Risks

16 pages, 498 KiB

Open AccessArticle

Can Unrealistic Optimism Among Consumers Precipitate Economic Recessions?

by Hyun-Soo Doh and Jiahao Pan

Risks 2025, 13(5), 83; https://doi.org/10.3390/risks13050083 (registering DOI) - 26 Apr 2025

This paper examines the macroeconomic implications of unrealistic optimism, a psychological bias that has been largely overlooked in economic models. While traditional models often link optimism to speculative bubbles and excessive risk taking, this study challenges that view by demonstrating that unrealistic optimism [...] Read more.

This paper examines the macroeconomic implications of unrealistic optimism, a psychological bias that has been largely overlooked in economic models. While traditional models often link optimism to speculative bubbles and excessive risk taking, this study challenges that view by demonstrating that unrealistic optimism may rather accelerate recessions. Specifically, we develop a model in which consumers, under the influence of unrealistic optimism, believe that negative aggregate shocks will affect others but not themselves. This misjudgment leads to a premature fall in output prices, reducing production and triggering recessions. Additionally, we show that government intervention, when optimally timed, can mitigate the adverse effects of unrealistic optimism, offering important policy implications for stabilizing economies. By highlighting the possibility of optimism-induced downturns, this paper provides new insights into behavioral macroeconomics and offers a novel perspective on policy design. Full article

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28 pages, 2632 KiB

Open AccessFeature PaperArticle

A Neural Network Approach for Pricing Correlated Health Risks

by Alessandro G. Laporta, Susanna Levantesi and Lea Petrella

Risks 2025, 13(5), 82; https://doi.org/10.3390/risks13050082 - 24 Apr 2025

Abstract

In recent years, the actuarial literature involving machine learning in insurance pricing has flourished. However, most actuarial machine learning research focuses on property and casualty insurance, while using such techniques in health insurance is yet to be explored. In this paper, we discuss [...] Read more.

In recent years, the actuarial literature involving machine learning in insurance pricing has flourished. However, most actuarial machine learning research focuses on property and casualty insurance, while using such techniques in health insurance is yet to be explored. In this paper, we discuss the use of neural networks to set the price of health insurance coverage following the structure of a classical frequency-severity model. In particular, we propose negative multinomial neural networks to jointly model the frequency of possibly correlated medical claims and Gamma neural networks to estimate the expected claim severity. Using a case study based on real-world health insurance data, we highlight the overall better performance of the neural network models with respect to more established regression models, both in terms of accuracy (frequency models achieve an average out-of-sample deviance of 8.54 compared to 8.61 for classical regressions) and risk diversification, as indicated by the ABC lift metric, which is

5.62 \times 10^{- 3}

for neural networks versus

8.27 \times 10^{- 3}

for traditional models. Full article

(This article belongs to the Special Issue Market-Consistent Actuarial Valuation and Risk-Based Capital Assessment)

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50 pages, 6937 KiB

Open AccessArticle

The Impact of Economic Policies on Housing Prices: Approximations and Predictions in the UK, the US, France, and Switzerland from the 1980s to Today

by Nicolas Houlié

Risks 2025, 13(5), 81; https://doi.org/10.3390/risks13050081 (registering DOI) - 23 Apr 2025

Abstract

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, [...] Read more.

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine learning allows for the discrimination of two periods within the dataset. First, and up to 2015, I show that, although the US Treasury rates level is the most critical parameter to explain the change of house-price indices, other macroeconomic factors (e.g., consumer price indices) are essential to include in the modeling because they highlight the degree of openness of an economy and the contribution of the economic context to price changes. Second, and for the period from 2015 to today, I show that, to explain the most recent price evolution, it is necessary to include the datasets of the European Central Bank programs, which were designed to support the economy since the beginning of the 2010s. Indeed, unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models’ relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties. Full article

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12 pages, 2274 KiB

Open AccessArticle

A New Approach on Country Risk Monitoring

by Christos E. Kountzakis and Christos Floros

Risks 2025, 13(5), 80; https://doi.org/10.3390/risks13050080 - 22 Apr 2025

Abstract

Most of indexes regarding Credit Rating of the national debt bonds are associated to Gross National Product, which involves the well-known Keynesian Multiplicator of the IS-LM Equilibrium. Specifically, a common way of Sovereign Debt evaluation is its percentage of the Gross National Product [...] Read more.

