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on Risk Management |
| By: | Qingkai Zhang; L. Jeff Hong; Houmin Yan |
| Abstract: | The rapid expansion of cross-border e-commerce (CBEC) has created significant opportunities for small and medium-sized enterprises (SMEs), yet financing remains a critical challenge due to SMEs' limited credit histories. Third-party logistics (3PL)-led supply chain finance (SCF) has emerged as a promising solution, leveraging in-transit inventory as collateral. We propose an advanced credit risk management framework tailored for 3PL-led SCF, addressing the dual challenges of credit risk assessment and loan size determination. Specifically, we leverage conditional generative modeling of sales distributions through Quantile-Regression-based Generative Metamodeling (QRGMM) as the foundation for risk estimation. We propose a unified framework that enables flexible estimation of multiple risk measures while introducing a functional risk measure formulation that systematically captures the relationship between these risk measures and varying loan levels, supported by theoretical guarantees. To capture complex covariate interactions in e-commerce sales data, we integrate QRGMM with Deep Factorization Machines (DeepFM). Extensive experiments on synthetic and real-world data validate the efficacy of our model for credit risk assessment and loan size determination. This study represents a pioneering application of generative AI in CBEC SCF risk management, offering a solid foundation for enhanced credit practices and improved SME access to capital. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.15305 |
| By: | Xin Tian |
| Abstract: | This report presents a comprehensive evaluation of three Value-at-Risk (VaR) modeling approaches: Historical Simulation (HS), GARCH with Normal approximation (GARCH-N), and GARCH with Filtered Historical Simulation (FHS), using both in-sample and multi-day forecasting frameworks. We compute daily 5 percent VaR estimates using each method and assess their accuracy via empirical breach frequencies and visual breach indicators. Our findings reveal severe miscalibration in the HS and GARCH-N models, with empirical breach rates far exceeding theoretical levels. In contrast, the FHS method consistently aligns with theoretical expectations and exhibits desirable statistical and visual behavior. We further simulate 5-day cumulative returns under both GARCH-N and GARCH-FHS frameworks to compute multi-period VaR and Expected Shortfall. Results show that GARCH-N underestimates tail risk due to its reliance on the Gaussian assumption, whereas GARCH-FHS provides more robust and conservative tail estimates. Overall, the study demonstrates that the GARCH-FHS model offers superior performance in capturing fat-tailed risks and provides more reliable short-term risk forecasts. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.05646 |
| By: | Roberto Baviera; Michele Domenico Massaria |
| Abstract: | We investigate the asymptotic behaviour of the Implied Volatility in the Bachelier setting, extending the framework introduced by Benaim and Friz for the Black-Scholes setting. Exploiting the theory of regular variation, we derive explicit expressions for the Bachelier Implied Volatility in the wings of the smile, linking these to the tail behaviour of the underlying's returns' distribution. Furthermore, we establish a direct connection between the analyticity strip of the returns' characteristic function and the asymptotic formula for the Implied Volatility smile at extreme moneyness. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.08067 |
| By: | Dangxing Chen |
| Abstract: | In recent years, machine learning models have achieved great success at the expense of highly complex black-box structures. By using axiomatic attribution methods, we can fairly allocate the contributions of each feature, thus allowing us to interpret the model predictions. In high-risk sectors such as finance, risk is just as important as mean predictions. Throughout this work, we address the following risk attribution problem: how to fairly allocate the risk given a model with data? We demonstrate with analysis and empirical examples that risk can be well allocated by extending the Shapley value framework. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.06653 |
| By: | Muñoz, Manuel A. (Bank of England); Smets, Frank (Bank for International Settlements, Ghent University and CEPR) |
