|
on Risk Management |
| By: | Demetrio Lacava |
| Abstract: | This paper introduces a new extension of the Conditional Autoregressive Value at Risk (CAViaR) model aimed at improving tail risk forecasting across assets. The proposed component-based model, CAViaR with Spillover Effects (CAViaR-SE), decomposes the conditional Value at Risk into a proper-risk component and a spillover component driven by a linear combination of tail risks from influential assets. These assets are selected via a recursive partial correlation algorithm, allowing multiple spillover sources with minimal parameterization. The spillover component acts as a predictable quantile shifter, directly affecting the conditional quantile dynamics rather than the volatility scale. Empirical results on Dow Jones Industrial Average stocks show that spillover effects account for a substantial share of total tail risk and significantly improve out-of-sample tail risk forecasts. Backtesting procedures, together with Model Confidence Set (MCS) analysis, confirm that CAViaR-SE provides well-calibrated risk measures and statistically superior forecasts compared to standard and augmented CAViaR models. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.25217 |
| By: | Adele Ravagnani; Mattia Chiappari; Andrea Flori; Piero Mazzarisi; Marco Patacca |
| Abstract: | Shorting for hedging exposes to risk when the market dynamics is uncertain. Managing uncertainty and risk exposure is key in portfolio management practice. This paper develops a robust framework for dynamic minimum-variance hedging that explicitly accounts for forecast uncertainty in volatility estimation to achieve empirical stability and reduced turnover, further improving other standard performance metrics. The approach combines high-frequency realized variance and covariance measures, autoregressive models for multi-step volatility forecasting, and a box-uncertainty robust optimization scheme. We derive a closed-form solution for the robust hedge ratio, which adjusts the standard minimum-variance hedge by incorporating variance forecast uncertainty. Using a diversified sample of equity, bond, and commodity ETFs over 2016-2024, we show that robust hedge ratios are more stable and entail lower turnover than standard dynamic hedges. While overall variance reduction is comparable, the robust approach improves downside protection and risk-adjusted performance, particularly when transaction costs are considered. Bootstrap evidence supports the statistical significance of these gains. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.02126 |
| By: | A. Fernandez-Perez; A.-M. Fuertes; J. Miffre (Audencia Business School) |
| Abstract: | This paper presents a comprehensive analysis of traditional versus selective hedging strategies in commodity futures markets. Traditional hedging aims solely to reduce spot price risk, while selective hedging also seeks to enhance returns by predicting movements in commodity futures prices. We construct selective hedges using a range of forecasting techniques, from simple historical averages to advanced machine learning models, and evaluate their performance based on the expected mean-variance utility of hedge portfolio returns. Out-of-sample results for 24 commodities do not favor selective hedging over traditional hedging, as the former increases risk without delivering additional returns. These findings are robust across various hedge reformulations, expanding estimation windows, and rebalancing frequencies. |
| Keywords: | Commodity futures markets, Expected utility, Selective hedging, Traditional hedging |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05563835 |
| By: | Francesca Centrone; Asmerilda Hitaj; Elisa Mastrogiacomo; Emanuela Rosazza Gianin |
| Abstract: | This paper develops a unified framework for the robustification of risk measures beyond the classical convex and cash-additive setting. We consider general risk measures on Lp spaces and construct their robust counterparts through families of uncertainty sets that capture ambiguity. Two complementary mechanisms generate robust quasi-convex measures: in the first, quasi-convexity is inherited from the initial risk measure under convex uncertainty sets; in the second it comes from the quasi-convex (or c-quasi-convex) structure of the uncertainty sets themselves. Building on Cerreia-Vioglio et al. (2011); Frittelli and Maggis (2011), we derive dual (penalty-type) representations for robust quasi-convex and cash-subadditive risk measures, showing that the classical convex cash-additive case arises as a special instance. We further analyze acceptance families and capital allocation rules under robustification, highlighting how ambiguity affects acceptability and the distribution of capital. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.17954 |