Most of indexes regarding Credit Rating of the national debt bonds are associated to Gross National Product, which involves the well-known Keynesian Multiplicator of the IS-LM Equilibrium. Specifically, a common way of Sovereign Debt evaluation is its percentage of the Gross National Product in terms of a spot value. Another index is the spot value of the percentage of the annual interest rate payments of the state to the owners of sovereign debt. These indexes provide an inefficient evaluation of the national debt and moreover they are sensitive in their calculative aspect. Hence, we propose another index of national debt evaluation, which is more realistic, since public debt is a part of the balance sheet of the state itself. Moreover, this index may be translated into growth variables of the national economy. Since Gross National Product relies on consumption of the Economy, more consumption implies an ’illusion’ about sovereign debt. On the other hand, this index has limits to its credibility because it depends on the size of the annual investments. Full article

(This article belongs to the Special Issue Computational Methods and Models in the Financial Risk Management Process)

22 pages, 600 KiB

Open AccessArticle

Exploring the Principle of Multi-Dimensional Risk Analysis and a Case Study in Two-Dimensional Risk

by Yundong Huang

Risks 2025, 13(4), 79; https://doi.org/10.3390/risks13040079 - 21 Apr 2025

Abstract

By examining the significant flaws in multivariate risk analysis and integrated risk analysis, this article introduces a new approach to evaluating the total risk within complex risk systems: the principle of multi-dimensional risk (MDR) analysis. Under this framework, the scope of each individual [...] Read more.

By examining the significant flaws in multivariate risk analysis and integrated risk analysis, this article introduces a new approach to evaluating the total risk within complex risk systems: the principle of multi-dimensional risk (MDR) analysis. Under this framework, the scope of each individual risk is first defined, and the risk-bearing entity is identified. Each risk is then analyzed independently, and the results are subsequently integrated to provide a comprehensive view of MDR. Multivariate risk analysis becomes increasingly impractical as the number of factors grows, due to the correspondingly large sample size required—often unattainable in real-world conditions. Integrated risk analysis methods, such as weighted combinations and Copula techniques, are heavily influenced by subjective factors, which compromise the reliability of their results. In contrast, MDR analysis involves fewer variables per individual risk, reducing the sample size requirement and making data collection more feasible. Individual risks can be quantified using objective physical indicators such as economic loss or physical injury, enabling more accurate calculations of the total risk across the system. A case study involving two-dimensional risks—flood and earthquake—demonstrated that these events often have vastly different occurrence cycles. When these risks are entangled in conventional analysis, the resulting annual total risk value can be severely distorted. By analyzing individual risks separately, maintaining the focus on overall system risk, and treating the total risk as an MDR problem, a more reliable foundation for policy-making and risk management can be established. There are at least three types of MDR relationships: independent, compounding, and negatively correlated. As a result, no universal MDR analysis model exists. Full article

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31 pages, 867 KiB

Open AccessArticle

Investor Psychology in the Bangladesh Equity Market: An Examination of Herding Behavior Across Diverse Market States

by Muhammad Enamul Haque and Mahmood Osman Imam

Risks 2025, 13(4), 78; https://doi.org/10.3390/risks13040078 - 17 Apr 2025

Abstract

The results reveal significant evidence of herding in the overall, bearish, and extended crisis market phases during extreme downturns, while the magnitude of market returns in the tail distribution is considered. Asymmetric herding behavior is more pronounced and prevalent, conditioned by market dimensions [...] Read more.

