|
on Risk Management |
| By: | Soane, Emma |
| Abstract: | Organizational risk exposure arises from external sources and internal activities intended to reduce the potential for risks to cause harm. While organizational risk exposure is necessary to realize opportunities, misalignment between risk management and risk exposure may achieve the opposite outcomes. Through an inductive, qualitative case study, I explore how 73 managers in a technology services company perceive and manage risk and consider how their risk management contributes to organizational risk exposure. Through analysing informants' accounts, I show how managers perceive risk in terms of uncertainty. These perceptions motivate risk management that involves knowledge acquisition and knowledge sharing. By identifying these individual‐level concepts and how they shape risk management, I elucidate how interdependence between practices and the supporting structures necessary for acquiring and sharing knowledge constitutes cross‐level mechanisms that connect individual behaviour with organizational outcomes. Yet variable and undeveloped structures hamper the effectiveness of risk management by reducing the visibility of risks. The corollary is that executives' decisions and actions are not informed by risk knowledge, and organizational risk exposure is increased unknowingly. I advance psychological microfoundations research with these findings and make novel contributions to theorizing about cross‐level mechanisms involving interdependence between risk management and organizational structures. |
| Keywords: | microfoundations; organizational risk; risk management; risk perception |
| JEL: | J50 G32 |
| Date: | 2026–05–11 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:138316 |
| By: | Simone Alfarano (Universitat Jaume I); Omar Blanco-Arroyo (Universitat de València) |
| Abstract: | We study how rising concentration in the U.S. stock market affects the transmis- sion of firm-level risk to aggregate volatility. Using a variance decomposition that separates common and granular components of market returns, we document a shift in the composition of idiosyncratic risk since the mid-2010s. Whereas idiosyncratic volatility previously reflected cross-firm comovement, it is now increasingly driven by the weighted variances of a small number of large firms. We show that this change is not explained by concentration alone, but by a reallocation of idiosyncratic risk toward dominant firms. As a result, aggregate volatility becomes more sensitive to firm-specific shocks at the top of the size distribution. |
| Keywords: | granularity; market concentration; idiosyncratic volatility; firm size distribution; aggregate volatility |
| JEL: | G12 G14 E44 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:eec:wpaper:2608 |
| By: | Jing He; Shuzhen Yang |
| Abstract: | In this paper, we investigate a portfolio investment problem under volatility uncertainty and short-sale constraints market via sublinear expectation which is used to model volatility uncertainty. We assume the stocks admit volatility uncertainty. Thus the related portfolio has upper variance (maximum risk) and lower variance (minimum risk). By introducing a risk factor $w$ to conduct coupled modeling of the maximum and minimum risks, a simplified Sublinear Expectation Mean-Uncertainty Variance (SLE-MUV) model is constructed. Theoretically, we show that the Pareto frontier of the SLE-MUV model is a continuous convex curve, and its optimal solution can be expressed as a polynomial analytical expression with respect to the risk factor $w$. Empirically, we systematically test the practical performance of the SLE-MUV model and conduct comparative analysis with the traditional Mean-Variance (MV) model as the benchmark based on three sets of samples -- simulated generated data, data of the US stock market and the A-share market. The empirical results show that the SLE-MUV model can significantly improving the risk-adjusted return of the investment portfolio. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.02666 |
| By: | Jiayu Yi; Minxuan Hu; Wenxi Sun; Ziheng Chen |
| Abstract: | This research establishes ESG as a state dependent insurance mechanism against equity crashes by addressing the decoupling of unconditional alpha from tail risk resilience. By validating market stress regimes as distinct economic states through a drawdown-based truncation rule, the study demonstrates that high ESG ratings materially reduce the incidence of discrete crash events during systemic drawdowns. To address the selection bias and high-dimensional confounding inherent in traditional linear frameworks, we implement Double Machine Learning as a structural deconfounding layer. Unlike simple predictive modeling, the Double Machine Learning framework utilizes machine learning to handle complex nuisance parameters, allowing us to isolate the asymmetric treatment effects of ESG across different market states. Distributional analysis reveals the underlying mechanism as ESG specifically attenuates the severity of realized tail losses at the most adverse quantiles instead of shifting the entire return distribution. Confirmed by structural estimates, this protection functions as priced insurance that incurs performance drags during stable periods while providing critical resilience when tail risks are most acute. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.04479 |