| By: | Belhouari, Oussama (Université catholique de Louvain, LIDAM/ISBA, Belgium); Barigou, Karim (Université catholique de Louvain, LIDAM/ISBA, Belgium); Devolder, Pierre (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | In the Solvency II framework for insurance, the cost of capital rate is a critical metric that encapsulates the cost of holding capital to meet regulatory solvency requirements, while also reflecting the investor’s opportunity cost of capital allocation. It is therefore essential for insurers to rigorously justify the magnitude of this rate, particularly from the perspective of investors who perceive it as a required rate of return on capital. Albrecher et al. (2022) investigated the magnitude of this rate in the economic triangle of the policyholder, the shareholder, and the regulator. This paper seeks to extend that analysis by incorporating access to the financial market and focusing on hybrid life liabilities, which combine financial and mortality risks, thereby affording an asset-liability management perspective that insurers can employ to optimize business run-off. Furthermore, by incorporating partial hedging strategies, we show how hedging can affect both the numerator (i.e. the risk margin) and the denominator (i.e. the solvency capital requirement) of the cost of capital ratio. First, we assess whether the hedging operation has improved the insurer’s overall capital position. We then focus precisely on when the hedging operation is beneficial for both the policyholder and the shareholder, in the sense that it leads to a simultaneous reduction in their respective contributions. |
| Keywords: | Cost of capital rate ; partial hedging strategies ; the economic triangle ; asset-liability ; Solvency II |
| Date: | 2026–03–26 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2026005 |
| By: | Brunella Bruno (Bocconi University); Imma Marino (University of Naples Federico II and CSEF) |
| Abstract: | We examine how banks adjust credit allocation when hidden credit risk is revealed. Using supervisory risk disclosure data from the European Central Bank’s 2014 Asset Quality Review, we find that banks experiencing larger increases in non-performing loans and provisions significantly reduce risk-weighted exposures while keeping total credit volumes largely unchanged. This suggests that de-risking primarily occurs through portfolio reallocation-particularly within portfolios-rather than through credit contraction. We document heterogeneous responses depending on the rating approach used to measure credit risk and we show that capital constraints amplify, but are not the sole driversof, de-risking. Finally, we provide evidence that supervisory risk disclosure plays a key role in shaping banks’ risk-taking behavior, even in the absence of observable adjustments in their financial statements. |
| Keywords: | Transparency, Bank Supervision, Credit risk, Non-performing loans |
| JEL: | G21 G28 M48 |
| Date: | 2026–03–17 |
| URL: | https://d.repec.org/n?u=RePEc:sef:csefwp:773 |
| By: | Massimiliano Caporin; Daniele Girolimetto; Emanuele Lopetuso |
| Abstract: | We assess the advantage of combining univariate and multivariate portfolio risk forecasts with the aid of forecast reconciliation techniques. In our analyzes, we assume knowledge of portfolio weights, a standard for portfolio risk management applications. With an extensive simulation experiment, we show that, if the true covariance is known, forecast reconciliation improves over a standard multivariate approach, in particular when the adopted multivariate model is misspecified. However, if noisy proxies are used, correctly specified models and the misspecified ones (for instance, neglecting spillovers) turn out to be, in several cases, indistinguishable, with forecast reconciliation still providing improvements. The noise in the covariance proxy plays a crucial role in determining the improvement of both the forecast reconciliation and the correct model specification. An empirical analysis shows how forecast reconciliation can be adopted with real data to improve traditional GARCH-based portfolio variance forecasts. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.17463 |
| By: | Wenxuan Zhang; Zhouchi Lin; Benzhuo Lu |
| Abstract: | Accurately characterizing the implied volatility curves is a central challenge in option pricing and risk management. The classical SABR model by Hagan et al. has been widely adopted in practice due to its well-defined stochastic volatility structure and its tractable closed-form approximation for Black implied volatility. However, under complex market conditions, its fitting accuracy for implied volatility curves remains limited. To address this issue, this paper proposes an extended model within the SABR framework, referred to as skew-SABR. Specifically, the proposed approach introduces an extension to the stochastic dynamics of the underlying asset price and its variance process, under which a corresponding Black implied volatility expression is derived. By further simplifying and reorganizing the resulting formula, the implied volatility can be expressed in a form that explicitly incorporates a skew parameter, thereby enabling a direct characterization of the asymmetry in the implied volatility curve. The resulting expression preserves the structural simplicity of the Hagan-SABR formula, while significantly enhancing the model's flexibility in capturing complex volatility smile patterns. From a theoretical perspective, the paper provides a systematic analysis of the model specification and the financial interpretation of its parameters. From an empirical perspective, a comprehensive comparison is conducted using data from the Chinese options market over the period 2018--2025. The skew-SABR model is evaluated against the classical Hagan-SABR model, the SVI parameterization, polynomial fitting, and spline-based methods. Numerical results show that, across different market regimes and a wide range of implied volatility curve shapes, the skew-SABR model consistently achieves high and stable fitting accuracy. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.27501 |
