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on Risk Management |
| By: | Reza Habibi |
| Abstract: | Systemic risk has essential impact on of a firm. In the current paper, under scenario of systemic crisis, the behavior of credit risk measure is studied and its relation to market and climate betas are proposed. Then, optimum leverage ratio and prudential coefficient are derived. Finally, using the Mote Carlo simulation method, stress tests of risk measure under systemic risk crisis event is studied. It is seen that changes throughout changing capital structure, leverage ratio, systemic risk index, market and climate betas. |
| Keywords: | Climate crisis scenario, Credit risk, Monte Carlo, Stress test, Systemic risk. |
| JEL: | C63 G21 G28 G32 G33 |
| Date: | 2026–01–07 |
| URL: | https://d.repec.org/n?u=RePEc:eei:rpaper:eeri_rp_2026_07 |
| By: | Dario Caldara; Haroon Mumtaz; Molin Zhong |
| Abstract: | We characterize asymmetric tail risk across over one hundred U.S. macroeconomic and financial variables using a dynamic factor model with stochastic volatility. A single mechanism unifies growth-at-risk, inflation-at-risk, and sectoral risk heterogeneity: common factors and their volatilities move together, while heterogeneous loadings transmit the resulting asymmetry unevenly across variables. We find that asymmetric tail risk is pervasive but heterogeneous. The heterogeneity is systematic: factor exposures, especially to financial conditions and inflation, explain over half of the cross-sectional variation in tail asymmetry across variables. These exposures determine where in the economy vulnerabilities concentrate and how the balance of tail risks shifts over time. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.05676 |
| By: | Jilang Miao; Nonna Sorokina |
| Abstract: | We study whether nuclear and energy-adjacent equity options exhibit a harvestable variance risk premium. Using CRSP and OptionMetrics data for 2000-2024, we construct a systematic cash-secured short-put strategy on a curated universe of nuclear-related firms. The strategy compares at-the-money put implied volatility with GARCH-based realized volatility forecasts, then evaluates unconditional and IV/RV-filtered put-writing portfolios. The results show positive average option premia, high win rates, and substantially lower volatility than an equal-weight stock benchmark, though performance is measured before transaction costs and with a fixed universe. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.01183 |
| By: | Giovanni Covi (Bank of England); Tihana Škrinjarić (Bank of England) |
| Abstract: | This study develops a stochastic balance sheet based microstructural banking model to quantify the dynamic interplay between solvency and liquidity risks – two traditionally distinct dimensions in stress testing. By incorporating endogenous bank reactions, feedback loops, and amplification mechanisms, the model captures how management responses to shocks can escalate financial distress, potentially leading to insolvency and illiquidity. We apply the model to granular loan and security exposure data from UK banks over 2015–24, estimating capital and liquidity at risk and deriving a systemic default probability indicator. Results indicate an average one-year bank default probability of 0.7%, consistent with market-implied estimates but diverging during stress episodes. Amplification effects, driven by balance sheet constraints and behavioural responses, account for approximately one third of default risk on average. Counterfactual analyses further evaluate the effectiveness of capital requirements and identify optimal capital levels under hypothetical stress scenarios. |
| Keywords: | Banking stability;solvency-liquidity interactions;financial contagion;macroprudential stress test |
| JEL: | D85 G21 G32 L14 |
| Date: | 2025–08–29 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:023262 |
| By: | Hervé Andrès (Milliman France, CERMICS - Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique - ENPC - École nationale des ponts et chaussées - IP Paris - Institut Polytechnique de Paris); Alexandre Boumezoued (Milliman France); Benjamin Jourdain (MATHRISK - Mathematical Risk Handling - UPEM - Université Paris-Est Marne-la-Vallée - ENPC - École nationale des ponts et chaussées - Centre Inria de Paris - Inria - Institut National de Recherche en Informatique et en Automatique, CERMICS - Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique - ENPC - École nationale des ponts et chaussées - IP Paris - Institut Polytechnique de Paris, MATHRISK - Mathematical Risk Handling - Centre Inria de Paris - Inria - Institut National de Recherche en Informatique et en Automatique - Université Gustave Eiffel - CERMICS UMR 9032 - Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique - CNRS - Centre National de la Recherche Scientifique - ENPC - École nationale des ponts et chaussées - IP Paris - Institut Polytechnique de Paris) |