| By: | Yosuke Fukunishi (Graduate School of Economics, The University of Tokyo); Haorong Qiu (Formerly Graduate School of Economics, The University of Tokyo); Akihiko Takahashi (School of Interdisciplinary Mathematical Sciences/Graduate School of Advanced Mathematical Sciences, Meiji University) |
| Abstract: | Modeling the probability distribution of stock returns is a fundamental challenge in quantitative finance, with significant implications for risk management, derivative pricing, and portfolio optimization. This paper proposes a diffusion-based generative framework tailored to the statistical characteristics of financial return distributions. By incorporating learned reverse-process variance, velocity parameterization, and a sigmoid noise schedule, the proposed model aims to improve distributional fidelity, particularly in the tails. The framework is further extended to regime-conditional generation, enabling controlled simulation of distinct market states. Empirical evaluations demonstrate that the proposed approach outperforms classical parametric models such as Geometric Brownian Motion and GARCH, deep generative baselines like VAEs, and existing diffusion-based methods across multiple distributional metrics, including higher-order moments and tail behaviors. The results highlight the potential of diffusion models as robust tools for synthetic return generation and scenario analysis in finance. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:cfi:fseres:cf627 |
| By: | Lillian Han; Ali Ozdagli; Dylan Ryfe |
| Abstract: | Despite asset managers playing an increasingly pivotal role in investment decisions—leading to more similar portfolios—analysis of life insurance firms and their advisers reveals a relatively small threat to financial stability. |
| Keywords: | asset managers; banking; finance; insurance companies |
| Date: | 2025–08–19 |
| URL: | https://d.repec.org/n?u=RePEc:fip:d00001:101536 |
| By: | Lutz Kilian; Michael D. Plante; Alexander W. Richter |
| Abstract: | Notwithstanding the attention geopolitical events in oil markets have attracted, we find that geopolitical oil price risk is unlikely to generate sizable recessionary effects. |
| Keywords: | energy; oil prices; international economics |
| Date: | 2025–02–18 |
| URL: | https://d.repec.org/n?u=RePEc:fip:d00001:99566 |
| By: | Alexandros E. Tzikas; Emmanuel J. Cand\`es; Trevor Hastie; Stephen P. Boyd; Mykel J. Kochenderfer; Ronald N. Kahn |
| Abstract: | Estimating the covariance of asset returns, i.e., the risk model, is a key component of financial portfolio construction and evaluation. Most risk modeling approaches produce a factor model that decomposes the asset variability into two components: the first attributed to a small number of factors that are common among the assets and the second attributed to the idiosyncratic behavior of each asset. Third-party providers typically provide risk models to investors, and while these models are typically of high quality, they may fail to capture important information, e.g., changing market regimes and transient factors. To overcome these limitations, we propose a systematic method based on maximum likelihood estimation to enhance an existing factor model by both refining the given model and adding new statistical factors. Our approach relies only on the observed sequence of realized returns and on the choice of two hyperparameters: the number of additional factors and the half-life parameter that determines the weights assigned to returns in the log-likelihood objective. Importantly, our methodology applies to the situation where asset returns may be missing, making it suitable for typical equity datasets. We demonstrate our approach on the Barra short-term US risk model, a high-quality risk model used in practice, for a universe of US high-capitalization equities. We show that the proposed extension captures structure in the returns that is missed by the original model. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.12977 |
| By: | Oprica, Silviu; Bobeică, Gabriel |
| Abstract: | We empirically analyse the role of judgement in assigning overall scores by the euro area supervisors as part of the yearly Supervisory Review and Evaluation Process (SREP), which evaluates banks’ risks and sets supervisory actions. We also analyse its role in shaping the drivers of the Pillar 2 capital requirement (P2R) that banks must fulfil. We find that supervisors actively adjust the weight of the components of the overall score to reflect qualitative information, thereby smoothing fluctuations in the final assessment. The analysis reveals a common supervisory judgement channel, which could reflect shared priorities and concerns, such as systemic vulnerabilities or macroeconomic conditions. We also show that certain risks, such as credit risk, can play a decisive role in the overall assessment of a bank’s viability. These findings underpin the critical role of judgement in adapting supervisory frameworks to evolving risks and systemic conditions, providing flexibility at both the individual and system-wide levels. JEL Classification: G21, G28, C23, E58 |