| By: | D. J. Manuge |
| Abstract: | The purpose of this paper is to describe and extend the use of the newly-introduced measure, residual estimation risk. Following the seminal work of Bignozzi and Tsanakas, the quantification of residual estimation risk is proposed in a multivariate framework. Our aim is to provide a succinct and practical introduction to the concept, to motivate its use as a back-testing measure, and to provide examples related to credit risk parameter estimation. In section 2, we introduce residual estimation risk defined by various risk measures, and illustrate the calculation using R and SAS. In section 3, we propose a back-testing criterion for the measure, which can be altered to assess model performance for both accuracy and conservatism. In section 4, we conduct back-testing on risk parameter estimates of retail credit portfolios, including multiple back-testing measures for comparison. Finally, we conclude our findings and propose areas for future work in section 5. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.17792 |
| By: | Michel Denuit; Jos\'e Miguel Flores-Contr\'o; Christian Y. Robert |
| Abstract: | This paper studies proportional risk sharing at claim occurrence time in community-based insurance. Each participant is modeled by an individual Cram\'er-Lundberg surplus process, and, whenever a claim is reported within the pool, its cost is redistributed according to a fixed allocation matrix. We compare the infinite-time ruin probability of each participant under stand-alone operation and under pool participation. Our main result shows that pooling reduces, for every participant, the infinite-time ruin probability when claim severities belong to a common scale family, the allocation rule satisfies full allocation and actuarial fairness, and each transfer remains bounded by an individual capacity condition. The proof relies on a convex-order comparison between the losses borne inside the pool and the corresponding stand-alone losses. We also clarify the role of these assumptions by showing that, outside this framework, pooling need not be beneficial for all participants. Numerical illustrations with Exponential and LogNormal severities support the theoretical findings and highlight how the design of proportional sharing rules affects solvency. The paper thus provides simple and interpretable sufficient conditions under which transparent linear risk-sharing arrangements improve individual solvency in community-based insurance. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.29530 |
| By: | Marcelo Righi; Rodrigo Targino |
| Abstract: | We develop an averaging approach to robust risk measurement under payoff uncertainty. Instead of taking a worst-case value over an uncertainty neighborhood, we weight nearby payoffs more heavily under a chosen metric and average the baseline risk measure. We prove continuity in the neighborhood radius and provide a stable large-radius behavior. In Banach lattices, the approach leads to a convex risk measure and under separability of the space, a dual representation through a penalty term based on an inf-convolution taken over a Gelfand integral constraint. We also relate our veraging to aggregation at the distribution and quantile levels of payoffs, obtaining dominance and coincidence results. Numerical illustrations are conducted to verify calibration and sensitivity. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.24349 |
| By: | Yan Dolinsky; Xin Zhang |
| Abstract: | We study scaled trinomial models converging to the Black--Scholes model, and analyze exponential certainty-equivalent prices for path-dependent European options. As the number of trading dates $n$ tends to infinity and the risk aversion is scaled as $nl$ for a fixed constant $l>0$, we derive a nontrivial scaling limit. Our analysis is purely probabilistic. Using a duality argument for the certainty equivalent, together with martingale and weak-convergence techniques, we show that the limiting problem takes the form of a volatility control problem with a specific penalty. For European options with Markovian payoffs, we analyze the optimal control problem and show that the corresponding delta-hedging strategy is asymptotically optimal for the primal problem. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.28948 |
| By: | Atsushi Hane |