The results reveal significant evidence of herding in the overall, bearish, and extended crisis market phases during extreme downturns, while the magnitude of market returns in the tail distribution is considered. Asymmetric herding behavior is more pronounced and prevalent, conditioned by market dimensions like return direction, trading volume, and volatility, with CSSD proving more effective than CSAD in detecting asymmetric patterns. Notably, herding strongly appears in the COVID-19 market during periods of abnormally high market volatility, reflecting heightened market sentiment. Applying Dow Theory to delineate bull and bear market phases significantly improved the methodological complexity and analytical depth related to herding behavior. These findings suggest policy implications for regulators and market participants in minimizing herding effects to create an efficient market environment through enhanced market surveillance, improved investor education, and the use of advanced technologies. Full article

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29 pages, 1456 KiB

Open AccessArticle

Inter-Market Mean and Volatility Spillover Dynamics Between Cryptocurrencies and an Emerging Stock Market: Evidence from Thailand and Sectoral Analysis

by Yanjia Zhang, Shih-tse Lo and Dhanoos Sutthiphisal

Risks 2025, 13(4), 77; https://doi.org/10.3390/risks13040077 - 15 Apr 2025

Abstract

The increasing interaction between the equity market and cryptocurrencies has raised concerns about volatility spillovers; however, empirical evidence about sectoral-specific spillover effects in emerging markets is scarce and hard to find. Existing research mainly concentrates on developed markets and aggregate equity indices, leaving [...] Read more.

The increasing interaction between the equity market and cryptocurrencies has raised concerns about volatility spillovers; however, empirical evidence about sectoral-specific spillover effects in emerging markets is scarce and hard to find. Existing research mainly concentrates on developed markets and aggregate equity indices, leaving a research gap in comprehending how sectoral indices variations impact market interactions in developing financial markets like Thailand. This article investigates the mean and volatility spillover effects between the Thai stock market and leading cryptocurrencies from April 2019 to April 2024. Applying bivariate VAR (1)-BEKK-GARCH (1,1) with an asymmetry model, this study examines the aggregate and sectoral-specific mean and volatility spillovers across major Thai stock market sectors. The findings reveal the significant mean spillover effect from cryptocurrencies to the Thai stock market with sectoral variation, while sectors such as industrials and financials exerted significant linkages, and the agricultural and food sector remains unaffected. Additionally, volatility spillovers were predominantly transmitted from the Thai equity market to cryptocurrency. Moreover, asymmetry effects were observed, with the asymmetry effects mainly transmitted from the Thai equity market to cryptocurrency. These findings provide critical insights for both individual and institutional investors on risk management and portfolio diversification while also helping policymakers with guidance on regulatory measures to mitigate systemic risks in emerging financial markets. Full article

(This article belongs to the Special Issue Applied Econometrics and International Finance: Analysis, Modeling, and Development)

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28 pages, 4546 KiB

Open AccessArticle

A Study on the Topological Insights and Network Visualization Mapping of the Indian Equity Market

by Biplab Bhattacharjee and Moinak Maiti

Risks 2025, 13(4), 76; https://doi.org/10.3390/risks13040076 - 14 Apr 2025

Abstract

The primary objective of this empirical study is to investigate the Indian equity market network by analyzing its topological properties using the disparity filtering technique, and a minimum spanning tree. It investigates the backbone structure of the reduced weighted equity network and highlights [...] Read more.

The primary objective of this empirical study is to investigate the Indian equity market network by analyzing its topological properties using the disparity filtering technique, and a minimum spanning tree. It investigates the backbone structure of the reduced weighted equity network and highlights the sector-based cluster formation. This study also examines the relative importance of each sector by utilizing different key network metrics, with comparative analysis against other emerging markets. It observes a high sector-specific dominance, power imbalance, disparity, and risk concentration in the healthcare and technology sectors. It also finds that fast-moving consumer goods and the healthcare sector can play important roles in maintaining economic stability, public health, and social wellbeing. The findings of this study are highly useful in understanding the market structure, risk management, and investment decisions in the emerging market context of India. Full article

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16 pages, 340 KiB

Open AccessArticle

The Poverty Alleviation Role of the “Insurance+Futures” Pattern—Evidence from 10 Chinese Provinces

by Jinhong Han

Risks 2025, 13(4), 75; https://doi.org/10.3390/risks13040075 - 14 Apr 2025

Abstract

This paper sorts out the poverty reduction mechanism of the “insurance+futures” pattern and uses actual data from the top 10 provinces in China with the highest underwriting value in the “insurance+futures” pattern since 2016 to test it by a panel data model. The [...] Read more.

