indices, such as the Nasdaq, and specialized AI and robotics ETFs. We evaluate the role of these stocks in diversifying portfolios,
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In this study, we delve into the financial market to compare the performance of prominent AI and robotics-related stocks against traditional IT indices, such as the Nasdaq, and specialized AI and robotics ETFs. We evaluate the role of these stocks in diversifying portfolios, analyzing their return potential and risk profiles. Our analysis includes various investment scenarios, focusing on common AI-related stocks in the United States. We explore the influence of risk management strategies, ranging from “buy and hold” to daily rebalancing, on AI stock portfolios. This involves investigating long-term strategies like buy and hold, as well as short-term approaches, such as daily rebalancing. Our findings, covering the period from 30 April 2021, to 15 September 2023, show that AI-related stocks have not only outperformed in recent years but also highlight the growing “AI bubble” and the increasing significance of AI in investment decisions. The study reveals that these stocks have delivered superior performance, as indicated by metrics like Sharpe and Treynor ratios, providing insights into market trends and financial returns in the technology and robotics sectors. The results are particularly relevant for investors and traders in the AI sector, offering a balanced view of potential returns against the risks in this rapidly evolving market. This paper adds to the financial market literature by demonstrating that investing in emerging trends, such as AI, can be more advantageous in the short term compared to traditional markets like the Nasdaq.
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Open AccessArticle
Assessing Financial Stability in Turbulent Times: A Study of Generalized Autoregressive Conditional Heteroskedasticity-Type Value-at-Risk Model Performance in Thailand’s Transportation Sector during COVID-19
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Danai Likitratcharoen and Lucksuda Suwannamalik
Risks 2024, 12(3), 51; https://doi.org/10.3390/risks12030051 - 13 Mar 2024
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The Value-at-Risk (VaR) metric serves as a pivotal tool for quantifying market risk, offering an estimation of potential investment losses. Predominantly employed within financial sectors, it aids in adhering to regulatory mandates and in devising capital reserve strategies. Nonetheless, the predictive precision of
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The Value-at-Risk (VaR) metric serves as a pivotal tool for quantifying market risk, offering an estimation of potential investment losses. Predominantly employed within financial sectors, it aids in adhering to regulatory mandates and in devising capital reserve strategies. Nonetheless, the predictive precision of VaR models frequently faces scrutiny, particularly during crises and heightened uncertainty phases. Phenomena like volatility clustering impinge on the accuracy of these models. To mitigate such constraints, conditional volatility models are integrated to augment the robustness and adaptability of VaR approaches. This study critically evaluates the efficacy of GARCH-type VaR models within the transportation sector amidst the Thai stock market’s volatility during the COVID-19 pandemic. The dataset encompasses daily price fluctuations in the Transportation Sector index (TRANS), the Service Industry index (SERVICE), and 17 pertinent stocks within the Stock Exchange of Thailand, spanning from 28 December 2018 to 28 December 2023, thereby encapsulating the pandemic era. The employed GARCH-type VaR models include GARCH (1,1) VaR, ARMA (1,1)—GARCH (1,1) VaR, GARCH (1,1)—M VaR, IGARCH (1,1) VaR, EWMA VaR, and csGARCH (1,1) VaR. These are juxtaposed with more traditional, less computationally intensive models like the Historical Simulation VaR and Delta Normal VaR. The backtesting methodologies encompass Kupiec’s POF test, the Independence Test, and Christoffersen’s Interval Forecast test. Intriguingly, the findings reveal that the Historical Simulation VaR model surpasses GARCH-type VaR models in failure rate accuracy. Within the GARCH-type category, the EWMA VaR model exhibited superior failure rate accuracy. The csGARCH (1,1) VaR and EWMA VaR models emerged as notably robust. These findings bear significant implications for managerial decision-making in financial risk management.
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Open AccessArticle
Value-at-Risk Effectiveness: A High-Frequency Data Approach with Semi-Heavy Tails
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Mario Ivan Contreras-Valdez, Sonal Sahu, José Antonio Núñez-Mora and Roberto Joaquín Santillán-Salgado
Risks 2024, 12(3), 50; https://doi.org/10.3390/risks12030050 - 13 Mar 2024
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In the broader landscape of cryptocurrency risk management, this study delves into the nuanced estimation of Value-at-Risk (VaR) for a uniformly weighted portfolio of cryptocurrencies, employing the bivariate Normal Inverse Gaussian distribution renowned for its semi-heavy tails. Utilizing high-frequency data spanning between 1
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In the broader landscape of cryptocurrency risk management, this study delves into the nuanced estimation of Value-at-Risk (VaR) for a uniformly weighted portfolio of cryptocurrencies, employing the bivariate Normal Inverse Gaussian distribution renowned for its semi-heavy tails. Utilizing high-frequency data spanning between 1 January 2017 and 25 October 2022, with a primary focus on Bitcoin and Ethereum, our research seeks to accentuate the resilience of VaR methodology as a paramount risk assessment tool. The essence of our investigation lies in advancing the comprehension of VaR accuracy by quantitatively comparing the observed returns of both cryptocurrencies with their corresponding estimated values, with a central theme being the endorsement of the Normal Inverse Gaussian distribution as a potent model for risk measurement, particularly in the domain of high-frequency data. To bolster the statistical reliability of our results, we adopt a forward test methodology, showcasing not only a contribution to the evolution of risk assessment techniques in Finance but also underscoring the practicality of sophisticated distributional models in econometrics. Our findings not only contribute to the refinement of risk assessment methods but also highlight the applicability of such models in precisely modeling and forecasting financial risk within the dynamic realm of cryptocurrencies, epitomized by the case study of Bitcoin and Ethereum.
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Open AccessArticle
The Role of Longevity-Indexed Bond in Risk Management of Aggregated Defined Benefit Pension Scheme
by
Xiaoyi Zhang, Yanan Li and Junyi Guo
Risks 2024, 12(3), 49; https://doi.org/10.3390/risks12030049 - 06 Mar 2024
Abstract
Defined benefit (DB) pension plans are a primary type of pension schemes with the sponsor assuming most of the risks. Longevity-indexed bonds have been used to hedge or transfer risks in pension plans. Our objective is to study an aggregated DB pension plan’s
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Defined benefit (DB) pension plans are a primary type of pension schemes with the sponsor assuming most of the risks. Longevity-indexed bonds have been used to hedge or transfer risks in pension plans. Our objective is to study an aggregated DB pension plan’s optimal risk management problem focusing on minimizing the solvency risk over a finite time horizon and to investigate the investment strategies in a market, comprising a longevity-indexed bond and a risk-free asset, under stochastic nominal interest rates. Using the dynamic programming technique in the stochastic control problem, we obtain the closed-form optimal investment strategy by solving the corresponding Hamilton–Jacobi–Bellman (HJB) equation. In addition, a comparative analysis implicates that longevity-indexed bonds significantly reduce solvency risk compared to zero-coupon bonds, offering a strategic advantage in pension fund management. Besides the closed-form solution and the comparative study, another novelty of this study is the extension of actuarial liability (AL) and normal cost (NC) definitions, and we introduce the risk neutral valuation of liabilities in DB pension scheme with the consideration of mortality rate.
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(This article belongs to the Special Issue Optimal Investment and Risk Management)
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