| Abstract: | We study the problem of optimally hedging the price exposure of liquidity positions in constant-product automated market makers (AMMs) when the hedge is funded by collateralized borrowing. A liquidity provider (LP) who borrows tokens to construct a delta-neutral position faces a trade-off: higher hedge ratios reduce price exposure but increase liquidation risk through tighter collateral utilization. We model token prices as correlated geometric Brownian motions and derive the hedge ratio h that maximizes risk-adjusted return subject to a liquidation-probability constraint expressed via a first-passage-time bound. The unconstrained optimum h* admits a closed-form expression, but at h* the liquidation probability is prohibitively high. The practical optimum h** = min(h*, h_bar(alpha)) is determined by the binding liquidation constraint h_bar(alpha), which we evaluate analytically via the first-passage-time formula and confirm with Monte Carlo simulation. Simulations calibrated to on-chain data validate the analytical results, demonstrate robustness across realistic parameter ranges, and show that the optimal hedge ratio lies between 50% and 70% for typical DeFi lending conditions. Practical guidelines for rebalancing frequency and position sizing are also provided. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.19716 |
| By: | Konstantinos Chatziandreou; Sven Karbach |
| Abstract: | We develop a semi-static framework for the variance-optimal hedging of multi-asset derivatives exposed to correlation and covariance risk. The approach combines continuous-time dynamic trading in the underlying assets with a static portfolio of auxiliary contingent claims. Using a multivariate Galtchouk--Kunita--Watanabe decomposition, we show that the resulting global mean-variance problem decouples naturally into an inner continuous-time projection onto the space spanned by the underlying assets and an outer finite-dimensional quadratic optimization over the static hedging instruments. To systematically select suitable auxiliary claims, we leverage multidimensional functional spanning theory, establishing that otherwise unhedgeable cross-gamma exposures can be structurally mitigated through static strips of vanilla, product, and spread options. As a central application, we derive explicit semi-static replication formulas for covariance swaps and geometric dispersion trades. Our framework accommodates a broad class of asset dynamics, including quadratic and stochastic Volterra covariance models, as well as affine stochastic covariance models with jumps, yielding tractable semi-closed-form solutions via Fourier transform techniques. Extensive numerical experiments demonstrate that incorporating optimally weighted static strips of cross-asset instruments substantially reduces the mean-squared hedging error relative to purely dynamic benchmark strategies across various model classes. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.25320 |
| By: | Wing Fung Chong; Runhuan Feng; Kenneth Tsz Hin Ng |
| Abstract: | This article proposes a new class of risk-sharing rules by exploring the relationship between capital allocation and risk sharing. While the former is concerned with ex-ante allocating capitals to different lines of business within a corporation based on the relationship among the individual risks, often also through the aggregate risk, the latter is an arrangement which collects risks from and allocates them to, also ex-ante, a group of participants. Drawing on this analogy, we introduce a novel idea of inducing risk-sharing rules by randomizing existing capital allocation principles. Such an approach derives new risk-sharing rules complementing known results in the literature, which were largely based on economic principles and Pareto optimality. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.26491 |
| By: | Frangiamore, Francesco; Saadaoui, Jamel |
| Abstract: | Using local projections, this paper documents that neither global geopolitical risk (GPR) shocks nor GPR shocks originating in smaller euro area countries have a significant impact on Euro Area sovereign stress, whereas GPR shocks originating in Germany generate sizable effects, against the backdrop of the recent surge in geopolitical risk following the Russian invasion of Ukraine. |
| Keywords: | Geopolitical Risk, Sovereign Stress, Local Projections. |
| JEL: | E44 F51 G01 |
| Date: | 2026–01–26 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127823 |
| By: | Valter T. Yoshida Jr.; Rafael Schiozer; Alan de Genaro; Toni R.E. dos Santos |
| Abstract: | This paper addresses the challenge of forecasting the best-performing credit scoring model in outof-time settings, focusing on the decision between segmented (bank-specific) and full data (financial system-wide) models. Building upon the Credit Scoring Model Risk (CSMR) metric, defined as one minus the correlation between observed defaults and predicted scores, we highlight the instability of in-sample performance measures when applied to evolving loan portfolios and changing macroeconomic conditions. We propose three complementary approaches to predict out-of-time model performance: (i) an analytical method based on Copas shrinkage concept utilizing estimated covariances and prediction variances; (ii) a Monte Carlo simulation leveraging average model predictions to simulate default events; and (iii) a Bayesian estimation framework for covariances grounded in conditional expectations of predictions given default. Empirical analysis using a large Brazilian loan dataset reveals that segmented models outperform full data models in in-sample contexts but not consistently out-of-time. Among the approaches, the Monte Carlo simulation achieved the highest accuracy (70.8%) in forecasting the superior out-of-time model, followed by the Bayesian method (66.7%) and the analytical shrinkage approach (54.2%). The study underscores the importance of considering population shifts via the Population Stability Index (PSI) to detect model decalibration and overfitting. The proposed methodologies offer practitioners and regulators practical tools for informed model selection, enhancing predictive reliability over time amid portfolio and economic dynamics. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:bcb:wpaper:645 |
