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on Risk Management |
| By: | Lydia J. Gabric; Kenneth Q. Zhou |
| Abstract: | Natural hedging allows life insurers to manage longevity risk internally by offsetting the opposite exposures of life insurance and annuity liabilities. Although many studies have proposed natural hedging strategies under different settings, calibration methods, and mortality models, a unified framework for constructing and evaluating such hedges remains undeveloped. While graphical risk assessment has been explored for index-based longevity hedges, no comparable metric exists for natural hedging. This paper proposes a structured natural hedging framework paired with a graphical risk metric for hedge evaluation. The framework integrates valuation, calibration, and evaluation, while the graphical metric provides intuitive insights into residual dependencies and hedge performance. Applied to multiple hedging scenarios, the proposed methods demonstrate flexibility, interpretability, and practical value for longevity risk management. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.18721 |
| By: | Federico Gatta; Fabrizio Lillo; Piero Mazzarisi |
| Abstract: | We propose a new approach, termed Realized Risk Measures (RRM), to estimate Value-at-Risk (VaR) and Expected Shortfall (ES) using high-frequency financial data. It extends the Realized Quantile (RQ) approach proposed by Dimitriadis and Halbleib by lifting the assumption of return self-similarity, which displays some limitations in describing empirical data. More specifically, as the RQ, the RRM method transforms intra-day returns in intrinsic time using a subordinator process, in order to capture the inhomogeneity of trading activity and/or volatility clustering. Then, microstructural effects resulting in non-zero autocorrelation are filtered out using a suitable moving average process. Finally, a fat-tailed distribution is fitted on the cleaned intra-day returns. The return distribution at low frequency (daily) is then extrapolated via either a characteristic function approach or Monte Carlo simulations. VaR and ES are estimated as the quantile and the tail mean of the distribution, respectively. The proposed approach is benchmarked against the RQ through several experiments. Extensive numerical simulations and an empirical study on 18 US stocks show the outperformance of our method, both in terms of the in-sample estimated risk measures and in the out-of-sample risk forecasting |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.16526 |
| By: | Yaxuan Kong; Yoontae Hwang; Marcus Kaiser; Chris Vryonides; Roel Oomen; Stefan Zohren |
| Abstract: | We introduce M2VN: Multi-Modal Volatility Network, a novel deep learning-based framework for financial volatility forecasting that unifies time series features with unstructured news data. M2VN leverages the representational power of deep neural networks to address two key challenges in this domain: (i) aligning and fusing heterogeneous data modalities, numerical financial data and textual information, and (ii) mitigating look-ahead bias that can undermine the validity of financial models. To achieve this, M2VN combines open-source market features with news embeddings generated by Time Machine GPT, a recently introduced point-in-time LLM, ensuring temporal integrity. An auxiliary alignment loss is introduced to enhance the integration of structured and unstructured data within the deep learning architecture. Extensive experiments demonstrate that M2VN consistently outperforms existing baselines, underscoring its practical value for risk management and financial decision-making in dynamic markets. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.20699 |
| By: | Mario Ghossoub; Qinghua Ren; Ruodu Wang |
| Abstract: | We study Pareto-optimal risk sharing in economies with heterogeneous attitudes toward risk, where agents' preferences are modeled by distortion risk measures. Building on comonotonic and counter-monotonic improvement results, we show that agents with similar attitudes optimally share risks comonotonically (risk-averse) or counter-monotonically (risk-seeking). We show how the general $n$-agent problem can be reduced to a two-agent formulation between representative risk-averse and risk-seeking agents, characterized by the infimal convolution of their distortion risk measures. Within this two-agent framework, we establish necessary and sufficient conditions for the existence of optimal allocations, and we identify when the infimal convolution yields an unbounded value. When existence fails, we analyze the problem under nonnegative allocation constraints, and we characterize optima explicitly, under piecewise-linear distortion functions and Bernoulli-type risks. Our findings suggest that the optimal allocation structure is governed by the relative strength of risk aversion versus risk seeking behavior, as intuition would suggest. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.18236 |
| By: | Daniel Maas; Roberto Panzica; Martín Saldías |
| Abstract: | This paper introduces the Macroprudential Two-Mode Network Analysis Toolbox (M2MN), a modular framework designed to assess credit risk shocks and contagion through overlapping exposures in banking systems. The M2MN toolbox uses a weighted two-mode network structure linking banks to grouped credit exposures, capturing indirect interconnectedness and systemic vulnerabilities arising from portfolio overlaps. The framework comprises three integrated modules: (i) a network diagnostics module that computes exposure-based metrics and community structures; (ii) a first-round sensitivity analysis simulating credit losses and capital impacts under CRR2 regulatory thresholds; and (iii) a second-round effects module. The toolbox is applied to supervisory data for 31 Portuguese banks, with calibrated scenarios targeting key exposures. Results show that most losses are absorbed by voluntary capital buffers, with limited contagion under conservative stress assumptions, reflecting the strong capitalization of the system. The M2MN toolbox provides a flexible and empirically grounded platform for systemic risk monitoring, buffer calibration, and supervisory scenario design, contributing to the refinement of macroprudential tools within the regulatory framework. |