| Abstract: | We reconcile theory and recent evidence on the benefits of building releasable bank capital buffers when there is headroom for doing so by building a quantitative macro-banking model that provides a rationale for static bank capital requirements and dynamic capital buffers due to externalities arising from bank risk failure and collateral constraints. Optimal dynamic capital buffers gradually build in response to expected upward shifts in bank net interest margins. In the absence of pecuniary externalities due to collateral constraints, such capital buffers are ineffective. The model also captures previous empirical findings such as the negative effect of a capital requirement tightening on short-term lending and the optimality of setting static bank capital requirements at relatively conservative levels. We present an application of our quantitative analysis in the form of a simple framework for calibrating the so-called ‘positive neutral counter-cyclical capital buffer’ (PN-CCyB). |
| Keywords: | Macroprudential policy; pecuniary externalities; borrowing limits; bank default risk; bank lending spread |
| JEL: | E44 G21 |
| Date: | 2025–05–30 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:1128 |
| By: | Kerkhofs, Ruben; Bernhofen, Mark; Borsuk, Marcin; Baer, Moritz; Ranger, Nicola; Schoutens, Wim; Shrimali, Gireesh |
| Abstract: | Extreme weather events pose a risk to the economic and financial system. To understand the materiality of these risks, financial institutions are beginning to conduct climate stress testing exercises. This requires climate risk models to be integrated with financial risk models. In this paper, we introduce an open, modular, and reproducible framework for the assessment of asset-level physical risk and the translation of these risks into portfolio-level impacts. The proposed framework addresses key limitations of previous research by including multiple financial transmission channels, and the incorporation of spatial correlations between weather events for bottom-up, asset-level, estimation of portfolio-level tail risks. By incorporating direct capital damages, business disruptions, and insurance coverage, we provide an overview of the direct financial impact of extreme weather events. Through an application of the framework for the assessment of flood risk to a portfolio of power firms located in India, we show that these extensions have material impacts on the risk estimates. We further show how different assumptions related to spatial correlations can lead to large under- or overestimations of portfolio-level tail risks. |
| Keywords: | spatial correlations; financial tail risk; extreme weather events; copulas |
| JEL: | N0 F3 G3 |
| Date: | 2025–06–30 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128395 |
| By: | Aryan Singh; Paul O Reilly; Daim Sharif; Patrick Haughey; Eoghan McCarthy; Sathvika Thorali Suresh; Aakhil Anvar; Adarsh Sajeev Kumar |
| Abstract: | A multivariate risk analysis for VaR and CVaR using different copula families is performed on historical financial time series fitted with DCC-GARCH models. A theoretical background is provided alongside a comparison of goodness-of-fit across different copula families to estimate the validity and effectiveness of approaches discussed. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.06950 |
| By: | Austin Pollok |
| Abstract: | The discrepancy between realized volatility and the market's view of volatility has been known to predict individual equity options at the monthly horizon. It is not clear how this predictability depends on a forecast's ability to predict firm-level volatility. We consider this phenomenon at the daily frequency using high-dimensional machine learning models, as well as low-dimensional factor models. We find that marginal improvements to standard forecast error measurements can lead to economically significant gains in portfolio performance. This makes a case for re-imagining the way we train models that are used to construct portfolios. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.07928 |
| By: | Dan Li; Lubomir Petrasek; Mary Tian |
| Abstract: | Self-imposed risk limits effectively limit dealers' appetite for risks and their capacity to intermediate in Treasury markets in times of market stress. Using granular and high frequency regulatory data on US dealers' Treasury securities trading desk positions and desk-level Value-at-Risk limits, we show that dealers are more inclined to reduce their positions as they get closer to their internal risk limit, consistent with such limit being meaningful and costly for traders to breach. Dealers actively manage their inventories away from their limits by selling longer-term securities and requiring higher compensation to take on additional risks. During the height of the Covid-crisis in 2020, dealer desks that were closer to their VaR limits sold more Treasury securities to the Fed and accepted lower prices in the emergency open market operations. Our findings complement studies that link post-GFC bank regulations to market liquidity by showing that self-imposed risk limits can explain the risk-averse behavior by dealers, and provide a micro-foundation for the link between market volatility and market liquidity in dealer-intermediated OTC markets. In times of crisis, policy prescriptions such as deregulation alone may not be sufficient to induce risk-taking by dealer intermediaries. Moreover, to address market functioning issues, policy actions that address the funding costs of intermediaries would not be as effective as policies that remove risks from intermediary balance sheets directly. |
