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on Risk Management |
| By: | Sam Schulhofer-Wohl |
| Abstract: | Opening remarks delivered by Sam Schulhofer-Wohl at the Third-party Service Provider Risks in the Economy and Financial System workshop. |
| Keywords: | risk management |
| Date: | 2025–10–16 |
| URL: | https://d.repec.org/n?u=RePEc:fip:feddsp:101974 |
| By: | Shuping Shi (Department of Economics, Macquarie University); Jun Yu (Faculty of Business Administration, University of Macau); Chen Zhang (Department of Economics, Sun Yat-sen University) |
| Abstract: | Forecasting realized volatility (RV) is central to financial econometrics, with important implications for risk management, asset allocation, and derivative pricing. Motivated by the ongoing debate on volatility modeling, this paper provides a comprehensive empirical comparison of many alternative models. We evaluate leading continuous time models estimated using state-of-the-art methods from the rough volatility literature, together with both standard long-memory autoregressive fractionally integrated moving average (ARFIMA) models and their rough-volatility extensions, as well as several variants of the heterogeneous autoregressive (HAR) model and their logarithmic counterparts. The models are applied to a large panel of equities and cryptocurrencies, with performance assessed using both statistical and economic criteria. Our results show that for equities, continuous time models consistently outperform discrete time alternatives across all evaluation criteria and forecasting horizons. The fractional Brownian motion model for log RV performs best at short horizons, while the fractional Ornstein Uhlenbeck model for log RV dominates in the long run. For cryptocurrencies, a mild divergence emerges between economic and statistical performance: based on realized utility, the quarticity-augmented heterogeneous autoregressive (HARQ) model for RV leads in the short term and the Brownian semistationary models prevail at longer horizons, whereas the HAR-type models for log RV deliver superior statistical accuracy. |
| Keywords: | Realized volatility, Continuous-time models, Discrete-time models, forecasting, economic utility |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:boa:wpaper:202537 |
| By: | Gabriele Casto |
| Abstract: | We introduce the Historical and Dynamic Volatility Ratios (HVR/DVR) and show that equity and index volatilities are cointegrated at intraday and daily horizons. This allows us to construct a VECM to forecast portfolio volatility by exploiting volatility cointegration. On S&P 500 data, HVR is generally stationary and cointegration with the index is frequent; the VECM implementation yields substantially lower mean absolute percentage error (MAPE) than covariance-based forecasts at short- to medium-term horizons across portfolio sizes. The approach is interpretable and readily implementable, factorizing covariance into market volatility, relative-volatility ratios, and correlations. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.23533 |
| By: | Joshi, Satyadhar |
| Abstract: | The rapid adoption of generative AI in various sectors, particularly in finance, has introduced new challenges and opportunities for model risk management (MRM). This paper provides a comprehensive review of the current state of MRM in the context of generative AI, focusing on the risks, regulatory frameworks, and mitigation strategies. We explore the implications of generative AI on financial institutions, the evolving regulatory landscape, and the role of advanced MRM frameworks in ensuring compliance and mitigating risks. By synthesizing insights from 50+ recent articles, this paper aims to provide a roadmap for future research and practical applications of MRM in the generative AI era. It examines the key risks associated with these models, including bias, lack of transparency, and potential for misuse, and explores the regulatory frameworks and best practices being developed to mitigate these risks. We delve into the specific challenges faced by financial institutions in adapting their MRM strategies to encompass generative AI, and highlight the emerging tools and technologies that can support effective risk management. This paper also discusses quantitative methods for risk quantification, such as probabilistic frameworks, Monte Carlo simulations, and adversarial risk metrics, which are essential for assessing the reliability and robustness of generative AI models. Foundational metrics, including fairness measures like demographic parity and equalized odds, are explored to address bias and ensure ethical AI deployment. Additionally, the paper presents pseudocode for key algorithms, such as risk quantification and adversarial risk