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on Risk Management |
| By: | Mingzhi Yang; Sheng Wang; Chao Zhang; Ruikun Li |
| Abstract: | Modeling the dynamics of option implied volatility surface (IVS) is crucial for pricing, hedging, and risk-managing option portfolios. We develop a universal conditional diffusion model that learns to jointly generate next-day IVS increments and the underlying stock's returns. The model is trained on pooled data from 50 stocks and evaluated on a test set comprising 50 in-sample stocks and 50 out-of-sample stocks excluded from training. The training objective is primarily the minimization of MSE, with the variants that jointly impose surface smoothness, and that penalize the presence of static-arbitrage, which are all economically meaningful constraints. Across in-sample and out-of-sample stocks, our diffusion model outperforms the VolGAN benchmark in reducing arbitrage violations, improving stock risk prediction, and aligning explained variance ratios by the first three principal components. The fact that the model extrapolates well to stocks that are excluded from training indicates that the learned dynamics can be shared across stocks. These findings support a universal conditional diffusion as a credible framework for multi-stock implied volatility surface scenario generation. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.22893 |
| By: | Onur Polat (Institute of Informatics, Hacettepe University, Beytepe Campus, 06800 Çankaya, Ankara, Türkiye); Yuxiang Ye (School of Economics and Finance, University of the Witwatersrand, Johannesburg, South Africa); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | This study examines whether decomposing the geopolitical risk (GPR) index into oil- and non-oil-related components yields incremental explanatory power for U.S. industry stock market volatility. Using daily conditional volatilities derived from a GJR-GARCH model with time-varying skewness and kurtosis (GJR-SK) across 49 Fama-French industries, we estimate autoregressive specifications with exogenous inputs (ARX). Each model incorporates rich macro-financial controls, including market portfolio volatility, the Aruoba-Diebold-Scotti business conditions index, and the shadow-rate-augmented federal funds rate. Through a sequence of properly nested F-tests, we address three core empirical questions: (i) whether non-oil GPR adds predictive content beyond the macro baseline; (ii) whether oil-specific GPR provides incremental information once non-oil GPR is controlled for; and (iii) whether a geographic decomposition across eight oil-producing regions enhances explanatory power beyond aggregate metrics. We find that non-oil GPR is incrementally significant in 35 of 49 industries. Conditional on non-oil GPR, aggregate oil-related risk remains incrementally significant in 23 industries, while its regional breakdown increases significance to 29 industries. Estimated coefficients demonstrate marked sign heterogeneity: for oil-intensive sectors, non-oil GPR reduces volatility while oil-specific risk elevates it, supporting a commodity-price information-resolution channel; for non-oil-intensive sectors, the pattern reverses, consistent with the classical risk-aversion channel of Bloom (2009). Regionally, geopolitical risk originating in the Middle East, Venezuela, and Asia systematically amplifies industry volatility, whereas risk originating in Russia and Africa exerts a stabilizing, volatility-dampening effect. |
| Keywords: | stock price volatility, industry sectors, geopolitical risk, oil geopolitical risk, ARX, nested F-test |
| JEL: | C22 G12 F51 Q41 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202626 |
| By: | Guo, Honfei; Marín Díazaraque, Juan Miguel; Veiga, Helena |
| Abstract: | Rankings of volatility models often change with the market, the evaluation period, and the forecast criterion, so a gain found in one setting may not carry over to another. We test this directly. Eight volatility models, four observation-driven and four stochastic volatility, are estimated for five equity indices by data cloning and compared on one-step-ahead forecasts. We assess joint value-at-risk and expected shortfall accuracy, calibration, predictive density accuracy, and forecast availability. In 2018–2023, nominally significant gains cluster in DAX and NIKKEI, with weaker evidence in FTSE. Neither holdout comparison is corroborated: the DAX estimate changes sign, and the NIKKEI estimate keeps its direction but is imprecise. Different criteria also favour different models. The results argue for validating a model in its intended market and for the criterion it is meant to serve, and they show how a prespecified holdout in a later period can test whether an advantage persists. |
