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on Risk Management |
| By: | Ahnert, Toni; Bertsch, Christoph; Leonello, Agnese; Marquez, Robert |
| Abstract: | Shocks to a bank’s ability to raise liquidity at short notice can trigger depositor panics. Why don’t banks take a more active role in managing these risks? We study contingent risk management (hedging) in a standard global-games model of a bank run. Banks fail to hedge precisely when the exposure to a shock is most severe, just when risk management would have the biggest impact. Higher bank capital and broader deposit-insurance coverage crowd out hedging by banks that already manage risk, yet encourage more banks to establish risk management desks in the first place. The model also yields testable implications for hedging incentives and policy design. JEL Classification: G01, G21, G23 |
| Keywords: | bank runs, hedging, interim asset valuation, liquidity risk |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253076 |
| By: | Majid Asadi; Jeffrey S. Racine; Ehsan S. Soof; Shaomin Wu |
| Abstract: | We propose a unified approach for developing financial risk measures that are tailored to decision making in the face of two sources of uncertainty, namely, market-based and prudence-based. Common risk measures, including Value at Risk (VaR), Expected Shortfall (ES), and their extensions, are typically assessed at a fixed quantile of the risk variable’s probability distribution, given a prudence level p, thereby only considering market-based uncertainty. We advocate for risk assessment measures that incorporate both sources of uncertainty using a novel decomposition which we present as a representation theorem. Our representation theorem introduces a Mean-Covariance (MCov) decomposition of co-monotonically additive and coherent risk measures, expressed through the expected value of an investment and a covariance functional. Among these market-based uncertainty risk measures, ES has become the dominant measure used in financial decision analysis. We define a Bayesian risk measure as the expected value of ES, incorporating a variable prudence level governed by a prior probability distribution. Within a decision-theoretic framework, this Bayes Expected Shortfall (BES) serves as the optimal shortfall forecast under quadratic loss, termed the prior Bayes estimate. BES has a MCov decomposition where the covariance functional is determined by the prior distribution and is characterized by the properties given by the representation theorem. Specific prudence level distributions yield some premium principles in the existing literature. We explore both parametric and nonparametric methods for its estimation. |
| Keywords: | Decision analysis; Risk analysis; Bayes expected shortfall; Coherent risk measure; Gini |
| JEL: | D81 G11 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:mcm:deptwp:2025-04 |
| By: | Ayush Jha; Abootaleb Shirvani; Ali Jaffri; Svetlozar T. Rachev; Frank J. Fabozzi |
| Abstract: | This paper develops and estimates a multivariate affine GARCH(1, 1) model with Normal Inverse Gaussian innovations that captures time-varying volatility, heavy tails, and dynamic correlation across asset returns. We generalize the Heston-Nandi framework to a multivariate setting and apply it to 30 Dow Jones Industrial Average stocks. The model jointly supports three core financial applications: dynamic portfolio optimization, wealth path simulation, and option pricing. Closed-form solutions are derived for a Constant Relative Risk Aversion (CRRA) investor's intertemporal asset allocation, and we implement a forward-looking risk-adjusted performance comparison against Merton-style constant strategies. Using the model's conditional volatilities, we also construct implied volatility surfaces for European options, capturing skew and smile features. Empirically, we document substantial wealth-equivalent utility losses from ignoring time-varying correlation and tail risk. These findings underscore the value of a unified econometric framework for analyzing joint asset dynamics and for managing portfolio and derivative exposures under non-Gaussian risks. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.12198 |
| By: | Eduardo Abi Jaber (CMAP - Centre de Mathématiques Appliquées de l'Ecole polytechnique - Inria - Institut National de Recherche en Informatique et en Automatique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique); Louis-Amand Gérard (UP1 - Université Paris 1 Panthéon-Sorbonne) |
| Abstract: | We consider a stochastic volatility model where the dynamics of the volatility are given by a possibly infinite linear combination of the elements of the time extended signature of a Brownian motion. First, we show that the model is remarkably universal, as it includes, but is not limited to, the celebrated Stein-Stein, Bergomi, and Heston models, together with some path-dependent variants. Second, we derive the joint characteristic functional of the log-price and integrated variance provided that some infinitedimensional extended tensor algebra valued Riccati equation admits a solution. This allows us to price and (quadratically) hedge certain European and path-dependent options using Fourier inversion techniques. We highlight the efficiency and accuracy of these Fourier techniques in a comprehensive numerical study. |
| Keywords: | stochastic volatility, path signature, pricing, hedging, calibration, Fourier methods |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04435238 |