calculation, to provide a practical understanding of these methods. A detailed gap analysis identifies critical shortcomings in current MRM frameworks, such as the lack of standardized validation methods and inadequate handling of adversarial robustness. Based on these gaps, the paper proposes solutions, including the development of advanced validation frameworks, integration of fairness metrics, and alignment with regulatory standards. These findings and proposals aim to guide financial institutions in adopting generative AI responsibly while addressing the unique risks it poses. This paper serves as a valuable resource for professionals and researchers seeking to understand and navigate the complexities of MRM in the age of generative AI. |
| Keywords: | Model Risk Management, Generative AI, Financial Institutions, Regulatory Compliance, Risk Mitigation, AI Governance. |
| JEL: | C52 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125221 |
| By: | Andreas Fuster; Teodora Paligorova; James Vickery |
| Abstract: | We use bond-level data to study how US banks manage risk in their securities portfolios, focusing on the period of rapidly-rising interest rates in 2022-23, and examine the role of financial and regulatory frictions in shaping bank behavior. Interest rate risk in bank portfolios increased sharply as rates rose, with significant cross-bank heterogeneity depending on ex ante holdings of bonds with embedded options. In response, exposed banks shortened the duration of bond purchases but did not actively sell risky securities or expand “qualified” hedging activity; securities also played a limited role in banks’ responses to deposit outflows. We identify two frictions that can help explain this inertia. First, we find that banks are highly averse to selling underwater bonds at a discount to book value—e.g., banks were 8-9 times more likely to trade bonds with unrealized gains than unrealized losses in 2022-23. This “strategic” trading is more pronounced for banks that do not recognize unrealized losses in regulatory capital and banks facing stock market pressure. Second, frictions in establishing qualified accounting hedges limited hedging activity depending on bond type and accounting classification. Banks did, however, reduce the interest-rate sensitivity of regulatory capital by classifying the riskiest bonds as held-to-maturity. |
| Keywords: | securities; gains trading; bank; capital regulation; bonds |
| JEL: | G11 G21 G23 G28 |
| Date: | 2025–10–17 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpwp:101960 |
| By: | Meng cai; Tianze Li |
| Abstract: | The Heston stochastic-local volatility (HSLV) model is widely used to capture both market calibration and realistic volatility dynamics, but simulating its CIR-type variance process is numerically challenging.This paper compare two alternative schemes for HSLV simulation: the truncated Euler method and the backward Euler method with the conventional Euler and almost exact simulation methods in \cite{van2014heston} by using a Monte Carlo method.Numerical results show that the truncated method achieves strong convergence and remains robust under high volatility, while the backward method provides the smallest errors and most stable performance in stress scenarios, though at higher computational cost. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.24449 |
| By: | Yee, Brandon |
| Abstract: | We develop a dynamic asset pricing model to analyze investor behavior around high-uncertainty events such as earnings announcements and FDA approvals. Our key innovations include: (1) a two-risk framework distinguishing between directional news risk (uncertainty about event outcomes) and impact uncertainty (uncertainty about market response magnitude); (2) a three-phase volatility process (pre-event rise, event-day peak, post-event dynamics) modeled through GJR-GARCH specifications; and (3) integration of heterogeneous investor beliefs and asymmetric transaction costs. Investors with mean-variance preferences trade an event-related asset and a generic risky asset in a multi-period framework. We solve for equilibrium prices with three investor types: informed, uninformed, and liquidity traders. Our model generates a testable hypothesis predicting that risk-adjusted returns, specifically return-to-variance and Sharpe ratios, peak during the post-event rising phase due to high volatility and biased expectations. Empirical validation using 2000-2024 data from earnings announcements and FDA approvals provides exceptionally strong support for our predictions, with return-to-variance ratios showing 4.4x amplification for FDA approvals and 9.5x enhancement for earnings announcements during the post-event rising phase. The framework provides insights for risk management and investment timing around high-uncertainty events. |