| By: | Yuechen Dai; Richard Watt (University of Canterbury); Kuntal Das (University of Canterbury) |
| Abstract: | Over the past decade, the number of operational satellites in lower-earth orbit (LEO) has experienced exponential growth. This has led to levels of traffic congestion in the LEO environment that are making it increasingly risky. Satellites can collide with each other, and they can also be destroyed or damaged by orbital debris. While satellite insurance for such risks is not new, it does not seem to have grown at the rate one might expect given the increased number of insurable assets, and the increased risks they face. In fact, some insurers that once offered to underwrite orbital satellites now prefer to stay out of that particular market, citing excessive risk. This article uses standard financial economics modelling to explore whether this is indeed optimal. We find that in fact an expected utility maximizing insurer should always dedicate some of their underwriting capacity to orbital satellites. We also carry out a simulation to show that including orbital satellite insurance within an optimally structured insurance portfolio has the effect of enhancing long-run profitability. |
| Keywords: | Satellite insurance; capital allocation; portfolio optimization; insurance underwriting; diversification; CRRA utility; efficient frontier; space risk |
| JEL: | G22 G11 D81 G32 C61 |
| Date: | 2026–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:cbt:econwp:26/02 |
| By: | Bogdan Grechuk; Anton Malandii; Terry Rockafellar; Stan Uryasev |
| Abstract: | This paper revisits and extends the 2013 development by Rockafellar and Uryasev of the Risk Quadrangle (RQ) as a unified scheme for integrating risk management, optimization, and statistical estimation. The RQ features four stochastics-oriented functionals -- risk, deviation, regret, and error, along with an associated statistic, and articulates their revealing and in some ways surprising interrelationships and dualizations. Additions to the RQ framework that have come to light since 2013 are reviewed in a synthesis focused on both theoretical advancements and practical applications. New quadrangles -- superquantile, superquantile norm, expectile, biased mean, quantile symmetric average union, and $\varphi$-divergence-based quadrangles -- offer novel approaches to risk-sensitive decision-making across various fields such as machine learning, statistics, finance, and PDE-constrained optimization. The theoretical contribution comes in axioms for ``subregularity'' relaxing ``regularity'' of the quadrangle functionals, which is too restrictive for some applications. The main RQ theorems and connections are revisited and rigorously extended to this more ample framework. Examples are provided in portfolio optimization, regression, and classification, demonstrating the advantages and the role played by duality, especially in ties to robust optimization and generalized stochastic divergences. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.27370 |
| By: | Wenyuan Li; Haoqi Lyu |
| Abstract: | This paper investigates the valuation of variable annuity contracts with an early surrender option under non-Markovian models. Moreover, policyholders are provided with guaranteed minimum maturity and death benefits to protect against the downside risk. Unlike the existing literature, our variable annuity account value is linked to two non-Markovian processes: an equity index modeled by a rough Heston model and a force of mortality following a Volterra-type stochastic model. In this case, the early surrender feature introduces an optimal stopping problem where continuation values depend on the entire path history, rendering traditional numerical methods infeasible. We develop a deep signature Least Squares Monte Carlo approach to learn optimal surrender strategies on a discretized time grid. To mitigate the curse of dimensionality arising from the path-dependent model, we use truncated rough-path signatures to encode the historical paths and approximate the continuation values using a neural network. Numerically, we find that the fair fee increases with the Hurst parameters of both the stock volatility and the force of mortality. Finally, a convergence proof is provided to further support the stability of our method. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.00472 |
| By: | Liao, Yanjun (Penny) (Resources for the Future); Whitlock, Zach (Resources for the Future); Kaiser, Brooks; Sølvsten, Simon |
