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on Risk Management |
| By: | Satyadhar Joshi (Bank of America, Touro University, Bar-Ilan University [Israël], Independent Researcher) |
| Abstract: | The rapid advancement of generative artificial intelligence (Gen AI) has revolutionized various domains, including financial analytics. This paper provides a comprehensive review of the applications, challenges, and future directions of Gen Al in financial analytics. We explore its role in risk management, credit scoring, feature engineering, and macroeconomic simulations, while addressing limitations such as data quality, interpretability, and ethical concerns. By synthesizing insights from recent literature, we highlight the transformative potential of Gen AI and propose frameworks for its effective integration into financial workflows. This paper presents a systematic examination of generative artificial intelligence (Gen AI) applications in financial risk management, focusing on architectural frameworks and implementation methodologies. We analyze the integration of large language models (LLMs) with traditional quantitative finance pipelines, addressing key challenges in feature engineering, risk modeling, and regulatory compliance. The study demonstrates how transformer-based architectures enhance financial analytics through automated data processing, risk factor extraction, and scenario generation. Technical implementations leverage hybrid cloud platforms and specialized Python libraries for model deployment, achieving measurable improvements in accuracy and efficiency. Our findings reveal critical considerations for production systems, including computational optimization, model interpretability, and governance protocols. The proposed architecture combines LLM capabilities with domain-specific modules for credit scoring, value-at-risk calculation, and macroeconomic simulation. Empirical results highlight trade-offs between model complexity and operational constraints, providing actionable insights for financial institutions adopting Gen Al solutions. The paper concludes with recommendations for future research directions in financial Al systems. |
| Keywords: | Generative AI financial analytics risk management credit scoring large language models feature engineering, Generative AI, financial analytics, risk management, credit scoring, large language models, feature engineering |
| Date: | 2025–05–29 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05101589 |
| By: | Christopher J. Neely |
| Abstract: | In April 2025, U.S. financial markets experienced a sharp, temporary rise in volatility. How did this level of volatility compare with the levels seen in other periods since 1990? |
| Keywords: | financial markets; volatility |
| Date: | 2025–06–20 |
| URL: | https://d.repec.org/n?u=RePEc:fip:l00001:101153 |
| By: | Fadina, Tolulope (Center for Mathematical Economics, Bielefeld University); Schmidt, Thorsten (Center for Mathematical Economics, Bielefeld University) |
| Abstract: | This paper discusses ambiguity in the context of single-name credit risk. We focus on uncertainty on the default intensity but also discuss uncertainty on the recovery in a fractional recovery of the market value. This approach is a first step towards integrating uncertainty in credit risky term structure models and can profit from its simplicity. We derive drift conditions in a Heath-Jarrow-Morton forward rate setting in the case of ambiguous default intensity in combination with zero recovery, and in the case of ambiguous fractional recovery of the market value. |
| Keywords: | Model ambiguity, default time, credit risk, no-arbitrage, reduced- form HJM models, recovery process. |
| Date: | 2025–06–24 |
| URL: | https://d.repec.org/n?u=RePEc:bie:wpaper:710 |
| By: | Issa Kachaou (UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12, UPEC FSEG - Faculté de sciences Economiques et de Gestion - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12) |
| Abstract: | This work applies the Capital Asset Pricing Model (CAPM) to estimate the beta of NVIDIA stock, using the NASDAQ index as a benchmark. The objective is to differentiate between systematic and idiosyncratic risks by running a linear regression on log-differenced daily returns. The study finds a beta significantly greater than 1, indicating high sensitivity to market movements. It also emphasizes the importance of reassessing beta over time as market conditions and investor behavior change. This presentation highlights both the usefulness of the CAPM in risk analysis and its theoretical and empirical limitations. |
| Abstract: | Ce travail applique le modèle d'évaluation des actifs financiers (CAPM) à l'estimation du bêta de l'action NVIDIA, en utilisant l'indice NASDAQ comme benchmark. L'objectif est de distinguer les risques systématiques des risques spécifiques, en mettant en œuvre une régression linéaire sur les rendements journaliers logarithmiques. L'étude révèle un bêta significativement supérieur à 1, suggérant une forte sensibilité de l'action aux variations du marché. Les résultats soulignent également l'importance de réévaluer régulièrement le bêta, dans un contexte où les préférences des investisseurs et les conditions de marché évoluent. Cette présentation met en lumière à la fois la pertinence du CAPM pour l'analyse des risques et ses limites théoriques et empiriques. |
| Keywords: | CAPM, beta estimation, systematic risk, idiosyncratic risk, stock volatility, NVIDIA, NASDAQ, financial modeling, market sensitivity, asset pricing, empirical finance, risk analysis, time series regression, linear regression, investment risk |
| Date: | 2025–03–13 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05053311 |
| By: | Green, Alicia |
