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on Risk Management |
| By: | Teemu Pennanen; Waleed Taoum |
| Abstract: | Thousands of SOFR derivatives are available in exchanges and OTC, but the market remains illiquid and incomplete. Such a market is beyond the scope of classic risk-neutral approaches that imply linear pricing rules and, at best, approximate hedging strategies whose hedging error may be difficult to quantify. This paper develops an indifference pricing model which is consistent with observed derivative quotes, the agent's financial position and views about the uncertain future as well as risk preferences as described by a convex risk measure. In addition to prices and hedging strategies, the model gives an explicit description of the hedging error and the associated risk. The approach is illustrated numerically using hundreds of CME-listed derivatives to price and hedge unreplicable OTC SOFR derivatives. The indifference prices are computed in less than a minute on a regular PC. We find that the optimal hedging portfolios tend to be sparse but still provide good approximations of the derivative payouts. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.10711 |
| By: | Marcin Pietrzak |
| Keywords: | Geopolitical risk; financial tightening; risk-off pricing; block-exogenous BVAR; heterogeneous transmission |
| JEL: | C32 E44 F51 G12 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:cxu:wpaper:63 |
| By: | Lifeng Hao; Shaolin Ji |
| Abstract: | Implied volatility surface forecasting is essential for option valuation, hedging, and risk management, but remains difficult because future surfaces are stochastic while pricing inputs must satisfy static no-arbitrage shape restrictions. We propose a decoupled generative refinement framework for IVS forecasting as an operational risk surface modeling problem. The first stage uses a conditional diffusion model to learn the conditional distribution of future surfaces. The generated ensemble captures predictive distributional variation, and its median provides a robust representative surface for subsequent refinement. The second stage introduces a Surface Aware Attention Module (SAAM), a cross sectional refinement operator that improves fit to market observations and staticno-arbitrage diagnostics for the representative surface. This design separates distribution learning from surface refinement, allowing the diffusion model to capture stochastic market dynamics while SAAM controls static no-arbitrage residual violations on the final surface. We evaluate the framework on CSI 300 index options from June 2020 to September 2024 under daily and minute level forecasting protocols. The diffusion stage improves forecasting accuracy and produces predictive intervals that vary across moneyness, maturity, and sampling frequency. The refinement stage improves fitting accuracy against market observations and reduces measured static no-arbitrage residual violations, with stronger gains at the minute level. Attention diagnostics suggest that SAAM performs adaptive cross sectional refinement rather than fixed local smoothing |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.29220 |
| By: | H\'el\`ene Gu\'erin; Michel Mandjes; Jean-Fran\c{c}ois Renaud; Arsene Brice Zotsa Ngoufack |
| Abstract: | Epidemic dynamics introduce time-varying heterogeneity into insured populations, as individuals' risk profiles depend on their evolving health status, thereby challenging classical insurance models based on homogeneity. Motivated by this phenomenon, we develop in this paper actuarial risk models in which the claim process is directly linked to an underlying general stochastic population dynamics, resulting in interacting subpopulations with different claim frequencies and severities. We propose both collective and individual modeling frameworks that track the changing composition of the insured population and its impact on aggregate claims. For these models, we derive scaling limits and provide bounds and approximations for ruin probabilities, offering tractable tools for risk assessment. Applications in an SIS context are detailed for different classes of risk processes, thereby contributing to a better understanding of how contagion-driven changes in population structure affect insurer solvency and supporting more realistic modeling of insurance portfolios in the presence of epidemic risk. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.28031 |
| By: | Besley, Timothy (London School of Economics); Lambert, Peter John (London School of Economics and University of Warwick and CAGE); Michalski-Roland, Isabelle (Bank of England); Van Reenen, John (London School of Economics) |
| 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. |
| Keywords: | productivity, default risk, credit frictions, misallocation JEL Classification: D24, E32, L11, O47 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:cge:wacage:818 |
