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on Risk Management |
| By: | L\'eonard Vincent |
| Abstract: | Conventional wisdom warns against "putting all your eggs in one basket, " and diversification is widely regarded as a reliable strategy for reducing risk. Yet under certain extreme conditions, this intuition not only fails - it reverses. This paper explores such reversals by identifying new settings in which diversification increases risk. Our main result - the one-basket theorem - provides sufficient conditions under which a weighted average of independent risks is larger, in the sense of first-order stochastic dominance, than a corresponding mixture model that concentrates all exposure on a single risk chosen at random. Our framework handles non-identically distributed risks and includes new examples, such as infinite-mean discrete Pareto variables and the St. Petersburg lottery. We further show that these reversals are not isolated anomalies, but boundary cases of a broader phenomenon: diversification always increases the likelihood of exceeding small thresholds, and under specific conditions, this local effect extends globally, resulting in first-order stochastic dominance. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.16265 |
| By: | Rodrigo Alfaro; Catalina Estefó |
| Abstract: | In this paper we propose a framework for building tail-risk indicators for the Chilean Peso (CLP) based on time-variant volatility models [e.g., Engle (1982), Taylor (1982), Nelson (1991), Heston and Nandi (2000)], which we estimate by combining: (i) daily returns, (ii) option-implied volatility (IV), and (iii) intraday realized volatility (RV). Our empirical results show that the in-sample fit of the models improves when volatility measures (IV or RV) are added. We provide an application of the framework to evaluate extreme scenarios. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:chb:bcchwp:1041 |
| By: | Angelique Acquatella; Victoria Marone |
| Abstract: | Health insurance lowers the out-of-pocket price of healthcare, and it is well-established that this leads to higher utilization of care. This type of "moral hazard" is typically viewed as a social cost of insurance. Within a standard model, we show that there are two important ways in which the consumer's ability to change her behavior in response to insurance can play a central role in the ability of insurance to protect her from risk. These are (i) by allowing optimal exploitation of real income gained in bad states, and (ii) by enabling more resources to be shifted to bad states than otherwise could be. We provide a theoretical characterization of these cases and quantify their importance empirically. Under standard parameterizations of demand for healthcare and health insurance, estimates in the literature imply that moral hazard accounts for as much as half of the total value of risk protection derived from insurance. Preventing consumers from changing their behavior in response to insurance would lower costs, but also result in a major loss of risk protection, on-net reducing consumer welfare in the population we study. Our results suggest that under-utilization of healthcare may thus be an equally important threat to welfare as over-utilization. |
| JEL: | D81 I13 I18 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34156 |
| By: | Ahmed Aboulhassane (ESCA Ecole de Management, Morocco); Azzeddine Allioui (ESCA Ecole de Management, Morocco) |
| Abstract: | This study explores the introduction of Overnight Index Swaps (OIS) to the Moroccan financial market. OIS are financial derivatives that involve the exchange of fixed interest rate payments for floating payments linked to an overnight index, and they are widely used for interest rate risk management. The primary goal of this research is to assess the feasibility and potential impact of OIS in Morocco through a thorough analysis of their characteristics, benefits, and the regulatory environment. A detailed examination of OIS reveals their potential advantages for Moroccan businesses and financial institutions, including improved interest rate risk management and increased liquidity. The study also evaluates the current regulatory framework in Morocco, assessing its readiness to support the introduction of OIS, and identifies key market participants and their needs for such financial instruments. To provide insights into market dynamics and future trends, the study employs quantitative models such as ARIMA (Auto Regressive Integrated Moving Average), SARIMA (Seasonal ARIMA), and the Simple Moving Average (SMA) method. These models are used to analyze historical interest rate data, identify patterns, and forecast future movements, thereby aiding in understanding the potential impact of OIS on the Moroccan market. The findings suggest that OIS could significantly enhance risk management practices and contribute to market stability in Morocco. By providing effective hedging against interest rate volatility, OIS can reduce financial uncertainty for institutions and corporations. Additionally, the introduction of OIS could attract more foreign investment and stimulate the growth of Morocco's financial derivatives market. |
| Keywords: | Overnight Index Swaps (OIS), Moroccan Financial Market, Morrocan Overnight Index Average, ARIMA, SARIMA, Risk Management, Financial Derivatives, Market Stability |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:smo:raiswp:05 |
| By: | James W. Taylor; Chao Wang |
