Risks

23 pages, 1158 KB

Open AccessArticle

Digital Financial Literacy and the Formation of Horizon-Specific Inflation Expectations: Evidence from Japanese Investors

by Sumeet Lal, Aliyu Ali Bawalle, Jargalmaa Amarsanaa and Yoshihiko Kadoya

Risks 2026, 14(6), 125; https://doi.org/10.3390/risks14060125 - 27 May 2026

Abstract

Inflation expectations play an important role in monetary transmission, yet little is known about whether digital financial literacy (DFL) is associated with how individuals form such expectations across different forecasting horizons. This study examines the association between DFL and inflation expectations at the [...] Read more.

Inflation expectations play an important role in monetary transmission, yet little is known about whether digital financial literacy (DFL) is associated with how individuals form such expectations across different forecasting horizons. This study examines the association between DFL and inflation expectations at the one-, three-, and five-year horizons using a large-scale online survey of more than 150,000 Japanese investors. DFL is measured as a multidimensional construct capturing digital and financial knowledge, awareness and use of digital financial services, financial attitudes and behaviors, and self-protection capabilities. Ordered probit models are employed to estimate the association between DFL and horizon-specific inflation expectations while controlling for demographic, socioeconomic, and behavioral characteristics. The results indicate a clear horizon-dependent pattern. Higher DFL is negatively associated with one-year-ahead inflation expectations but positively associated with inflation expectations at the three- and five-year horizons. Marginal-effects estimates indicate that higher DFL shifts the short-run distribution toward lower inflation categories, but shifts the medium- and long-run distributions toward higher inflation categories. These findings are consistent with the possibility that individuals with higher DFL process short-term and longer-term inflation-related information differently. However, because the analysis is based on cross-sectional observational data, the results should be interpreted as conditional associations rather than causal effects. Full article

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30 pages, 730 KB

Open AccessFeature PaperArticle

Longevity Option and Longevity Swap De-Risking Strategies Under Frailty-Based Mortality Models

by Maria Carannante, Valeria D’Amato, Steven Haberman and Massimiliano Menzietti

Risks 2026, 14(6), 124; https://doi.org/10.3390/risks14060124 - 27 May 2026

Abstract

In this research, we develop longevity option and longevity swap de-risking strategies based on a frailty-based mortality model in order to obtain more effective longevity risk transfer by means of unbiased projections. The main findings show that when an appropriate de-risking strategy is [...] Read more.

In this research, we develop longevity option and longevity swap de-risking strategies based on a frailty-based mortality model in order to obtain more effective longevity risk transfer by means of unbiased projections. The main findings show that when an appropriate de-risking strategy is not selected, the longevity risk transfer becomes ineffective. The suitable strategy, in turn, is strongly affected by the mortality model. Full article

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51 pages, 7192 KB

Open AccessArticle

Forward-Modeling Approaches to American Option Valuation: Additive and Multiplicative HJM Representations

by Kushantha Fernando and Vajira Manathunga

Risks 2026, 14(6), 123; https://doi.org/10.3390/risks14060123 - 26 May 2026

Abstract

This paper introduces an HJM-style forward modeling framework for valuing American options. Instead of modeling the dynamics of the underlying asset, we model the maturity-indexed forward drift of the gain process, leading to two no-arbitrage representations of the option value. The first is [...] Read more.

This paper introduces an HJM-style forward modeling framework for valuing American options. Instead of modeling the dynamics of the underlying asset, we model the maturity-indexed forward drift of the gain process, leading to two no-arbitrage representations of the option value. The first is an additive model, where the American option price equals the current gain plus an integral of forward drifts. This representation embeds the early-exercise premium directly and yields a forward drift characterization of the optimal stopping rule. The second is a multiplicative model that provides an arbitrage-free term structure of option values across maturities via a forward rate, in the spirit of the HJM interest rate theory. While it does not determine the early exercise boundary, it is useful for modeling European option price curves and their evolution. We develop the corresponding drift restrictions, spot consistency conditions, and valuation formulas for both representations and provide numerical examples. Full article

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25 pages, 463 KB

Open AccessArticle

ESG Performance and Open Innovation: The Moderating Role of Common Institutional Ownership

by Qiong Li, Norfaiezah Sawandi and Mohd Farid Asraf Md Hashim

Risks 2026, 14(6), 122; https://doi.org/10.3390/risks14060122 - 25 May 2026

Abstract

Inspired by the limited research regarding the interrelationships among Environmental, Social, and Governance (ESG) performance, common institutional investors and open innovation, this study adopts stakeholder theory and social network theory as the analytical framework to examine this issue. This study uses data from [...] Read more.