| Keywords: | Dealer intermediation capacity; Treasury market; Risk limits; Regulation; Market liquidity |
| JEL: | G01 G23 E52 |
| Date: | 2025–05–15 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-34 |
| By: | Hazell, Peter B. R.; Timu, Anne G. |
| Abstract: | Much of the recent literature on agricultural insurance focuses on ways to increase farmers’ demand for insurance, but this paper revisits the supply side of the insurance market. To better understand the conditions under which private insurance has been successful or failed the paper draws on the available empirical and theoretical literature, on case studies, and interviews with selected insurers. While there are many examples of innovative solutions to some of the product design, marketing and delivery challenges facing agricultural insurance, our review suggests that private unsubsidized insurance can only play a limited role in terms of the overall risk management needs of agriculture. Fundamentally, agricultural insurance can only address certain types of risks, and these are often not the most important from the farmers’ perspective. For most farmers insurance is best seen as part of a broader risk management approach, and its relevance for commercial farmers linked to value chains can be quite different from that for more subsistence-oriented smallholders. Commercial farmers generally have the most options for managing risk and may benefit most from specific types of indemnity or index-based products to protect specific agricultural investments and there are many examples of insurers meeting this need on an affordable and unsubsidized basis. On the other hand, subsistence-oriented farmers, especially poor and vulnerable ones, need insurance that can help protect their household income and consumption from negative shocks. This kind of insurance is expensive and difficult to supply without subsidies and requires strong public sector support. Even if targeted in this way, private unsubsidized insurance will only thrive given a supporting policy environment and, to keep costs down and improve the relevance and delivery of its products, insurers need to take full advantage of new and emerging digital and remote sensing innovations, and where possible, partner with intermediaries who can bundle their insurance with credit, farm inputs and other services. |
| Keywords: | agricultural insurance; case studies; farmers; literature review; private sector |
| Date: | 2024–12–14 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:gsspwp:169010 |
| By: | Stella C. Dong; James R. Finlay |
| Abstract: | This paper develops a novel multi-agent reinforcement learning (MARL) framework for reinsurance treaty bidding, addressing long-standing inefficiencies in traditional broker-mediated placement processes. We pose the core research question: Can autonomous, learning-based bidding systems improve risk transfer efficiency and outperform conventional pricing approaches in reinsurance markets? In our model, each reinsurer is represented by an adaptive agent that iteratively refines its bidding strategy within a competitive, partially observable environment. The simulation explicitly incorporates institutional frictions including broker intermediation, incumbent advantages, last-look privileges, and asymmetric access to underwriting information. Empirical analysis demonstrates that MARL agents achieve up to 15% higher underwriting profit, 20% lower tail risk (CVaR), and over 25% improvement in Sharpe ratios relative to actuarial and heuristic baselines. Sensitivity tests confirm robustness across hyperparameter settings, and stress testing reveals strong resilience under simulated catastrophe shocks and capital constraints. These findings suggest that MARL offers a viable path toward more transparent, adaptive, and risk-sensitive reinsurance markets. The proposed framework contributes to emerging literature at the intersection of algorithmic market design, strategic bidding, and AI-enabled financial decision-making. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.13113 |
| By: | Maria Andraos; Mario Ghossoub; Michael B. Zhu |
