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on Risk Management |
| By: | Noureddinne Benlaghaa (Department of Finance and Economics, College of Business and Economics, Qatar University, Doha, Qatar); Fahad Shafiqa (Department of Finance and Economics, College of Business and Economics, Qatar University, Doha, Qatar); Rashid Hassan Al-Derham (Department of Finance and Economics, College of Business and Economics, Qatar University, Doha, Qatar); Nur Ain Shahrier (The South East Asian Central Banks (SEACEN) Research and Training Centre) |
| Abstract: | This paper investigates the asymmetric connectedness between global banks and insurance companies under varying market conditions, with a particular focus on tail risk transmission. Motivated by the growing integration between banking and insurance sectors, we move beyond traditional average-based models and adopt a quantile vector autoregression (QVAR) framework to capture nonlinear spillovers across the 5th, 50th, and 95th percentiles of daily return distributions (2016–2025). Our analysis reveals three key findings: (1) Total connectedness intensifies sharply during both distress and exuberance, highlighting strong state dependence in systemic risk; (2) banks consistently act as net receivers of shocks at both tails, whereas certain insurers, particularly those with broader financial exposure, emerge as persistent net transmitters; and (3) connectedness in the tails is largely symmetric, though marginally stronger during downturns, underscoring heightened vulnerability in periods of stress. These findings emphasise the limitations of mean-based approaches and reinforce the value of tail-sensitive models for capturing regime shifts in financial contagion. The framework offers a replicable, data-driven approach to systemic risk monitoring that is especially relevant for SEACEN member economies aiming to strengthen macroprudential surveillance in the face of increasingly complex cross-sector linkages. |
| Keywords: | Systemic risks, bank-insurance linkages, financial contagion, quintile VAR (QVAR), quintile connectedness |
| JEL: | G15 G21 C32 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:sea:wpaper:wp60 |
| By: | Marc Schmitt |
| Abstract: | Risk forecasts drive trading constraints and capital allocation, yet losses are nonstationary and regime-dependent. This paper studies sequential one-sided VaR control via conformal calibration. I propose regime-weighted conformal risk control (RWC), which calibrates a safety buffer from past forecast errors using exponential time decay and regime-similarity weights from regime features. RWC is model-agnostic and wraps any conditional quantile forecaster to target a desired exceedance rate. Finite-sample coverage is established under weighted exchangeability, and approximation bounds are derived under smoothly drifting regimes. On the CRSP U.S.\ equity portfolio, time-weighted conformal calibration is a strong default under drift, while regime weighting can improve regime-conditional stability in some settings with modest conservativeness changes. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.03903 |
| By: | Kubitza, Christian; Oehmke, Martin |
| Abstract: | Central clearing counterparties (CCPs) manage counterparty risk by requiring clearing members to post margins. This paper explores the role of margins as “canaries in the coal mine:” By inducing defaults of fragile counterparties before contract maturity, margin calls enable CCPs to transfer these contracts to other counterparties, thereby preserving risk sharing. Our model reveals a pecking order of CCP risk management tools. When fragility is low, loss sharing among original counterparties suffices. When fragility is high, such that defaults at contract maturity would trigger cascading failures among clearing members, the CCP optimally complements loss sharing with margins. It is optimal to use margins as canaries when the balance sheets of fragile counterparties are severely impaired. Our findings highlight the complementary nature of CCP risk management tools: margins, loss sharing, and counterparty replacement. JEL Classification: G22, G23, D82 |
| Keywords: | central clearing counterparties (CCPs), counterparty replacement, counterparty risk, margin requirements, risk sharing |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263187 |
| By: | Murad Farzulla; Andrew Maksakov |