| Abstract: | We propose a new model for the forecasting of both the implied volatility surfaces and the underlying asset price. In the spirit of Guyon and Lekeufack (2023) who are interested in the dependence of volatility indices (e.g. the VIX) on the paths of the associated equity indices (e.g. the S&P 500), we first study how vanilla options implied volatility can be predicted using the past trajectory of the underlying asset price. Our empirical study reveals that a large part of the movements of the at-the-money-forward implied volatility for up to two years time-to-maturities can be explained using the past returns and their squares. Moreover, we show that this feedback effect gets weaker when the time-to-maturity increases. Building on this new stylized fact, we fit to historical data a parsimonious version of the SSVI parameterization (Gatheral and Jacquier, 2014) of the implied volatility surface relying on only four parameters and show that the two parameters ruling the at-the-money-forward implied volatility as a function of the time-to-maturity exhibit a path-dependent behavior with respect to the underlying asset price. Finally, we propose a model for the joint dynamics of the implied volatility surface and the underlying asset price. The latter is modelled using a variant of the path-dependent volatility model of Guyon and Lekeufack and the former is obtained by adding a feedback effect of the underlying asset price onto the two parameters ruling the at-the-money-forward implied volatility in the parsimonious SSVI parameterization and by specifying Ornstein-Uhlenbeck processes for the residuals of these two parameters and Jacobi processes for the two other parameters. Thanks to this model, we are able to simulate highly realistic paths of implied volatility surfaces that are free from static arbitrage. |
| Keywords: | Implied volatility modelling SSVI Path-dependent volatility Simulation Arbitrage, Arbitrage, Simulation, Path-dependent volatility, SSVI, Implied volatility modelling |
| Date: | 2026–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04362544 |
| By: | Demetrio Lacava; Paolo Santucci de Magistris |
| Abstract: | Market efficiency relies fundamentally on stable liquidity. Consequently, forecasting liquidity dynamics is a priority for both investors and regulators. We introduce a new tail-risk metric, Illiquidity-at-Risk (IlliQaR), designed to quantify the magnitude of extreme liquidity dry-ups. Relying upon the realized Amihud (a precise illiquidity measurement derived from high-frequency data as the ratio of realized volatility to trading volume) we assess the predictive power of various linear and non-linear econometric models, with a specific focus on the impact of discontinuous jump components. Accounting for these jumps is essential for achieving accurate probability coverage and better IlliQaR predictions during periods of systemic stress, where standard continuous models systematically underestimate the severity of liquidity evaporation. Our empirical analysis, encompassing the S&P 500 index and a cross-section of 25 large U.S. equities, demonstrates that incorporating jumps significantly improves forecasts of illiquidity. Our results suggest that individual stock IlliQaR violations often cluster during periods of S&P 500 liquidity stress. This indicates that Illiquidity at Risk is not just a localized concern but a systemic one, where the main index acts as a leading indicator for extreme dry-ups in individual stock liquidity. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.00943 |
| By: | Guillaume Flament; Christophe Hurlin; Quentin Lajaunie; Yoann Pull |
| Abstract: | Credit stress testing requires impulse responses of portfolio default probabilities, not only macro-financial drivers. We derive closed-form generalized impulse responses for the mean, quantiles (PD-at-Risk), and expected shortfall in a modular framework combining a Bayesian VAR, a Gaussian satellite, and the Merton-Vasicek model underlying Basel IRB regulation. Results extend to any probit-Gaussian mapping of a latent factor. Nonlinearity makes responses depend on conditional means and variances; plug-in evaluations understate projected default probability levels by 6-8% and miss tail quantiles. For U.S. geopolitical risk shocks, 99%-quantile responses exceed mean responses by 50%, and peak responses vary 4.6-fold across the credit cycle. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.04469 |
| By: | Ziang Li; Derek Wenning |
| Abstract: | This paper explores how financial institutions pass interest rate risk through to product markets using the life insurance industry as a setting. We show theoretically that it is optimal for insurers to distort product issuance across maturities to offset duration gaps. We examine insurers exogenously exposed to interest rate risk through their variable annuity liabilities after the 2008 financial crisis. Consistent with our mechanism, exposed insurers developed negative duration gaps, increased markups on long-duration products, and shifted issuance toward shorter-duration products to hedge. As a result, long-term life insurance coverage declined by 31% of GDP between 2005 and 2023. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.04925 |
| By: | Marcus Gawronsky; Chun-Sung Huang |
