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on Risk Management |
| By: | Nolan Alexander; Frank Fabozzi |
| Abstract: | This paper develops a decomposition of standard Risk Contribution (RC) into two economically interpretable components: inherent risk and correlation risk. Using a leave-one-out representation, each position's RC separates into a term reflecting its own volatility contribution independent of the portfolio and a term capturing its covariance with the remainder of the portfolio. The inherent component is always positive, arising from the intrinsic volatility of the position, while the correlation component may amplify or mitigate total portfolio risk depending on how the position moves relative to other holdings. Because the decomposition operates within standard RC, it preserves the property of strict additivity. This separation provides diagnostic insight not visible from aggregate risk contributions alone. It distinguishes whether a position contributes risk because it is volatile in isolation or because it is highly correlated with the rest of the portfolio, and it clarifies when a negatively correlated position functions as an effective hedge. Two approaches to time-series analysis are presented to track how inherent and correlation risk evolve across market regimes, revealing whether changes in portfolio risk during stress periods are driven by volatility shocks, correlation shifts, or both. Empirical illustrations suggest that the decomposition provides stable, transparent, and easily implementable risk diagnostics that can support portfolio risk reporting, stress testing, and performance attribution. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.10375 |
| By: | Hilde C. Bjornland; Nicolas Hardy; Dimitris Korobilis |
| Abstract: | We develop a Quantile Bayesian Vector Autoregression (QBVAR) to forecast real oil prices across different quantiles of the conditional distribution. The model allows predictor effects to vary across quantiles, capturing asymmetries that standard mean-focused approaches miss. Using monthly data from 1975 to 2025, we document three findings. First, the QBVAR improves median forecasts by 2-5\% relative to Bayesian VARs, demonstrating that quantile-specific dynamics matter even for point prediction. Second, uncertainty and financial condition variables strongly predict downside risk, with left-tail forecast improvements of 10-25\% that intensify during crisis episodes. Third, right-tail forecasting remains difficult; stochastic volatility models dominate for upside risk, though forecast combinations that include the QBVAR recover these losses. The results show that modeling the conditional distribution yields substantial gains for tail risk assessment, particularly during major oil market disruptions. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.12927 |
| By: | Sebastien Gallet; Julja Prodani; Kitty Rang |
| Abstract: | This paper presents a top-down stress testing framework for estimating the financial (stability) impact of nature degradation. The methodology links the three components of the NGFS conceptual framework on nature-related risks: nature, the economy, and the financial sector. In the first step, a shock on nature, e.g. water scarcity, is calibrated based on the macroeconomic impact of proxy scenarios of nature degradation. We then estimate the impact of this shock on nature on companies. For this, we modify the Merton model (Merton, Robert C. 1974) to account for the vulnerability of companies to nature. The resulting higher probabilities of default are the main driver of credit and market risk losses for banks and insurers respectively. While the framework we introduce is general and can be applied to multi-dimensional nature shocks and joint climate-nature shocks, in quantification we focus on water as a sub-category of nature. The results show that the financial‑stability implications of nature‑related disruptions can be quantified in a coherent manner. Losses are allocated according to sectoral, geographical and ecosystem‑service vulnerabilities. The framework delivers granular indicators – from sectoral production impacts to market revaluations and prudential ratios – supporting a wide set of analytical and supervisory applications. |
| Keywords: | nature degradation; ecosystem services; biodiversity loss; dependence score; financial stability; risk; credit risk; market risk; Merton model |
| JEL: | G21 G28 Q57 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:dnb:dnbwpp:857 |
| By: | J\'er\^ome Lelong (LJK); V\'eronique Maume-Deschamps (ICJ, PSPM); William Thevenot (ICJ, PSPM) |
| Abstract: | We study a discrete-time multi-period portfolio optimization problem under an explicit constraint on the Deviation Conditional Value-at-Risk (DCVaR), defined as the excess of Conditional Value-at-Risk over expected terminal wealth. The objective is to maximize expected return subject to a global tail-risk constraint, leading to a time-inconsistent precommitment problem. We propose a recurrent neural-network-based approach to approximate the optimal precommitment policy, which accommodates path-dependent risk constraints and highdimensional state dynamics without relying on dynamic programming. The explicit constraint formulation allows for exact penalty methods and provides a transparent notion of feasibility. The methodology is validated in a classical complete-market financial model and extended to a multi-period portfolio allocation problem in (re)insurance, capturing the long-term risk dynamics of insurance liabilities. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.14439 |
| By: | Zhenfeng Zou |
| Abstract: | This paper introduces the Lambda extension of the R\'{e}nyi entropic value-at-risk ($\Lambda$-EVaR), a novel family of risk measures that unifies the flexible confidence level structure of the $\Lambda$-framework with the higher-moment sensitivity of EVaR. We define $\Lambda$-EVaR, establish its foundational properties including monotonicity, cash subadditivity, and quasi-convexity, and provide a complete axiomatic characterization showing that convexity, concavity in mixtures and cash additivity hold only when $\Lambda$ is constant. A dual representation and an extended Rockafellar-Uryasev-type formula are derived, enabling efficient computation. We further analyze the worst-case behavior of $\Lambda$-EVaR under Wasserstein and mean-variance uncertainty, obtaining closed-form expressions that reveal its robustness properties. The proposed measure bridges the gap between adaptive risk tolerance and moment-sensitive risk assessment, offering a versatile tool for modern risk management. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.10657 |