| Abstract: | Cryptocurrency markets have grown to represent over $3 trillion in capitalization, yet no unified index exists to monitor the systemic risks arising from the interconnection between decentralized finance (DeFi) protocols and traditional financial institutions. This paper introduces the Aggregated Systemic Risk Index (ASRI), a composite measure comprising four weighted sub-indices: Stablecoin Concentration Risk (30%), DeFi Liquidity Risk (25%), Contagion Risk (25%), and Regulatory Opacity Risk (20%). We derive theoretical foundations for each component, specify quantitative formulas incorporating data from DeFi Llama, Federal Reserve FRED, and on-chain analytics, and validate the framework against historical crisis events including the Terra/Luna collapse (May 2022), the Celsius/3AC contagion (June 2022), the FTX bankruptcy (November 2022), and the SVB banking crisis (March 2023). Event study analysis detects statistically significant abnormal signals for all four crises (t-statistics 5.47-32.64, all p |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.03874 |
| By: | Barbagli, Matteo (Université catholique de Louvain, LIDAM/LFIN, Belgium); Vrins, Frédéric (Université catholique de Louvain, LIDAM/LFIN, Belgium) |
| Abstract: | In this paper we address the explicit exclusion of credit concentration risk from the Pillar 1 minimum capital requirements formulas of the Basel framework. Leveraging on a well established Gaussian multi-factor model, we introduce a novel control variate estimator of value-at-risk (VaR), suitable for measuring sector concentration risk under the Pillar 2 guidelines. This estimator integrates the precision of Monte Carlo simulations with the speed and simplicity of the Large Pool approximation, aiming for a more efficient quantile estimation tool. We conduct numerical experiments in a two systematic factor setup to test the validity of our methodology, achieving consistent variance reduction compared to the benchmark Monte Carlo estimator. Our results are robust across various pool parameters and increasing number of Monte Carlo simulations. |
| Keywords: | Credit risk ; Factor model ; Control variate ; Value-at-risk ; Basel regulation |
| JEL: | G21 G28 G32 |
| Date: | 2025–08–06 |
| URL: | https://d.repec.org/n?u=RePEc:ajf:louvlf:2025003 |
| By: | Jérôme Lelong (DAO - Données, Apprentissage et Optimisation - LJK - Laboratoire Jean Kuntzmann - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes); Véronique Maume-Deschamps (ICJ - Institut Camille Jordan - ECL - École Centrale de Lyon - Université de Lyon - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - INSA Lyon - Institut National des Sciences Appliquées de Lyon - Université de Lyon - INSA - Institut National des Sciences Appliquées - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique, PSPM - Probabilités, statistique, physique mathématique - ICJ - Institut Camille Jordan - ECL - École Centrale de Lyon - Université de Lyon - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - INSA Lyon - Institut National des Sciences Appliquées de Lyon - Université de Lyon - INSA - Institut National des Sciences Appliquées - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique); William Thevenot (ICJ - Institut Camille Jordan - ECL - École Centrale de Lyon - Université de Lyon - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - INSA Lyon - Institut National des Sciences Appliquées de Lyon - Université de Lyon - INSA - Institut National des Sciences Appliquées - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique, PSPM - Probabilités, statistique, physique mathématique - ICJ - Institut Camille Jordan - ECL - École Centrale de Lyon - Université de Lyon - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - INSA Lyon - Institut National des Sciences Appliquées de Lyon - Université de Lyon - INSA - Institut National des Sciences Appliquées - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique, SCOR SE [Paris]) |
| Abstract: | We consider optimal allocation problems with Conditional Value-At-Risk (CVaR) constraint. We prove, under very mild assumptions, the convergence of the Sample Average Approximation method (SAA) applied to this problem, and we also exhibit a convergence rate and discuss the uniqueness of the solution. These results give (re)insurers a practical solution to portfolio optimization under market regulatory constraints, i.e. a certain level of risk. |
| Keywords: | Value-At-Risk, Conditional Value-At-Risk, Expected shortfall, Sample average approximation, Portfolio optimization, Insurance, Reinsurance, Uniform strong large law of numbers, Central limit theorem |
| Date: | 2025–02–05 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04733015 |
| By: | Junyu Chen; Tom Boot; Lingwei Kong; Weining Wang |