| Abstract: | Portfolio risk assessment ordinarily relies on reliable estimates of cross-asset return covariances, which are difficult to obtain in short, high-dimensional panels. We show that firm-level distribution-valued characteristics can instead provide one-sided certificates of portfolio risk. Under maintained links from characteristics to systematic exposures and from exposures to returns, multi-firm Wasserstein-2 dispersion yields a sharp upper bound on systematic portfolio variance and a corresponding bound for standardized returns. A weighted pairwise relaxation produces an objective that is convex under a checkable condition and requires marginal volatility scales but no cross-asset return covariances. With zero firm-specific slack, the common-map scale changes the certified variance reduction but not the normalized allocation, which depends only on observed information geometry. In a 52-firm panel from 2018-2022, an allocation constructed from Qwen3-Embedding-8B news representations lies between the 0.69th and 1.33rd in-sample variance percentiles across four prespecified capped portfolio populations; equal risk weighting lies between the 21.1st and 28.6th percentiles. The lower in-sample variance ranking relative to equal risk also appears across the reported frozen language-model representations. The framework therefore distribution-valued firm information into a coherent risk bound and an implementable allocation rule constructed without cross-asset return covariances. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.29692 |
| By: | Carole Bernard; Silvana M. Pesenti |
| Abstract: | We introduce a framework for preference-robust decision making when preferences over risk are modelled through generalised distortion risk measures. Unlike distributional robustness, our approach addresses ambiguity in the risk functional itself. We construct ambiguity sets on distortion (weight) functions using the Wasserstein distance and Bregman divergences, and derive closed-form expressions for the worst- and best-case distortion risk measures. We further extend the framework to rank-dependent utility, yielding preference-robust behavioural models. In particular, rank-dependent utility appears as a robustification of the expected utility model, yielding a novel way to address the Allais paradox. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.02854 |
| By: | Nils Bundi |
| Abstract: | Similar to banks, DeFi protocols expose depositors to operational risk (USD 9.45 billion across 1, 075 events since 2020). Unlike banks, they are not required to hold capital against it. A protocol may maintain a buffer voluntarily. Absent one, the risk falls on the depositor, who should then demand a risk premium in the supply yield. I quantify the underlying tail on one benchmark, a per-sector Basel loss-distribution approach fitted to a new operational risk event dataset, and test both margins against it. Tails in the four core sectors are no heavier than the Moscadelli banking band $[0.85, 1.39]$. Bridge, Derivatives, and the residual Other sector exhibit cyber-loss-level tails ($\hat\xi \approx 1.6$), with point estimates past the infinite-mean boundary. The Lending tail implies a $\mathrm{VaR}_{99.9}$ capital buffer of 18% of TVL and of the ten largest Lending venues, the four holding a buffer cover on average 5% of it. Under market discipline, depositors should demand a higher yield in compensation where a venue does not maintain a buffer. I find that venues without a buffer pay a higher premium than those with (a 125-bps gap in medians): evidence the market discriminates in the right direction. However, the premium falls far short of an adequately priced tail. This unpriced tail falls disproportionately on the retail depositor, who sees only the posted rate but lacks the information and skills to price it. Because these products are not bank-regulated, I recommend disclosure over capital mandates: protocols, and any service providers that front access to it, should publish standardized losses, existing capital buffers and tail coverage. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.00911 |
| By: | Bhattarai, Chandan; Tack, Jesse |
| Keywords: | Risk and Uncertainty |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404418 |
| By: | Peng Liu; Yang Liu |
| Abstract: | Modern portfolio theory identifies diversification as the primary tool for risk reduction. However, under model uncertainty, this cornerstone may no longer remain optimal. This paper investigates the tension between portfolio diversification and concentration under dependence uncertainty. In the absence of model uncertainty, we employ the framework of the majorization order and doubly stochastic matrices to formalize the degree of diversification, and prove that quasi-convexity is a necessary and sufficient property for a risk functional to be weakly consistent with the majorization order. We further derive worst-case risk measure inequalities and solve robust portfolio selection problems for a broad class of risk measures, including VaR, ES, Range-VaR (RVaR), and standard deviation (SD). Our results reveal a ''concentration paradox'' for many widely-used risk functionals: when the dependence structure is fully ambiguous, robust optimization often recommends concentrating investment in a single asset to hedge against the worst-case dependence scenario. As an application, we propose a weighted robustness formulation that interpolates between a reference dependence structure and the worst-case structure. The formulation is structurally analogous to the constrained/unconstrained Expected Shortfall blend in the Fundamental Review of the Trading Book (FRTB) and provides a theoretical foundation for balancing diversification against robustness in the presence of model uncertainty. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.04496 |
| By: | F\'elix B. Tambe-Ndonfack |