| By: | Stavrakeva, Vania; Tang, Jenny |
| Abstract: | In this paper, we study how the volatility of both \textit{realized} and \textit{expected} macroeconomic variables relates to the variation in exchange rate volatility through the prism of the Great Moderation hypothesis. We find significant heterogeneity in exchange rate trend volatility across currency pairs despite decreases in the volatility of expected future interest rate differentials and of realized yields themselves. We argue that time variation in the relationship between macroeconomic variables and exchange rates has prevented the Great Moderation in realized yield volatility from translating to a decrease in exchange rate volatility. Considering a Campbell-Shiller-type decomposition of exchange rate changes into forward-looking components linked to inflation, policy rate, and currency risk premia expectations, we find that the Great Moderation in volatility of expected yield differentials cannot explain the patterns in exchange rate volatility we observe. The main drivers of these patterns were trends in the volatility of the currency risk premium component and in the covariance between the components capturing the strength of the Fama puzzle and the expected responsiveness of monetary policy to inflation. |
| Keywords: | Exchange rates; International finance; Foreign exchange volatility; Currency risk premiums; Fama puzzle |
| JEL: | E44 F31 G15 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19265 |
| By: | Nocciola, Luca; Scaglioni, Samuele |
| Abstract: | We analyze the Probability of Default (PD) of non-financial corporations in Europe using Random Forests (RF) and assess implications for stress testing the banking sector. To this end, we exploit data on firms’ financial statements (Orbis) and banks’ credit registry (Anacredit). We show that RF displays stronger risk sensitivity than logistic regression in stress testing, shedding new light on the non-linear effect of scenario severity on PD. Moreover, we show how RF-based PD can be used in a network of banks and firms to stress test the banking sector through loan exposures as a key transmission channel of adverse scenarios. A granular inspection of banks’ riskiness indices derived from this network sheds light also on RF’s superior ability in capturing non-linearity thanks to its capability in identifying “tail banks”. Our work is relevant for central banks and banking supervisors alike. JEL Classification: C53, C55, C58, G17, G21 |
| Keywords: | banking supervision, corporate exposures, credit risk, financial stability, random forests |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263277 |
| By: | Henrik Karlholm; Marlon Moresco; Marcelo Righi |
| Abstract: | This paper develops a theory of monetary risk measures on metric state spaces. We propose the space of Lipschitz functions vanishing at a reference state as a natural domain for financial positions. The associated Lipschitz-free space provides its canonical predual, linking anchored Lipschitz payoffs to transport-based dual variables interpreted as redistributions of mass around the benchmark. Since the domain lacks constants and need not be a Banach lattice under the Lipschitz norm, standard cash-additive methods do not apply directly. We address this by using additivity along benchmark-deviation instruments and derive dual representations for convex and coherent risk measures. The framework covers temporal cash flows, path-dependent payoffs, network risk, and model uncertainty. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.17020 |
| By: | Lee, Woongki (Yonsei University) |
| Abstract: | A basic intuition in asset pricing is that higher perceived risk is associated with higher expected return. Yet this intuition alone does not fully explain how expected return is translated into realized return. An important link is therefore still missing. To address that gap, this study moves beyond the static risk-return tradeoff and focuses on three intertemporal elements: information processing, repricing in response to revisions in perceived risk, and hedging against future reinvestment risk. By bringing these elements together, the study explains how asset prices evolve over time and how returns take shape over time. |
| Date: | 2026–08–05 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:pjhau_v1 |
| By: | Miles, David; Sefton, James |