This paper sorts out the poverty reduction mechanism of the “insurance+futures” pattern and uses actual data from the top 10 provinces in China with the highest underwriting value in the “insurance+futures” pattern since 2016 to test it by a panel data model. The results show that the “insurance+futures” pattern has a significant effect on reducing poverty. By replacing the proxy variable for the dependent variable and changing the samples for testing, the results remained significantly valid, demonstrating the robustness of the conclusion. Full article

13 pages, 696 KiB

Open AccessArticle

Fuzzy Non-Payment Risk Management Rooted in Optimized Household Consumption Units

by Gregorio Izquierdo Llanes and Antonio Salcedo

Risks 2025, 13(4), 74; https://doi.org/10.3390/risks13040074 - 11 Apr 2025

Abstract

Traditionally, business risk management models have not taken into consideration household composition for the purposes of credit granting or project financing in order to manage the risk of default. In this research, an improvement in the risk management model was obtained by introducing [...] Read more.

Traditionally, business risk management models have not taken into consideration household composition for the purposes of credit granting or project financing in order to manage the risk of default. In this research, an improvement in the risk management model was obtained by introducing household composition as a new exogenous variable. With the application of generalized reduced gradient nonlinear optimization modeling, improved consumption units are determined according to the different types of household size and the age of their members. Estimated household economies of scale show a consistent pattern even in the year 2020, corresponding with the COVID-19 outbreak. Thus, an adjusted estimation of the household equivalized disposable income is obtained. Based on this more accurate equivalized income estimation, acceptable debt levels can be determined. The estimation of probabilities of default allows the household risk of default to be managed. In this way, a novel model is proposed by incorporating household composition into credit risk evaluation using fuzzy clustering and optimization techniques. Companies can assess the expected loss of a credit exposure through a model that can help them in the process of making evidence-informed decisions. Full article

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28 pages, 527 KiB

Open AccessArticle

A Multistate Analysis of Policyholder Behaviour in Life Insurance—Lasso-Based Modelling Approaches

by Lucas Reck, Johannes Schupp and Andreas Reuß

Risks 2025, 13(4), 73; https://doi.org/10.3390/risks13040073 - 9 Apr 2025

Abstract

Holders of life insurance policies can exercise various options that lead to contract modifications, e.g., full surrender, partial surrender, and paid-up and dynamic premium increase options. Transitions between these contract states materially affect (current and future) cash flows and thus represent a serious [...] Read more.

Holders of life insurance policies can exercise various options that lead to contract modifications, e.g., full surrender, partial surrender, and paid-up and dynamic premium increase options. Transitions between these contract states materially affect (current and future) cash flows and thus represent a serious source of uncertainty for an insurance company. It is common practice to determine best-estimate assumptions for these transitions independently, i.e., without considering joint determinants of the different aspects of policyholder behaviour. The recent literature also incorporates multistate classical statistical models. Our paper shows how consistent best-estimate transition rates for multiple status transitions can be derived using data science methods. More specifically, we extend existing multivariate approaches based on established statistical models (generalised linear models) with the Lasso method, such that the key drivers for each transition can be identified automatically. We discuss the performance, the complexity and the practical applicability of the different modelling approaches based on data from a European insurer. Full article

(This article belongs to the Special Issue Statistical Models for Insurance)

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21 pages, 999 KiB

Open AccessArticle

Can Environmental Variables Predict Cryptocurrency Returns? Evidence from Bitcoin, Ethereum, and Tether Using a Time-Varying Coefficients Vector Autoregression Model

by Kamel Touhami, Ilyes Abidi, Mariem Nsaibi and Maissa Mejri

Risks 2025, 13(4), 72; https://doi.org/10.3390/risks13040072 - 7 Apr 2025

Abstract

This study investigates the impact of environmental variables, such as carbon emissions and temperature anomalies, on cryptocurrency returns. While existing research has primarily focused on economic and financial determinants, the influence of environmental factors remains underexplored. Using Dynamic Conditional Correlation GARCH (DCC-GARCH) and [...] Read more.