| Abstract: | We develop a structural credit-risk model under incomplete information in which investors observe firm value only indirectly through noisy market signals and scheduled corporate disclosures. While disclosure dates are known in advance, their informational content is random, leading to stochastic discontinuities in the observation process. We derive the Kushner-Stratonovich equation for structural credit-risk models with endogenous default by applying the nonlinear filtering framework with predictable jumps. We then study the valuation and local risk-minimization hedging for default-sensitive securities under partial information. The interaction between predictable disclosure events and endogenous default produces discrete adjustments in the conditional default compensator, leading to announcement-driven distortions in credit spreads and hedge ratios that are absent from classical diffusion-based and inaccessible-jump models. Numerical experiments illustrate how scheduled disclosures affect filtered default probabilities, Credit Default Swaps (CDS) spreads, and hedging strategies, generating characteristic pre-announcement dynamics in credit spreads. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.19221 |
| By: | J. Carter Braxton; Kyle F. Herkenhoff; Chengdai Huang; Michael Nattinger; Jonathan L. Rothbaum; Lawrence D.W. Schmidt |
| Abstract: | We document an increase in U.S. income risk from 1969 to 2019 using newly digitized IRS tax returns, distinguishing permanent from transitory risk. Since the 1970s, permanent income risk increased across the distribution, but most sharply among high earners, rising nearly 70% among the top 5%. We show that, even among top earners, large negative income shocks strongly predict financial distress and higher income risk is linked with higher savings. In a quantitative life-cycle model, rising income risk concentrated at the top lowers the risk-free rate by 0.7pp, increases wealth inequality, and contributes to the "savings glut of the rich." |
| JEL: | D15 E21 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35664 |
| By: | Timothy J. Besley; Peter John Lambert; Isabelle A. Michalski-Roland; John Van Reenen |
| Abstract: | This paper examines the impact of credit frictions arising from firm-level default risk on aggregate economic performance. We build a micro-to-macro model with heterogeneous firms and sector-specific production functions, showing that perceived default risk is a sufficient statistic for credit frictions. Using UK administrative data (2004–2019) matched to S&P risk measures, counterfactual estimates reveal that relaxing frictions raises output by 25% and wages by 23%. Ignoring equilibrium wage adjustments overstates output gains, while fixed-capital misallocation approaches understate them. Most gains reflect aggregate capital accumulation. Credit frictions remain above pre-crisis levels, reshape firm size dynamics, increase misallocation across firms, and dampen productivity growth over time. |
| JEL: | D24 E32 L11 O47 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35552 |
| By: | Fairbanks, Jackson |
| Abstract: | Perpetual preferred equity issued against a Bitcoin reserve produces a forward-coverage problem. The measure of forward solvency is the Bitcoin Coverage Ratio (BCR): BTC reserve value over annual dividend obligation, in years of forward coverage. BCR collapses the preferred-to-reserve ratio and dividend rate into a single coverage statistic. Primary failure occurs at BCR |
| Keywords: | Bitcoin Coverage Ratio (BCR), Perpetual Preferred Equity, Digital Credit, Bitcoin Treasury, Bitcoin, Credit Risk, Solvency, Coverage Ratio, Drawdown, Backtesting |
| JEL: | G01 G23 G32 G33 |
| Date: | 2026–07–29 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:130390 |
| By: | Turan G. Bali; Bryan T. Kelly; Mathis Mörke |
| Abstract: | We construct a novel stock-level measure of volatility disagreement as the cross-sectional dispersion of realized variance forecasts built from heterogeneous information sets and machine learning models. Sorting single-name delta-hedged straddles on this measure yields a long-short return of −5.14% per month, robust to known option return predictors and not subsumed by disagreement about first moments or the variance risk premium. A one-standard-deviation increase in volatility disagreement is associated with a 30% rise in option position opening. The cross-sectional patterns of our disagreement measure align with recent theoretical advances of beliefs and pricing of variance claims. Evidence on attention, ownership, and arbitrage costs is more consistent with mispricing than risk compensation. |
| JEL: | G12 G13 G14 G41 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35500 |
| By: | Dominik Manuel Buchegger; Lukas Gonon |
| Abstract: | Implied volatility surfaces summarise the option market and are central to many financial applications. Forecasting their future evolution requires modelling two-dimensional geometry, temporal dependence, and predictive uncertainty while preserving economic admissibility. We propose a conditional latent diffusion framework for generating joint 30-step trajectories of implied volatility surfaces and underlying returns. An arbitrage-aware autoencoder learns a low-dimensional surface representation, while the diffusion model captures the conditional joint evolution. Evaluated on SPX surfaces, the framework generates realistic probabilistic multi-step scenarios while also outperforming the persistence benchmark in point forecasting. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.22478 |
