Risks

20 pages, 639 KiB

Open AccessFeature PaperArticle

AI-Powered Reduced-Form Model for Default Rate Forecasting

by Jacopo Giacomelli

Risks 2025, 13(8), 151; https://doi.org/10.3390/risks13080151 (registering DOI) - 13 Aug 2025

This study aims to combine deep and recurrent neural networks with a reduced-form portfolio model to predict future default rates across economic sectors. The industry-specific forecasts for Italian default rates produced with the proposed approach demonstrate its effectiveness, achieving significant levels of explained [...] Read more.

This study aims to combine deep and recurrent neural networks with a reduced-form portfolio model to predict future default rates across economic sectors. The industry-specific forecasts for Italian default rates produced with the proposed approach demonstrate its effectiveness, achieving significant levels of explained variance. The results obtained show that enhancing a reduced-form model by integrating it with neural networks is possible and practical for multivariate forecasting of future default frequencies. In our analysis, we utilize the recently proposed RecessionRisk⁺, a reduced-form latent-factor model developed for default and recession risk management applications as an improvement of the well-known CreditRisk⁺ model. The model has been empirically verified to exhibit some predictive power concerning future default rates. However, the theoretical framework underlying the model does not provide the elements necessary to define a proper estimator for forecasting the target default rates, leaving space for the application of a neural network framework to retrieve the latent information useful for default rate forecasting purposes. Among the neural network models tested in combination with RecessionRisk⁺, the best results are obtained with shallow LSTM networks. Full article

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38 pages, 2503 KiB

Open AccessArticle

Volatility Spillovers Between the U.S. and Romanian Markets: The BET–SFT-500 Dynamic Under Political Uncertainty

by Kamer-Ainur Aivaz, Lavinia Mastac, Dorin Jula, Diane Paula Corina Vancea, Cristina Duhnea and Elena Condrea

Risks 2025, 13(8), 150; https://doi.org/10.3390/risks13080150 - 13 Aug 2025

Abstract

This paper analyzes the volatility relationship between the Romanian BET index and the U.S. SFT-500 index during the period 2019–2024, with a particular focus on the impact of political and geopolitical shocks. The study investigates whether financial markets in emerging economies react symmetrically [...] Read more.

This paper analyzes the volatility relationship between the Romanian BET index and the U.S. SFT-500 index during the period 2019–2024, with a particular focus on the impact of political and geopolitical shocks. The study investigates whether financial markets in emerging economies react symmetrically or asymmetrically to external shocks originating from mature markets, especially during periods of political uncertainty. The research period includes four major systemic events: the COVID-19 pandemic, the military conflict in Ukraine, the 2024 U.S. presidential elections, and the 2024 Romanian elections, all of which generated significant volatility in global markets. The methodological approach combines time series econometrics with the Impulse Indicator Saturation (IIS) technique to identify structural breaks and outliers, without imposing exogenous assumptions about the timing of events. The econometric model includes autoregressive and lagged exogenous variables to estimate the influence of the SFT-500 index on the BET index, while IIS variables capture unanticipated political and economic shocks. Additionally, a Fractionally Integrated GARCH (FIGARCH) specification is applied to model the persistence of volatility over time, capturing the long-memory behavior often observed in emerging markets like Romania. The results confirm a statistically significant but partial synchronization between the two markets, with lagged and contemporaneous effects from the SFT-500 index on the BET index. Volatility in Romania is markedly higher and longer-lasting during domestic political episodes, confirming that local factors are a primary source of market instability. For investors, this underscores the need to embed political risk metrics into emerging market portfolios. For policymakers, it highlights how stronger institutions and transparent governance can dampen election- and crisis-related turbulence. Full article

(This article belongs to the Special Issue Risk Analysis in Financial Crisis and Stock Market)

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26 pages, 498 KiB

Open AccessArticle

What Determines Digital Financial Literacy? Evidence from a Large-Scale Investor Study in Japan

by Sumeet Lal, Aliyu Ali Bawalle, Mostafa Saidur Rahim Khan and Yoshihiko Kadoya

Risks 2025, 13(8), 149; https://doi.org/10.3390/risks13080149 - 12 Aug 2025

Abstract

The digitalization of financial systems has intensified risks such as cyber fraud, data breaches, and financial exclusion, particularly for individuals with low digital financial literacy (DFL). As digital finance becomes ubiquitous, DFL has emerged as a critical competency. However, the determinants of DFL [...] Read more.