| Keywords: | Forecast evaluation; Holdout validation; Model comparison; Stochastic volatility; Tail risk forecasting; Volatility asymmetry |
| JEL: | C22 C52 C53 G17 |
| Date: | 2026–09–18 |
| URL: | https://d.repec.org/n?u=RePEc:cte:wsrepe:50798 |
| By: | Jaehyung Choi |
| Abstract: | We develop Entropic Value-at-Risk (EVaR) parity for tempered stable returns. EVaR-based inverse risk parity (IRP) and equal risk contribution (ERC) portfolios are constructed using multivariate normal tempered stable models and independent component analysis with tempered stable components. We derive the corresponding asset-level EVaR and EVaR-deviation contributions and use the latter to separate the fitted location term from EVaR risk contributions. Under Gaussian returns, EVaR-deviation IRP and ERC recover conventional volatility IRP and ERC weights. We evaluate the resulting portfolios in three investment universes. Empirically, EVaR-based ERC portfolios achieve positive Sharpe differences relative to equal weight across the universes. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.11905 |
| By: | Chizhe Cheng; Paan Jindapon; Ajalavat Viriyavipart |
| Abstract: | Informal risk sharing enables individuals to smooth consumption when access to formal insurance is limited. While previous studies have examined the roles of income correlation and initial income inequality, little is known about how differences in individual risk exposure affect voluntary risk sharing. We investigate this question using a laboratory experiment based on an indefinitely repeated risk-sharing game. Subjects are randomly assigned to either homogeneous income risk, in which both members of a pair face the same level of risk, or heterogeneous income risk, in which one subject faces high income risk and the other faces low income risk. The benchmark repeated-game model predicts that transfers should be highest under homogeneous high income risk and lower, but comparable, under homogeneous low income risk and heterogeneous income risk. Consistent with this prediction, transfers are higher when both subjects face high income risk than when both face low income risk. However, subjects facing heterogeneous income risk exhibit a higher incidence of positive transfers than those in the homogeneous-risk configurations. Although average transfers under heterogeneous income risk lie between those observed under homogeneous high and homogeneous low income risk, they are considerably closer to the former than the latter. These findings provide the first experimental evidence that heterogeneous income risk does not undermine voluntary risk sharing and may instead encourage greater participation in reciprocal transfer arrangements. |
| Keywords: | Risk sharing; Heterogeneous risk; Cooperation; Infinitely repeated games; Economic experiment |
| JEL: | D81 C91 C73 O17 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:pui:dpaper:262 |
| By: | Kok, Christoffer; Barquero, Javier Arranz; Bertelsen, Christian Hellum |
| Abstract: | This paper examines how bank capital conditions the effect of competition on credit risk in lending markets, informing the debate on banking competition, deregulation, and risk-based supervision. Using ECB supervisory data for 146 euro area banks across 19 countries over 2020Q2–2025Q3, we analyze whether this relationship depends on banks’ regulatory capital positions. We find that greater market power is associated with higher subsequent credit risk, while stronger capitalization is associated with lower risk. Crucially, competition reduces credit risk primarily for well-capitalized banks, whereas the effect is weak or absent for banks with lower capital ratios. By aligning the measurement of competition and risk with the pricing-based mechanism, the paper provides a direct empirical test of the borrower-risk channel and offers an explanation for mixed evidence in the competition–risk literature. The results highlight the importance of considering the interaction between competition and prudential capital requirements when assessing financial stability. JEL Classification: G21, G28, L11, C23 |
| Keywords: | bank capital, bank competition, borrower-risk channel, credit risk, financial stability |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263285 |
| By: | Yinbin Han; Jack Yuxiang Zhang; Manuel Torres; Fernando Acero; Renyuan Xu |