| By: | Xin Guo; Binnan Wang; Ruixun Zhang |
| Abstract: | We propose an interpretable machine-learning framework for dynamic hedging using the It\^o signature transform, which turns asset-price paths into a set of linear features that universally represent nonlinear functions on time-series. We show that each discretized It\^o signature component can be perfectly replicated by a simple self-financing strategy using only the underlying assets and cash, which turns It\^o signature components into tradable and transparent hedging bases. This allows nonlinear derivative payoffs to be approximated by linear combinations of signature terms and hedged through the corresponding combination of trading strategies. We further establish a new approximation result for the It\^o signature and derive theoretical bounds for both in-sample and out-of-sample hedging errors. Our method is computationally efficient, easy to implement, and avoids the estimation of future conditional expectations, which makes it attractive for real-world applications. In simulations, our method delivers strong sample efficiency at substantially lower computational cost than neural-network benchmarks. In an empirical study of S\&P 500 index options, it performs robustly across vanilla and path-dependent contracts, with the signature-kernel weighted version providing further gains by localizing estimation to similar historical market paths. Overall, the paper identifies the It\^o signature as a practical, transparent, and model-agnostic implementation framework for dynamic hedging. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.18120 |
| By: | Jaskaran Singh |
| Abstract: | Let a finite population of n labelled examples carry a class-weighted loss, with pi*n in a rare positive class weighted by N0/N1. We study estimation of total risk from a subsample K |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.04420 |
| By: | Tetsuya Takaishi |
| Abstract: | Herein, we propose a quantum circuit learning framework for modeling the realized volatility (RV) of Bitcoin and investigate the statistical properties of the predicted time series through multifractal analysis. Unlike conventional GARCH-type models, which require a pre-specified functional form for the volatility process, a parameterized quantum circuit directly approximates the volatility function from empirical data, eliminating the need for explicit model selection. Using five-minute Bitcoin price data, we construct daily RV, train a single-qubit parameterized quantum circuit, and generate a long synthetic time series from the optimized quantum circuit. Multifractal Detrended Fluctuation Analysis is applied to calculate the generalized Hurst exponent $h(q)$, the singularity spectrum $f(\alpha)$, and the multifractal scaling exponent $\tau(q)$. The predicted return series exhibits $h(2)\approx 0.5$, consistent with near-random dynamics, and both the predicted and the empirical return series display multifractality that partially persists after random shuffling. The increment series of RV shows pronounced anti-persistence with $h(2)\approx 0.05$--$0.1$, consistent with the rough volatility hypothesis. These results demonstrate that a simple single-qubit parameterized quantum circuit captures qualitatively some observed properties in Bitcoin volatility dynamics. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.04569 |
| By: | Yucheng Guo; Qinxin Yan |
| Abstract: | We study a systemic-risk control problem in which a central planner allocates losses generated by bank defaults across the surviving institutions. Banks are modeled through their distances to default, evolving as absorbed Brownian motions with downward jumps induced by redistributed default losses. Unlike bailout models, the planner cannot inject external capital or reduce the aggregate loss, and the only admissible intervention is to decide how each endogenous loss is assigned among solvent banks. The objective is to maximize terminal system health, including survival mass as a leading special case and, more generally, increasing concave welfare functionals of the terminal distribution. Our main result identifies an optimal allocation rule with a simple economic interpretation: losses should be concentrated on the currently healthiest institutions. In discrete time, this rule takes the form of a cutoff or taxing-the-richest policy, which reduces banks above an endogenous threshold down to that threshold while leaving weaker banks untouched. We prove convergence of the time-discretized mean-field control problem as the allocation time step tends to zero and characterize the limiting problem as a singular mean-field control problem. The optimally controlled law is described by a reflected free-boundary formulation, in which the cutoff becomes the moving upper edge of the support, and the associated value function satisfies a Hamilton-Jacobi equation on Wasserstein space. Finally, we formulate the corresponding finite-particle control problem and show, under suitable assumptions, that the cutoff-controlled particle system converges to the continuous-time mean-field model. This provides a finite-system foundation for the optimal mean-field loss-allocation rule. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.18113 |
| By: | Lorenzo Quirini |