Inspired by the limited research regarding the interrelationships among Environmental, Social, and Governance (ESG) performance, common institutional investors and open innovation, this study adopts stakeholder theory and social network theory as the analytical framework to examine this issue. This study uses data from China’s A-share listed firms between 2018 and 2024, comprising 25,440 firm-year observations. Fixed-effects OLS models are employed to estimate the main relationships. Empirical findings demonstrate that ESG performance significantly promotes open innovation among Chinese companies. Furthermore, the moderating results indicate common institutional investors strengthen the positive association between ESG performance and open innovation. Further analysis confirms that each of the three dimensions of ESG can independently drive open innovation, yet the moderating effect of common institutional investors positively regulates only the relationships between social performance and open innovation as well as between corporate governance performance and open innovation, while exerting no significant impact on the relationship between environmental performance and open innovation. Overall, this study underscores the positive effects of ESG practices by focusing on the perspective of open innovation and integrating common institutional investors, which provides insights for enterprises to optimize their ESG practices and enhance their open innovation capabilities by virtue of external governance. Full article

38 pages, 16573 KB

Open AccessArticle

Energy Dependence, Environmental Quality and Banking Sector Capital: New Evidence from OECD Countries

by Angelo Leogrande, Fabio Anobile, Alberto Costantiello, Carlo Drago and Massimo Arnone

Risks 2026, 14(6), 121; https://doi.org/10.3390/risks14060121 - 22 May 2026

Abstract

The current study investigates the relationships among environmental variables, energy sector characteristics, and the resilience of the financial sector using a panel dataset of OECD countries covering 2004–2021. For that purpose, information from the World Bank Global Financial Development Database and Sovereign ESG [...] Read more.

The current study investigates the relationships among environmental variables, energy sector characteristics, and the resilience of the financial sector using a panel dataset of OECD countries covering 2004–2021. For that purpose, information from the World Bank Global Financial Development Database and Sovereign ESG Data was used, along with the indicator of financial stability—bank capitalization, represented by the capital-to-asset ratio. This work uses an integrated empirical framework that includes panel regressions, clustering techniques, and machine learning models. The findings from fixed-effects panel regression indicate that methane emissions, PM2.5 air pollution, and energy dependence are negatively correlated with bank capitalization, whereas renewable energy consumption is positively correlated. Contrariwise, fossil fuel consumption is positively correlated with the dependent variable, perhaps indicating the financial conditions prevailing at the moment, but not accounting for the long-run sustainable perspective. Robustness checks, such as excluding major economies, using lagged specifications, and adding control variables, confirm the robustness of the main empirical relationships, yet the results need to be interpreted conditionally. Through clustering analysis, various regimes are observed across the sample, each characterized by different combinations of environmental, energy, and financial features. On the other hand, the machine learning results obtained using K-Nearest Neighbors and Random Forest algorithms are consistent with the regression analysis, revealing non-linearities in the data. Full article

(This article belongs to the Special Issue Climate Risk in Financial Markets and Institutions)

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22 pages, 409 KB

Open AccessArticle

Do ESG Risks Constitute a Financial Deterrent to Investment Attractiveness? An Empirical Multi-Country Analysis

by Abdelouaret El Wardi, Hind Hammouch, Kenza Hammouch and Sonal Trivedi

Risks 2026, 14(5), 120; https://doi.org/10.3390/risks14050120 - 20 May 2026

Abstract

The growing incorporation of environmental, social, and governance (ESG) considerations into global financial systems has significantly influenced investment decision-making. Previous studies have mainly concentrated on ESG performance and their associated implications for businesses and have failed to examine the role of ESG risks [...] Read more.