| Abstract: | We develop a diffusion-model framework for dynamic implied-volatility surface generation and evaluate its economic usefulness through data-driven hedging. The framework consists of two models. AD-Seq-Vol jointly learns the conditional evolution of the underlying asset return and the high-dimensional implied-volatility surface, generating adapted multi-period scenarios by sequentially updating the realized market history. AD-Seq-Vol-FT further incorporates option-market structure through post-training penalties for violations of static no-arbitrage conditions. Using daily SPX option data from 2000 to 2023, we show that the proposed models generate coherent surface trajectories while capturing both cross-sectional and temporal dependence. AD-Seq-Vol produces fewer and less severe static-arbitrage violations than the training data and the GAN-based benchmark, while AD-Seq-Vol-FT reduces these violations to nearly zero. We then integrate the generated conditional scenarios into an optimization-based hedging framework. Compared to a range of classical and data-driven benchmarks, the diffusion-based hedges maintain tracking errors near zero, substantially reduce tail risk, and exhibit particularly stable performance during the COVID-19 market disruption. These results establish Adaptive Sequential Diffusion Models as a promising class of market-consistent and economically useful financial scenario generators. Our code is available at: https://github.com/yinbinhan/volatility- surface-simulation. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.13402 |
| By: | Masaaki Fukasawa |
| Abstract: | We prove that the skew stickiness ratio converges to two at short maturity under local volatility models. This appears to be the first rigorous proof of this limit for a general time-dependent local volatility function. As a by-product, we strengthen the one-half rule of the implied volatility skew by removing uniform ellipticity and global bounds on spatial derivatives of order at least two. The proof uses a first-order Watanabe expansion. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.11586 |
| By: | Antonio Cabrales; Gregorio Curello; Olivier Gossner; Roberto Serrano |
| Abstract: | We study how willingness to pay for information depends on risk aversion when a decision maker faces background risk and can acquire information before choosing from a menu of assets. We distinguish investment menus, whose payoffs are procyclical with background wealth, from insurance menus, whose payoffs are countercyclical. Our main results show that the interaction between asset cyclicality and the tail geometry of background risk determines the direction of the comparative statics. When the density of background risk is log-concave, willingness to pay for information decreases with risk aversion for investment menus, whereas with downward-log-convex background risk it increases with risk aversion for insurance menus. The proofs compare the distributions of terminal wealth with and without information and develop new aggregation arguments for state-dependent single-crossing comparisons. We also construct reversals under strictly log-convex tails for investment menus and super-exponential left tails for insurance menus. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.12149 |
| By: | Paula Prakash; Leon Stolle |
| Abstract: | We analyze how long-term electricity hedging design affects industrial investments in load-shifting capacity. These investments enable flexible electricity consumption and play a crucial role in integrating variable renewable energy. We develop a theoretical model of flexibility investment by a risk-averse industrial firm facing uncertainty about renewable generation and electricity prices. We compare a conventional baseload hedge with a renewable energy profile (REP) hedge, a hedge with a fixed price but varying quantity proportional to renewable generation. The key difference is the consumer’s risk exposure and how it affects flexibility investment. Under the baseload hedge, flexibility provides an uncertain arbitrage value from shifting consumption across hours, so risk aversion reduces investment. Under the REP hedge, flexibility acts as a physical hedge against renewable profile risk by aligning the firm’s consumption with the contracted profile. This insurance value can increase flexibility investment under risk aversion. Numerical simulations illustrate the mechanism for realistic hourly price and renewable generation profiles. We further show that the REP hedge can reduce risk-adjusted total costs for flexible firms once baseload contract prices reflect observed profile risk premia. The results identify long-term hedging design as a potential channel to unlock industrial demand-side flexibility. |
| Keywords: | Industrial demand response, long-term contracts, renewable energy, investment, risk aversion, profile risk |
| JEL: | Q41 Q48 D21 D81 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:diw:diwwpp:dp2180 |
| By: | Giancarlo Mazzoni (Bank of Italy) |