| Abstract: | We use stochastic simulations to analyse the probability distribution of outcomes for the UK University pension scheme (the USS) in the light of conflicting claims about its sustainability. We use the results to draw wider conclusions about the nature of defined benefit (DB) pension schemes and whether they bring benefits to members based on risk sharing. We find that a substantial investment in riskier assets (equities) makes the average outcome one in which the scheme is comfortably able to pay accrued benefits. But the risk of having far fewer funds than needed to pay existing pension promises is significant and the chances of large deficits is substantial. The ambiguity about how pension fund surpluses or deficits would be allocated between scheme members and the scheme sponsor (for the USS that is Universities) means that agreement on the optimal portfolio allocation for the scheme's funds is not likely. Among scheme members of different ages and different attitudes towards risk agreement on what are acceptable trade-offs between risk and return on assets is unlikely. We contrast this with the position for defined contribution pensions. |
| Keywords: | Pensions |
| JEL: | G11 G50 G22 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19254 |
| By: | Francesco Landolfi |
| Abstract: | How deep and how long should the drawdowns of a systematic trading strategy run, given its Sharpe ratio and the statistical structure of its returns? Building on the drawdown framework of Rej, Seager and Bouchaud (2017), we develop the answer in three steps. We first reframe their closed-form results as a transparent Monte-Carlo experiment, validate it against their analytic benchmarks, and extend the mapping from drawdowns to four decision-relevant measures: maximum drawdown, maximum loss, final negative time and longest recovery time. We then relax the Gaussian assumption, holding the true Sharpe and volatility fixed while varying skewness, fat tails, volatility clustering and Sharpe-estimation uncertainty across strategy archetypes; the four measures move differently, so a single Gaussian table mis-warns. We finally replace short-memory persistence with fractional Brownian motion and show that the apparent amplification of drawdown risk under persistence is, for maximum-drawdown depth, almost entirely self-similar dispersion scaling (T^(H-1/2)) rather than path geometry: a failure of square-root-of-time calibration, not intrinsic danger. We provide reproducible lookup tables and a practical calibration recipe. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.00127 |
| By: | Jongrim Ha; Haroon Mumtaz; Franz Ruch |
| Abstract: | We develop a new dynamic factor model with stochastic volatility to quantify inflation tail risk across a large cross section of countries. The framework accommodates unbalanced panels and mixed-frequency data, allowing estimation of the full predictive distribution of inflation for over 200 economies over 1971-2023. Inflation risk - defined as the probability that inflation exceeds 5 percent over a twelve-month horizon - declined during the Great Moderation but rose sharply following the COVID-19 pandemic, with the global probability surpassing 50 percent from early 2021 through 2023. Exploiting the joint predictive distribution of inflation and real activity, we document a brief surge in global stagflation risk in late 2021. While inflation risk responds to both structural demand and supply shocks, it tends to decline during monetary policy tightening cycles. Cross-country evidence further shows that economies with greater trade and financial openness, stronger monetary policy frameworks, and fixed exchange rate regimes face systematically lower inflation risk, while commodity-exporting countries exhibit higher tail exposures. Overall, the results under-score the importance of monitoring inflation risks alongside inflation forecasts and highlight the role of institutions in mitigating macroeconomic tail vulnerabilities. |
| Keywords: | monetary policy, risk, FAVAR, stochastic volatility |
| JEL: | C32 E44 E52 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2026-72 |
| By: | Remy Levin; Daniela Vidart |
| Abstract: | We design a method for measuring the risk preferences of agents in the deep past. The method combines a structural model of crop choice as a portfolio allocation with machine-learning prediction of expected crop returns, using historic agronomic and climate data. We estimate county-level risk preferences for the United States and farmer-level preferences in Kansas from 1889 to 1929. More risk averse farmers leveraged less, were less likely to purchase novel WWI Liberty Bonds, and were more likely to participate in local risk-sharing institutions. We show that higher risk aversion predicts slower tractor adoption and farm mechanization during the 1920s. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.15876 |
| By: | Lorenzo Quirini |