The digitalization of financial systems has intensified risks such as cyber fraud, data breaches, and financial exclusion, particularly for individuals with low digital financial literacy (DFL). As digital finance becomes ubiquitous, DFL has emerged as a critical competency. However, the determinants of DFL remain insufficiently explored. This study aims to validate a comprehensive, theory-driven model that identifies the key sociodemographic, economic, and psychological factors that influence DFL acquisition among investors. Drawing on six established learning and behavioral theories—we analyze data from 158,169 active account holders in Japan through ordinary least squares regression. The results show that higher levels of DFL are associated with being male, younger or middle-aged, highly educated, and unemployed and having greater household income and assets. In contrast, being married, having children, holding a myopic view of the future, and high risk aversion are linked to lower DFL. Interaction effects show a stronger income–DFL association for males and a diminishing return for reduced education with age. Robustness checks using a probit model with a binary DFL measure confirmed the OLS results. These findings highlight digital inequalities and behavioral barriers that shape DFL acquisition. This study contributes a validated framework for identifying at-risk groups and supports future interventions to enhance inclusive digital financial capabilities in increasingly digital economies. Full article

12 pages, 1125 KiB

Open AccessArticle

Algorithmic Trading System with Adaptive State Model of a Binary-Temporal Representation

by Michal Dominik Stasiak

Risks 2025, 13(8), 148; https://doi.org/10.3390/risks13080148 - 4 Aug 2025

Viewed by 224

Abstract

In this paper a new state model is introduced, an adaptative state model in a binary temporal representation (ASMBRT) as well as its application in constructing an algorithmic trading system. The presented model uses the binary temporal representation, which allows for a precise [...] Read more.

In this paper a new state model is introduced, an adaptative state model in a binary temporal representation (ASMBRT) as well as its application in constructing an algorithmic trading system. The presented model uses the binary temporal representation, which allows for a precise analysis of exchange rates without losing any informative value of the data. The basis of the model is the trajectory analysis for the ensuing changes in price quotations and dependencies between the duration of each change. The main advantage of the model is to eliminate the threshold analysis, used in existing state models. This solution allows for a more accurate identification of investor behavior patterns, which translates into a reduction of investment risk. In order to verify obtained results in practice, the paper presents a concept of creating an algorithmic trading system and an analysis of its financial effectiveness for the exchange rate most popular among investors, namely EUR/USD. Full article

(This article belongs to the Special Issue Advances in Risk Models and Actuarial Science)

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26 pages, 20835 KiB

Open AccessFeature PaperArticle

Reverse Mortgages and Pension Sustainability: An Agent-Based and Actuarial Approach

by Francesco Rania

Risks 2025, 13(8), 147; https://doi.org/10.3390/risks13080147 - 4 Aug 2025

Viewed by 328

Abstract

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree [...] Read more.

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree welfare and supporting pension system resilience under demographic and financial uncertainty. We explore Reverse Mortgage Loans (RMLs) as a potential financial instrument to support retirees while alleviating pressure on public pensions. Unlike prior research that treats individual decisions or policy outcomes in isolation, our hybrid model explicitly captures feedback loops between household-level behavior and system-wide financial stability. To test our hypothesis that RMLs can improve individual consumption outcomes and bolster systemic solvency, we develop a hybrid model combining actuarial techniques and agent-based simulations, incorporating stochastic housing prices, longevity risk, regulatory capital requirements, and demographic shifts. This dual-framework enables a structured investigation of how micro-level financial decisions propagate through market dynamics, influencing solvency, pricing, and adoption trends. Our central hypothesis is that reverse mortgages, when actuarially calibrated and macroprudentially regulated, enhance individual financial well-being while preserving long-run solvency at the system level. Simulation results indicate that RMLs can improve consumption smoothing, raise expected utility for retirees, and contribute to long-term fiscal sustainability. Moreover, we introduce a dynamic regulatory mechanism that adjusts capital buffers based on evolving market and demographic conditions, enhancing system resilience. Our simulation design supports multi-scenario testing of financial robustness and policy outcomes, providing a transparent tool for stress-testing RML adoption at scale. These findings suggest that, when well-regulated, RMLs can serve as a viable supplement to traditional retirement financing. Rather than offering prescriptive guidance, this framework provides insights to policymakers, financial institutions, and regulators seeking to integrate RMLs into broader pension strategies. Full article

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

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17 pages, 2439 KiB

Open AccessArticle

Monte Carlo-Based VaR Estimation and Backtesting Under Basel III

by Yueming Cheng

Risks 2025, 13(8), 146; https://doi.org/10.3390/risks13080146 - 1 Aug 2025

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Abstract

Value-at-Risk (VaR) is a key metric widely applied in market risk assessment and regulatory compliance under the Basel III framework. This study compares two Monte Carlo-based VaR models using publicly available equity data: a return-based model calibrated to historical portfolio volatility, and a [...] Read more.