| Abstract: | Escalating climate-related catastrophe losses are placing increasing strain on private insurance markets, raising concerns about the long-term insurability of natural hazards. This paper describes the evolving roles of private and public institutions in sustaining catastrophe risk transfer. We first examine private catastrophe risk transfer mechanisms and discuss how rising loss volatility and modeling uncertainty are constraining private market capacity. We then compare catastrophe insurance arrangements across 13 countries and US states, identifying four institutional regimes that differ in the extent and form of government involvement. Across these regimes, we analyze the economic logic underlying public sector involvement, with particular emphasis on its roles in expanding risk pooling and enabling cross-subsidization to sustain insurance markets. We also discuss complementary policies that improve data availability and promote risk mitigation. Our analysis provides a framework for understanding how public-private arrangements can sustain insurance availability and enhance financial resilience under worsening climate risk. |
| Date: | 2026–03–27 |
| URL: | https://d.repec.org/n?u=RePEc:rff:dpaper:dp-26-06 |
| By: | Gustavo Silva Araujo; Claudio Henrique da Silveira Barbedo; Hugo Araujo Costa; Aziz Baruque |
| Abstract: | The objective of this work is to analyze whether the risk of differences between the closing price and the opening price of the subsequent day, the opening price gap rate, is priced in the Brazilian stock market. This inquiry stems from the recognition that an investor is impacted by the volatility of these gaps. Long and Short strategies are simulated, entailing long positions in stocks with higher overnight volatility and short positions on those with lower overnight volatility. The one-year volatility of daily gap returns is utilized to define the portfolio for the subsequent year, and to categorize the strategy into long and short positions. The results reveal that the strategy generates positive abnormal returns across all models (CAPM, Fama-French Three Factor Model and Fama-French Extended Model), indicating that the gap risk is not priced in the Brazilian market. The strategy yields an annualized abnormal return exceeding 13%. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:bcb:wpaper:644 |
| By: | Roberto Daluiso; H\'ector Folgar-Came\'an; Andrea Pallavicini; Carlos V\'azquez |
| Abstract: | In this paper, we develop a general rough volatility model for commodities that provides an automatic calibration of the initial term structure of the futures prices and an appropriate treatment of the Samuelson effect. After the theoretical analysis of this general model, we focus on the rBergomi and rHeston models and their calibration to market data of vanilla futures options on WTI Crude Oil. Finally, numerical results illustrate the performance of the proposed rough volatility models for commodities pricing. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.26514 |
| By: | Tim J. Boonen; Engel John C. Dela Vega; Len Patrick Dominic M. Garces |
| Abstract: | This paper considers an insurer with two collaborating business lines that faces three critical decisions: (1) dividend payout, (2) reinsurance coverage, and (3) capital injection between the lines, in the presence of model uncertainty. The insurer considers the reference model to be an approximation of the true model, and each line has its own robustness preference. The reserve level of each line is modeled using a diffusion process. The objective is to obtain a robust strategy that maximizes the expected weighted sum of discounted dividends until the first ruin time, while incorporating a penalty term for the distortion between the reference and alternative models in the worst-case scenario. We completely solve this problem and obtain the value function and optimal (equilibrium) strategies in closed form. We show that the optimal dividend-capital injection strategy is a barrier strategy. The optimal proportion of risk ceded to the reinsurer and the deviation of the worst-case model from the reference model are decreasing with respect to the aggregate reserve level. Finally, numerical examples are presented to show the impact of the model parameters and ambiguity aversion on the optimal strategies. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.25350 |
| By: | Bingzheng Chen; Jan Dhaene; Chun Liu; Shunzhi Pang |
| Abstract: | This paper develops a dynamic equilibrium model of the insurance market that jointly characterizes insurers' underwriting, investment, recapitalization, and dividend policies under model uncertainty and financial frictions. Competitive insurers maximize shareholder value under a subjective worst-case probability measure, giving rise to liquidity-driven underwriting cycles and flight-to-quality behavior. While an equilibrium typically fails to exist in such dynamic liquidity management framework with external financial investment, we show that incorporating model uncertainty restores equilibrium existence under plausible parameter conditions. Moreover, the model uncovers a novel relationship between the correlation of insurance and financial market risks and the equilibrium insurance price: negative loadings may emerge when insurance gains and financial returns are positively correlated, contrary to conventional intuition. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.18962 |