| Abstract: | Value-at-risk (VaR) and expected shortfall (ES) have become widely used measures of risk for daily portfolio returns. As a result, many methods now exist for forecasting the VaR and ES. These include GARCH-based modelling, approaches involving quantile-based autoregressive models, and methods incorporating measures of realised volatility. When multiple forecasting methods are available, an alternative to method selection is forecast combination. In this paper, we consider the combination of a large pool of VaR and ES forecasts. As there have been few studies in this area, we implement a variety of new combining methods. In terms of simplistic methods, in addition to the simple average, the large pool of forecasts leads us to use the median and mode. As a complement to the previously proposed performance-based weighted combinations, we use regularised estimation to limit the risk of overfitting due to the large number of weights. By viewing the forecasts of VaR and ES from each method as the bounds of an interval forecast, we are able to apply interval forecast combining methods from the decision analysis literature. These include different forms of trimmed mean, and a probability averaging method that involves a mixture of the probability distributions inferred from the VaR and ES forecasts. Among other methods, we consider smooth transition between two combining methods. Using six stock indices and a pool of 90 individual forecasting methods, we obtained particularly strong results for a trimmed mean approach, the probability averaging method, and performance-based weighting combining. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.16919 |
| By: | Diego Fernando Cuesta-Mora; Camilo Gómez |
| Abstract: | This paper presents a stress test model used by the Financial Stability Department of the Banco de la República to assess the financial vulnerability of Colombian non financial firms. The model supports the Central Bank’s biannual Financial Stability Report and informs policy decisions by identifying firms that are exposed to credit risk under adverse economic conditions. The proposed model integrates three components: a dynamic balance sheet simulation framework; a suite of machine learning models to estimate credit default probabilities; and a final module that identifies firms at risk of default. This tool strengthens the Central Bank’s capacity to monitor and evaluate risks in the corporate sector with a forward-looking perspective. The paper details each component and illustrates the model’s results using a stress scenario. *****RESUMEN: Este documento presenta un modelo de prueba de estrés empleado por el Departamento de Estabilidad Financiera del Banco de la República para evaluar la vulnerabilidad financiera de las firmas no financieras colombianas. El modelo apoya el Reporte de Estabilidad Financiera semestral del Banco de la República y aporta al diseño de políticas al identificar firmas expuestas al riesgo crediticio en condiciones macroeconómicas adversas. El modelo propuesto integra tres componentes: un marco dinámico de simulación de balances; un conjunto de modelos de machine learning para estimar probabilidades de incumplimiento crediticio; y un módulo final que identifica firmas en riesgo de incumplimiento crediticio. Esta herramienta fortalece la capacidad del Banco de la República para monitorear y evaluar riesgos en el sector empresarial de forma prospectiva. El documento detalla cada componente e ilustra los resultados mediante un escenario de estrés. |
| Keywords: | Stress Testing, Credit Risk, Credit Default, Machine Learning, Prueba de estrés, Riesgo crediticio, Incumplimiento crediticio. |
| JEL: | G3 G21 G01 G17 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:bdr:borrec:1325 |
| By: | Jing Wang (Numerical Analysis, Delft University of Technology, Delft, the Netherlands); Shuaiqiang Liu (Numerical Analysis, Delft University of Technology, Delft, the Netherlands; ING Bank, Amsterdam, the Netherlands); Cornelis Vuik (Numerical Analysis, Delft University of Technology, Delft, the Netherlands) |
| Abstract: | This paper presents a deep generative modeling framework for controllably synthesizing implied volatility surfaces (IVSs) using a variational autoencoder (VAE). Unlike conventional data-driven models, our approach provides explicit control over meaningful shape features (e.g., volatility level, slope, curvature, term-structure) to generate IVSs with desired characteristics. In our framework, financially interpretable shape features are disentangled from residual latent factors. The target features are embedded into the VAE architecture as controllable latent variables, while the residual latent variables capture additional structure to preserve IVS shape diversity. To enable this control, IVS feature values are quantified via regression at an anchor point and incorporated into the decoder to steer generation. Numerical experiments demonstrate that the generative model enables rapid generation of realistic IVSs with desired features rather than arbitrary patterns, and achieves high accuracy across both single- and multi-feature control settings. For market validity, an optional post-generation latent-space repair algorithm adjusts only the residual latent variables to remove occasional violations of static no-arbitrage conditions without altering the specified features. Compared with black-box generators, the framework combines interpretability, controllability, and flexibility for synthetic IVS generation and scenario design. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.01743 |