This study investigates the impact of environmental variables, such as carbon emissions and temperature anomalies, on cryptocurrency returns. While existing research has primarily focused on economic and financial determinants, the influence of environmental factors remains underexplored. Using Dynamic Conditional Correlation GARCH (DCC-GARCH) and Time-Varying Coefficients Vector Autoregression (TVC-VAR) models, this study provides empirical evidence that environmental variables significantly affect the volatility and returns of Bitcoin, Ethereum, and Tether. The results show that Bitcoin and Ethereum are highly sensitive to CO₂ emissions and temperature fluctuations, while Tether demonstrates a more moderate response. Moreover, the impact of these environmental factors evolves over time, underscoring their dynamic nature in cryptocurrency valuation. These findings highlight the importance of incorporating environmental variables into forecasting models to enhance risk management and investment strategies. This study contributes to the literature by bridging the gap between environmental concerns and cryptocurrency market behavior, offering valuable insights for investors, regulators, and policymakers. Full article

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19 pages, 784 KiB

Open AccessArticle

Determinants of Firms’ Propensity to Use Intercorporate Loans: Empirical Evidence from India

by Biswajit Ghose, Prasenjit Roy, Yeshi Ngima, Kiran Gope, Pankaj Kumar Tyagi, Premendra Kumar Singh and Asokan Vasudevan

Risks 2025, 13(4), 71; https://doi.org/10.3390/risks13040071 - 2 Apr 2025

Abstract

Several studies have investigated the determinants of firms’ capital structure choices. Though an intercorporate loan is an essential source of corporate debt, there are no studies that examine the determinants of firms’ preference to use the intercorporate loan as a source of debt. [...] Read more.

Several studies have investigated the determinants of firms’ capital structure choices. Though an intercorporate loan is an essential source of corporate debt, there are no studies that examine the determinants of firms’ preference to use the intercorporate loan as a source of debt. This study examines the relevance of the conventional capital structure determinants in explaining firms’ tendency to use intercorporate loans. The study is based on a dataset of 53,112 firm-year observations comprising 3739 non-financial listed Indian firms for 21 years from 2002 to 2022. The random effect logistic regression model is used to investigate the objectives. The conventional capital structure determinants are relevant in explaining firms’ decisions to use intercorporate loans. Firm size, asset tangibility, and earnings volatility favorably influence the tendency to use intercorporate loans, whereas profitability, growth, uniqueness, dividend payment, ownership concentration, and foreign promoter holdings adversely affect the same. The results reveal that the influence of firm size, uniqueness, earnings volatility, and ownership concentration are not unidirectional for group-affiliated and standalone firms. The findings are mostly consistent with the arguments of conventional capital structure theories. The results of this study will be pragmatic for financial managers in their capital structure decisions. Full article

(This article belongs to the Special Issue Valuation Risk and Asset Pricing)

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27 pages, 497 KiB

Open AccessArticle

Minimal Entropy and Entropic Risk Measures: A Unified Framework via Relative Entropy

by Moritz Sohns

Risks 2025, 13(4), 70; https://doi.org/10.3390/risks13040070 - 1 Apr 2025

Abstract

We introduce a new coherent risk measure, the minimal-entropy risk measure, which is built on the minimal-entropy

σ

-martingale measure—a concept inspired by the well-known minimal-entropy martingale measure used in option pricing. While the minimal-entropy martingale measure is commonly used for pricing and [...] Read more.