| Abstract: | Conditional Value-at-Risk (CoVaR) quantifies systemic financial risk by measuring the loss quantile of one asset, conditional on another asset experiencing distress. We develop a Transformer-based methodology that integrates financial news articles directly with market data to improve CoVaR estimates. Unlike approaches that use predefined sentiment scores, our method incorporates raw text embeddings generated by a large language model (LLM). We prove explicit error bounds for our Transformer CoVaR estimator, showing that accurate CoVaR learning is possible even with small datasets. Using U.S. market returns and Reuters news items from 2006--2013, our out-of-sample results show that textual information impacts the CoVaR forecasts. With better predictive performance, we identify a pronounced negative dip during market stress periods across several equity assets when comparing the Transformer-based CoVaR to both the CoVaR without text and the CoVaR using traditional sentiment measures. Our results show that textual data can be used to effectively model systemic risk without requiring prohibitively large data sets. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.12490 |
| By: | Walid Chkili (University of Tunis El Manar); Samir Mabrouk (University of Sousse) |
| Abstract: | This paper examines the risk dependence between clean energy, oil prices and GCC stock markets during the period 2015-2023 covering the two recent events of COVID-19 pandemic and Russia-Ukrainian conflict. The main purpose is to investigate the volatility spillovers of clean and dirty energy markets versus GCC stock indices. We use two methodologies namely the Diebold and Yilmaz (2012, 2014) volatility spillover index and the wavelet coherence analysis. The Diebold-Yilmaz connectedness index shows that oil prices, KSA and Kuwait stock markets are the net transmitter of shocks while clean energy index and the stock markets of UAE, Qatar, Bahrain and Oman are net receiver of volatility. The wavelet coherency approach reveals that the dependence between clean energy/oil prices and the stock markets varies across time scales and considered countries. The intense coherence is detected during the oil crash and the COVID-19 crisis at low frequencies (high scales). The findings have several financial implications for investors and portfolio managers. The GCC investors should add either clean energy or crude oil to their portfolio of stocks in order to minimize the risk of portfolio. The hedging ratios show that both clean energy and crude oil offer effective hedging strategies. Finally, the hedging effectiveness index reveals a higher reduction of hedged portfolio risk involving clean energy than crude oil. |
| Date: | 2024–12–20 |
| URL: | https://d.repec.org/n?u=RePEc:erg:wpaper:1764 |
| By: | Palermo, Tommaso; Pirozzi, Lorenzo |
| Abstract: | Nature risks are a central challenge for companies and society (Dasgupta, 2021; TNFD, 2024; World Economic Forum, 2025), and a compelling area of study for scholars interested in measurement, the construction of “risk objects” (Hilgartner, 1992) and their translation into risk management action (Hardy et al., 2020). As the (fictional) vignette shows, it is difficult, often impossible, to capture the full set of trade‑offs arising from well‑intentioned decisions. What is at stake is sound data sources and measures, as well as clarity about values‑based judgment on which risks, and how much risk, companies are willing to take. This report presents preliminary findings from a study funded by the LSE Global School of Sustainability (webpage). The report explores these themes and how data availability, measurement and risk considerations can construct new objects of concern—even something as complex and multifaceted as nature—and make them amenable to management |
| JEL: | G32 R14 J01 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:137011 |
| By: | Lassance, Nathan (Université catholique de Louvain, LIDAM/LFIN, Belgium); Vanderveken, Rodolphe (Université catholique de Louvain, LIDAM/LFIN, Belgium); Vrins, Frédéric (Université catholique de Louvain, LIDAM/LFIN, Belgium) |
| Abstract: | We introduce analytical linear and nonlinear shrinkage estimators of the sample covariance matrix that are optimal for mean-variance portfolio choice. Unlike the classical estimators based on statistical loss functions like the mean squared error, our shrinkage covariance matrices optimize the expected out-of-sample portfolio utility and account for estimation errors in mean returns. Our estimators shrink the sample eigenvalues more intensively than conventional methods, and they especially diminish the contribution of principal components with small squared Sharpe ratios. By jointly estimating the covariance matrix and the optimal portfolio in one step, our method delivers significant empirical performance gains relative to the usual two-step shrinkage approach. Our portfolios also help reduce turnover and outperform recent regularized mean-variance portfolio strategies. |
