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on Risk Management |
| By: | Laura Alfaro; Julian Caballero; Bryan Hardy |
| Abstract: | This paper examines optimal foreign currency (FX) hedging by non-financial corporations globally. Using a cross-country, firm-level dataset, we first document key patterns of FX borrowing across advanced (AEs) and emerging market economies (EMEs). We find that while FX debt is prevalent in both groups, its intensity varies considerably. We assess the optimality of firms' exchange rate exposures using a risk-management framework where hedging serves to minimize the impact of cash flow volatility on firm value. Our results indicate that most firms hedge optimally, as exposures from FX debt are largely offset by other exposures, like foreign revenues and assets. While the distribution of exchange rate risk is broadly similar between AE and EME firms, the EME distribution has thicker tails, revealing a larger concentration of firms with significant, unhedged depreciation risk. |
| Keywords: | foreign currency debt, currency risk, currency hedging |
| JEL: | F31 F34 G30 G32 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1303 |
| By: | Peng Liu; Steven Vanduffel; Yi Xia |
| Abstract: | We establish sharp upper and lower bounds for distortion risk metrics under distributional uncertainty. The uncertainty sets are characterized by four key features of the underlying distribution: mean, variance, unimodality, and Wasserstein distance to a reference distribution. We first examine very general distortion risk metrics, assuming only finite variation for the underlying distortion function and without requiring continuity or monotonicity. This broad framework includes notable distortion risk metrics such as range value-at-risk, glue value-at-risk, Gini deviation, mean-median deviation and inter-quantile difference. In this setting, when the uncertainty set is characterized by a fixed mean, variance and a Wasserstein distance, we determine both the worst- and best-case values of a given distortion risk metric and identify the corresponding extremal distribution. When the uncertainty set is further constrained by unimodality with a fixed inflection point, we establish for the case of absolutely continuous distortion functions the extremal values along with their respective extremal distributions. We apply our results to robust portfolio optimization and model risk assessment offering improved decision-making under model uncertainty. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.08662 |
| By: | Matteo Bonato (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France; B-CCaS, University of Edinburgh Business School); Oguzhan Cepni (Ostim Technical University, Ankara, Turkiye; University of Edinburgh Business School, Centre for Business, Climate Change, and Sustainability; Department of Economics, Copenhagen Business School, Denmark); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany) |
| Abstract: | We study the out-of-sample forecasting value of and state-level and market-wideoverall commercial, industrial, and residential electricity sales for monthly state-level (1995--2025) realized stock market volatility (RV) of the United States (U.S.). We control for state-level and market-wide realized moments (leverage, skewness, kurtosis, and tail risks). We estimate our forecasting models using a boosting algorithm, and two alternative statistical learning algorithms (forward best predictor selection and random forests). We find evidence that realized moments have predictive power for subsequent RV at forecast horizons up to one year in some model configurations, while evidence of predictive power of the growth rate of electricity sales, whether measured at state-level or at the market-level, is mixed and mainly concentrated, on average across states, at the short forecast horizon. |
| Keywords: | Stock market, Realized volatility, Electricity sales, Statistical learning, Forecasting |
| JEL: | C22 C53 G10 G17 Q41 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202540 |
| By: | Dmitrii Vlasiuk |
| Abstract: | We develop a finite-horizon model in which liquid-asset returns exhibit Levy-stable scaling on a data-driven window [tau_UV, tau_IR] and aggregate into a finite-variance regime outside. The window and the tail index alpha are identified from the log-log slope of the central body and a two-segment fit of scale versus horizon. With an anchor horizon tau_0, we derive horizon-correct formulas for Value-at-Risk, Expected Shortfall, Sharpe and Information ratios, Kelly under a Value-at-Risk constraint, and one-step drawdown, where each admits a closed-form Gaussian-bias term driven by the exponent gap (1/alpha - 1/2). The implementation is nonparametric up to alpha and fixed tail quantiles. The formulas are reproducible across horizons on the Levy window. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.07834 |
| By: | Miquel Noguer i Alonso |
| Abstract: | This paper introduces a comprehensive framework for Financial Information Theory by applying information-theoretic concepts such as entropy, Kullback-Leibler divergence, mutual information, normalized mutual information, and transfer entropy to financial time series. We systematically derive these measures with complete mathematical proofs, establish their theoretical properties, and propose practical algorithms for estimation. Using S&P 500 data from 2000 to 2025, we demonstrate empirical usefulness for regime detection, market efficiency testing, and portfolio construction. We show that normalized mutual information (NMI) behaves as a powerful, bounded, and interpretable measure of temporal dependence, highlighting periods of structural change such as the 2008 financial crisis and the COVID-19 shock. Our entropy-adjusted Value at Risk, information-theoretic diversification criterion, and NMI-based market efficiency test provide actionable tools for risk management and asset allocation. We interpret NMI as a quantitative diagnostic of the Efficient Market Hypothesis and demonstrate that information-theoretic methods offer superior regime detection compared to traditional autocorrelation- or volatility-based approaches. All theoretical results include rigorous proofs, and empirical findings are validated across multiple market regimes spanning 25 years of daily returns. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.16339 |