| By: | Hardhik Mohanty; Bhaskar Krishnamachari |
| Abstract: | Daily probability changes in Kalshi macro prediction markets forecast cryptocurrency realized volatility through two distinct channels. The monetary policy channel, measured by Fed rate repricing on KXFED contracts, predicts Bitcoin volatility in sample with t = 3.63 and p |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.01431 |
| By: | Dominic Gribben; Carolina Allende; Alba Villarino; Aser Cortines; Mazen Ali; Rom\'an Or\'us; Pascal Oswald; Noureddine Lehdili |
| Abstract: | We develop a tensor-network surrogate for option pricing, targeting large-scale portfolio revaluation problems arising in market risk management (e.g., VaR and Expected Shortfall computations). The method involves representing high-dimensional price surfaces in tensor-train (TT) form using TT-cross approximation, constructing the surrogate directly from black-box price evaluations without materializing the full training tensor. For inference, we use a Laplacian kernel and derive TT representations of the kernel matrix and its closed-form inverse in the noise-free setting, enabling TT-based Gaussian process regression without dense matrix factorization or iterative linear solves. We found that hyperparameter optimization consistently favors a large kernel length-scale and show that in this regime the GPR predictor reduces to multilinear interpolation for off-grid inputs; we also derive a low-rank TT representation for this limit. We evaluate the approach on five-asset basket options over an eight dimensional parameter space (asset spot levels, strike, interest rate, and time to maturity). For European geometric basket puts, the tensor surrogate achieves lower test error at shorter training times than standard GPR by scaling to substantially larger effective training sets. For American arithmetic basket puts trained on LSMC data, the surrogate exhibits more favorable scaling with training-set size while providing millisecond-level evaluation per query, with overall runtime dominated by data generation. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.26318 |
| By: | Nabeel Ahmad Saidd |
| Abstract: | Multi-horizon price forecasting is central to portfolio allocation, risk management, and algorithmic trading, yet deep learning architectures have proliferated faster than rigorous financial benchmarks can evaluate them. This study provides a controlled comparison of nine architectures (Autoformer, DLinear, iTransformer, LSTM, ModernTCN, N-HiTS, PatchTST, TimesNet, and TimeXer) spanning Transformer, MLP, CNN, and RNN families across cryptocurrency, forex, and equity index markets at 4-hour and 24-hour horizons. A total of 918 experiments were conducted under a strict five-stage protocol including fixed-seed Bayesian hyperparameter optimization, configuration freezing per asset class, multi-seed retraining, uncertainty aggregation, and statistical validation. ModernTCN achieves the best mean rank (1.333) with a 75 percent first-place rate, followed by PatchTST (2.000). Results reveal a clear three-tier ranking structure and show that architecture explains nearly all performance variance, while seed randomness is negligible. Rankings remain stable across horizons despite 2 to 2.5 times error amplification. Directional accuracy remains near 50 percent across all configurations, indicating that MSE-trained models lack directional skill at hourly resolution. The findings highlight the importance of architectural inductive bias over raw parameter count and provide reproducible guidance for multi-step financial forecasting. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.16886 |
| By: | Débora Allam-Firley (CEPN - Centre d'Economie de l'Université Paris Nord - Université Sorbonne Paris Nord, CREDDI - Centre de Recherche en Economie et en Droit du Développement Insulaire [UR7_2] - UA - Université des Antilles); Marc-Hubert Depret (UP - Université de Poitiers = University of Poitiers, LéP [Poitiers] - Laboratoire d'économie de Poitiers [UR 13822] - UP - Université de Poitiers = University of Poitiers, ENSAR [Niort] - École nationale supérieure des sciences applicatives et du risque - UP - Université de Poitiers = University of Poitiers, Département Sciences du risque et de la donnée [ENSAR] - ENSAR [Niort] - École nationale supérieure des sciences applicatives et du risque - UP - Université de Poitiers = University of Poitiers); Céline Merlin-Brogniart (CLERSÉ - Centre Lillois d’Études et de Recherches Sociologiques et Économiques - UMR 8019 - Université de Lille - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | The digital transformation of non-life insurance services in France is a prime example of the technological disruption that is reshaping the financial services sector. Driven primarily by the adoption of artificial intelligence and the emergence of insurtech solutions, this transformation presents a complex interplay between operational gains and systemic challenges. While AI has