| Keywords: | Estimation risk ; linear shrinkage ; mean-variance portfolio ; nonlinear shrinkage ; out-of-sample utility ; parameter uncertainty |
| JEL: | G11 |
| Date: | 2025–07–11 |
| URL: | https://d.repec.org/n?u=RePEc:ajf:louvlf:2025002 |
| By: | T. Di Matteo; L. Riso; M. G. Zoia |
| Abstract: | This paper proposes a machine learning-based framework for asset selection and portfolio construction, termed the Best-Path Algorithm Sparse Graphical Model (BPASGM). The method extends the Best-Path Algorithm (BPA) by mapping linear and non-linear dependencies among a large set of financial assets into a sparse graphical model satisfying a structural Markov property. Based on this representation, BPASGM performs a dependence-driven screening that removes positively or redundantly connected assets, isolating subsets that are conditionally independent or negatively correlated. This step is designed to enhance diversification and reduce estimation error in high-dimensional portfolio settings. Portfolio optimization is then conducted on the selected subset using standard mean-variance techniques. BPASGM does not aim to improve the theoretical mean-variance optimum under known population parameters, but rather to enhance realized performance in finite samples, where sample-based Markowitz portfolios are highly sensitive to estimation error. Monte Carlo simulations show that BPASGM-based portfolios achieve more stable risk-return profiles, lower realized volatility, and superior risk-adjusted performance compared to standard mean-variance portfolios. Empirical results for U.S. equities, global stock indices, and foreign exchange rates over 1990-2025 confirm these findings and demonstrate a substantial reduction in portfolio cardinality. Overall, BPASGM offers a statistically grounded and computationally efficient framework that integrates sparse graphical modeling with portfolio theory for dependence-aware asset selection. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.03325 |
| By: | Diego Bonelli (BANCO DE ESPAÑA) |
| Abstract: | Inflation risk explains a significant share of the systematic residual variation in yield spread changes beyond credit factors and intermediation frictions. Movements in expected inflation directly affect the real value of debt and, consequently, bond prices. I show that shocks to inflation expectations, volatility, and cyclicality – derived from inflation swap prices – are important determinants of yield spread movements. Load-ing patterns become more pronounced with higher ex-ante default risk and cash-flow flexibility but weaken with refinancing intensity. To rationalize the findings, I show that the same patterns emerge in a model of debt rollover risk with stochastic inflation and sticky cash flows. |
| Keywords: | inflation risk, corporate bonds, yield spread changes, inflation-linked derivatives |
| JEL: | G10 G12 G20 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2603 |
| By: | Divya Choudhary (IIM Lucknow - Indian Institute of Management Lucknow); Ajay Kumar (EM - EMLyon Business School); Yeming Gong (EM - EMLyon Business School); Thanos Papadopoulos |
| Abstract: | We perform a multidimensional and integrated investigation of risks associated with circular supply chains (CSC), drawing on Transition Management Theory (TMT). This research focuses on e-waste from the Indian electronics industry, a waste stream with significant recovery potential and one of the fastest-growing in emerging economies. Drawing on TMT, the study (i) institutionalises risk management activities in circular systems to operationalise the transition towards CE; (ii) quantifies CSC risks at operational, tactical, and strategic levels and measure the total risk exposure of CSCs; (iii) comprehensively cogitates the operational, socio-environmental, and financial implications of CSCs risks and (iv) considers uncertainty in operations research (OR) models by applying a fuzzy set theory, evidential reasoning algorithm, and expected utility theory based model to evaluate and profile the CSCs risks. The proposed model contributes to the application of decision analysis and risk analysis approaches in the sustainability domain and can efficiently model uncertain, subjective, and incomplete data. Our findings reveal that customers' reluctance to purchase reprocessed products represents the most critical challenge to the effectiveness of CSCs. Furthermore, contrary to conventional perspectives, organizations are strategically shifting toward adopting circular practices. However, they often lack the practical means and resources to implement these strategies effectively. |