| By: | Eckhard Platen; Kevin Fergusson |
| Abstract: | The hypothesis that there do not exist free lunches with vanishing risk (FLVRs) in the real market underpins the popular risk-neutral pricing and hedging methodology in quantitative finance. The paper documents the fact that this hypothesis can be safely rejected. It performs extremely accurately the hedging of an extreme-maturity zero-coupon bond (ZCB). This hedge is part of a portfolio that starts with zero initial wealth and invests dynamically in a total return stock market index and the savings account to generate at the maturity date of the extreme-maturity ZCB a strictly positive amount with strictly positive probability, which represents an FLVR. The fact that FLVRs naturally exist in the real market can be accommodated theoretically under the benchmark approach. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.07108 |
| By: | Friederike Niepmann; Leslie Sheng Shen |
| Abstract: | How do banks respond to geopolitical risk, and is this response distinct from other macroeconomic risks? Using U.S. supervisory data and new geopolitical risk indices, we show that banks reduce cross-border lending to countries with elevated geopolitical risk but continue lending to those markets through foreign affiliates—unlike their response to other macro risks. Furthermore, banks reduce domestic lending when geopolitical risk rises abroad, especially when they operate foreign affiliates. A simple banking model in which geopolitical shocks feature expropriation risk can explain these findings: Foreign funding through affiliates limits downside losses, making affiliate divestment less attractive and amplifying domestic spillovers. |
| Keywords: | geopolitical risk; bank lending; credit risk; international spillovers |
| JEL: | F34 F36 G21 |
| Date: | 2025–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedbwp:101470 |
| By: | Mikael Juselius; Aurea Ponte Marques; Nikola Tarashev |
| Abstract: | In managing their capital, banks balance the risk of breaching regulatory requirements against the cost of maintaining and speedily restoring "management" buffers. Using 68 quarters of data on 17 US and 17 euro-area banks, we find systematic reductions in steady-state management buffer targets and attendant rises in regulatory risk tolerance (RRT) following the Great Financial Crisis (GFC). This phenomenon is particularly pronounced at banks with higher capital requirements post GFC. In parallel, banks facing more volatile management buffer shocks set higher management buffer targets, suggesting that RRT is a conscious choice. High-RRT banks tend to respond to a depletion of their management buffers by cutting lending, whereas low-RRT banks reduce the riskiness of their assets in other ways - thus highlighting real-economy effects of capital management strategies. |
| Keywords: | capital management, management buffer target, speed of reversion, regulatory regimes |
| JEL: | G21 G28 E51 G31 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1287 |
| By: | Eduardo Abi Jaber; Louis-Amand G\'erard |
| Abstract: | We investigate the use of path signatures in a machine learning context for hedging exotic derivatives under non-Markovian stochastic volatility models. In a deep learning setting, we use signatures as features in feedforward neural networks and show that they outperform LSTMs in most cases, with orders of magnitude less training compute. In a shallow learning setting, we compare two regression approaches: the first directly learns the hedging strategy from the expected signature of the price process; the second models the dynamics of volatility using a signature volatility model, calibrated on the expected signature of the volatility. Solving the hedging problem in the calibrated signature volatility model yields more accurate and stable results across different payoffs and volatility dynamics. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.02759 |
| By: | Abdullah Karasan; \"Ozge Sezgin Alp |
| Abstract: | Filtering signal from noise is fundamental to accurately assessing spillover effects in financial markets. This study investigates denoised return and volatility spillovers across a diversified set of markets, spanning developed and developing economies as well as key asset classes, using a neural network-based denoising architecture. By applying denoising to the covariance matrices prior to spillover estimation, we disentangle signal from noise. Our analysis covers the period from late 2014 to mid-2025 and adopts both static and time-varying frameworks. The results reveal that developed markets predominantly serve as net transmitters of volatility spillovers under normal conditions, but often transition into net receivers during episodes of systemic stress, such as the Covid-19 pandemic. In contrast, developing markets display heightened instability in their spillover roles, frequently oscillating between transmitter and receiver positions. Denoising not only clarifies the dynamic and heterogeneous nature of spillover channels, but also sharpens the alignment between observed spillover patterns and known financial events. These findings highlight the necessity of denoising in spillover analysis for effective monitoring of systemic risk and market interconnectedness. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.01156 |
| By: | Gonzalo Ramirez-Carrillo; David Ortiz-Mora; Alex Aguilar-Larrotta |