| By: | Sabrina Aufiero; Antonio Briola; Tesfaye Salarin; Fabio Caccioli; Silvia Bartolucci; Tomaso Aste |
| Abstract: | This paper investigates the evolving link between cryptocurrency and equity markets in the context of the recent wave of corporate Bitcoin (BTC) treasury strategies. We assemble a dataset of 39 publicly listed firms holding BTC, from their first acquisition through April 2025. Using daily logarithmic returns, we first document significant positive co-movements via Pearson correlations and single factor model regressions, discovering an average BTC beta of 0.62, and isolating 12 companies, including Strategy (formerly MicroStrategy, MSTR), exhibiting a beta exceeding 1. We then classify firms into three groups reflecting their exposure to BTC, liquidity, and return co-movements. We use transfer entropy (TE) to capture the direction of information flow over time. Transfer entropy analysis consistently identifies BTC as the dominant information driver, with brief, announcement-driven feedback from stocks to BTC during major financial events. Our results highlight the critical need for dynamic hedging ratios that adapt to shifting information flows. These findings provide important insights for investors and managers regarding risk management and portfolio diversification in a period of growing integration of digital assets into corporate treasuries. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.14655 |
| By: | Anubha Goel; Puneet Pasricha; Martin Magris; Juho Kanniainen |
| Abstract: | Time series foundation models (FMs) have emerged as a popular paradigm for zero-shot multi-domain forecasting. These models are trained on numerous diverse datasets and claim to be effective forecasters across multiple different time series domains, including financial data. In this study, we evaluate the effectiveness of FMs, specifically the TimesFM model, for volatility forecasting, a core task in financial risk management. We first evaluate TimesFM in its pretrained (zero-shot) form, followed by our custom fine-tuning procedure based on incremental learning, and compare the resulting models against standard econometric benchmarks. While the pretrained model provides a reasonable baseline, our findings show that incremental fine-tuning, which allows the model to adapt to new financial return data over time, is essential for learning volatility patterns effectively. Fine-tuned variants not only improve forecast accuracy but also statistically outperform traditional models, as demonstrated through Diebold-Mariano and Giacomini-White tests. These results highlight the potential of foundation models as scalable and adaptive tools for financial forecasting-capable of delivering strong performance in dynamic market environments when paired with targeted fine-tuning strategies. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.11163 |
| By: | Dylan Hogg; Hossein Jebeli |
| Abstract: | Assessing insolvency dynamics is essential for evaluating the financial health of non-financial corporations and mitigating macroeconomic and financial stability risks. This study leverages a newly created Statistics Canada dataset linking insolvency records with firm-level financial data to develop a robust framework for monitoring insolvency risk. We employ two complementary approaches: a univariate threshold method that establishes critical financial ratio benchmarks and a multivariate econometric model that accounts for interactions among financial indicators. These methods produce debt-at-risk measures that enhance risk assessment by combining simplicity with analytical depth. Finally, we apply these metrics to timely firm-level data, enabling continual monitoring of financial vulnerabilities. |
| Keywords: | Credit and credit aggregates; Econometric and statistical methods; Financial stability; Firm dynamics |
| JEL: | D22 G33 L20 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocadp:25-10 |
| By: | Kubra Bolukbas; Ertan Tok |
| Abstract: | The goal of this study is to identify the most effective model for predicting credit risk, the likelihood a commercial loan defaults (become a non-performing loan) in the Turkish banking sector and to determine which firm and loan characteristics influence that risk. The analysis draws on an unbalanced dataset of 1.2 million firm-level observations for 2018–2023, combining financial ratios with detailed loan- and firm-specific information. Class imbalance is addressed through oversampling (including SMOTE) and multiple down-sampling schemes. Although the risk is assessed ex-ante, model performance is evaluated ex-post using the ROC-AUC metric. Within tested conventional econometric and machine learning approaches accompanied with different sampling techniques, Extreme Gradient Boosting (XGBoost) with oversampling delivers the best result with a ROC-AUC score of 0.914. Compared with logistic regression under the same sampling setup, a 4.9- percentage-point increase in test ROC-AUC is attained, confirming the model’s superior predictive performance over conventional approaches. Accordingly, the study finds that the industry and location in which a firm operates, its loan-restructuring status, loan cost and type (fixed vs. floating rate), the firm’s record of bad checks, and core ratios capturing profitability, liquidity and leverage to be the most influential predictors of credit risk. |