| Keywords: | Circular supply chains, evidential reasoning algorithm (ERA), expected utility theory, risk quantification, transition management theory |
| Date: | 2025–02–03 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05489667 |
| By: | Teichmann, Fabian |
| Abstract: | The EU Artificial Intelligence Act (AI Act) establishes a novel risk-based regulatory model for AI systems, categorising uses into four tiers: unacceptable (prohibited), high-risk (tightly regulated), limited-risk (transparency obligations), and minimal-risk (largely unregulated). This article develops a rigorous conceptual framework to analyse the Act’s logic of risk, reasonableness, and residual harm. It explains how the principles of precaution and proportionality shape the AI Act’s ex ante controls, requiring providers to anticipate reasonably foreseeable misuse and apply measures that reflect the state of the art. 1 We propose criteria for calibrating key requirements (data governance, transparency, human oversight, robustness or cybersecurity) to the severity and uncertainty of risks, drawing on risk-regulation theory (e.g., Baldwin and Black’s responsive regulation and Sunstein’s cost-benefit rationality). The analysis also situates the EU approach within a comparative context, noting alignments and divergences with US and OECD AI frameworks – for example, the EU’s precautionary bans on biometric mass surveillance contrast with the US reliance on voluntary risk management guidelines. Specific high-impact use cases (biometric identification in public spaces, AI in critical infrastructure) illustrate how risk severity triggers stricter controls. The article concludes by discussing policy implications for implementation, including the role of harmonised standards and presumptions of conformity, the interface with parallel cybersecurity regimes (NIS2, DORA) as “risk multipliers, ” and the need for further guidance and delegated acts to ensure that the AI Act’s proportional safeguards remain effective in the face of technological change. |
| Keywords: | EU artificial intelligence act; harmonised standards; residual risk management; proportionality principle; risk-based regulation |
| JEL: | G32 |
| Date: | 2026–01–20 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:130944 |
| By: | Rafique, Amir; Ali, Amjad; Audi, Marc |
| Abstract: | This study analyzes the impact of liquidity management on the performance of banks in Canada. The Canadian economy is significantly reliant on the banking sector, which plays a vital role by offering financial services, including lending to corporate, commercial, and retail clients. The stability of the banking system is essential for the continuity of successful economic activities within the country. Strong liquidity ratios are indicative of financial stability and serve as a foundation for customer confidence. This study employs descriptive, correlation, and regression analyses and compares the liquidity and performance of Canadian banks during the period from 2022 to 2024, using financial data primarily obtained from banks’ financial statements. The findings indicate that the relationship between liquidity and profitability is mixed, varying from positive, sometimes negative or insignificant according to the specific variables and factors considered in the analysis. In general, a stable liquidity position contributes to greater stakeholder confidence, improved business activity, and higher income and profitability. Regulatory authorities should maintain vigilant oversight of banks’ liquidity metrics to safeguard financial stability. External influences such as ongoing tariff conflict with United States and unstable geopolitical conditions can significantly affect bank performance and erode customer confidence. To address such challenges, banks should maintain sufficient buffer assets to meet liquidity demands. Deposit runs, whether triggered internally or externally, can become unmanageable; therefore, a conservative liquidity approach is necessary to preserve customer trust. Complex and high-risk financial products must be rigorously monitored. Additionally, concentration risk should be managed such that banks diversify their exposure across different sectors of the economy, ensuring that under-performance in a single industry segment does not jeopardize the overall stability of the banking sector. |