We introduce a new coherent risk measure, the minimal-entropy risk measure, which is built on the minimal-entropy

σ

-martingale measure—a concept inspired by the well-known minimal-entropy martingale measure used in option pricing. While the minimal-entropy martingale measure is commonly used for pricing and hedging, the minimal-entropy

σ

-martingale measure has not previously been studied, nor has it been analyzed as a traditional risk measure. We address this gap by clearly defining this new risk measure and examining its fundamental properties. In addition, we revisit the entropic risk measure, typically expressed through an exponential formula. We provide an alternative definition using a supremum over Kullback–Leibler divergences, making its connection to entropy clearer. We verify important properties of both risk measures, such as convexity and coherence, and extend these concepts to dynamic situations. We also illustrate their behavior in scenarios involving optimal risk transfer. Our results link entropic concepts with incomplete-market pricing and demonstrate how both risk measures share a unified entropy-based foundation. Full article

(This article belongs to the Special Issue Stochastic Modelling in Financial Mathematics, 2nd Edition)

24 pages, 1066 KiB

Open AccessArticle

Interest Rate Sensitivity of Callable Bonds and Higher-Order Approximations

by Scott S. Dow and Stefanos C. Orfanos

Risks 2025, 13(4), 69; https://doi.org/10.3390/risks13040069 - 1 Apr 2025

Abstract

Certain fixed-income securities, such as callable bonds and mortgage-backed securities subject to prepayment, typically exhibit negative convexity at low yields and cannot be adequately immunized through duration and convexity-matching alone. To address this residual risk, we examine the concepts of bond tilt and [...] Read more.

Certain fixed-income securities, such as callable bonds and mortgage-backed securities subject to prepayment, typically exhibit negative convexity at low yields and cannot be adequately immunized through duration and convexity-matching alone. To address this residual risk, we examine the concepts of bond tilt and bond agility. We provide explicit calculations and derive several approximation formulas that incorporate higher-order terms. With the help of these methods, we are able to track the price-yield dynamics of callable bonds remarkably well, achieving mean absolute errors below 2.5% across a wide variety of callable bonds for parallel yield shifts of up to ±200 basis points. Full article

(This article belongs to the Special Issue Financial Risk, Actuarial Science, and Applications of AI Techniques)

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21 pages, 385 KiB

Open AccessArticle

Nonlinear Nexus Between ESG Scores and Corporate Performance of Insurance Companies in the MENAT Region: Moderating the Effect of Institutional Quality

by Rewayda Tobar

Risks 2025, 13(4), 68; https://doi.org/10.3390/risks13040068 - 1 Apr 2025

Abstract

Although the relationship between ESG performance and firm performance has been the subject of several studies, the nonlinear relationship between ESG performance and the corporate performance of insurance companies remains less explored, specifically in the Middle East, North Africa, and Turkey (MENAT) region. [...] Read more.

Although the relationship between ESG performance and firm performance has been the subject of several studies, the nonlinear relationship between ESG performance and the corporate performance of insurance companies remains less explored, specifically in the Middle East, North Africa, and Turkey (MENAT) region. Moreover, the moderating effect of institutional quality on this relationship has not been examined. To fill this gap, this paper investigates the nonlinear impact of ESG performance on the financial performance of insurance companies in the MENAT region, as well as the moderating effect of institutional quality. To achieve this, a sample of 31 insurance companies located in the seven MENAT countries was constructed over the period 2017–2022. The sample was selected based on the completeness and availability of ESG-related data. This ensured a standardized dataset to enhance the reliability of the results. To estimate this relationship, the System Generalized Method of Moments (SGMM) was used. This technique was used to address endogeneity issues. The empirical results indicate that the performance of the insurance companies is better for those with better ESG performance. Moreover, the quality of institutions is an even more important factor in enhancing the ESG practices–corporate performance nexus. More in-depth analysis is needed to show how these various relationships might be altered with ESG criteria. The findings of this research would, therefore, be beneficial to insurers in terms of an increased understanding of how effective integration of ESG practices, both at the institutional and company level, could be streamlined to enhance their long-term competitiveness and profitability. Full article

24 pages, 696 KiB

Open AccessArticle

ESG Controversies and Firm Investment Efficiency: Impact and Mechanism Examination

by Shijin Ma and Tao Ma

Risks 2025, 13(4), 67; https://doi.org/10.3390/risks13040067 - 1 Apr 2025

Abstract

In the context of increasingly severe global climate change, both companies and investors are placing greater emphasis on investment philosophies centered around environmental protection, social responsibility, and corporate governance (ESG). This paper, based on data from 847 Chinese A-share listed companies over the [...] Read more.