| Abstract: | We consider a model of a reinsurance market consisting of multiple insurers on the demand side and multiple reinsurers on the supply side, thereby providing a unifying framework and extension of the recent literature on optimality and equilibria in reinsurance markets. Each insurer has preferences represented by a general Choquet risk measure and can purchase coverage from any or all reinsurers. Each reinsurer has preferences represented by a general Choquet risk measure and can provide coverage to any or all insurers. Pricing in this market is done via a nonlinear pricing rule given by a Choquet integral. We model the market as a sequential game in which the reinsurers have the first-move advantage. We characterize the Subgame Perfect Nash Equilibria in this market in some cases of interest, and we examine their Pareto efficiency. In addition, we consider two special cases of our model that correspond to existing models in the related literature, and we show how our findings extend these previous results. Finally, we illustrate our results in a numerical example. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.07291 |
| By: | Mihaela Nistor |
| Abstract: | Remarks at the XLoD Global – New York Conference, New York City. |
| Keywords: | risk; risk shifting; structural risk; technology; automation |
| Date: | 2025–06–04 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednsp:101132 |
| By: | Martijn Boermans |
| Abstract: | Governments across the world have issued inflation-linked debt to finance their deficits. Recent advances in asset pricing models recognize that there may be clientele effects that affect relative prices, especially in bond markets. We study investor demand for inflation-linked bonds using detailed bond portfolio data. Our analysis reveals pronounced market segmentation: insurance companies, with predominantly nominal liabilities, underinvest in inflation-linked securities, while pension funds overinvest. Investors hedging inflation risk exhibit a strong preference for bonds indexed to domestic rather than foreign inflation. A regulatory reform announcement provides quasi-experimental evidence that the demand for inflation-linked bonds may be shaped by regulatory requirements. |
| Keywords: | sovereign bonds; inflation-linked bonds; TIPS; investor clientele; securities holdings |
| JEL: | F21 G11 G15 G22 G23 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:dnb:dnbwpp:838 |
| By: | Wenhao Guo; Yuda Wang; Zeqiao Huang; Changjiang Zhang; Shumin ma |
| Abstract: | In the complex landscape of traditional futures trading, where vast data and variables like real-time Limit Order Books (LOB) complicate price predictions, we introduce the FutureQuant Transformer model, leveraging attention mechanisms to navigate these challenges. Unlike conventional models focused on point predictions, the FutureQuant model excels in forecasting the range and volatility of future prices, thus offering richer insights for trading strategies. Its ability to parse and learn from intricate market patterns allows for enhanced decision-making, significantly improving risk management and achieving a notable average gain of 0.1193% per 30-minute trade over state-of-the-art models with a simple algorithm using factors such as RSI, ATR, and Bollinger Bands. This innovation marks a substantial leap forward in predictive analytics within the volatile domain of futures trading. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.05595 |
| By: | Benedikt Koch; Kosuke Imai |
| Abstract: | Classical statistical decision theory evaluates treatment choices based solely on observed outcomes. However, by ignoring counterfactual outcomes, it cannot assess the quality of decisions relative to feasible alternatives. For example, the quality of a physician's decision may depend not only on patient survival, but also on whether a less invasive treatment could have produced a similar result. To address this limitation, we extend standard decision theory to incorporate counterfactual losses--criteria that evaluate decisions using all potential outcomes. The central challenge in this generalization is identification: because only one potential outcome is observed for each unit, the associated risk under a counterfactual loss is generally not identifiable. We show that under the assumption of strong ignorability, a counterfactual risk is identifiable if and only if the counterfactual loss function is additive in the potential outcomes. Moreover, we demonstrate that additive counterfactual losses can yield treatment recommendations that differ from those based on standard loss functions, provided that the decision problem involves more than two treatment options. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.08908 |
| By: | Roger J. A. Laeven; Matteo Ferrari; Emanuela Rosazza Gianin; Marco Zullino |