| Keywords: | Liquidity Risk, Banks Performance, Canada |
| JEL: | G2 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127486 |
| By: | Osei, Prince (Center for Mathematical Economics, Bielefeld University) |
| Abstract: | We study the ambiguity-adjusted return distribution induced by an investor with smooth ambiguity preferences à la Klibanoff et al. (2005), who faces uncertainty about the variance of asset returns. The variance uncertainty is modeled using a gamma distribution, a second-order prior over the family of normally distributed returns. Our main results present a density distortion that exponentially tilts this prior into an ambiguity-adjusted gamma distribution, characterized by its distorted rate parameter and shape parameter. A smaller distorted rate parameter implies greater weight on high-variance returns. This paper derives the ambiguity-adjusted return distribution as a symmetric variance–gamma distribution reflecting the investor’s risk and ambiguity aversion. The ambiguity-averse investor assigns a variance–gamma distribution with a higher likelihood of extreme returns, while the ambiguity-neutral investor assigns a distribution more peaked around the mean. We obtain the ambiguity-adjusted return variance as an increasing function of risk and ambiguity aversion. An empirical comparison is performed to calibrate the ambiguity aversion parameter of an in- vestor investing in gold. |
| Keywords: | sset returns, Smooth ambiguity aversion, Variance uncertainty, Variance– Gamma distribution |
| Date: | 2026–02–11 |
| URL: | https://d.repec.org/n?u=RePEc:bie:wpaper:764 |
| By: | Lang, Jan Hannes; Menno, Dominik |
| Abstract: | Under which conditions do usability constraints for regulatory capital buffers emerge? To answer this question, we build a non-linear structural banking sector model with a minimum capital requirement that banks are not allowed to breach, and a capital buffer requirement (CBR) that banks can breach but if they do so potential stigma applies. We prove that even very low stigma costs induce large buffer usability constraints, i.e. when faced with losses banks will deleverage significantly to avoid that their capital ratio falls below the CBR. Our findings imply that non-releasble regulatory capital buffers are unlikely to fully achieve their macro stabilisation goal to support aggregate loan supply when the banking system faces losses. JEL Classification: D21, E44, E51, G21, G28 |
| Keywords: | bank capital requirements, buffer usability, capital buffers, loan supply, macroprudential policy |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263188 |
| By: | Vespignani, Joaquin L.; Smyth, Russell; Saadaoui, Jamel; Wang, Yitian |
| Abstract: | We develop novel, stage-specific, geopolitical risk indicators to examine how geopolitical risk is distributed across the supply-chain for lithium and copper, two minerals which are vital for low-carbon technologies. We find that refining is the geopolitical bottleneck for both minerals, reflecting that refining capacity is highly concentrated in China. We examine refining diversification, strategic stockpiling, and AI-driven productivity gains as complementary policy instruments for mitigating exposure to geopolitical risk at the refining stage. We show that reducing China’s refining share substantially lowers refining-stage geopolitical risk, with larger gains for lithium than for copper. We find that stockpiling plays a critical role in buffering near-term geopolitical shocks, but significantly increases the projected shortfall in copper and lithium which is needed to realize the clean energy transition under alternative Net Zero pathways. We demonstrate that AI-driven productivity gains will be needed to narrow the projected supply gaps for both minerals. Our results suggest that ensuring effective security of critical minerals requires a coordinated policy mix, combining refining diversification, strategic stockpiling, and productivity-enhancing technological change. |
| Keywords: | Critical Minerals; Copper; Lithium; Geopolitical Risk; Refining bottlenecks |
| JEL: | C14 Q20 Q41 Q43 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127877 |
| By: | Brenzel-Weiss, Janosch; Koeniger, Winfried; Valladares-Esteban, Arnau |