| Keywords: | Credit Risk, Machine Learning Techniques, Financial Ratios, Banking Sector, Macro-Financial Stability, Feature Importance |
| JEL: | C52 C53 C55 G17 G2 G32 G33 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:tcb:wpaper:2508 |
| By: | Tony Paul, Nitin |
| Abstract: | Traditional GARCH models, while robust, are deterministic and their long-horizon forecasts converge to a static mean, failing to capture the dynamic nature of real markets. Conversely, classical stochastic volatility models often introduce significant implementation and calibration complexity. This paper introduces GARCH-FX (GARCH Forecasting eXtension), a novel and accessible framework that augments the classic GARCH model to generate realistic, stochastic volatility paths without this prohibitive complexity. GARCH-FX is built upon the core strength of GARCH—its ability to estimate long-run variance—but replaces the deterministic multi-step forecast with a stochastic simulation engine. It injects controlled randomness through a Gamma-distributed process, ensuring the forecast path is non-smooth and jagged. Furthermore, it incorporates a modular regime-switching multiplier, providing a flexible interface to inject external views or systematic signals into the forecast’s mean level. The result is a powerful and intuitive framework for generating dynamic long-term volatility scenarios. By separating the drivers of mean-level shifts from local stochastic behavior, GARCHFX aims to provide a practical tool for applications requiring realistic market simulations, such as stress-testing, risk analysis, and synthetic data generation. |
| Keywords: | Stochastic Volatility Forecasting, GARCH Extensions, Regime-Switching Volatility, Gamma-Distributed, Volatility, Volatility Forecast Uncertainty, Nonlinear GARCH Models, Stochastic Vol Forecast, Financial Time Series, Heteroskedasticity Dynamics, Gamma Noise in Volatility |
| JEL: | C22 C53 C6 |
| Date: | 2025–07–10 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125321 |
| By: | Manon Costa (IMT - Institut de Mathématiques de Toulouse UMR5219 - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - INSA Toulouse - Institut National des Sciences Appliquées - Toulouse - INSA - Institut National des Sciences Appliquées - UT - Université de Toulouse - UT2J - Université Toulouse - Jean Jaurès - UT - Université de Toulouse - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique); Sébastien Gadat (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Lorick Huang (INSA Toulouse - Institut National des Sciences Appliquées - Toulouse - INSA - Institut National des Sciences Appliquées - UT - Université de Toulouse) |
| Abstract: | This article studies and solves the problem of optimal portfolio allocation with CV@R penalty when dealing with imperfectly simulated financial assets. We use a Stochastic biased Mirror Descent to find optimal resource allocation for a portfolio whose underlying assets cannot be generated exactly and may only be approximated with a numerical scheme that satisfies suitable error bounds, under a risk management constraint. We establish almost sure asymptotic properties as well as the rate of convergence for the averaged algorithm. We then focus on the optimal tuning of the overall procedure to obtain an optimized numerical cost. Our results are then illustrated numerically on simulated as well as real data sets |
| Keywords: | Stochastic mirror descent, Biased observations, Risk management constraint, Portfolio selection, Discretisation |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05147991 |
| By: | Macchiati, Valentina; Cappiello, Lorenzo; Giuzio, Margherita; Ianiro, Annalaura; Lillo, Fabrizio |
| Abstract: | We propose a novel framework to assess systemic risk stemming from the inadequate liquidity preparedness of non-bank financial institutions (NBFIs) to derivative margin calls. Unlike banks, NBFIs may struggle to source liquidity and meet margin calls during periods of significant asset price fluctuations, potentially triggering asset fire sales and amplifying market volatility. We develop a set of indicators and statistical methods to assess liquidity preparedness and examine risk transmission through common asset holdings and counterparty exposures. Applying our framework to euro area NBFIs during the Covid-19 turmoil and the 2022–2023 monetary tightening, we observe an increase in distressed entities, which, in turn, seem to exhibit more liquidity-driven selling behaviours than their non-distressed peers. Network analysis suggests that certain counterparties of distressed entities appear particularly vulnerable to margin call-induced liquidity shocks. Our framework offers policymakers valuable tools to enhance the monitoring and resilience of the NBFI sector. JEL Classification: C02, E52, G01, G11, G23 |
| Keywords: | derivative margin calls, financial stability, liquidity risk, network analysis, non-bank financial institutions |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253074 |
| By: | Perukrishnen Vytelingum; Rory Baggott; Namid Stillman; Jianfei Zhang; Dingqiu Zhu; Tao Chen; Justin Lyon |