| JEL: | C63 D85 G01 G10 G21 G28 G32 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ptu:wpaper:w202512 |
| By: | Daniela I. Flores-Silva; Miguel A. Sordo; Alfonso Su\'arez-Llorens |
| Abstract: | We extend the "probability-equivalent level of VaR and CoVaR" (PELCoV) methodology to accommodate bivariate risks modeled by a Student-t copula, relaxing the strong dependence assumptions of earlier approaches and enhancing the framework's ability to capture tail dependence and asymmetric co-movements. While the theoretical results are developed in a static setting, we implement them dynamically to track evolving risk spillovers over time. We illustrate the practical relevance of our approach through an application to the foreign exchange market, monitoring the USD/GBP exchange rate with the USD/EUR series as an auxiliary early warning indicator over the period 1999-2024. Our results highlight the potential of the extended PELCoV framework to detect early signs of risk underestimation during periods of financial stress. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.15934 |
| By: | Yimeng Qiu; Feihuang Fang |
| Abstract: | We study whether liquidity and volatility proxies of a core set of cryptoassets generate spillovers that forecast market-wide risk. Our empirical framework integrates three statistical layers: (A) interactions between core liquidity and returns, (B) principal-component relations linking liquidity and returns, and (C) volatility-factor projections that capture cross-sectional volatility crowding. The analysis is complemented by vector autoregression impulse responses and forecast error variance decompositions (see Granger 1969; Sims 1980), heterogeneous autoregressive models with exogenous regressors (HAR-X, Corsi 2009), and a leakage-safe machine learning protocol using temporal splits, early stopping, validation-only thresholding, and SHAP-based interpretation. Using daily data from 2021 to 2025 (1462 observations across 74 assets), we document statistically significant Granger-causal relationships across layers and moderate out-of-sample predictive accuracy. We report the most informative figures, including the pipeline overview, Layer A heatmap, Layer C robustness analysis, vector autoregression variance decompositions, and the test-set precision-recall curve. Full data and figure outputs are provided in the artifact repository. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.20066 |
| By: | Jian'an Zhang |
| Abstract: | We present a white-box, risk-sensitive framework for jointly hedging SPX and VIX exposures under transaction costs and regime shifts. The approach couples an arbitrage-free market teacher with a control layer that enforces safety as constraints. On the market side, we integrate an SSVI-based implied-volatility surface and a Cboe-compliant VIX computation (including wing pruning and 30-day interpolation), and connect prices to dynamics via a clipped, convexity-preserving Dupire local-volatility extractor. On the control side, we pose hedging as a small quadratic program with control-barrier-function (CBF) boxes for inventory, rate, and tail risk; a sufficient-descent execution gate that trades only when risk drop justifies cost; and three targeted tail-safety upgrades: a correlation/expiry-aware VIX weight, guarded no-trade bands, and expiry-aware micro-trade thresholds with cooldown. We prove existence/uniqueness and KKT regularity of the per-step QP, forward invariance of safety sets, one-step risk descent when the gate opens, and no chattering with bounded trade rates. For the dynamics layer, we establish positivity and second-order consistency of the discrete Dupire estimator and give an index-coherence bound linking the teacher VIX to a CIR-style proxy with explicit quadrature and projection errors. In a reproducible synthetic environment mirroring exchange rules and execution frictions, the controller reduces expected shortfall while suppressing nuisance turnover, and the teacher-surface construction keeps index-level residuals small and stable. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.15937 |
| By: | Samuel Perreault; Silvana M. Pesenti; Daniyal Shahzad |
| Abstract: | Risk assessment in casualty insurance, such as flood risk, traditionally relies on extreme-value methods that emphasizes rare events. These approaches are well-suited for characterizing tail risk, but do not capture the broader dynamics of environmental variables such as moderate or frequent loss events. To complement these methods, we propose a modelling framework for estimating the full (daily) distribution of environmental variables as a function of time, that is a distributional version of typical climatological summary statistics, thereby incorporating both seasonal variation and gradual long-term changes. Aside from the time trend, to capture seasonal variation our approach simultaneously estimates the distribution for each instant of the seasonal cycle, without explicitly modelling the temporal dependence present in the data. To do so, we adopt a framework inspired by GAMLSS (Generalized Additive Models for Location, Scale, and Shape), where the parameters of the distribution vary over the seasonal cycle as a function of explanatory variables depending only on the time of year, and not on the past values of the process under study. Ignoring the temporal dependence in the seasonal variation greatly simplifies the modelling but poses inference challenges that we clarify and overcome. We apply our framework to daily river flow data from three hydrometric stations along the Fraser River in British Columbia, Canada, and analyse the flood of the Fraser River in early winter of 2021. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.18639 |