Value-at-Risk (VaR) is a key metric widely applied in market risk assessment and regulatory compliance under the Basel III framework. This study compares two Monte Carlo-based VaR models using publicly available equity data: a return-based model calibrated to historical portfolio volatility, and a CAPM-style factor-based model that simulates risk via systematic factor exposures. The two models are applied to a technology-sector portfolio and evaluated under historical and rolling backtesting frameworks. Under the Basel III backtesting framework, both initially fall into the red zone, with 13 VaR violations. With rolling-window estimation, the return-based model shows modest improvement but remains in the red zone (11 exceptions), while the factor-based model reduces exceptions to eight, placing it into the yellow zone. These results demonstrate the advantages of incorporating factor structures for more stable exception behavior and improved regulatory performance. The proposed framework, fully transparent and reproducible, offers practical relevance for internal validation, educational use, and model benchmarking. Full article

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20 pages, 1104 KiB

Open AccessArticle

Smile-Consistent Spread Skew

by Dan Pirjol

Risks 2025, 13(8), 145; https://doi.org/10.3390/risks13080145 - 31 Jul 2025

Viewed by 218

Abstract

We study the shape of the Bachelier-implied volatility of a spread option on two assets following correlated local volatility models. This includes the limiting case of spread options on two correlated Black–Scholes (BS) assets. We give an analytical result for the at-the-money (ATM) [...] Read more.

We study the shape of the Bachelier-implied volatility of a spread option on two assets following correlated local volatility models. This includes the limiting case of spread options on two correlated Black–Scholes (BS) assets. We give an analytical result for the at-the-money (ATM) skew of the spread-implied volatility, which depends only on the components’ ATM volatilities and skews. We also compute the ATM convexity of the implied spread option for the case when the assets follow correlated BS models. The results are extracted from the short-maturity asymptotics for basket options obtained previously by Avellaneda, Boyer-Olson, Busca and Friz and, thus, become exact in the short-maturity limit. Numerical testing of the short-maturity analytical results under the Black–Scholes model and in a local volatility model show good agreement for strikes sufficiently close to the ATM point. Numerical experiments suggest that a linear approximation for the spread Bachelier volatility constructed from the ATM spread volatility and skew gives a good approximation for the spread volatility for highly correlated assets. Full article

(This article belongs to the Special Issue Financial Derivatives and Their Applications)

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27 pages, 525 KiB

Open AccessArticle

An Analytical Review of Cyber Risk Management by Insurance Companies: A Mathematical Perspective

by Maria Carannante and Alessandro Mazzoccoli

Risks 2025, 13(8), 144; https://doi.org/10.3390/risks13080144 - 31 Jul 2025

Viewed by 276

Abstract

This article provides an overview of the current state-of-the-art in cyber risk and cyber risk management, focusing on the mathematical models that have been created to help with risk quantification and insurance pricing. We discuss the main ways that cyber risk is measured, [...] Read more.

This article provides an overview of the current state-of-the-art in cyber risk and cyber risk management, focusing on the mathematical models that have been created to help with risk quantification and insurance pricing. We discuss the main ways that cyber risk is measured, starting with vulnerability functions that show how systems react to threats and going all the way up to more complex stochastic and dynamic models that show how cyber attacks change over time. Next, we examine cyber insurance, including the structure and main features of the cyber insurance market, as well as the growing role of cyber reinsurance in strategies for transferring risk. Finally, we review the mathematical models that have been proposed in the literature for setting the prices of cyber insurance premiums and structuring reinsurance contracts, analysing their advantages, limitations, and potential applications for more effective risk management. The aim of this article is to provide researchers and professionals with a clear picture of the main quantitative tools available and to point out areas that need further research by summarising these contributions. Full article

16 pages, 899 KiB

Open AccessArticle

Public Funding, ESG Strategies, and the Risk of Greenwashing: Evidence from Greek Financial and Public Institutions

by Kyriaki Efthalitsidou, Vasileios Kanavas, Paschalis Kagias and Nikolaos Sariannidis

Risks 2025, 13(8), 143; https://doi.org/10.3390/risks13080143 - 29 Jul 2025

Viewed by 310

Abstract

The increasing pressure for environmental, social, and governance (ESG) accountability in publicly funded institutions has raised concerns about the authenticity and efficiency of ESG implementation. This study investigates the relationship between public ESG funding, disclosure quality, and organizational efficiency across Greek public and [...] Read more.