| Abstract: | In this paper, we describe a novel agent-based approach for modelling the transaction cost of buying or selling an asset in financial markets, e.g., to liquidate a large position as a result of a margin call to meet financial obligations. The simple act of buying or selling in the market causes a price impact and there is a cost described as liquidity risk. For example, when selling a large order, there is market slippage -- each successive trade will execute at the same or worse price. When the market adjusts to the new information revealed by the execution of such a large order, we observe in the data a permanent price impact that can be attributed to the change in the fundamental value as market participants reassess the value of the asset. In our ABM model, we introduce a novel mechanism where traders assume orderflow is informed and each trade reveals some information about the value of the asset, and traders update their belief of the fundamental value for every trade. The result is emergent, realistic price impact without oversimplifying the problem as most stylised models do, but within a realistic framework that models the exchange with its protocols, its limit orderbook and its auction mechanism and that can calculate the transaction cost of any execution strategy without limitation. Our stochastic ABM model calculates the costs and uncertainties of buying and selling in a market by running Monte-Carlo simulations, for a better understanding of liquidity risk and can be used to optimise for optimal execution under liquidity risk. We demonstrate its practical application in the real world by calculating the liquidity risk for the Hang-Seng Futures Index. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.15296 |
| By: | Qingyu Li; Chiranjib Mukhopadhyay; Abolfazl Bayat; Ali Habibnia |
| Abstract: | Recent advances in quantum computing have demonstrated its potential to significantly enhance the analysis and forecasting of complex classical data. Among these, quantum reservoir computing has emerged as a particularly powerful approach, combining quantum computation with machine learning for modeling nonlinear temporal dependencies in high-dimensional time series. As with many data-driven disciplines, quantitative finance and econometrics can hugely benefit from emerging quantum technologies. In this work, we investigate the application of quantum reservoir computing for realized volatility forecasting. Our model employs a fully connected transverse-field Ising Hamiltonian as the reservoir with distinct input and memory qubits to capture temporal dependencies. The quantum reservoir computing approach is benchmarked against several econometric models and standard machine learning algorithms. The models are evaluated using multiple error metrics and the model confidence set procedures. To enhance interpretability and mitigate current quantum hardware limitations, we utilize wrapper-based forward selection for feature selection, identifying optimal subsets, and quantifying feature importance via Shapley values. Our results indicate that the proposed quantum reservoir approach consistently outperforms benchmark models across various metrics, highlighting its potential for financial forecasting despite existing quantum hardware constraints. This work serves as a proof-of-concept for the applicability of quantum computing in econometrics and financial analysis, paving the way for further research into quantum-enhanced predictive modeling as quantum hardware capabilities continue to advance. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.13933 |
| By: | Agnello, Luca; Castro, Vítor; Sousa, Ricardo M.; Hammoudeh, Shawkat |
| Abstract: | Using a rich high-frequency and a cross-country panel of daily sovereign CDS spreads, we employ local projections to estimate the dynamic response of sovereign risk to the occurrence of natural disasters. We find that climatological and, to a lesser extent, hydrological events have a small and short-lived effect on the sovereign CDS spreads. We also explore whether anticipatory effects arise before a disaster unfolds, and confirm that the expectations of imminent disasters do not substantially affect CDS pricing. On the other hand, we show that the sovereign risk is dominated by regional and global financial spillovers, thus reflecting the systemic nature of the sovereign credit markets. Our results also suggest that governments may benefit from developing disaster-specific risk reduction and fiscal resilience strategies, as well as early-warning models that integrate disaster forecasting into risk monitoring frameworks. Sovereigns’ coordination and risk-pooling mechanisms may also be essential in times of regional calamities. Moreover, portfolio hedging strategies should include short-term protective positions in the vulnerable sovereigns during known disaster seasons. Disaster-integrated ESG strategies could also enhance the portfolio resilience. |
| Keywords: | expectations; natural disasters; credit default swaps; sovereign risk; local projections; spillovers |
| JEL: | Q54 H30 H60 |
| Date: | 2025–07–03 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128535 |
| By: | Herrera, Luis; Pirovano, Mara; Scalone, Valerio |
| Abstract: | This paper proposes a novel yet intuitive method for the calibration of the CCyB through the cycle in the euro area, including the positive neutral CCyB rate. The paper implements the Risk-to-Buffer framework by Couaillier and Scalone (2024) in both a DSGE and macro time series setting and proposes a calibration of the PN CCyB aimed to reduce the macroeconomic amplification of shocks occurring in an environment where risks are neither subdued nor elevated. The suggested positive neutral CCyB rates for the euro area are consistent across methodologies and robust to alternative specifications, ranging between 1% and 1.5%. The results also highlight the role of different shocks and sources of cyclical systemic risk for the calibration of the CCyB through the cycle. The flexibility of the method regarding the modeling tools, the selection of specific levels of risks as well as the choice of state variables and of exogenous shocks make it particularly suitable to be tailored to national specificities and policymakers’ preferences. JEL Classification: C32, E51, E58, G01 |