| Abstract: | This paper develops a latent-state framework for recovering borrower-level posterior beliefs in credit-risk analysis. Creditworthiness and financial fragility are represented as latent dimensions, while observed borrower scores follow a finite Gaussian mixture model and default depends on the latent profile. Borrower-specific probabilities of default are obtained by averaging profile-specific default probabilities over the posterior distribution of latent states. A joint Expectation--Maximization procedure is used to estimate the mixture structure and the profile-specific default probabilities from observed score--default pairs. After estimation, predictive posterior beliefs are computed using the observed scores alone, thereby preserving the information available before default realization. A controlled simulation experiment evaluates the recovery of structural parameters, posterior beliefs, and borrower-level probabilities of default. Posterior distributions are interpreted as points on the probability simplex, and their recovery is assessed using both conventional error measures and information-geometric divergences. The results provide a controlled benchmark for studying the interaction between latent economic structure, posterior uncertainty, and credit-risk prediction. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.29786 |
| By: | Yueman Fen; Wenyuan Li; Mengyi Xu; Pengyu Wei |
| Abstract: | This paper studies the investment and insurance strategies of defined-contribution (DC) pension plans under the mean-variance framework. We consider a stochastic environment with time-varying interest rates, contributions, and mortality risk. The DC plan members are allowed to decide their bond and stock allocations, as well as their life insurance coverage. Adopting the martingale approach, we derive the closed-form optimal strategies and the mean-variance efficient frontier. Further numerical analysis investigates how mortality improvements affect investment and insurance decisions, as well as the sensitivity of the optimal decision to market parameters. Our analysis suggests that longevity raises expectations of future contributions, allowing pension members to adopt a less risky investment strategy. Meanwhile, insurance strategy shifts toward early adulthood to protect the high value of future income and decreases significantly at later ages. Moreover, we conduct sensitivity analyses on the target expected wealth, market price of risk, and contribution growth. These findings provide practical guidance for pension members on investment and offer insights for the design of DC pension plans. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.04532 |
| By: | Shaowei Ke; Mu Zhang |
| Abstract: | Choice alternatives are often multidimensional and risky. We introduce and axiomatize the \textit{structured multidimensional expected utility} representation, a unified framework that generalizes existing approaches to evaluating such alternatives. The representation uses a \textit{rooted clustered tree} to organize the joint, separate, and conditional evaluation of risk across dimensions within a common structure. We analyze the uniqueness of the representation and characterize useful special cases. We apply the representation to inequality across individuals, groups, and generations and to multisource income, characterizing the implications of bracketing for stochastic dominance and the avoidance of multidimensional risk. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.05043 |
| By: | Kaniska Dam; Rajdeep Sengupta |
| Abstract: | This paper examines the relationship between bank capital and reliance on insured deposit funding. Contrary to the conventional moral-hazard view underlying risk-based capital regulation, U.S. bank data reveal a robust negative association between capital and the share of insured deposits. We develop a delegated-monitoring model in which banks choose between insured and uninsured deposit financing. Although monitoring increases with capital under both funding regimes, its sensitivity to capital is greater when deposits are uninsured, strengthening the relative attractiveness of uninsured funding for well-capitalized banks. Our contribution is to show that the relationship between bank capital and deposit insurance depends not only on the direct effect of capital on risk-taking, but also on how capitalization changes a bank’s incentives to monitor under different funding arrangements. |
| Keywords: | bank monitoring; deposit insurance; bank capital |
| JEL: | C78 D82 G11 |
| Date: | 2026–08–31 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedkrw:103726 |
| By: | Geoffrey Heal; Marcella Lucchetta |
| Abstract: | This paper provides a novel framework for assessing the effect of ambiguity on asset values within the Klibanoff-Marinacci-Mukerji (KMM) smooth ambiguity framework. By shifting the analysis into a continuous space of prior probabilities, we establish that ambiguity leads to an adjustment of beliefs (“ambiguity-adjusted probabilities” or “distorted probabilities”) characterized by First-Order Stochastic Dominance (FSD). Leveraging this property, we introduce a systematic economic decomposition of asset valuation separating the baseline risky valuation from the structural cost of uncertainty. Our continuous framework shows that increased ambiguity aversion depresses optimal asset demand. |
| JEL: | D81 G10 G12 Q20 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35488 |