| Abstract: | This study applies the Hierarchical Risk Parity (HRP) portfolio allocation methodology to the NUAM market, a regional holding that integrates the markets of Chile, Colombia and Peru. As one of the first empirical analyses of HRP in this newly formed Latin American context, the paper addresses a gap in the literature on portfolio construction under cross-border, emerging market conditions. HRP leverages hierarchical clustering and recursive bisection to allocate risk in a manner that is both interpretable and robust--avoiding the need to invert the covariance matrix, a common limitation in the traditional mean-variance optimization. Using daily data from 54 constituent stocks of the MSCI NUAM Index from 2019 to 2025, we compare the performance of HRP against two standard benchmarks: an equally weighted portfolio (1/N) and a maximum Sharpe ratio portfolio. Results show that while the Max Sharpe portfolio yields the highest return, the HRP portfolio delivers a smoother risk-return profile, with lower drawdowns and tracking error. These findings highlight HRP's potential as a practical and resilient asset allocation framework for investors operating in the integrated, high-volatility markets like NUAM. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.03712 |
| By: | Shuyu Gong; Taizhong Hu; Zhenfeng Zou |
| Abstract: | This paper proposes a novel class of generalized Expected-Shortfall (ES) norms constructed via distortion risk measures, establishing a unified analytical framework for risk quantification. The proposed norms extend conventional ES methodology by incorporating flexible distortion functions. Specifically, we develop the mathematical duality theory for generalized-ES norms to support portfolio optimization tasks, while demonstrating their practical utility through projection problem solutions. The generalizedES norms are also applied to detect anomalies of financial time series data. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.09444 |
| By: | Kiarash Firouzi |
| Abstract: | Extreme volatility, nonlinear dependencies, and systemic fragility are characteristics of cryptocurrency markets. The assumptions of normality and centralized control in traditional financial risk models frequently cause them to miss these changes. Four components-volatility stress testing, stablecoin hedging, contagion modeling, and Monte Carlo simulation-are integrated into this paper's modular simulation framework for crypto portfolio risk analysis. Every module is based on mathematical finance theory, which includes stochastic price path generation, correlation-based contagion propagation, and mean-variance optimization. The robustness and practical relevance of the framework are demonstrated through empirical validation utilizing 2020-2024 USDT, ETH, and BTC data. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.08915 |
| By: | Maximiliano San Millán |
| Abstract: | We examine the cross-border effects of bank capital requirements using a two-country DSGE model with financial frictions, calibrated to match Euro Area banking flows. Regulation follows a host country principle, applying uniformly to all bank exposures within a country, regardless of the banks' nationality. We find that increasing capital requirements in one country leads to a short run credit contraction in interconnected countries. However, long run credit spillovers are negligible. Instead, we find positive long run welfare spillovers, primarily due to higher bank dividend payouts to foreign bank owners, rather than increased financial stability in the foreign country. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:chb:bcchwp:1046 |
| By: | Leonardo Bargigli |
| Abstract: | Using a novel Generalized Autoregressive Score (GAS) methodology applied to EUR/USD high-frequency interdealer data on price variations and net demand in 2016, this paper provides evidence of a substantial violation of market efficiency in the foreign exchange market. The analysis shows that endogenous factors amplified efficient price fluctuations by at least 46\% on average, underscoring the importance of informational asymmetry and feedback trading in exchange rate dynamics. The key implication is that excess volatility of the EUR/USD exchange rate is not only sizeable but also structural, as it arises from mechanisms intrinsic to market functioning. |
| Keywords: | excess volatility, foreign exchange, high frequency data, score-driven model, GARCH, SVAR. |
| JEL: | G14 C32 C58 F31 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:frz:wpaper:wp2025_13.rdf |
| By: | Genjis A. Ossa; Luis H. Restrepo |
| Abstract: | This study investigates the impact of the pandemic on the most traded stocks in the Colombian stock market for the date of January 17, 2024. Based on the daily data of the most traded companies in Colombia for said date and covering a period general from 2015 to 2023, in a summarized way our analysis reveals that in the period 2015-2019, the return reached 5.70%, with a relatively low risk of 18.45%. However, in the following period 2016 -2020, although the yield decreased to 5.40%, the risk experienced a significant increase, reaching 24.64%. The beta also showed variations, being lowest in 2015-2019 with 0.61 and increasing to 1.02 in 2016-2020. The capital market line (LMC) in the constructed portfolios has a downward trend, indicating that the portfolio offers an expected rate of return lower than the risk-free rate. This finding is supported by the Sharpe index, which shows negative values throughout the periods studied. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.15111 |
| By: | Us-Salam, Danish (Central Bank of Ireland) |