| Abstract: | We propose an information-geometric framework for credit risk monitoring in which a bank's knowledge of a borrower is represented by a posterior distribution over latent dimensions of creditworthiness and financial fragility. Under a linear-Gaussian specification, Bayesian updating maps observed behavioural scores into Gaussian posterior beliefs, which form a statistical manifold endowed with the Fisher information metric. In the common-covariance case, the induced geometry reduces to the Mahalanobis metric on posterior means, while borrower-specific covariance matrices allow informational distances to account for both expected risk and assessment uncertainty. Simulation exercises illustrate how Kullback-Leibler and Jeffreys divergences can be used to compare portfolio segments. The framework provides a geometric interpretation of credit monitoring as the evolution of posterior beliefs over borrower risk. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.01294 |
| By: | Joy Buchanan; Joshua Foster |
| Abstract: | Language models increasingly settle real resource tradeoffs on behalf of principals yet their economic preferences remain unobserved. We demonstrate their generation rule is isomorphic to the random utility model of discrete choice. This allows internal logit scores to structurally identify preferences. Estimating risk attitudes across twelve models in a portfolio task reveals universal but heterogeneous risk aversion. Although models reject strictly dominated options, their elicited preferences fail invariance tests and violate the independence of irrelevant alternatives across varying experimental prompts. Finally, fine tuning establishes that a principal can explicitly engineer a target risk attitude. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.26288 |
| By: | Haelim Anderson; Charles W. Calomiris; Jennifer S. Rhee |
| Abstract: | We study how extended stockholder liability shaped bank resolution during and after the Panic of 1893, comparing California state banks (unlimited liability) with national banks (double liability). Using newly assembled data linking California state bank presidents to census records and national bank stock ownership data from Examination Reports, we measure managers’ personal exposures to extended liability. For California state banks, traditional fundamentals predict involuntary liquidations but do not explain voluntary exits. Instead, voluntary exits are driven by managers’ liability exposure, local economic risk, and personal wealth, consistent with managers responding to unlimited liability by initiating preemptive, orderly resolutions. For national banks under double liability, voluntary and involuntary liquidations are more similar, and personal exposure plays a smaller role in risk management. These findings show that substantial personal liability exposure can operate for modern prudential tools like compensation clawbacks and living wills. However, such extended liability also magnifies credit contractions during downturns, highlighting a fundamental tradeoff between micro-stability and macroeconomic fragility in regulatory design. |
| JEL: | D82 E32 G21 N12 N22 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35598 |
| By: | Vivien Czofa; Tibor Szendrei; Katalin Varga |
| Abstract: | This paper employs a Threshold Bayesian Vector Autoregression (TBVAR) to estimate the regime-dependent macroeconomic effects of capital regulation in Hungary. Using the Factor-based Index of Systemic Stress (FISS) as the threshold variable, the model identifies normal and stress regimes consistent with the occasionally binding constraints literature. The TBVAR offers a practical multivariate alternative to Growth-at-Risk for data-constrained economies. Generalised impulse responses reveal a pronounced asymmetry: releasing regulatory capital during stress raises GDP growth at the peak, with effects persisting for roughly twenty months, while the cost of accumulating capital in the normal regime is economically negligible. These findings are robust to alternative Cholesky orderings, sample periods, and credit variable definitions, providing direct empirical support for the countercyclical operation of the capital buffer. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.14307 |
| By: | Nicola F. Zaugg; Lech A. Grzelak |
| Abstract: | We propose the VIX-derived volatility (VDV) model, a VIX-first framework for joint SPXVIX modeling. In the model, we define explicit dynamics for the VIX process to price VIX futures and options, yielding a VIX-side calibration that is independent of the SPX dynamics. Using the rolling-window definition of the VIX, we then derive a coupling function to obtain the SPX volatility process as a latent process consistent with the calibrated VIX dynamics. This produces a stochastic-volatility representation for SPX that can be further calibrated to SPX options without altering the VIX dynamics. Relative to existing joint-calibration approaches based on global optimization or highly flexible black-box dynamics, the method offers an interpretable and tractable decomposition of the joint problem by separating the calibration of the VIX from the calibration of the SPX. In a numerical experiment, we show that a VDV model with local volatility and mean-reverting dynamics for the VIX achieves a close model fit to the VIX futures market, VIX option market, and the SPX option market at the same time, providing a consistent joint framework. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.01479 |