The growing incorporation of environmental, social, and governance (ESG) considerations into global financial systems has significantly influenced investment decision-making. Previous studies have mainly concentrated on ESG performance and their associated implications for businesses and have failed to examine the role of ESG risks in shaping barriers to cross-border investment. In this regard, this paper attempts to analyze the effects of ESG risks on foreign direct investment (FDI) inflows based on an unbalanced panel dataset for up to 250 countries spanning the years 2000 to 2024, coupled with cross-sectional data for 2020. This study uses a two-dimensional approach, whereby structural ESG risks are evaluated using panel FMOLS regression, while ESG risk exposures are assessed using cross-sectional models. This research also considers moderating factors such as economic development, industrial composition, and innovation capabilities. Based on the use of the national-level ESG risk, it is evident that ESG risks considerably reduce inward foreign direct investment. Full article

(This article belongs to the Special Issue Corporate Governance and Risk Management at Financial Institutions)

20 pages, 439 KB

Open AccessArticle

An Assessment of Liquidity, Profitability and Working Capital Management Strategy in Polish Manufacturing Companies in the Pressure-Casting Industry During the Crisis

by Grzegorz Zimon, Ahmed Mohamed Habib, Hossein Tarighi, Sergen Gursoy and Magdalena Kawalec

Risks 2026, 14(5), 119; https://doi.org/10.3390/risks14050119 - 19 May 2026

Abstract

This study assesses liquidity, profitability, and working capital management (WCM) strategy in Polish manufacturing companies in the pressure-casting industry, drawing on evidence from the pre-COVID-19, COVID-19, and Russia–Ukraine war periods. Using panel data from 19 companies representing 90% of the Polish aluminum diecasting [...] Read more.

This study assesses liquidity, profitability, and working capital management (WCM) strategy in Polish manufacturing companies in the pressure-casting industry, drawing on evidence from the pre-COVID-19, COVID-19, and Russia–Ukraine war periods. Using panel data from 19 companies representing 90% of the Polish aluminum diecasting industry, we employ non-parametric tests (Mann–Whitney U and Kruskal–Wallis) to analyze the data. The period after the COVID-19 crisis coincides with the Russian–Ukrainian war. These countries are Poland’s neighbors. This period of uncertainty for Poland has led to supply chain disruptions and reduced investments. For manufacturing companies, this is dangerous because they have limited development opportunities. The results indicate the adoption of a conservative WCM strategy in Polish aluminum foundries during the pre-COVID-19, COVID-19, and Russia–Ukraine war periods, characterized by increased inventory levels, extended operating cycles in large firms. Additionally, the results showed reduced the level of receivables in large companies and visible decrease in the level of financial liquidity and profitability—however, these differences are not statistically significant. Polish aluminum foundries are adapting their WCM strategies toward an optimal, conservative approach that incorporates both safe and risky elements to ensure continued operations and profits. In addition, larger Polish aluminum foundries exhibit distinct liquidity patterns relative to smaller foundries, particularly in indicators of inventory, receivables, and fixed assets. In addition, the Russia–Ukraine war period exhibits distinct liquidity characteristics in Polish aluminum foundries compared with the COVID-19 and pre-COVID-19 periods, particularly in inventory turnover and operating cycle. The results of this study offer several novel contributions to the existing literature on financial security indicators by examining unexplored factors related to size and period. The results of this study have several practical implications for business leaders seeking to adopt an optimal liquidity, profitability, and WCM strategy. Full article

21 pages, 912 KB

Open AccessReview

A Review of Management Reserves in U.S. Government Construction Cost Estimation

by Geoffrey Rothwell

Risks 2026, 14(5), 118; https://doi.org/10.3390/risks14050118 - 18 May 2026

Cited by 1

Abstract

While there is some agreement on estimating construction cost contingency for “known unknowns,” there is little consensus on estimating management reserves for “unknown unknowns.” Definitions of risk and uncertainty also differ between the economics and finance literature and the cost engineering literature. This [...] Read more.