| Abstract: | The growing popularity of crypto assets has driven increased engagement, often fuelled by promotional content that highlights past returns while downplaying risks. This paper evaluates the effectiveness of behaviourally informed risk warnings in such a setting. Using an online randomized controlled trial, participants viewed simulated investment promotions for two financial products: stocks and crypto assets. Treatments combined behaviorally informed risk warnings with past return information, the same information but with returns shown before warnings, or risk warnings paired with price volatility cues. The first treatment significantly improved risk comprehension and perception by 5% and 4%. These effects are further magnified by the order in which information is presented and by increasing the salience of risk information. Showing risk warnings after potential returns increases risk comprehension by 12% and risk perception by 6%, suggesting evidence in favor of recency bias. Similarly, showing risk warnings and price volatility cues improves risk comprehension by 10% and risk perception by 7%, reflecting the effect of heightened risk salience. These effects are driven by at-risk investors, defined as individuals who follow crypto market updates on social media but have not yet invested in crypto assets. In line with prior evidence, we find no effect among those who have previously invested in crypto assets, likely because their decisions are shaped more by past investment outcomes than by ex-ante warnings. |
| Keywords: | Crypto Assets, Risk Warnings, Order of Information, Recency Bias, Salience. |
| JEL: | D83 G11 G41 C93 G53 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:cbi:wpaper:9/rt/25 |
| By: | Carlo Nicolini; Matteo Manzi; Hugo Delatte |
| Abstract: | Portfolio optimization is a fundamental challenge in quantitative finance, requiring robust computational tools that integrate statistical rigor with practical implementation. We present skfolio, an open-source Python library for portfolio construction and risk management that seamlessly integrates with the scikit-learn ecosystem. skfolio provides a unified framework for diverse allocation strategies, from classical mean-variance optimization to modern clustering-based methods, state-of-the-art financial estimators with native interfaces, and advanced cross-validation techniques tailored for financial time series. By adhering to scikit-learn's fit-predict-transform paradigm, the library enables researchers and practitioners to leverage machine learning workflows for portfolio optimization, promoting reproducibility and transparency in quantitative finance. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.04176 |
| By: | William Du; Adrian Monninger; Xincheng Qiu; Tao Wang |
| Abstract: | We backcast subjective expectations on job finding and separation in the Survey of Consumer Expectations to 1978, and use real-time machine learning forecasting to proxy their objective counterparts. We document stickiness in job finding and separation expectations in reflecting changes in real-time job finding and separation risks and their substantial heterogeneity across observable and unobservable dimensions. Calibrating these facts into a heterogeneous-agent consumption-saving model reveals that belief stickiness attenuates the precautionary saving channel. As a result, workers under-insure during recessions, leading to a more sluggish recovery afterwards. The combination of high risk exposure and under-insurance due to belief stickiness operates as a novel amplification mechanism over the business cycle. |
| Keywords: | Business fluctuations and cycles; Labour markets; Monetary policy and uncertainty |
| JEL: | D14 E21 E71 G51 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocawp:25-23 |
| By: | Yin, Wei (School of Economics and Management, Southeast University, Nanjing, China); Wu, Fan (School of Economics and Management, Southeast University, Nanjing, China); Zhou, Peng (Cardiff Business School, Cardiff University, Cardiff, UK); Kirkulak-Uludag, Berna (Faculty of Business, Dokuz Eylul University, Ä°zmir, Turkiye) |
| Abstract: | The cryptocurrency market is characterized by rapid risk transmission, strong interconnectedness, and substantial downside risk, driven by technical similarities among major cryptocurrencies and herd behavior of investors. To analyze these dynamics, we construct a directed, weighted cryptocurrency risk spillover network consisting of 20 leading cryptocurrencies, using the DCC-GARCH-Copula-ΔCoVaR model. The market is segmented into six groups based on the interdependence of market values. The study evaluates the resilience of the network under a range of scenarios, including both random failures and intentional attacks, and validates the findings through a real-world case study of the 2022 Luna collapse. The results show that the overall resilience of the cryptocurrency risk network has improved as the market matures. Leading cryptocurrencies act as net risk receivers, enhancing the network's robustness. In contrast, active cryptocurrencies can accelerate the contagion of risks across the market. These findings suggest that effective risk management in the cryptocurrency market requires not only the stabilization of major cryptocurrencies but also the ongoing monitoring of smaller, high-activity cryptocurrencies. |
| Keywords: | Cryptocurrency, Risk spillover, Complex network, Resilience |
| JEL: | G11 G12 G15 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:cdf:wpaper:2025/18 |