| Abstract: | Geopolitical developments have become a persistent and structurally relevant factor shaping the operating environment of banks. Their prudential significance does not arise from the existence of a distinct 'geopolitical' risk category, but from their capacity to amplify established vulnerabilities, including credit deterioration, market repricing, liquidity stress, funding instability, operational disruption, and governance weaknesses. This reading is also consistent with the broader SSM and European supervisory approach, which frames geopolitical developments as cross-cutting drivers of existing prudential risks rather than as a new standalone risk category. In this context, it is analytically more appropriate to refer to geopolitical uncertainty rather than geopolitical risk in a narrow sense. Geopolitical uncertainty is inherently cross-cutting, heterogeneous, nonlinear, and frequently transmitted through indirect channels. It therefore does not fit neatly within the traditional banking risk taxonomy but propagates through existing prudential categories and affects both sides of banks' balance sheets. This feature is particularly relevant for less significant institutions (LSIs), whose exposure is often not direct or cross-border, but embedded in domestic economic structures, sectoral concentrations, borrower vulnerabilities, and operational dependencies. Against this background, the paper documents how the supervisory approach of Banca d’Italia integrates geopolitical considerations within the standard prudential framework, rather than treating them as a separate supervisory silo. Evidence from recent supervisory cycles suggests that geopolitical uncertainty is reflected indirectly through its impact on governance, credit quality, provisioning adequacy, liquidity, operational resilience, funding sustainability, and capital demand. The paper also develops an original structural framework, inspired by contingent claims analysis and by the Merton approach, but adapted to the specific economics of banking. Relative to the standard Merton model, the proposed framework introduces asset-class heterogeneity, liability-class dynamics, endogenous funding costs, and a payout structure that makes it possible to model banks while preserving analytical tractability. The contribution is therefore not measurement in a narrow sense, but a consistent analytical framework to explain how geopolitical uncertainty affects asset performance, volatility, correlations, funding conditions, distance to default, and equity values. Its main aim is to support supervisory assessment and structured scenario analysis in an environment of fundamental uncertainty. |
| Keywords: | geopolitical uncertainty, banking supervision, prudential risk, contingent claims analysis |
| JEL: | G28 G32 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:bdi:opques:qef_1052_26 |
| By: | LiYang Wang (Washington University in St. Louis); Zhen Zhong (Georgetown University); Zhen Tian (University of Glasgow); Keyu Chen (Wuyi University); Keyu Chen (Wuyi University) |
| Abstract: | Advancements in data fusion and real-time analytics technologies have opened new avenues for addressing complex domain challenges. Financial risk early warning systems often suffer from inefficiency due to information silos and monitoring delays. This paper proposes a credit risk early warning system based on heterogeneous information fusion. The system employs a model architecture integrating deep neural networks and attention mechanisms to extract multidimensional features from diverse data sources such as transaction behaviors and social networks, thereby establishing an early identification mechanism for corporate and individual credit risks. System testing demonstrates that this approach significantly enhances the accuracy and timeliness of risk warnings, outperforming traditional rule-based engine solutions. The findings offer innovative insights for early intervention in financial risks, holding practical significance for safeguarding financial stability. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.15744 |
| By: | Mikkel Bennedsen; Kim Christensen; Peter Korsbakke Christensen; Jun Yu; Chen Zhang |