| Abstract: | The integration of artificial intelligence (AI) into financial intelligence systems enables automated risk detection and strategic decision support in African markets. This paper examines the technical architectures and AI methodologies (supervised learning, anomaly detection, natural language processing) employed in real-world African financial applications. We discuss data pipelines combining structured and unstructured data (market transactions, social media, news, macro indicators) and outline algorithmic models for credit risk, market risk, systemic risk, and financial crime detection. Specific cases from Nigeria, Kenya, and South Africa illustrate AI use in fraud/AML detection, credit scoring with alternative data, and portfolio stress-testing. Quantitative indicators (e.g., Nigeria’s NGN1.56 quadrillion digital payments in H1 2024 and 468\% surge in fraud cases) underscore the scale of data and risks. Regulatory contexts (e.g., CBN’s AI‑AML framework, SARB guidelines) and infrastructure constraints (limited data connectivity, power) are highlighted. The paper proposes a system framework comprising data integration, machine learning engines, continuous risk scoring, and visualization dashboards. Key applications include dynamic capital allocation, real-time AML monitoring, and scenario-based stress testing. We conclude by identifying ethical challenges (data privacy, model bias, transparency) and suggesting future directions such as hybrid AI-rule systems, localized language models, and cross-border data sharing platforms. |
| Date: | 2025–06–18 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:ynph2_v1 |
| By: | Jan Bena (Sauder School of Business, University of British Columbia); Andrew Ellul (Indiana University, CSEF, CEPR and ECGI.); Marco Pagano (University of Naples Federico II, CSEF and EIEF.); Valentina Rutigliano (Sauder School of Business, University of British Columbia) |
| Abstract: | Entrepreneurs with more diversified portfolios of private firms provide more insurance against labor income risk: in a sample of over 524, 000 Canadian firms and 858, 000 owners, firms owned by such entrepreneurs offer more stable jobs and earnings to employees. In firms whose owners’ portfolios are one standard deviation more diversified, the passthrough rates of foreign sales shocks to layoffs and labor earnings are 13% and 41% lower, respectively. These entrepreneurs reduce their own compensation and increase firm leverage to fund labor income insurance. Enhanced insurance is associated with better retention of valuable human capital and fewer costly terminations, potentially improving firm performance. |
| Keywords: | labor income risk; portfolio diversification; firm shocks. |
| JEL: | G32 J30 J63 L20 |
| Date: | 2025–06–20 |
| URL: | https://d.repec.org/n?u=RePEc:sef:csefwp:754 |
| By: | Xia Zou (Vrije Universiteit Amsterdam and Tinbergen Institute); Yicong Lin (Vrije Universiteit Amsterdam and Tinbergen Institute); André Lucas (Vrije Universiteit Amsterdam and Tinbergen Institute) |
| Abstract: | Point forecasts of score-driven models have been shown to behave at par with those of state-space models under a variety of circumstances. We show, however, that density rather than point forecasts of plain-vanilla score-driven models substantially underperform their state-space counterparts in a factor model context. We uncover the origins of this phenomenon and show how a simple adjustment of the measurement density of the score-driven model can put score-driven and state-space models approximately back on an equal footing again. The score-driven models can subsequently easily be extended with non-Gaussian features to fit the data even better without complicating parameter estimation. We illustrate our findings using a factor model for the implied volatility surface of S&P500 index options data. |
| JEL: | C32 C38 |
| Date: | 2025–05–30 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20250036 |
| By: | Carol Bertaut; Valentina Bruno; Hyun Song Shin |
| Abstract: | We highlight the role of duration and exchange rate risks on portfolio flows by using a unique and comprehensive database of US investor flows into emerging market government bonds denominated in local currency. Borrowing long-term mitigates roll-over risk but amplifies valuation changes that further interact with currency movements. Our analysis highlights the double-edged nature of long-term borrowing and draws attention to market stress dynamics due to strategic complementarities among mutual fund investors. |
| JEL: | F3 F65 G23 H63 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33816 |
| By: | Sergio A. Correia; Stephan Luck; Emil Verner |
| Abstract: | Why do banks fail? We create a panel covering most commercial banks from 1863 through 2024 to study the history of failing banks in the United States. Failing banks are characterized by rising asset losses, deteriorating solvency, and an increasing reliance on expensive noncore funding. These commonalities imply that bank failures are highly predictable using simple accounting metrics from publicly available financial statements. Failures with runs were common before deposit insurance, but these failures are strongly related to weak fundamentals, casting doubt on the importance of non-fundamental runs. Furthermore, low recovery rates on failed banks' assets suggest that most failed banks were fundamentally insolvent, barring strong assumptions about the value destruction of receiverships. Altogether, our evidence suggests that the primary cause of bank failures and banking crises is almost always and everywhere a deterioration of bank fundamentals. |
| Keywords: | Bank failure; Banking History; banking fundamentals |
| JEL: | G01 G21 N20 N24 |
| Date: | 2025–06–09 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedrwp:101149 |
| By: | Jihene Arfaoui; Harald Uhlig |
| Abstract: | Inspired by the Silicon Valley Bank run and building on Diamond- Dybvig (1993), we develop a model in which asset price fluctuations can trigger bank runs. Liquidation amounts to selling assets at their market price. Depositors can buy and hold the assets after paying an idiosyncratic cost. We characterize the equilibria. We introduce a withdrawal pressure function to distinguish between fundamental runs, driven by market price declines, and self-enforcing runs triggered by depositor panic. Deposit insurance can prevent self-enforcing runs but incurs losses during fundamental runs. Regulatory measures ensuring price resilience reduce run risks, but at the expense of depositor welfare. |
| JEL: | E43 E44 G01 G21 G28 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33955 |