| Abstract: | We calibrate a lifecycle portfolio-choice model of homeowners facing uninsurable income risk to show that tax deductions for mortgage interest payments and voluntary pension contributions have sizable effects on household portfolios and macroprudential risks. The deductions reduce the after-tax cost of debt and increase the after-tax return of pension savings so that the mortgage incidence increases and portfolios shift from home equity and liquid assets towards pension savings. Because the consumption responses to a house-price decline are heterogeneous, the distribution of household debt shapes the quantitative effect of the tax deductions on the homeowners' resilience after a house price bust. |
| Keywords: | Mortgage amortization, Tax incentives, Household consumption, Portfolio choice, Housing busts, Economic stability, Macroprudential policy |
| JEL: | D14 D15 D31 E21 G11 G21 H24 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:cfswop:336755 |
| By: | Akyildirim, Erdinc; Corbet, Shaen; Muñiz, Jose Antonio; Scrimgeour, Frank |
| Abstract: | Negative ESG‐related reputational events generate significant corporate risks, particularly within sensitive sectors such as the pharmaceutical industry. Using novel reputational data, this research investigates investor perceptions of the consequences of experienced ESG breaches among US pharmaceutical firms. Specifically, we consider the magnitude, timing, and persistence of abnormal returns, testing whether firm‐specific characteristics and event‐related attributes moderate and account for identified market response differentials. Results indicate the presence of significant negative abnormal returns before the identified media release date, suggesting market anticipation or information leakage, followed by a pronounced negative shock upon formal announcement, with firm size the most robust mitigating factor. Market response shows substantial heterogeneity, while environmental incidents generate significant, delayed negative returns, whereas social and governance events show negligible investor response, indicating a lack of market concern. Companies experiencing recurring incidents experience further deterioration of returns than first‐time offenders. Neither the initial news source's reach nor the assessed severity significantly affects the magnitude of market response. These findings highlight the context‐dependent nature of ESG materiality in the pharmaceutical sector. |
| Keywords: | reputational risk; CSR; ESG; abnormal returns; pharmaceutical industry |
| JEL: | G14 G32 L65 M14 Q56 |
| Date: | 2026–02–03 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:137054 |
| By: | Hong Beng Lim; Mengyi Xu; Kenneth Q. Zhou |
| Abstract: | Extant literature on fair pricing methods for actuarial contexts has primarily focused on the regression setting. While such approaches are well-suited to short-term products, it is unclear how they generalize to long-term products, whose pricing essentially relies on estimating transition rates in multi-state models. To address this gap, we propose a unified framework that recasts the estimation of any given multi-state transition model as a set of Poisson regression problems. This reformulation enables the direct application of existing fair pricing methods, which together constitute our proposed methodology. As an illustration, we apply the framework to a fair pricing exercise for a stylized long-term care insurance product using data from the University of Michigan Health and Retirement Study (HRS), focusing on a post-processing approach. We further explain how the framework readily accommodates pre-processing and in-processing fairness methods. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.04791 |
| By: | Hainaut, Donatien (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | This article proposes a parametric model explaining dierences in mortality across European NUTS2 regions using economic and environmental variables. We extenda multi-group version of the LeeCarter framework by incorporating economic and environmental time series as driving factors. The marginal eects of these factors are modeled with B-splines. Compared to the Li and Lee framework, our model oersseveral advantages. First, it is interpretable and allows assessment of the impact on mortality resulting from changes in economic or environmental policies. Second, theparameterization limits the model's degrees of freedom, enabling reliable estimation over shorter time windows. We illustrate the eciency of our approach by explaining regional mortality patterns in France, Italy, and BelgiumNetherlands. |
| Keywords: | Mortality forecasting ; Lee-Carter model ; multi-group mortality ; life insurance |
| Date: | 2026–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2026001 |