| Keywords: | overall SREP score, panel data, Pillar 2 capital requirements (P2R), supervisory judgement, Supervisory Review and Evaluation Process (SREP) |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263233 |
| By: | Anand, Kartik; Leonello, Agnese; Panetti, Ettore; Kazinnik, Sophia |
| Abstract: | Does artificial intelligence (AI) pose a threat to financial stability? We study AI investor behavior, specifically Q-learning and large language model (LLM) investors, in a mutual fund redemption problem with economic and strategic uncertainty. Different AI architectures generate systematically different outcomes. Q-learning investors coordinate well but under default risk exhibit excessive redemption that amplifies fragility. LLM investors internalize equilibrium structure but display belief heterogeneity, weakening coordination and predictability. Our findings show that AI architecture is a first-order determinant of financial stability. JEL Classification: G01, G23, C63 |
| Keywords: | AI agents, coordination games, financial stability, large language models, Q-learning, strategic uncertainty |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263225 |
| By: | Isaiah Spellman; Xiaoqing Zhou |
| Abstract: | While hostilities between Iran and Israel ended quickly in June 2025 without a major oil supply disruption, it is worthwhile to explore the impact on inflation and inflation expectations if this geopolitical event had turned out differently. |
| Keywords: | energy; inflation; international economics |
| Date: | 2025–08–21 |
| URL: | https://d.repec.org/n?u=RePEc:fip:d00001:101537 |
| By: | Tom Roullier; Justin Larouzée (CRC - Centre de recherche sur les Risques et les Crises - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres) |
| Abstract: | In a globalized world, industrial risks linked to the hazard potential of physic-chemical and/or biological processes and phenomena transcend borders, yet there are disparities in regulations leading to differences in risk management practices and hence protection worldwide. This article examines the factors that can explain and influence the diversity of regulations governing industrial and natural hazards in different countries. The analysis shows that differences can be explained by three main factors: (1) political systems, (2) economic priorities and (3) cultural and historical legacies. These disparities raise questions about risk management in multinational companies and international cooperation. While total harmonization seems both impossible and undesirable, the study recommends a middle way of strengthened and flexible international cooperation, respecting local specificities while promoting the sharing of expertise. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05533221 |
| By: | Hugo De Vere; Srini Ramaswamy; Sam Schulhofer-Wohl |
| Abstract: | The Fed has floating-rate liabilities as well as long-lived, zero-interest liabilities. A barbell of floating-rate and long-duration assets would best offset the interest rate risk from these liabilities. Investing in a more diversified mix of durations, while matching the average duration of assets, could be more practical than the barbell approach but would leave a substantial portion of interest rate risk unhedged. |
| Keywords: | assets; banking and finance; Federal Reserve; monetary policy |
| Date: | 2025–08–07 |
| URL: | https://d.repec.org/n?u=RePEc:fip:d00001:101409 |
| By: | Matthijs Leusen; Harry Garretsen; Francesco Giumelli; Tristan Kohl |
| Abstract: | We examine how Western sanctions imposed in response to Russia’s 2022 invasion of Ukraine shaped firm-level stock market performance in the EU and the US. To measure sanction exposure, we link firms to the specific products targeted by US and EU sanctions at the HS6 level, producing a detailed, firm-level indicator. We use a classical finance event study to analyze investor reactions around three salient moments in the pre-invasion period: Biden’s initial sanction threat, the collapse of US-Russia diplomatic talks, and the first sanctions announcement. We complement this with a staggered difference-indifferences design that exploits cross-firm variation in the timing of product level listings to trace the effects of sanctions as they accumulated over time. Stock market losses in the EU are substantially larger than in the US, and the gap between sanctioned and non-sanctioned firms is modest and shortlived. Investors price in sanction risk ahead of formal implementation, and subsequent expansions of existing sanction regimes generate little additional market response. |
| Keywords: | economic sanctions, event study, geopolitical risk, Russia-Ukraine war, anticipation effects, sender costs |
| JEL: | F51 F13 F14 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12650 |