While there is some agreement on estimating construction cost contingency for “known unknowns,” there is little consensus on estimating management reserves for “unknown unknowns.” Definitions of risk and uncertainty also differ between the economics and finance literature and the cost engineering literature. This paper examines how cost engineering guidance on estimating management reserves is applied in government-sponsored project cost estimates. This lack of consensus is evident in a specific program: the management, treatment, and disposal of 212,000 cubic meters of mixed radioactive and hazardous chemical waste generated by plutonium production at the Hanford Nuclear Site. Over $30 billion has been invested in treatment facilities, vitrification plants, and laboratories analyzing gases, liquids, sludges, and salt cake from 177 aging storage tanks. The remaining construction and operating costs are highly uncertain, with estimates ranging from $300 billion to $640 billion. Analyses of alternatives for constructing Hanford waste treatment facilities assume 15% contingencies and 40% management reserves. A method is presented to compute the implicit moments of Extreme Value distributions of cost estimates for different options, helping determine whether one alternative’s cost estimate stochastically dominates others. Adopting industry definitions of contingency and management reserves by federal government agencies could improve construction cost estimation in government-financed programs. Full article

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31 pages, 644 KB

Open AccessArticle

Board Governance as a Risk Management Tool: Insights from Carbon Emission Disclosure in ASEAN Firms

by Sad Abu Alim and Marwan Mansour

Risks 2026, 14(5), 117; https://doi.org/10.3390/risks14050117 - 18 May 2026

Abstract

This study examines how internal board governance influences carbon emission disclosure (CED), conceptualized as both a key dimension of ESG transparency and a mechanism for managing disclosure-related risks. Using panel data from 175 listed firms across six ASEAN countries during 2014–2021, we develop [...] Read more.

This study examines how internal board governance influences carbon emission disclosure (CED), conceptualized as both a key dimension of ESG transparency and a mechanism for managing disclosure-related risks. Using panel data from 175 listed firms across six ASEAN countries during 2014–2021, we develop a composite Board Effectiveness Score (BES) that integrates five governance attributes: gender diversity, board size, board independence, meeting frequency, and the presence of an environmental committee. Unlike prior single-attribute studies, the BES captures governance complementarities and the interactive effects of board structures. Applying a multi-method empirical framework—including fixed-effects estimation, quantile regression, two-step system GMM, Heckman selection correction, Propensity Score Matching (PSM), and Two-Stage Least Squares (2SLS)—we find that gender-diverse boards, environmental committees, and higher BES values significantly enhance carbon emission disclosure. The results remain robust across alternative disclosure measures and additional econometric specifications, with the strongest effects observed among low- and mid-level disclosing firms. These findings highlight the role of internal board governance as a potential substitute for weak institutional oversight in emerging markets. The study offers practical implications for regulators and investors seeking to strengthen climate transparency, ESG accountability, and governance-based risk management in ASEAN economies. Full article

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22 pages, 811 KB

Open AccessArticle

Why Are Female Investors Trapped in Multi-Level Marketing (MLM) Schemes in Fintech? Insights from Pi Network in Vietnam

by Dung Hai Dinh, Thi Dang Minh Nguyen, Huyen Le Thanh Nguyen and Tobias Ametsbichler

Risks 2026, 14(5), 116; https://doi.org/10.3390/risks14050116 - 13 May 2026

Abstract

The rapid development of the fintech sector has facilitated the emergence of digital multi-level marketing (MLM) schemes, raising concerns about investor protection. Despite extensive literature on MLM schemes and pyramid schemes, there remains a significant research gap regarding the psychological mechanisms and cognitive [...] Read more.

The rapid development of the fintech sector has facilitated the emergence of digital multi-level marketing (MLM) schemes, raising concerns about investor protection. Despite extensive literature on MLM schemes and pyramid schemes, there remains a significant research gap regarding the psychological mechanisms and cognitive biases that drive investor participation behavior. This study investigates factors influencing Vietnamese female investors’ intention to participate in fintech MLM schemes, using Pi Network as a case study. Grounded in behavioral finance theories (Prospect Theory and Social Comparison Theory), the model empirically examines the impacts of herding bias and overconfidence bias, explaining participation intention through the mediating effect of the fear of missing out (FOMO) and perceived risk. A quantitative approach was employed using PLS-SEM analysis, with data collected from 264 female investors in Ho Chi Minh City. The results reveal that herding behavior and overconfidence significantly shape investors’ FOMO and perceived risk, with these biases significantly increasing FOMO and decreasing perceived risk. More importantly, these biases, mediated by FOMO and perceived risk, significantly shape participation intention in fintech MLM schemes. This study contributes empirical evidence showing the interaction between high social connectivity and cognitive-bias-driven vulnerabilities in a rapidly expanding and unregulated digital market such as Vietnam. This study has practical implications for policymakers and financial educators in protecting investors from financial schemes by monitoring social media to debunk “safety in numbers” narratives and prioritize the awareness of biases in financial education to mitigate impulse investments. Full article