| Abstract: | This paper proposes discrete-time approximations to rough continuous-time models of realized variance (RV). The leading rough models can be viewed as autoregressive processes driven by fractional Gaussian noise. We show that the Wold representation of this noise concentrates its dependence at the first lag when the Hurst parameter is below one half. Augmenting the autoregressive (AR) and heterogeneous autoregressive (HAR) models with a first-order moving-average (MA(1)) component therefore approximates the roughness, and the MA coefficient maps almost linearly into the Hurst parameter. We refer to these extensions as the "rough" AR and "rough" HAR models. Estimating them on the log RV of ten ETFs, we find negative MA coefficients for every asset, and the implied Hurst parameters align closely with the estimates from the continuous-time models. In the HAR literature, the negative MA(1) component is a significant feature that has been largely overlooked. In out-of-sample comparisons, the "rough" models outperform their classical counterparts for nearly every asset and horizon, with the largest gains at short horizons, and their accuracy is comparable to that of the rough continuous-time models but much easier to estimate by standard off-the-shelf software. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.21587 |
| By: | Wenjun Jiang; Yiying Zhang; Zhenfeng Zou |
| Abstract: | We characterize the worst- and best-case values of a mean-variance functional over a 2-Wasserstein ball. Using quantile representations and the geometry of attainable means and standard deviations, we reduce both infinite-dimensional problems to scalar equations and construct the extremal laws as location-scale transformations of the reference distribution. Their values depend on the reference law only through its first two moments. We then derive dual representations and formulate proportional risk sharing under heterogeneous beliefs as a finite-dimensional optimization problem. Under homogeneous beliefs, we show that the classical proportional allocation remains optimal for every ambiguity radius and $\alpha$-maxmin weight. Finally, we study a coupled distortion-variance functional and characterize its worst-case quantile through a convex-envelope construction, allowing the extremal law to change in shape as well as location and scale. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.23571 |
| By: | Mikkel Bennedsen (Aarhus University, Department of Economics and Business Economics, Denmark; Aarhus Center for Econometrics (ACE), Aarhus University, Denmark; Center for Research in Energy: Economics and Markets (CoRE), Aarhus University, Denmark); Kim Christensen (Aarhus University, Department of Economics and Business Economics, Denmark; Aarhus Center for Econometrics (ACE), Aarhus University, Denmark; Research fellow at the Danish Finance Institute (DFI)); Peter Korsbakke Christensen (Aarhus University, Department of Economics and Business Economics, Denmark; Aarhus Center for Econometrics (ACE), Aarhus University, Denmark); Jun Yu (University of Macau, Faculty of Business Administration, Macau); Chen Zhang (Sun Yat-sen University, Lingnan College, China) |
| Abstract: | This paper proposes discrete-time approximations to rough continuous-time models of realized variance (RV). The leading rough models can be viewed as autoregressive processes driven by fractional Gaussian noise. We show that the Wold representation of this noise concentrates its dependence at the first lag when the Hurst parameter is below one half. Augmenting the autoregressive (AR) and heterogeneous autoregressive (HAR) models with a first-order moving-average (MA(1)) component therefore approximates the roughness, and the MA coeffcient maps almost linearly into the Hurst parameter. We refer to these extensions as the “rough†AR and “rough†HAR models. Estimating them on the log RV of ten ETFs, we find negative MA coeffcients for every asset, and the implied Hurst parameters align closely with the estimates from the continuous-time models. In the HAR literature, the negative MA(1) component is a significant feature that has been largely overlooked. In out-of-sample comparisons, the “rough†models outperform their classical counterparts for nearly every asset and horizon, with the largest gains at short horizons, and their accuracy is comparable to that of the rough continuous-time models but much easier to estimate by standard off-the-shelf software. |
| Keywords: | Continuous-time models, roughness, HAR model, realized variance |
| JEL: | C10 C58 C80 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:boa:wpaper:202646 |
| By: | Xu, Yongdeng (Cardiff University, Cardiff, UK) |
| Abstract: | Realized measures are biased for the moments of returns. We propose a two-step approach to forecasting large correlation matrices: machine-learned forecasts of realized measures enter a dynamic conditional correlation model as drivers, and their weights are estimated by quasi-maximum likelihood on returns. The correlation recursion is run in a matrix-logarithm parametrization, so every forecast is a valid correlation matrix. For Dow Jones stocks, a learned volatility driver and this recursion improve on a dynamic conditional correlation model with realized drivers at horizons of one, five and 22 days. For S&P 500 stocks, the model fits monthly returns better than machine-learned projections of realized correlations, has no invalid forecast in any month, and trades less. The criterion that selects the weight on a realized driver matters as much as the model, and practitioners can obtain valid forecasts calibrated to returns with standard software. |
