Risks MDPI

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- 22 September

✔️ Enterprise Risk Management, Financial Reporting and Firm Operations

✍️ by Siwei Gao, Hsiao-Tang Hsu and Fang‑Chun Liu

Does a holistic approach to risk management pay off in the numbers? Analyzing 648 firms (2004‑2014), this study finds that Enterprise Risk Management (ERM) implementation is associated with higher reporting quality and reduced volatility in future operating cash flows and stock returns. Difference‑in‑differences analysis shows these effects strengthened after the SEC's 2010 risk disclosure rule—highlighting ERM's incremental value for firms and policymakers alike.

👉 https://brnw.ch/21x5S6o

Enterprise Risk Management, Financial Reporting and Firm Operations We examine financial reporting and firm operations, focusing specifically on the roles of ‘enterprise risk management’ (ERM), within which a holistic approach is taken to the conceptualization and management of all types of risk. We measure ERM implementation based on information obtained from 2...

🙌 - 22 September

✔️ Robust Estimation of the Tail Index of a Single Parameter Pareto Distribution from Grouped Data

✍️ by Chudamani Poudyal (चुडामणि पौडेल), PhD, ASA

How do you estimate extreme losses when you only have grouped data—not individual ? This paper introduces the Method of Truncated Moments (MTuM), a novel robust estimator for the tail index of a Pareto distribution from grouped loss severity data. Validated through simulation and grounded in the central limit theorem, the method offers a practical alternative to limited existing approaches like least squares and minimum Hellinger distance—strengthening actuarial risk assessment where granular data is unavailable.

👉 https://brnw.ch/21x5RB8

Robust Estimation of the Tail Index of a Single Parameter Pareto Distribution from Grouped Data | MDPI Numerous robust estimators exist as alternatives to the maximum likelihood estimator (MLE) when a completely observed ground-up loss severity sample dataset is available.

🙌 - 21 September

✔️ A Transfer Learning Approach for Testing the Adaptive Market Hypothesis: Evidence from BWP/USD to Cryptocurrency Markets

✍️ by Katleho Makatjane, claris shoko and Tiisetso Makatjane

Can knowledge from a stable fiat currency market improve predictions in the wildly volatile crypto market? This study tests the Adaptive Market Hypothesis by training deep learning models on Botswana Pula/US Dollar (BWP/USD) and transferring them to Bitcoin/US Dollar (BTC/USD). The recurrent temporal neural network (RTNN) consistently outperforms other architectures, while rolling long‑memory diagnostics confirm persistent departures from random walk behavior. The findings support time‑varying market efficiency and demonstrate that transfer learning can improve predictive stability across fiat and digital asset markets.

👉 https://brnw.ch/21x5QFY

A Transfer Learning Approach for Testing the Adaptive Market Hypothesis: Evidence from BWP/USD to Cryptocurrency Markets The efficient market hypothesis, which holds that prices completely reflect available information, is commonly used in financial market analysis. However, emerging empirical evidence shows that market efficiency develops with time, as posited by the adaptive market hypothesis (AMH), with predictabil...

📢 Call for Papers: Applications of Artificial Intelligence in Financial Risk Management and Modelling

Guest Editors:
Dr Wilson Tsakane Mongwe, PhD
Dr Farai Mlambo (PhD, Mathematical Statistics)

AI & ML are reshaping financial‑risk workflows while bringing new challenges around explainability, model governance and fairness. This Special Issue invites theoretical, methodological and applied work covering credit/market/insurance risk, generative‑AI risk use‑cases, model validation, fraud analytics and emerging‑market risk modelling.

Key Topics
Credit risk, default prediction and loss given default modelling;
Market and liquidity risk modelling;
AI in insurance: pricing, reserving, claims modelling and underwriting;
Explainable and interpretable AI for risk models and regulatory reporting;
Model risk management, validation and governance of AI and ML models;
Fairness, bias and ethics in AI-driven financial decision making;
Large language models and generative AI in risk management;
Stress testing, scenario analysis, robustness under regime change, and systemic risk using ML;
Fraud detection, anomaly detection and financial crime analytics;
AI and ML applications, data limitations, and structural challenges in emerging market finance.

✅ Deadline: 15 September 2027
✅ Types: Research articles, reviews, communications
✅ Single‑blind peer‑review; continuous OA publication
✅ Journal social‑media promotion & potential MDPI Books reprint

🔗 Submit & learn more: https://brnw.ch/21x5Qnr

🙌 - 18 September

✔️ Comparative Analysis of Weather‑Based Indexes and the Actuaries Climate Index™ for Crop Yield Prediction and Weather‑Derivative Pricing

✍️ by Cem Yavrum, A. Sevtap Selcuk‑Kestel and José Garrido

Can a single climate index serve both agriculture and finance? This study compares the Actuaries Climate Index™ (ACI) against established weather‑based indexes for predicting crop yields and pricing weather derivatives. Using 22 models with generalized statistical and machine learning approaches across six U.S. regions, the findings reveal wind speed and sea‑level changes—alongside temperature and precipitation—significantly impact crop yield variability. The ACI framework shows strong potential as a comprehensive climate risk indicator for both agricultural and financial applications.