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20 pages, 487 KB

Open AccessArticle

Risk Scoring for Crop Insurance at Enrollment: Evidence and Limits

by Constantin Colonescu, Subhadip Ghosh and Shahidul Islam

Risks 2026, 14(5), 115; https://doi.org/10.3390/risks14050115 - 13 May 2026

Abstract

Crop insurance has to be priced and screened before the season’s main losses are known. This paper asks how far an insurer can get using only the information already available when a contract is enrolled: where the farm is, what crop and practice [...] Read more.

Crop insurance has to be priced and screened before the season’s main losses are known. This paper asks how far an insurer can get using only the information already available when a contract is enrolled: where the farm is, what crop and practice are insured, the chosen coverage level, and the local hail rate history. Using administrative records from 2006 to 2024, we build a practical underwriting score that separates the chance of a claim from the likely size of a loss, then test it on recent years and compare it with simpler rules and alternative models. The score ranks contracts better than regional averages, hail rate rules, or premium-based sorting, and the highest-ranked fifth of contracts contains about one third of the realized losses. Still, it misses much of the most severe loss risk. Coverage choice helps with prediction but also reflects farmer decisions, and thus the score should be read as a contract risk measure rather than a causal measure of hazard. These results suggest an enrollment-time information constraint for the specifications tested here, while leaving room for richer administrative and hazard-based extensions. Better tail prediction may require weather, farm history, and spatial information not used in the strict score. Full article

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22 pages, 454 KB

Open AccessArticle

Climate Policy Uncertainty and Housing Prices: Analyzing Bidirectional Transmission Across U.S. Metropolitan Areas

by Sourav Batabyal and Alper Gormus

Risks 2026, 14(5), 114; https://doi.org/10.3390/risks14050114 - 9 May 2026

Abstract

This study examines the relationship between climate policy uncertainty (CPU) and residential housing prices across U.S. metropolitan areas using the U.S. CPU index developed by Gavriilidis in 2021 and monthly S&P CoreLogic Case-Shiller Home Price Indices, covering January 1991 to May 2024. Employing [...] Read more.

This study examines the relationship between climate policy uncertainty (CPU) and residential housing prices across U.S. metropolitan areas using the U.S. CPU index developed by Gavriilidis in 2021 and monthly S&P CoreLogic Case-Shiller Home Price Indices, covering January 1991 to May 2024. Employing a Fourier-augmented Toda–Yamamoto causality framework that accounts for both abrupt and gradual structural breaks, we document significant CPU → housing prices transmission in multiple metropolitan markets, with bidirectional transmission dynamics emerging in Los Angeles, New York, San Diego, and San Francisco, as well as at the U.S. national level. The results reveal substantial spatial heterogeneity across various market types. Coastal high-exposure markets exhibit strong CPU sensitivity, which may reflect the influence of physical climate risks and regulatory uncertainty; inland growth markets display housing prices → CPU feedback, likely operating through political economy channels; Midwest extreme-weather markets show persistent transmission despite their non-coastal locations; recession-sensitive markets become CPU-responsive following the Great Recession; and insulated markets show no significant transmission. The findings indicate that CPU operates as a priced systematic risk factor requiring integration into housing finance oversight, macroprudential frameworks, and investment strategies. These results have important implications for financial stability monitoring, mortgage credit risk assessment, and climate policy design as markets navigate transition risks in a low-carbon economy. Full article

(This article belongs to the Special Issue Climate Change and Financial Risks)

25 pages, 779 KB

Open AccessArticle

Determinants of Digital Asset Investment Intention Among Mutual Fund Investors in Thailand

by Wilasinee Jiaranaipayuk, Amnuay Saengnoree, Sujira Vuthisopon and Kaimuk Pattananupong

Risks 2026, 14(5), 113; https://doi.org/10.3390/risks14050113 - 8 May 2026

Abstract

This study examined the determinants of intentions to participate in digital asset investment among Thai mutual fund investors with medium-to-high risk tolerance by extending the Theory of Planned Behavior (TPB) to include financial literacy and trust. A quantitative survey was conducted with 360 [...] Read more.