| By: | Mantu Gupta; Anand Deo |
| Abstract: | We study stress-scenario generation for systems driven by multivariate heavy-tailed risk factors. Within regions where several financial losses are simultaneously extreme, stress analysis concerns both the conditional law of the risk factors and the most plausible configurations producing those losses. We show that both are governed by the same limiting tail law. Preserving its measure recovers rare-event probabilities and scaled conditional stress laws, while misspecifying extremal dependence distorts some regular joint-stress probability. Its density governs reverse-stress optimization, whose maximizers identify the most plausible stress configurations. To exploit this common structure in finite samples, we develop SSGEN (Self-Similar Generative Estimation), which learns extremal dependence from intermediate exceedances and extrapolates to rarer levels using a Pareto radial component. Even when the target event is absent from the sample, we establish convergence rates for the generated conditional law, and data-driven reverse-stress solutions. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.13056 |
| By: | Damiano Brigo; Vladimir Lucic |
| Abstract: | This article studies the conditional-density equation and its pathwise transformation in local stochastic rough volatility models, with rough Heston (rHeston) as the main explicit example. Under the stated common-filtration, measurability, predictability and spatial-regularity assumptions, we show that the Ito-Wentzell random-PDE reduction of the conditional density SPDE remains valid under local stochastic rough volatility. After fixing a common-environment realization and the associated stochastic flow, the transformed equation becomes a deterministic PDE with path-dependent coefficients. This yields a pathwise Fokker--Planck formulation that connects naturally with Rao--Blackwellized calibration. In the pure rough Heston case, the transformed coefficients simplify and the conditional density admits an explicit lognormal form. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.27588 |
| By: | Giulio Principi |
| Abstract: | This paper proposes a metric approach to measuring the complexity of lotteries. Starting by observing that degenerate lotteries are the simplest choice alternatives, the complexity of a lottery is evaluated by its distance from the closest degenerate lottery. Equivalently, a lottery is complex when it is difficult to approximate it by a single outcome. Given a metric over outcomes, the complexity index we consider is the minimum average distance between the lottery and one of its best degenerate proxies. The paper provides an axiomatic foundation for this representation, studies its main properties, and compares it with other measures of complexity. It then applies the index to choice under risk through a class of complexity adjusted expected utility preferences. Within this model, we study how complexity affects risk attitudes and consistency with stochastic dominance. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.14464 |
| By: | Robert A. Jarrow; Simon S. Kwok |
| Abstract: | We develop a unified modeling framework that connects two distinct types of bubbles defined in the literature: the rational bubbles (aka P-bubbles), and the local martingale bubbles (aka Q-bubbles). We show that the local martingale bubble model includes the classical rational bubble as a special case. We relate both types of bubbles to an equity's risk premium via a novel decomposition. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.01554 |
| By: | David Dillenberger (University of Pennsylvania); Jay Lu (University of California, Los Angeles) |
| Abstract: | We introduce a behavioral notion of domain-specific risk aversion that separates attitudes toward risk from deterministic utility: an agent is more pure risk averse in one domain than in another if, for prizes that are indifferent under certainty, he is more averse to risk in the former domain than in the latter. We develop a model that goes beyond expected utility by allowing risk attitudes to vary across domains, while preserving expected utility within each domain. The domains are subjective and need not be specified in advance; they are identified from choice behavior. We establish uniqueness of the model’s parameters and provide an axiomatic characterization. |
| Keywords: | domain-specific risk aversion; non-expected utility; subjective domains; prize interchangeability |
| JEL: | D81 D91 D80 |
| Date: | 2026–08–19 |
| URL: | https://d.repec.org/n?u=RePEc:pen:papers:26-011 |