| By: | Alok Das; Kiseop Lee |
| Abstract: | Deep hedging uses recurrent neural networks to hedge financial products that cannot be fully hedged in incomplete markets. Previous work in this area focuses on minimizing some measure of quadratic hedging error by calculating pathwise gradients, but doing so requires large batch sizes and can make training effective models in a reasonable amount of time challenging. We show that by adding certain topological features, we can reduce batch sizes substantially and make training these models more practically feasible without greatly compromising hedging performance. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.16938 |
| By: | Hishamuddin Abdul Wahab ("Universiti Sains Islam Malaysia, 71800, Bandar Baru Nilai, Malaysia " Author-2-Name: Author-2-Workplace-Name: Author-3-Name: Author-3-Workplace-Name: Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:) |
| Abstract: | " Objective - The study examines the relationship between foreign exchange risk and hedging strategies across multiple time horizons using a wavelet multiscaling approach involving nine listed healthcare firms in Malaysia. Methodology/Technique – The study involves a 3-stage analysis, where the first stage adopts the Maximal Overlap Discrete Wavelet Transform (MODWT) technique for time series decomposition. The second stage focuses on assessing scale-dependent foreign currency risk. The third stage employs cross-sectional regression analysis to determine the impact of various hedging strategies on foreign exchange risk at each time scale, spanning the period from January 2019 to December 2022. Findings – The study reveals that exchange rate risk increases non-monotonically across time scales and intensifies with a longer time horizon, indicating increased vulnerability of firm value to foreign exchange risk over time. For the risk-hedging relationship, the study identifies that foreign currency derivatives (FCDs) are effective in managing short-term exposure, while the effectiveness of long-term exposure remains inconclusive. Given the significant time scale effect in exchange risk pricing, the study suggests that the risk management framework should integrate time horizon considerations to enhance the effectiveness of hedging strategies. Novelty – The study integrates the application of the wavelet technique in quantifying foreign currency risk, assisting corporate managers and market players in managing foreign exchange risk over a defined time frame. Type of Paper - Empirical" |
| Keywords: | Currency risk; multi-horizon exchange rate; Healthcare Sectors; Wavelet Analysis; MODWT. |
| JEL: | F31 F39 |
| Date: | 2025–09–30 |
| URL: | https://d.repec.org/n?u=RePEc:gtr:gatrjs:jfbr230 |
| By: | Chujun He; Zhonghao Huang; Xiangguo Li; Ye Luo; Kewei Ma; Yuxuan Xiong; Xiaowei Zhang; Mingyang Zhao |
| Abstract: | We present a multi-agent, AI-driven framework for fundamental investing that integrates macro indicators, industry-level and firm-specific information to construct optimized equity portfolios. The architecture comprises: (i) a Macro agent that dynamically screens and weights sectors based on evolving economic indicators and industry performance; (ii) four firm-level agents -- Fundamental, Technical, Report, and News -- that conduct in-depth analyses of individual firms to ensure both breadth and depth of coverage; (iii) a Portfolio agent that uses reinforcement learning to combine the agent outputs into a unified policy to generate the trading strategy; and (iv) a Risk Control agent that adjusts portfolio positions in response to market volatility. We evaluate the system on the constituents by the CSI 300 Index of China's A-share market and find that it consistently outperforms standard benchmarks and a state-of-the-art multi-agent trading system on risk-adjusted returns and drawdown control. Our core contribution is a hierarchical multi-agent design that links top-down macro screening with bottom-up fundamental analysis, offering a robust and extensible approach to factor-based portfolio construction. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.21147 |
| By: | Jacek Wszo{\l}a; Krzysztof Burnecki; Marek Teuerle; Martyna Zdeb |
| Abstract: | This paper introduces a novel multidimensional insurance-linked instrument: a contingent convertible bond (CoCoCat bond) whose conversion trigger is activated by predefined natural catastrophes across multiple geographical regions. We develop such a model explicitly accounting for the complex dependencies between regional catastrophe losses. Specifically, we explore scenarios ranging from complete independence to proportional loss dependencies, both with fixed and random loss amounts. Utilizing change-of-measure techniques, we derive risk-neutral pricing formulas tailored to these diverse dependence structures. By fitting our model to real-world natural catastrophe data from Property Claim Services, we demonstrate the significant impact of inter-regional dependencies on the CoCoCat bond's pricing, highlighting the importance of multidimensional risk assessment for this innovative financial instrument. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.17221 |