This study examined the determinants of intentions to participate in digital asset investment among Thai mutual fund investors with medium-to-high risk tolerance by extending the Theory of Planned Behavior (TPB) to include financial literacy and trust. A quantitative survey was conducted with 360 respondents, and the proposed model was analyzed using structural equation modeling. The results indicate that attitudes toward behavior, subjective norms, and perceived behavioral control are positively associated with the intention to invest. Financial literacy and trust also demonstrate direct and indirect effects, with financial literacy emerging as the strongest overall predictor. In addition, perceived behavioral control and attitudes toward behavior serve as important mediating mechanisms linking cognitive and social factors to the intention to invest. The model explains 52% of the variance in investment intention, indicating moderate explanatory power. These findings suggest that intention to invest in digital assets among Thai mutual fund investors with medium-to-high risk tolerance is shaped by a combination of financial capability, social influence, perceived control, and evaluative judgment. However, these findings should be interpreted cautiously due to the purposive sampling approach and the fact that they are primarily applicable to financially experienced and relatively risk-tolerant investors rather than the broader Thai population. Full article

(This article belongs to the Special Issue Quantitative Analysis and Mathematical Modeling in Economics and Financial Decision-Making)

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23 pages, 1102 KB

Open AccessArticle

From Compliance to Resilience: Integrating Additional Risk Factors into AML Business Risk Assessments

by Yelena Popova, Olegs Cernisevs and Evita Kalmane-Pivkina

Risks 2026, 14(5), 112; https://doi.org/10.3390/risks14050112 - 8 May 2026

Abstract

Risk-based approaches constitute the cornerstone of contemporary anti-money laundering (AML) regulatory frameworks, requiring institutions to conduct Business Risk Assessments (BRAs). While the customer risk assessment component is well-defined across key regulatory dimensions, the “additional factors” capturing institution-specific risks remain unclear and underexplored, leaving [...] Read more.

Risk-based approaches constitute the cornerstone of contemporary anti-money laundering (AML) regulatory frameworks, requiring institutions to conduct Business Risk Assessments (BRAs). While the customer risk assessment component is well-defined across key regulatory dimensions, the “additional factors” capturing institution-specific risks remain unclear and underexplored, leaving a methodological gap in AML governance. This study examines this unaddressed issue by proposing and empirically validating a structured approach for identifying and evaluating institution-specific BRA factors. A PRISMA-guided literature review confirms the lack of standardized approaches for integrating additional factors into AML risk modelling. Empirical data from five European financial institutions were used to model 291 threats and 122 vulnerabilities via Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings demonstrate that six dimensions, governance, operational, ICT, ESG, human resources, and regulatory compliance, significantly influence AML vulnerability, explaining 80.9% of variance (R² = 0.809) with p < 0.001. Governance quality and ICT risk management emerge as the most critical drivers, underscoring the importance of institutional controls and technological resilience in mitigating AML risk. Full article

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21 pages, 651 KB

Open AccessArticle

Impact of Simultaneous Jumps in Mortality and Asset Markets on GMDB Riders

by Amin Hassan Zadeh, Arman Rostami and Kristina G. Stankova

Risks 2026, 14(5), 111; https://doi.org/10.3390/risks14050111 - 7 May 2026

Abstract

This study investigates the impact of jointly modeling jumps in asset prices and mortality rates on the valuation of insurance guarantees. Mortality dynamics are specified using two extended frameworks based on the classical Lee–Carter model, with and without the inclusion of jump components. [...] Read more.