the potential to significantly enhance productivity and reduce operational costs, its implementation raises important questions about strategic positioning, financial sustainability and ethical governance. To elucidate these dynamics, we employ the systemic framework proposed by Ivanov and Webster (2019), which links technological change to organisational restructuring and ecosystem reconfiguration. This framework allows us to examine the functional architecture of insurance enterprises, identifying which capabilities are candidates for externalisation versus internal retention, whilst also accounting for the firm's relational dynamics with ecosystem participants, including competing insurers, artificial intelligence providers, insurtech enterprises and technology giants. Our analysis reveals that AI and insurtech solutions catalyse profound structural reconfiguration within the insurance ecosystem. This manifests as three principal transformations: migration from ex-post indemnification to ex-ante risk prevention; evolution of pricing mechanisms from segmentation-based to behaviourally informed; and transition of risk management from retrospective to prospective orientation. Through this institutional lens, we identify the barriers that constrain rapid adoption and outline a research agenda to advance the empirical understanding of how these technological and organisational changes are reshaping insurance enterprises and the broader sectoral architecture. |
| Abstract: | La transformation numérique des services d'assurance non-vie en France s'accélère en raison notamment de l'adoption de l'intelligence artificielle et l'émergence des solutions insurtech. Si ces technologies augmentent la productivité et contribuent à réduire les coûts opérationnels, elles soulèvent simultanément des enjeux stratégiques, financiers et éthiques substantiels. Pour appréhender ces dynamiques, nous mobilisons le cadre systémique d'Ivanov et Webster ( 2019) qui articule les mutations technologiques avec les réorganisations intra-organisationnelles et les reconfigurations écosystémiques. Cette approche permet d'examiner l'architecture fonctionnelle de l'entreprise assurantielle (en particulier les fonctions susceptibles d'externalisation ou de maintien en interne) tout en interrogeant les rapports qu'elle entretient avec les autres acteurs du secteur (assureurs concurrents, prestataires d'IA, entreprises insurtech et GAFAM). Notre analyse établit que l'IA et les solutions insurtech induisent une reconfiguration structurelle majeure de l'écosystème assurantiel. Cette reconfiguration se manifeste par trois inflexions principales : le passage d'une logique d'indemnisation ex-post à une logique de prévention ex-ante, l'évolution de la tarification segmentaire vers une tarification comportementale, la transition d'une gestion rétrospective des risques vers une gestion prospective. Nous identifions également les obstacles substantiels à cette adoption et proposons un agenda de recherche visant à documenter précisément l'impact effectif de ces transformations sur les organisations assurantielles et sur la structure d'ensemble du secteur. |
| Keywords: | InnovationReverse product cycle, Ecosystem, Insurtech, Artificial intelligence, Digital, Service, Non-life insurance, Insurance, Assurance, Assurance non-vie, Numérique, Intelligence artificielle, Écosystème, Innovation, Cycle de vie du produit inversé |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05551244 |
| By: | Victor Medina-Olivares; Wangzhen Xia; Stefan Lessmann; Nadja Klein |
| Abstract: | We propose a semi-structured discrete-time multi-state model to analyse mortgage delinquency transitions. This model combines an easy-to-understand structured additive predictor, which includes linear effects and smooth functions of time and covariates, with a flexible neural network component that captures complex nonlinearities and higher-order interactions. To ensure identifiability when covariates are present in both components, we orthogonalise the unstructured part relative to the structured design. For discrete-time competing transitions, we derive exact transformations that map binary logistic models to valid competing transition probabilities, avoiding the need for continuous-time approximations. In simulations, our framework effectively recovers structured baseline and covariate effects while using the neural component to detect interaction patterns. We demonstrate the method using the Freddie Mac Single-Family Loan-Level Dataset, employing an out-of-time test design. Compared with a structured generalised additive benchmark, the semi-structured model provides modest but consistent gains in discrimination across the earliest prediction spans, while maintaining similar Brier scores. Adding macroeconomic indicators provides limited incremental benefit in this out-of-time evaluation and does not materially change the estimated borrower-, loan-, or duration-driven effects. Overall, semi-structured multi-state modelling offers a practical compromise between transparent effect estimates and flexible pattern learning, with potential applications beyond credit-transition forecasting. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.26309 |