👉 https://brnw.ch/21x5MSo

Comparative Analysis of Weather-Based Indexes and the Actuaries Climate IndexTM for Crop Yield Prediction and Weather-Derivative Pricing Climate change poses significant challenges to the agricultural and financial sectors, affecting crop productivity and the overall financial stability. This study evaluates the robustness of the Actuaries Climate IndexTM (ACI), a relatively recent tool to measure the impact of climate change, by com...

⏰ Final Call｜Risks Travel Award Application Closing Soon - 30 September 2026!

Open to PhD students & postdocs presenting (oral / poster) at 2026 international conferences — includes both upcoming and already‑completed conferences in financial risk & insurance.

✨ Prize: CHF 500 + official certificate. Gather your CV, abstract, justification letter and supervisor recommendation to submit your application.

🔗 Details: https://brnw.ch/21x5MpR

Risks Risks, an international, peer-reviewed Open Access journal.

🙌 - 17 September

✔️ Residualized Big Five Traits and Financial Risk Tolerance: Connecting Tolerance to Behavior

✍️ by John Grable and Eun Jin Kwak

Can overlapping personality traits distort what we think we know about financial risk‑taking? This study applies a two‑stage framework to adjust for suppressor effects among Big Five traits. Once shared variance is controlled, Openness to Experience and Extraversion emerge as the strongest descriptors of financial risk‑taking, while Agreeableness and Conscientiousness contribute only modestly. The findings show that ignoring suppression effects can mischaracterize personality's role in financial decisions—improving models of investor behavior and financial advice.

👉 https://brnw.ch/21x5L7j

Residualized Big Five Traits and Financial Risk Tolerance: Connecting Tolerance to Behavior Research on financial risk tolerance and risk-taking increasingly incorporates personality traits into predictive and descriptive models of risk-taking behavior; however, intercorrelations among traits can obscure the unique contributions of individual traits. This is known as the suppressor effect....

🙌 - 17 September

✔️ Profitability Drivers in European Banks: Analyzing Internal and External Factors in the Post‑2009 Financial Landscape

✍️ by Suzana Laporšek, Barbara Švagan, Mojca Stubelj and Igor Stubelj, University of Primorska

What drives bank profitability in a post‑crisis world of tighter regulation? This study analyzes 3,076 European banks across 34 countries (2013‑2018) using GMM estimation, examining ROA, ROE, NIM, and risk‑adjusted profitability measures. The findings reveal that capital adequacy, liquidity risk, and income diversification boost profitability—but not on a risk‑adjusted basis. Meanwhile, credit risk, management inefficiency, and excessive size consistently erode performance. GDP growth and inflation also matter significantly, offering key insights for regulators balancing stability with profitability.

👉 https://brnw.ch/21x5KE5

Profitability Drivers in European Banks: Analyzing Internal and External Factors in the Post-2009 Financial Landscape The paper examines the key determinants of European banks’ profitability by analyzing the return on assets (ROA), return on equity (ROE), net interest margin (NIM), and the risk-adjusted measures of profitability, RAROAA and RAROAE, across 34 European countries during the period from 2013 to 2018....

🙌 - 16 September

✔️ Assessing the Impact of Financial Risk and Ownership Structure on ESG Disclosure: Insights from the Energy Sector in Indonesia

✍️ by Aloysius Harry Mukti, Oda I. B. Hariyanto and Oswald Timothy Edward

Who really drives ESG transparency in Indonesia's energy sector—financial performance or ownership structure? Analyzing 98 firm-year observations (2020‑2024), this study finds institutional ownership significantly enhances ESG disclosure, while return on assets, liquidity, managerial ownership, and foreign ownership show no meaningful effect. The findings position institutional investors as key catalysts for sustainability reporting in one of the most environmentally intensive sectors of an emerging economy.

👉 https://brnw.ch/21x5JdU

Assessing the Impact of Financial Risk and Ownership Structure on ESG Disclosure: Insights from the Energy Sector in Indonesia | MDPI Environmental, social, and governance (ESG) disclosure has gained global prominence, yet its implementation in emerging markets particularly in environmentally intensive sectors remains fragmented.

🙌 - 16 September

✔️ A First Step Toward a CAT Model Framework: An ODE‑Based Risk Analysis of Urban Floods Triggered by Meteorological Events

✍️ by Beatriz A. Curioso, Manuel L. Esquível, Gracinda R. Guerreiro, Nadezhda P. Krasii and Pedro A. C. Sousa

Can physics equations price catastrophe risk better than statistics alone? This paper introduces a physics‑based ODE system to model urban flood hazards—simulating water accumulation, absorption, routing, and drainage across interconnected surfaces. Using Monte Carlo simulation of stochastic precipitation, the framework yields concrete risk metrics with proven mathematical properties and optimal computational efficiency. It offers a foundational hazard module for next‑generation CAT models, supporting the insurance industry's adaptation to climate change.

👉 https://brnw.ch/21x5IJh

A First Step Toward a CAT Model Framework: An ODE-Based Risk Analysis of Urban Floods Triggered by Meteorological Events | MDPI This paper presents a physics-based hazard model for catastrophe (CAT) modelling of urban flood risk—a first step toward a complete CAT modelling framework.