This study investigates the impact of jointly modeling jumps in asset prices and mortality rates on the valuation of insurance guarantees. Mortality dynamics are specified using two extended frameworks based on the classical Lee–Carter model, with and without the inclusion of jump components. Financial asset returns are modeled using Merton jump–diffusion processes. In the proposed specification, asset prices evolve according to a two-regime Merton model, where the regimes correspond to pandemic and non-pandemic market conditions. Using historical mortality data for the U.S. population and financial market data from the S&P 500 index, we evaluate the pricing implications for a Guaranteed Minimum Death Benefit (GMDB) rider. Contract values and Greeks are computed across multiple issue ages and policy maturities. The empirical results highlight the importance of accounting for simultaneous mortality and market jumps and demonstrate that their interaction has a material effect on the valuation of GMDB products. Full article

(This article belongs to the Special Issue Longevity and Morbidity Risks: Emerging Technologies and Perspectives)

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25 pages, 1641 KB

Open AccessArticle

E-Backtesting Expected Shortfall: What Defines a “Good” Forecasting Method for Chinese Regulators?

by Weihua Zhao

Risks 2026, 14(5), 110; https://doi.org/10.3390/risks14050110 - 7 May 2026

Abstract

Following the implementation of Basel IV, China’s financial regulators have replaced Value-at-Risk (VaR) with Expected Shortfall (ES) as the standard market risk measure, necessitating regulatory-oriented evaluation of ES forecasts. This study examines what constitutes a prudent ES forecasting method using e-backtesting, a sequential [...] Read more.

Following the implementation of Basel IV, China’s financial regulators have replaced Value-at-Risk (VaR) with Expected Shortfall (ES) as the standard market risk measure, necessitating regulatory-oriented evaluation of ES forecasts. This study examines what constitutes a prudent ES forecasting method using e-backtesting, a sequential and model-free evaluation framework designed for regulatory monitoring. We evaluate 11 forecasting methods, including parametric, semiparametric, empirical, and deep-learning models, across four asset classes and four portfolio strategies in the Chinese market under Basel IV-consistent settings. Results show that parametric and semiparametric candidates exhibit clustered backtesting detections and increased computational burden around major market regime shifts, whereas the deep-learning model demonstrates greater resilience and produces more conservative ES forecasts during turbulent periods. These findings suggest that robustness to regime shifts should be considered a key criterion in the regulatory evaluation of ES forecasting models in the Chinese market. Full article

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15 pages, 386 KB

Open AccessArticle

The Effect of Regulatory Liquidity Measure on Bank Capital Structure

by Ndonwabile Zimasa Mabandla and Godfrey Marozva

Risks 2026, 14(5), 109; https://doi.org/10.3390/risks14050109 - 6 May 2026

Abstract

This study investigates the effects of liquidity regulation, specifically the liquidity coverage ratio (LCR), on the capital structure of South African banks, with a focus on debt maturity composition. Using panel data covering the period 2015–2024, the analysis applies the Generalized Method of [...] Read more.

This study investigates the effects of liquidity regulation, specifically the liquidity coverage ratio (LCR), on the capital structure of South African banks, with a focus on debt maturity composition. Using panel data covering the period 2015–2024, the analysis applies the Generalized Method of Moments (GMM) estimator to address potential endogeneity concerns. The findings reveal a significant positive relationship between LCR and banks’ total and long-term debt ratios, indicating a shift towards more stable funding structures. In contrast, the LCR is negatively associated with short-term debt. These results suggest that stricter liquidity requirements encourage banks to rely less on short-term funding and more on long-term debt instruments. Although the analysis is limited to a small sample of leading South African banks, the findings provide important insights into the structural implications of liquidity regulation. The study highlights the need for regulators to consider how liquidity requirements shape banks’ financing decisions within broader macroprudential frameworks. By promoting stable funding structures, liquidity regulations enhance banking sector resilience, protect depositors, and support sustainable credit provision. This study contributes novel evidence from an emerging market and addresses a gap in the post-crisis financial regulation literature by linking liquidity regulation to debt maturity profiles. Full article

19 pages, 3707 KB

Open AccessArticle

ORAKULUM: An Information-Impact Asset Pricing Model Introducing a Jump-Diffusion Framework for Information-Driven Markets

by Zoltán Köntös and Ruszlan Megdetovics Rahimkulov

Risks 2026, 14(5), 108; https://doi.org/10.3390/risks14050108 - 6 May 2026

Abstract

Standard asset pricing models treat price dynamics as a stochastic process driven by undifferentiated random noise, rendering them agnostic about the primary engine of price discovery: the arrival of economically significant information. This paper introduces ORAKULUM, a structured Information-Impact Asset Pricing Model that [...] Read more.