| By: | Chen Jin; Ankush Agarwal |
| Abstract: | We introduce a conditional Denoising Diffusion Probabilistic Model (DDPM) for generating arbitrage-free implied volatility (IV) surfaces, offering a more stable and accurate alternative to existing GAN-based approaches. To capture the path-dependent nature of volatility dynamics, our model is conditioned on a rich set of market variables, including exponential weighted moving averages (EWMAs) of historical surfaces, returns and squared returns of underlying asset, and scalar risk indicators like VIX. Empirical results demonstrate our model significantly outperforms leading GAN-based models in capturing the stylized facts of IV dynamics. A key challenge is that historical data often contains small arbitrage opportunities in the earlier dataset for training, which conflicts with the goal of generating arbitrage-free surfaces. We address this by incorporating a standard arbitrage penalty into the loss function, but apply it using a novel, parameter-free weighting scheme based on the signal-to-noise ratio (SNR) that dynamically adjusts the penalty's strength across the diffusion process. We also show a formal analysis of this trade-off and provide a proof of convergence showing that the penalty introduces a small, controllable bias that steers the model toward the manifold of arbitrage-free surfaces while ensuring the generated distribution remains close to the real-world data. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.07571 |
| By: | Elizabeth Dadzie; Wilfried Kuissi-Kamdem; Marcel Ndengo |
| Abstract: | This paper investigates a robust optimal consumption, investment, and reinsurance problem for an insurer with Epstein-Zin recursive preferences operating under model uncertainty. The insurer's surplus follows the diffusion approximation of the Cram\'er-Lundberg model, and the insurer can purchase proportional reinsurance. Model ambiguity is characterised by a class of equivalent probability measures, and the insurer, being ambiguity-averse, aims to maximise utility under the worst-case scenario. By solving the associated coupled forward-backward stochastic differential equation (FBSDE), we derive closed-form solutions for the optimal strategies and the value function. Our analysis reveals how ambiguity aversion, risk aversion, and the elasticity of intertemporal substitution (EIS) influence the optimal policies. Numerical experiments illustrate the effects of key parameters, showing that optimal consumption decreases with higher risk aversion and EIS, while investment and reinsurance strategies are co-dependent on both financial and insurance market parameters, even without correlation. This study provides a comprehensive framework for insurers to manage capital allocation and risk transfer under deep uncertainty. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.03031 |
| By: | Alain Coen; Aya Nasreddine; Aurelie Desfleurs; Yasmine Essafi Zouari |
| Abstract: | This article analyzes the role of real estate risks in the dynamics of financial sector stock returns for a sample of 14 countries: Asia and Oceania (Australia, Hong Kong, Japan, and Singapore), Europe (Belgium, France, Italy, Netherlands, Sweden, Switzerland and the U.K.) and North America (Canada and the USA). Real estate risk measures are drawn from the FTSE/EPRA NAREIT indexes. The period includes the last twenty years running from February 2005 to December 2024 on a daily and a monthly basis. The wavelet quantile correlation (WQC) methodology is implemented to highlight the impact of domestic and U.S. real estate risks. The WQC allows us to deal with time-varying characteristics of time series and to capture tail dependence. Besides, it has the advantage of dissolving the correlation structure between returns across different timescales. Our results report that the response to real estate risk pressures varies significantly depending on the financial sector, the investment horizon, and the origin of the real estate risk. The dynamic dimensions of the domestic and U.S. real estate risks during a long period, marked by significant crises including the Global financial crisis and the COVID-19 pandemic, are heterogeneous in the international financial sector, with potential implications for investment managers and policymakers. |
| Keywords: | Financial sector; Real Estate Risk; REITs; Wavelet quantile correlation |
| JEL: | R3 |
| Date: | 2025–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2025_184 |
| By: | Tetsuya Takaishi |
| Abstract: | The finite sample effect on the Hurst exponent (HE) of realized volatility time series is examined using Bitcoin data. This study finds that the HE decreases as the sampling period $\Delta$ increases and a simple finite sample ansatz closely fits the HE data. We obtain values of the HE as $\Delta \rightarrow 0$, which are smaller than 1/2, indicating rough volatility. The relative error is found to be $1\%$ for the widely used five-minute realized volatility. Performing a multifractal analysis, we find the multifractality in the realized volatility time series, smaller than that of the price-return time series. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.03314 |
| By: | Christian Laudag\'e; J\"orn Sass |