| Abstract: | We propose the resilience rate as a measure of financial resilience. It captures the rate at which a dynamic risk evaluation recovers, i.e., bounces back, after the risk-acceptance set is breached. We develop the associated stochastic calculus by establishing representation theorems of a suitable time-derivative of solutions to backward stochastic differential equations (BSDEs) with jumps, evaluated at stopping times. These results reveal that our resilience rate can be represented as an expectation of the generator of the BSDE. We also introduce resilience-acceptance sets and study their properties in relation to both the resilience rate and the dynamic risk measure. We illustrate our results in several examples. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.07502 |
| By: | D’Amico , Marco (Uppsala University); Fazio, Martina (Bank of England) |
| Abstract: | This paper uses rich, administrative-quality data on earnings in the UK from the Annual Survey of Hours and Earnings (ASHE) to provide a detailed analysis of income risk and its patterns across individuals and over time. We develop a model of income dynamics that accounts for the broader state of the economy and successfully captures key features of the UK earnings growth distribution, including: a cyclical variance, procyclical skewness (more frequent negative earnings shocks during recessions), and a distribution that combines sharp peaks with long, heavy tails. The model is simple enough to be integrated into broader macroeconomic frameworks, such as heterogeneous agent models, and could be used to support policy scenario analysis. |
| Keywords: | Idiosyncratic risk; income dynamics; inequality; income process |
| JEL: | C15 C63 J01 |
| Date: | 2025–05–30 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:1129 |
| By: | Tobias Adrian; Domenico Giannone; Matteo Luciani; Mike West |
| Abstract: | We introduce methodology to bridge scenario analysis and model-based risk forecasting, leveraging their respective strengths in policy settings. Our Bayesian framework addresses the fundamental challenge of reconciling judgmental narrative approaches with statistical forecasting. Analysis evaluates explicit measures of concordance of scenarios with a reference forecasting model, delivers Bayesian predictive synthesis of the scenarios to best match that reference, and addresses scenario set incompleteness. This underlies systematic evaluation and integration of risks from different scenarios, and quantifies relative support for scenarios modulo the defined reference forecasts. The framework offers advances in forecasting in policy institutions that supports clear and rigorous communication of evolving risks. We also discuss broader questions of integrating judgmental information with statistical model-based forecasts in the face of unexpected circumstances. |
| Keywords: | Macroeconomic Forecasting; Mixtures of Scenarios; Misclassification Rates; Entropic Tilting; Bayesian Predictive Synthesis; Judgmental Forecasting; Forecast Risk Assessment |
| Date: | 2025–05–20 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-36 |
| By: | Mukashov, Askar; Robinson, Sherman; Thurlow, James; Arndt, Channing; Thomas, Timothy S. |
| Abstract: | This paper uses machine learning, simulation, and data mining methods to develop Systematic Risk Profiles of three developing economies: Kenya, Rwanda, and Malawi. We focus on three exogenous shocks with implications for economic performance: world market prices, capital flows, and climate-driven sectoral productivity. In these and other developing countries, recent decades have been characterized by increased risks associated with all these factors, and there is a demand for instruments that can help to disentangle them. For each country, we utilize historical data to develop multi-variate distributions of shocks. We then sample from these distributions to obtain a series of shock vectors, which we label economic uncertainty scenarios. These scenarios are then entered into economywide computable general equilibrium (CGE) simulation models for the three countries, which allow us to quantify the impact of increased uncertainty on major economic indicators. Finally, we utilize importance metrics from the random forest machine learning algorithm and relative importance metrics from multiple linear regression models to quantify the importance of country-specific risk factors for country performance. We find that Malawi and Rwanda are more vulnerable to sectoral productivity shocks, and Kenya is more exposed to external risks. These findings suggest that a country’s level of development and integration into the global economy are key driving forces defining their risk profiles. The methodology of Systematic Risk Profiling can be applied to many other countries, delineating country-specific risks and vulnerabilities. |
| Keywords: | climate; computable general equilibrium models; machine learning; risk; uncertainty; Kenya; Rwanda; Malawi; Africa; Eastern Africa; Sub-Saharan Africa |
| Date: | 2024–10–25 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:gsspwp:158180 |