| Keywords: | capital requirements, countercyclical capital buffer, financial stability, macroprudential policy |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253075 |
| By: | Gianluca De Nard; Damjan Kostovic |
| Abstract: | The paper introduces a new type of shrinkage estimation that is not based on asymptotic optimality but uses artificial intelligence (AI) techniques to shrink the sample eigenvalues. The proposed AI Shrinkage estimator applies to both linear and nonlinear shrinkage, demonstrating improved performance compared to the classic shrinkage estimators. Our results demonstrate that reinforcement learning solutions identify a downward bias in classic shrinkage intensity estimates derived under the i.i.d. assumption and automatically correct for it in response to prevailing market conditions. Additionally, our data-driven approach enables more efficient implementation of risk-optimized portfolios and is well-suited for real-world investment applications including various optimization constraints. |
| Keywords: | Covariance matrix estimation, linear and nonlinear shrinkage, portfolio management reinforcement learning, risk optimization |
| JEL: | C13 C58 G11 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:zur:econwp:470 |
| By: | Antonio Penta; Larbi Alaoui |
| Abstract: | We revisit the long-lasting debate about the meaning of the utility function used in the standard Expected Utility (EU) model. Despite the common view that EU forces risk aversion and diminishing marginal utility of wealth to be pegged to one another, here we show that this is not the case. Marginal utility for money is an input into risk attitude, but it is not its sole determinant. The attitude towards 'pure risk' is also a contributing factor, and it is independent from the former. We discuss several theoretical implications of this result, for the following topics: (i) non-neutral risk attitudes for profit maximizing firms; (ii) risk-aversion over time lotteries in the presence of discounting; (iii) the equity premium puzzle. We also discuss matters of identification: (i) for firms; (ii) via proxies ; (iii) via standard MLE-methods under parametric restrictions; and (iv) cross-context elicitation in multi-dimensional settings, and its relationship with the methods and results from the psychology literature. |
| Keywords: | Risk Aversion, utility function, marginal utility |
| JEL: | C72 C91 C92 D80 D91 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:bge:wpaper:1494 |
| By: | Matteo Buttarazzi; Claudia Ceci |
| Abstract: | This paper develops a continuous-time filtering framework for estimating a hazard rate subject to an unobservable change-point. This framework arises naturally in both financial and insurance applications, where the default intensity of a firm or the mortality rate of an individual may experience a sudden jump at an unobservable time, representing, for instance, a shift in the firm's risk profile or a deterioration in an individual's health status. By employing a progressive enlargement of filtration, we integrate noisy observations of the hazard rate with default-related information. We characterise the filter, i.e. the conditional probability of the change-point given the information flow, as the unique strong solution to a stochastic differential equation driven by the innovation process enriched with the discontinuous component. A sensitivity analysis and a comparison of the filter's behaviour under various information structures are provided. Our framework further allows for the derivation of an explicit formula for the survival probability conditional on partial information. This result applies to the pricing of credit-sensitive financial instruments such as defaultable bonds, credit default swaps, and life insurance contracts. Finally, a numerical analysis illustrates how partial information leads to delayed adjustments in the estimation of the hazard rate and consequently to mispricing of credit-sensitive instruments when compared to a full-information setting. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.13185 |
| By: | Kim, Dohan |
| Abstract: | One defining feature of financial crises, evident in U.S. and international data, is asymmetric bank distress—concentrated losses on a subset of banks. This paper proposes a model in which shocks to borrowers’ productivity dispersion lead to asymmetric bank losses. The framework exhibits a “bank distress amplifier, ” exacerbating economic downturns by causing costly bank failures and raising uncertainty about the solvency of banks, thereby pushing banks to deleverage. Quantitative analysis shows that the bank distress amplifier doubles investment decline and increases the spread by 2.5 times during the Great Recession compared to a standard financial accelerator model. The mechanism helps explain how a seemingly small shock can sometimes trigger a large crisis. |
| Date: | 2025–07–10 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:11170 |