| Abstract: | Under Solvency II, the Value-at-Risk (VaR) is applied, although there is broad consensus that the Expected Shortfall (ES) constitutes a more appropriate measure. Moving towards ES would necessitate specifying the corresponding ES level. The recently introduced Probability Equivalent Level of VaR and ES (PELVE) determines this by requiring that ES equals the prescribed VaR for a given future payoff, reflecting the situation of an individual insurer. We incorporate the regulator's perspective by proposing PELVE-inspired methods for multiple insurers. We analyze existence and uniqueness of the resulting ES levels, derive expressions for elliptically distributed payoffs and establish limit results for multivariate regularly distributed payoffs. A case study highlights that the choice of method is crucial when payoffs arise from different distribution families. Moreover, we recommend specific methods. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.03551 |
| By: | Yilong Zeng; Boyan Tang; Xuanhao Ren; Sherry Zhefang Zhou; Jianghua Wu; Raymond Lee |
| Abstract: | This paper introduces the Fractal-Chaotic Oscillation Co-driven (FCOC) framework, a novel paradigm for financial volatility forecasting that systematically resolves the dual challenges of feature fidelity and model responsiveness. FCOC synergizes two core innovations: our novel Fractal Feature Corrector (FFC), engineered to extract high-fidelity fractal signals, and a bio-inspired Chaotic Oscillation Component (COC) that replaces static activations with a dynamic processing system. Empirically validated on the S\&P 500 and DJI, the FCOC framework demonstrates profound and generalizable impact. The framework fundamentally transforms the performance of previously underperforming architectures, such as the Transformer, while achieving substantial improvements in key risk-sensitive metrics for state-of-the-art models like Mamba. These results establish a powerful co-driven approach, where models are guided by superior theoretical features and powered by dynamic internal processors, setting a new benchmark for risk-aware forecasting. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.10365 |
| By: | Alexander Alecio |
| Abstract: | We consider a model for systemic risk comprising of a system of diffusion processes, interacting through their empirical mean. Each process is subject to a confining double-well potential with some uncertainty in the coefficients, corresponding to fluctuations in height of the potential barrier seperating the two wells. This is equivalent to studying a single McKean-Vlasov SDE with explicit dependence on its moments and, novelly, independently varying additive and multiplicative noise. Such non-linear SDEs are known to possess two phases: stable (ordered) and unstable (disordered). When the potential is purely bistable, the phase changes from stable to unstable when noise intensity is increased past a critical threshold. With the recent advances, it will be shown that the behaviour here is far richer: indeed, depending on the interpretation of the stochastic integral, the system exhibits phase changes that cannot occur in any regime where there is no uncertainty in the potential. Strikingly, this allows for the phenomenon of noise induced stability; situations where more noise can reduce the risk of system failure. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.03358 |
| By: | Li, Mingzhe |
| Abstract: | The Merton framework has difficulty explaining empirical phenomena in household finance and life-cycle patterns. This paper proposes a continuous-time heterogeneous agent model with common noise to study portfolio choice for heterogeneous investors. Households pay an "asset-exposure premium" (AEP) based on their wealth for bearing common financial risk. The AEP effect serves as a non-monotonic third term in optimal portfolio choice. For households near the borrowing constraint, the AEP effect is large and negative, overwhelming other motives and explaining limited participation. As wealth increases, the AEP effect turns positive before disappearing at high wealth, resulting in a hump-shaped profile of risky holdings. The AEP enables households to identify the risk characteristics of their portfolio, explaining why the low wealth exhibits underdiversification and why middle-to-high wealth households hold diversified portfolios. Furthermore, this paper introduces a steady state and solves the degeneration problem of the wealth distribution in the continuous-time ABH framework. |
| Keywords: | portfolio choice; household finance; limited participation; wealth distribution; heterogeneous agent; continuous time; underdiversification; life cycle |
| JEL: | C73 D91 E21 G11 |
| Date: | 2025–06–17 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126642 |
| By: | Stella C. Dong |
| Abstract: | This paper develops a prudential framework for assessing the reliability of large language models (LLMs) in reinsurance. A five-pillar architecture--governance, data lineage, assurance, resilience, and regulatory alignment--translates supervisory expectations from Solvency II, SR 11-7, and guidance from EIOPA (2025), NAIC (2023), and IAIS (2024) into measurable lifecycle controls. The framework is implemented through the Reinsurance AI Reliability and Assurance Benchmark (RAIRAB), which evaluates whether governance-embedded LLMs meet prudential standards for grounding, transparency, and accountability. Across six task families, retrieval-grounded configurations achieved higher grounding accuracy (0.90), reduced hallucination and interpretive drift by roughly 40%, and nearly doubled transparency. These mechanisms lower informational frictions in risk transfer and capital allocation, showing that existing prudential doctrines already accommodate reliable AI when governance is explicit, data are traceable, and assurance is verifiable. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.08082 |