| Keywords: | Asset pricing, Two-risk framework, Directional news risk, Impact uncertainty, Event-driven returns, Volatility dynamics, Mean-variance preferences, Transaction costs, Asymmetric volatility, Liquidity trading |
| JEL: | C0 C01 C5 C50 C60 G0 G02 G11 G12 |
| Date: | 2025–05–30 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125993 |
| By: | Gregorio Curello; Ludvig Sinander; Mark Whitmeyer |
| Abstract: | We uncover a close link between outside options and risk attitude: when a decision-maker gains access to an outside option, her behaviour becomes less risk-averse, and conversely, any observed decrease of risk-aversion can be explained by an outside option having been made available. We characterise the comparative statics of risk-aversion, delineating how effective risk attitude (i.e. actual choice among risky prospects) varies with the outside option and with the decision-maker's 'true' risk attitude. We prove that outside options are special: among transformations of a decision problem, those that amount to adding an outside option are the only ones that always reduce risk-aversion. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.14732 |
| By: | Stefan Jacewitz |
| Abstract: | With the passage of the GENIUS Act of 2025, stablecoins are poised to play a greater role in the U.S. financial system. Although very similar to bank deposits, stablecoins lack the government guarantees offered for bank deposits in the form of deposit insurance. This paper is the first to analytically derive the price of hypothetical “deposit” insurance for stablecoins. The price of this insurance is shown to be a function of the volatility of the stablecoin’s price (the price of debt), reflecting Merton’s (1977) deposit insurance pricing model. Empirical estimates of the price of stablecoin insurance are developed in a novel way: using the high frequency data on the spot-price of issuer debt that is available for stablecoins, but not for bank deposits. |
| Keywords: | stablecoins; deposit insurance; bank deposits |
| JEL: | G21 G23 G28 G29 |
| Date: | 2025–10–17 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedkrw:101961 |
| By: | Julian Sester; Huansang Xu |
| Abstract: | In this paper, we propose an alternative valuation approach for CAT bonds where a pricing formula is learned by deep neural networks. Once trained, these networks can be used to price CAT bonds as a function of inputs that reflect both the current market conditions and the specific features of the contract. This approach offers two main advantages. First, due to the expressive power of neural networks, the trained model enables fast and accurate evaluation of CAT bond prices. Second, because of its fast execution the trained neural network can be easily analyzed to study its sensitivities w.r.t. changes of the underlying market conditions offering valuable insights for risk management. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.25899 |
| By: | Yan Dolinsky |
| Abstract: | In this work we study a continuous time exponential utility maximization problem in the presence of a linear temporary price impact. More precisely, for the case where the risky asset is given by the Ornstein-Uhlenbeck diffusion process we compute the optimal portfolio strategy and the corresponding value. Our method of solution relies on duality, and it is purely probabilistic. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.25472 |
| By: | Ortl, Aljoša; Sajinčič, Miha |
| Abstract: | Recent concerns about the German automotive slowdown and related US tariffs pose risks for small open economies. We focus on Slovenian companies integrated into these supply chains. Using comprehensive data-intensive analysis, we define “companies at risk” as companies operating in the automotive sector with at least 10% of revenues from German trade receivables. They account for approximately 2% of total bank exposures, 5.3% of value added, and 5% of operating revenues. At the municipal level, several clusters with higher concentrations of such firms are identified. To address data limitations and the arbitrariness of our classification, we conduct robustness checks. Using input–output tables, we also assess potential spillover effects across supply chains. Our granular company-level approach provides valuable insights for potential policy responses targeting the real economy, banking system, and/or local municipalities. |
| Keywords: | German automotive industry; input–output analysis; supply chains; companies at risk; potential risks; risk evaluation for banks; credit and economic risk assessment |
| JEL: | F14 G32 L62 |
| Date: | 2025–08–04 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126321 |