| Keywords: | DCC-GARCH-X; Realized measures; Covariance forecasting; Forecast evaluation; Positive definiteness; Log-correlation coordinates; Nonlinear shrinkage |
| JEL: | C32 C53 C58 G11 G17 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:cdf:wpaper:2026/12 |
| By: | Christopher Blier-Wong |
| Abstract: | Claim narratives, images and sensor data contain information about insured risks that is difficult to use through existing actuarial models. Foundation models learn patterns from large datasets before being adapted to particular tasks. By turning these high-dimensional sources into variables or numerical representations, they could help insurers use more of the information they already collect, potentially reducing the experience needed to develop each application. For example, a language model could identify a worsening injury in a new claim note, allowing a reserving model to recognise the change in expected cost before the payments reveal the deterioration. In this paper, we review language, vision, geospatial, time series, tabular and scientific models, explaining existing insurance applications and potential future uses. Scientific models extend this approach to future weather and climate conditions: their simulations can inform loss estimates once local hazards are linked to asset damage, repair costs and insurance coverage. We propose a process to connect these model outputs to actuarial calculations and to assess their predictive contribution, stability and compliance with rules on information use. Evaluating these applications is difficult when final claim costs become known only after long delays, large losses are rare or patterns learned elsewhere fail to transfer to the target portfolio. Richer data can reveal private information and support finer risk classification, which can change access to insurance. Reusing the same models across insurers also creates dependence on shared predictions and providers. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.14576 |
| By: | Nacira Agram; Jan Rems; Emanuela Rosazza Gianin |
| Abstract: | We study optimal stopping under dynamic risk measures with simultaneous ambiguity in the probability model and the discount rate. We introduce a paired ambiguity framework combining Girsanov model uncertainty with cash subadditive risk evaluation and characterize the stopping value by an upper reflected backward stochastic differential equation (BSDE). We establish structural properties of the resulting stopping operator and study quadratic drivers associated with entropic risk measures, obtaining explicit stopping rules in several benchmark cases. We then develop a deep learning scheme for the reflected quadratic BSDE. The convergence analysis uses discrete reflection and truncation to reduce the quadratic problem to a globally Lipschitz system and combines reflected BSDE discretization estimates with neural network approximation errors. Numerical experiments for American options illustrate the effects of discount rate and entropic ambiguity on stopping values and exercise decisions. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.23768 |
| By: | Xudong An; Jan Harren; Saket Hegde; Mete Kilic; Rodney Ramcharan |
| Abstract: | This paper shows that monetary policy changes the risk composition of bank lending by altering the perceived tail risk of bank equity: when FOMC announcements compress the likelihood of costly low-equity states, banks originate riskier commercial and industrial loans. We measure bank-specific tail-risk shocks using equity put option returns and link them to originations in supervisory loan data. The shift toward riskier lending emerges after announcements and extends to lending within existing bank-borrower relationships. Stress-test capital and banks' own reports tying lending standards to risk tolerance corroborate the mechanism. Short-term compensation, earnings pressure, and competition amplify this risk-taking channel. |
| Keywords: | Fed put, risk-taking channel, credit policy, monetary policy, bank equity tail risk |
| JEL: | E52 G12 G21 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12980 |
| By: | Said Khalil (TREE, INSEA); Fatima Zahrae Chaayra (INSEA) |