The increasing pressure for environmental, social, and governance (ESG) accountability in publicly funded institutions has raised concerns about the authenticity and efficiency of ESG implementation. This study investigates the relationship between public ESG funding, disclosure quality, and organizational efficiency across Greek public and financial entities. Using a mixed-methods approach—data envelopment analysis (DEA), qualitative ESG content scoring, and bibliometric mapping—we reveal that symbolic compliance remains prevalent, often decoupled from actual sustainability outcomes. Our DEA findings show that technical efficiency is strongly associated with reporting clarity, the use of verifiable metrics, and governance integration, rather than the mere volume of funding. The qualitative analysis further confirms that many disclosures reflect reputational signaling rather than impact-oriented transparency. Bibliometric results highlight a systemic underrepresentation of the public sector in ESG scholarship, particularly in Southern Europe, underscoring the need for regionally grounded empirical studies. This study provides practical implications for improving ESG accountability in publicly funded institutions and contributes a novel approach that integrates efficiency, content, and bibliometric analysis in the ESG context. Full article

(This article belongs to the Special Issue ESG and Greenwashing in Financial Institutions: Meet Risk with Action)

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14 pages, 379 KiB

Open AccessArticle

Overconfidence and Investment Loss Tolerance: A Large-Scale Survey Analysis of Japanese Investors

by Honoka Nabeshima, Mostafa Saidur Rahim Khan and Yoshihiko Kadoya

Risks 2025, 13(8), 142; https://doi.org/10.3390/risks13080142 - 23 Jul 2025

Viewed by 499

Abstract

Accepting a certain degree of investment loss risk is essential for long-term portfolio management. However, overconfidence bias within financial literacy can prompt excessively risky behavior and amplify susceptibility to other cognitive biases. These tendencies can undermine investment loss tolerance beyond the baseline level [...] Read more.

Accepting a certain degree of investment loss risk is essential for long-term portfolio management. However, overconfidence bias within financial literacy can prompt excessively risky behavior and amplify susceptibility to other cognitive biases. These tendencies can undermine investment loss tolerance beyond the baseline level shaped by sociodemographic, economic, psychological, and cultural factors. This study empirically examines the association between overconfidence and investment loss tolerance, which is measured by the point at which respondents indicate they would sell their investments in a hypothetical loss scenario. Using a large-scale dataset of 161,765 active investors from one of Japan’s largest online securities firms, we conduct ordered probit and ordered logit regression analyses, controlling for a range of sociodemographic, economic, and psychological variables. Our findings reveal that overconfidence is statistically significantly and negatively associated with investment loss tolerance, indicating that overconfident investors are more prone to prematurely liquidating assets during market downturns. This behavior reflects an impulse to avoid even modest losses. The findings suggest several possible practical strategies to mitigate the detrimental effects of overconfidence on long-term investment behavior. Full article

17 pages, 1363 KiB

Open AccessArticle

Navigating Risk in Crypto Markets: Connectedness and Strategic Allocation

by Nader Naifar

Risks 2025, 13(8), 141; https://doi.org/10.3390/risks13080141 - 23 Jul 2025

Viewed by 712

Abstract

This study examined the dynamic interconnectedness and portfolio implications within the cryptocurrency ecosystem, focusing on five representative digital assets across the core functional categories: Layer 1 cryptocurrencies (Bitcoin (BTC) and Ethereum (ETH)), decentralized finance (Uniswap (UNI)), stablecoins (Dai), and crypto infrastructure tokens (Maker [...] Read more.

This study examined the dynamic interconnectedness and portfolio implications within the cryptocurrency ecosystem, focusing on five representative digital assets across the core functional categories: Layer 1 cryptocurrencies (Bitcoin (BTC) and Ethereum (ETH)), decentralized finance (Uniswap (UNI)), stablecoins (Dai), and crypto infrastructure tokens (Maker (MKR)). Using the Extended Joint Connectedness Approach within a Time-Varying Parameter VAR framework, the analysis captured time-varying spillovers of return shocks and revealed a heterogeneous structure of systemic roles. Stablecoins consistently acted as net absorbers of shocks, reinforcing their defensive profile, while governance tokens, such as MKR, emerged as persistent net transmitters of systemic risk. Foundational assets like BTC and ETH predominantly absorbed shocks, contrary to their perceived dominance. These systemic roles were further translated into portfolio design, where connectedness-aware strategies, particularly the Minimum Connectedness Portfolio, demonstrated superior performance relative to traditional variance-based allocations, delivering enhanced risk-adjusted returns and resilience during stress periods. By linking return-based systemic interdependencies with practical asset allocation, the study offers a unified framework for understanding and managing crypto network risk. The findings carry practical relevance for portfolio managers, algorithmic strategy developers, and policymakers concerned with financial stability in digital asset markets. Full article

(This article belongs to the Special Issue Cryptocurrency Pricing and Trading)

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Risks, Volume 13, Issue 8 (August 2025) – 11 articles

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