In the context of increasingly severe global climate change, both companies and investors are placing greater emphasis on investment philosophies centered around environmental protection, social responsibility, and corporate governance (ESG). This paper, based on data from 847 Chinese A-share listed companies over the period 2007–2022, employs a two-way fixed effects model to investigate the relationship between ESG controversies and firm investment efficiency. The results indicate that ESG controversies significantly reduce overall firm investment efficiency. Further analysis reveals that ESG controversies affect investment efficiency by exacerbating agency costs and reducing audit quality. Meanwhile, financing constraints and robust internal control quality mitigate these negative effects. Heterogeneity analysis shows that the impact is more pronounced for firms with higher pollution levels, non-state-owned enterprises, those with higher analyst coverage, and firms with lower levels of digitalization. The findings have significant implications for encouraging companies to fulfill their social responsibilities and promote high-quality economic development. Full article

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25 pages, 2502 KiB

Open AccessArticle

Transition Risk in Climate Change: A Literature Review

by Elisa Di Febo and Eliana Angelini

Risks 2025, 13(4), 66; https://doi.org/10.3390/risks13040066 - 28 Mar 2025

Abstract

Climate risk is the negative effect of climate change on several aspects of the environment, business, and society. There are two categories of climate risks: physical risks include direct impacts due to extreme events and chronic changes due to climate modifications that have [...] Read more.

Climate risk is the negative effect of climate change on several aspects of the environment, business, and society. There are two categories of climate risks: physical risks include direct impacts due to extreme events and chronic changes due to climate modifications that have become commonplace; the transition risk arises from the economic and regulatory adjustments required to shift toward reducing greenhouse gas emissions and the transition to renewable energy. The problem, in financial terms, is the correct assessment and quantification of transition risk, as it is not univocal in the literature. This research aims to provide a literature review on transition risk that permits filling this gap and identifying the proxies used for its representation and evaluation. Moreover, the analysis considers the critical aspect of the connection between transition and credit risk, as firms exposed to high transition risks may face challenges in maintaining creditworthiness. Results highlight the most commonly used proxies, including carbon pricing, CO₂ or GHG emissions, and metrics from various databases. However, the findings emphasize the importance of integrating these indicators with broader factors, such as a company’s negative environmental impacts (e.g., waste production and water usage) and delays in technological adaptation from a forward-looking perspective. Full article

(This article belongs to the Special Issue Integrating New Risks into Traditional Risk Management)

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25 pages, 1023 KiB

Open AccessArticle

The Exponential Dispersion Family (EDF) Chain Ladder and Data Granularity

by Greg Taylor

Risks 2025, 13(4), 65; https://doi.org/10.3390/risks13040065 - 27 Mar 2025

Abstract

This paper is concerned with the choice of data granularity for application of the EDF (Exponential Dispersion Family) chain ladder model to forecast a loss reserve. As the duration of individual accident and development periods is decreased, the number of data points increases, [...] Read more.

This paper is concerned with the choice of data granularity for application of the EDF (Exponential Dispersion Family) chain ladder model to forecast a loss reserve. As the duration of individual accident and development periods is decreased, the number of data points increases, but the volatility of each point increases. This leads to a question as to whether a decrease in time unit leads to an increase or decrease in the variance of the loss reserve estimate. Is there an optimal granularity with respect to the variance of the loss reserve? A preliminary question is that of whether an EDF chain ladder that is valid for one duration (here called mesh size) remains so for another. The conditions under which this is so are established. There are various ways in which the mesh size of a data triangle may be varied. The paper identifies two of particular interest. For each of these two types of variation, the effect on variance of loss reserve is studied. Subject to some technical qualifications, the conclusion is that an increase in mesh size always increases the variance. It follows that one should choose a very high degree of granularity in order to maximize efficiency of loss reserve forecasting. Full article

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