| Abstract: | The 2027 Solvency II reform recalibrates the Risk Margin by reducing the prescribed cost-of-capital rate from 6% to 4.75% and introducing a time-dependent attenuation of future Solvency Capital Requirements. This paper develops an analytical and numerical framework for characterizing the effect of the final regulatory calibration. By normalizing discounted projected capital requirements into a probability distribution over run-off time, we obtain an exact representation of the ratio between the revised and previous Risk Margins. The framework yields sharp universal and horizon-specific bounds, characterizes the effect of later capital timing through stochastic dominance, and shows that mean run-off time alone does not determine the reform effect when temporal dispersion varies. Additional bounds are derived conditional on mean run-off time and horizon. Conditional on a given projected SCR path, the mechanical reduction lies between 20.83% and 60.42%. For proportional Best Estimate projections, an exact covariance decomposition identifies how departures from proportionality affect the relative reform ratio. Holding the projected SCR path fixed, the revised formula has a lower direct interest-rate semi-elasticity than the previous calibration. A reduced-form stochastic extension further quantifies convexity effects arising from uncertainty in capital persistence. Numerical applications reconstruct Risk Margin calculations from published actuarial run-off profiles and complement them with controlled long-horizon, interest-rate, and persistence experiments. The results show that the reform effect is governed by the temporal structure of future capital and provide tractable tools for assessing projected capital profiles, Risk Margin simplifications, and direct discount-rate sensitivity. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.15741 |
| By: | Yu Peng; Matloob Khushi; Josiah Poon |
| Abstract: | Managing drawdown, the peak-to-trough decline in an investment portfolio's value, is a precondition for long-term survival in practical investment management. However, mainstream stock forecasting methods predominantly optimize returns or Sharpe ratios under the independent and identically distributed (i.i.d.) assumption. Real markets do not follow this assumption, triggering catastrophic drawdowns. We propose a cross-asset state-space trading system (CAST), consisting of two components: The predictor, Cross-Asset Collaborative Kalman Filter (CoKF), estimates each asset's latent state online, coupling all assets through their correlations and adaptively fusing multiple integrated-random-walk orders. The controller, Model Predictive Control (MPC), converts the predictor's forecast into trades, using forecast uncertainty as an explicit risk penalty that controls drawdown. We evaluate CAST on four real-world stock markets over a 15-year test window and show that it consistently occupies the return-drawdown Pareto frontier, achieving strong risk-adjusted performance while maintaining substantially lower maximum drawdown than competitive baselines. A stress test across crisis periods further demonstrates robust behavior under market shocks and distribution shift. Because the predictor and controller interact only through the predicted price path, both are plug-and-play, making CAST a modular, interpretable trading system. The code is available at https://github.com/FanBroWell/CAST |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.14205 |
| By: | Maarten Meeuwis; Dimitris Papanikolaou; Lawrence D.W. Schmidt |
| Abstract: | We study how aggregate financial conditions shape firm insurance and, through it, labor income risk. In a directed search model with dynamic wage contracts and two-sided limited commitment, firm insurance against idiosyncratic shocks erodes when risk premia rise. Using U.S. administrative data, we document new evidence supporting the model: pass-through of firm shocks to earnings rises in bad times, especially for lower-paid workers near the separation margin. The model reproduces many untargeted time-series and cross-sectional features of earnings risk and implies substantial welfare costs of idiosyncratic risk, high private discount rates on human capital, and large gains from recession-contingent transfers. |
| JEL: | D52 E0 E24 E32 E44 G0 G12 J0 J08 J31 J41 J63 J64 J65 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35781 |
| By: | Craig A. Chikis (University of Chicago); Jonathan Goldberg (Board of Governors of the Federal Reserve System); David López-Salido (Banco de España) |
| Abstract: | Firms' hurdle rates exceed their financial cost of capital. This gap varies across firms and has widened in the aggregate. In our Schumpeterian model, two frictions drive this gap: firm decision-makers require an innovator's risk premium for undiversifiable innovation risk, and innovation profits are imperfectly pledgeable. Higher undiversifiable risk lowers the financial cost of capital while hurdle rates remain sticky or even rise. Widening gaps weaken creative destruction; profits and superstar valuations rise despite firms forgoing positive-NPV projects. The calibrated model matches cross-sectional patterns in gaps, R&D, market power and firm dynamics, and explains weak productivity growth and declining dynamism. |