Standard asset pricing models treat price dynamics as a stochastic process driven by undifferentiated random noise, rendering them agnostic about the primary engine of price discovery: the arrival of economically significant information. This paper introduces ORAKULUM, a structured Information-Impact Asset Pricing Model that reconceptualises the log-price as a signed information ledger. Each market-relevant event appends a weighted entry that either permanently revises the market consensus or temporarily disturbs it before decaying exponentially toward the new equilibrium. Mathematically, ORAKULUM is a jump-diffusion process combining a Wiener component for continuous micro-uncertainty with a Poisson-driven jump component for discrete macroeconomic and geopolitical shocks. The log-price identity

x (t) = x (0) + μ \cdot t + \sum A_{i} + \sum B_{i} \cdot e^{(- γ (t - t_{i}))} + σ \cdot W (t)

decomposes price dynamics into permanent and transient information impact, admits a natural event catalogue calibration, and supports Monte Carlo scenario simulation. We present the complete theoretical foundations, a closed-form expected path solution, a gradient-descent calibration procedure, and a fully documented Python3 reference implementation. An empirical illustration applies the model to XAU/USD and EUR/USD market data downloaded from Yahoo Finance, demonstrating ORAKULUM’s capacity to generate economically interpretable, real-time prediction clouds in response to central bank communications, inflation releases, and geopolitical shocks. Full article

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33 pages, 2622 KB

Open AccessFeature PaperArticle

Enhancing Enterprise Risk Management and Internal Audit Practices by Applying Machine Learning Models

by Reneta Duhova, Angel Duhov, Petia Georgieva and Milena Lazarova

Risks 2026, 14(5), 107; https://doi.org/10.3390/risks14050107 - 6 May 2026

Abstract

Organizations are currently in a stage where the volume of financial transactions and data is constantly growing. The same goes for risks associated with the use of data for risk management and strategic decision-making. The likelihood of transactional errors generally increases with data [...] Read more.

Organizations are currently in a stage where the volume of financial transactions and data is constantly growing. The same goes for risks associated with the use of data for risk management and strategic decision-making. The likelihood of transactional errors generally increases with data volume and process complexity, while fraud, although less frequent, may have more severe financial, compliance, and reputational consequences for organizations. Continuous auditing practices and well-established enterprise risk management (ERM) processes, combined with AI-driven pattern recognition, trend analysis and segmentation, can enhance timely detection and proper investigation of suspicious transactions. In areas with large volumes of transactions, the audit sampling process may be a lengthy process and pose a detection risk. Using machine learning (ML) models to support critical business processes could prove effective in managing enterprise risk overall. The current study offers new perspectives on managing risk and assurance with ML model output for flagging possible risky transactions within ERP (SAP) systems data. The study population consists of 69,158 finalized billing records extracted from the SAP production environment of a private sector organization, which covers a six-month operational period. The dataset was divided into an 80/20 train–test split, yielding 55,326 training and 13,832 test instances across six classification categories. The study examines the ML methods’ outcomes from billing datasets and their applicability in enhancing audit, assurance, and ERM processes by evaluating output data results from two supervised classification algorithms—multinomial logistic regression (SoftMax regression) and XGBoost—against various criteria generally accepted as risky in audit engagements. Model performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, and average precision (AP) from precision–recall curves. The results confirm that XGBoost achieves 99% overall accuracy with a macro F1-score of 0.965, outperforming logistic regression (macro F1 = 0.863), and that ML output allows early investigation and follow-up procedures to minimize the risk of fraud and errors and optimize risk management activities, thus strengthening internal control frameworks. Full article

(This article belongs to the Special Issue Artificial Intelligence Risk Management)

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