| By: | Christian Gollier (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements 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) |
| Abstract: | Because of risk aversion, any sensible investment valuation system should value less projects that contribute more to the aggregate risk. In theory, this is done by adjusting discount rates to consumption betas. But in reality, most public institutions use a dis-count rate that is rather insensitive to the risk profile of their investment projects. The economic consequences of the implied misallocation of capital are severe. I calibrate a Lucas model in which the investment opportunity set contains a constellation of projects with different expected returns and risk profiles. The model matches the traditional finan-cial and macro moments, together with the observed heterogeneity of assets' risk profiles. The welfare loss of using a single discount rate is equivalent to a permanent reduction in consumption that lies somewhere between 15% and 45% depending upon which single discount rate is used. |
| Keywords: | capital budgeting, rare disasters, WACC fallacy, Arrow-Lind theorem, carbon pricing, asset pricing, investment theory, Discounting |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05483623 |
| By: | Dylan Herman; Yue Sun; Jin-Peng Liu; Marco Pistoia; Charlie Che; Rob Otter; Shouvanik Chakrabarti; Aram Harrow |
| Abstract: | This paper explores advancements in quantum algorithms for derivative pricing of exotics, a computational pipeline of fundamental importance in quantitative finance. For such cases, the classical Monte Carlo integration procedure provides the state-of-the-art provable, asymptotic performance: polynomial in problem dimension and quadratic in inverse-precision. While quantum algorithms are known to offer quadratic speedups over classical Monte Carlo methods, end-to-end speedups have been proven only in the simplified setting over the Black-Scholes geometric Brownian motion (GBM) model. This paper extends existing frameworks to demonstrate novel quadratic speedups for more practical models, such as the Cox-Ingersoll-Ross (CIR) model and a variant of Heston's stochastic volatility model, utilizing a characteristic of the underlying SDEs which we term fast-forwardability. Additionally, for general models that do not possess the fast-forwardable property, we introduce a quantum Milstein sampler, based on a novel quantum algorithm for sampling L\'evy areas, which enables quantum multi-level Monte Carlo to achieve quadratic speedups for multi-dimensional stochastic processes exhibiting certain correlation types. We also present an improved analysis of numerical integration for derivative pricing, leading to substantial reductions in the resource requirements for pricing GBM and CIR models. Furthermore, we investigate the potential for additional reductions using arithmetic-free quantum procedures. Finally, we critique quantum partial differential equation (PDE) solvers as a method for derivative pricing based on amplitude estimation, identifying theoretical barriers that obstruct achieving a quantum speedup through this approach. Our findings significantly advance the understanding of quantum algorithms in derivative pricing, addressing key challenges and open questions in the field. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.03725 |
| By: | Tatsuru Kikuchi |
| Abstract: | This paper evaluates the causal impact of Generative Artificial Intelligence (GenAI) adoption on productivity and systemic risk in the U.S. banking sector. Using a novel dataset linking SEC 10-Q filings to Federal Reserve regulatory data for 809 financial institutions over 2018--2025, we employ two complementary identification strategies: Dynamic Spatial Durbin Models (DSDM) to capture network spillovers and Synthetic Difference-in-Differences (SDID) for causal inference using the November 2022 ChatGPT release as an exogenous shock. Our findings reveal a striking ``Productivity Paradox'': while DSDM estimates show that AI-adopting banks are high performers ($\beta > 0$), the causal SDID analysis documents a significant ``Implementation Tax'' -- adopting banks experience a 428-basis-point decline in ROE as they absorb GenAI integration costs. This tax falls disproportionately on smaller institutions, with bottom-quartile banks suffering a 517-basis-point ROE decline compared to 129 basis points for larger banks, suggesting that economies of scale provide significant advantages in AI implementation. Most critically, our DSDM analysis reveals significant positive spillovers ($\theta = 0.161$ for ROA, $p |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.02607 |