| Keywords: | hurdle rates, cost of capital, idiosyncratic risk, innovation, market power, firm valuations |
| JEL: | E43 G12 G31 G32 O31 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2628e |
| By: | Gangadharan, Lata; Gsottbauer, Elisabeth; Leslie, Gordon; Pretto, Madeline |
| Abstract: | Do households comprehend the nature of tail-risks inherent to real-time electricity pricing (RTP) plans? We develop a randomized and incentivized experiment calibrated to real-world price distributions and find that (a) probabilistic risk disclosure, beyond that offered in standard marketing materials, elicits greater demand for real-time pricing products relative to a low-risk fixed-price alternative, (b) products with tail-risk protection might not be highly sought, and (c) the experience of a RTP bill shock drives choice away from RTP. Personal experience receiving a tail price plays a greater role in risk comprehension and moving subsequent choices toward less risky plans than receipt of an additional ex-ante probabilistic risk disclosure. We discuss the implications these findings may have for regulators with a consumer protection mandate. |
| Keywords: | consumer protection;dynamic pricing;experimental economics;information provision;retail electricity markets;risk perception;tail-risk |
| JEL: | C91 D18 D81 L94 Q41 |
| Date: | 2026–09–30 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:140854 |
| By: | Aditya Gupta; Nick Polson |
| Abstract: | Robert Shiller proposed perpetual futures in 1993 to create derivative markets for assets that are illiquid or whose price cannot be observed directly, such as single family homes, human capital, and the consumer price index. The crypto markets later built the instrument for a different reason and with a different funding rule. We give a single no arbitrage result that nests both designs: the perpetual price is the present value of a benchmark flow discounted at the funding rate, so the funding rule chooses both the benchmark and the discount. We give a random time change representation in which the price is the expected spot at the first event of a clock whose intensity is the funding rate, use it to show that stochastic volatility moves the basis only through the carry, so a volatility risk premium and not volatility itself can break the peg, read price discovery as the convergence of a Doob martingale driven by a stochastic approximation, and give a segmented market equilibrium that makes the pre listing premium structural rather than behavioural. The June 2026 SpaceX pre-IPO market is the first large scale realization of the idea for an equity claim, and we find that the perpetual consensus forecast the secondary clearing price more accurately than the bookbuilt offer. We close with other equity applications. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.05433 |
| By: | Lilian Hu; Congxin He; Yue Kuen Kwok; Gongqiu Zhang |
| Abstract: | The standard Euler discretization schemes for numerical option pricing under stochastic volatility models are known to exhibit high biases and potential unreliability. The alternative use of the exact (unbiased) simulation approach invariably involves numerical evaluation of integrated variance (and / or volatility) conditional on terminal variance (volatility) value. To resolve the technical challenge, most simulation schemes either employ the tedious Fourier inversion of conditional characteristic function or numerical approximation by moment matched distribution. We propose a general framework of constructing efficient and reliable simulation schemes for stochastic volatility models via the Strang operator splitting approximation. The simulation procedure completely circumvents the necessity of evaluation of conditional integrated variance (and / or) volatility. Our simulation schemes compete favorably well with most existing exact simulation schemes and the biased Euler schemes in terms of accuracy, efficiency, reliability and ease of implementation. Extensive numerical tests were conducted to illustrate the versatility and success of our operator splitting approach for most common stochastic volatility models, such as the Heston-type models, lifted Heston model, Hull-White model, and Barndorff-Nielsen and Shephard model. We also establish the proof of second-order convergence of the operator splitting schemes. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.21301 |