| By: | Damiaan Chen; Roel Beetsma; Sweder van Wijnbergen |
| Abstract: | We explore to what extent real returns on investment portfolios can be hedged against inflation risk by using existing financial market instruments. We empirically find that inflation-linked bonds offer only limited protection against inflation risk, while nominal debt and stocks play at least comparable roles in this respect. These findings apply to both a static and a dynamic setting. To explain the empirical results, we develop a theoretical framework that incorporates real basis risk. The demonstrated limits of hedging inflation risk are of particular relevance for long-term investors, such as pension funds with participants concerned about the real value of their pension benefits. |
| Keywords: | unhedgeable inflation risk; incomplete markets; welfare loss; meanvariance frontiers; minimum-variance portfolio; nominal and index-linked bonds; |
| JEL: | C61 E21 G11 G23 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:dnb:dnbwpp:858 |
| By: | Guo, Hongfei; Marín Díazaraque, Juan Miguel; Veiga, Helena |
| Abstract: | We propose target-driven Bayesian stacking for a fixed six-model ensemble of GARCH and stochastic-volatility forecasts with realised- and VIX-based extensions. Two rolling stacking rules target either log predictive density or QLIKE. In S&P 500, the objective changes the preferred information channel: LPD stacking remains centred on GARCH-RV, whereas QLIKE stacking shifts toward GARCH-VIX. Across 56 rolling windows, the QLIKE stack improves certainty-equivalent returns by roughly one to one-and-a-half percentage points per year, depending on the investor's risk aversion. In the 30 windows where the QLIKE stack assigns material weight to implied volatility models, the gain exceeds two percentage points per year with a 90% win rate. However, LPD stacking delivers tighter 5% Value-at-Risk calibration |
| Keywords: | Bayesian stacking; QLIKE; Implied volatility; Realised variance; Value-at-risk; Volatility forecasting |
| JEL: | C11 G17 C53 |
| Date: | 2026–04–15 |
| URL: | https://d.repec.org/n?u=RePEc:cte:wsrepe:49851 |
| By: | Gabriel Santana; Jemirson Ramirez |
| Abstract: | Traditional risk measures in finance, predominantly based on the second moment of return distributions or tail risk heuristics (VaR/CVaR), fail to account for the intrinsic geometric structure of market dynamics. This paper introduces a rigorous mathematical framework utilizing Topological Data Analysis (TDA) to quantify risk as the structural instability of the reconstructed phase space. By applying Takens' Delay Embedding Theorem to cryptocurrency log-returns, we generate a point cloud representation of the underlying attractor. We analyze the evolution of the filtration of Vietoris-Rips complexes to compute persistent homology groups $H_k$. We define a "Topological Persistence Norm" to characterize market regimes and propose a leverage calibration heuristic based on the persistence of 1-dimensional cycles. This approach provides a coordinate-free, stability-invariant metric for risk assessment that is robust to high-frequency noise. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.13311 |
| By: | Santoni, Alessandro; Allali, Lamia; Dierick, Nicolas |
| Abstract: | This article provides a novel insight into whether earnings manipulation signals are reflected in banks’ internal credit risk estimates, as measured by the probability of default (PD) estimates, and whether such manipulation has an impact on credit risk (point in time or deferred). The hypothesis is that firms engaging in manipulation may be exposed to increased credit risk over time, which should be reflected in higher PD values. Using AnaCredit – a granular dataset covering credit exposures from European banks between 2019 and 2022 – and financial statement data from Orbis, we constructed a sample of 4, 649 publicly traded corporations, for which we computed the Beneish M-Scores that are used to detect potential earnings manipulation. This allowed us to determine the interrelation with PDs. Our results reveal a weak and negative correlation between M-Scores and PDs, suggesting that earnings manipulation signals are not fully absorbed by banks’ internal models. Further analysis shows that these results are driven by the high prevalence of firms with no earnings manipulation signals. Firms for which the M-Score effectively indicates potential earnings manipulation (8.9% of the sample) are observed to have higher PDs, which also increase further as the M-Score worsens. These findings support the hypothesis that earnings manipulation signals are not fully reflected in credit risk estimates over time, indicating that their impact – when it occurs – is deferred instead of being captured immediately in internal models. Our results indicate that the relationship between potential earnings manipulation and banks’ internal credit risk estimates is highly context-dependent and non-linear. Cross-sectional analyses by country and industry show consistent patterns linking default risk to M-Scores in selected countries and sectors. [...] JEL Classification: G32, M41, M42, C33 |
| Keywords: | AnaCredit, Beneish M-Score, credit risk modelling, earnings manipulation, financial misreporting, probability of default |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbops:2026385 |
| By: | Tenghan Zhong |
| Abstract: | Daily ETF risk monitoring can become unreliable when market data quality degrades, market conditions shift, or predictive performance becomes unstable. This paper develops a reliability-aware risk monitoring service for next-day tail-risk surveillance. The proposed framework combines service-time quality checks, lower-tail prediction, uncertainty scoring, and conservative risk adjustment. We evaluate the system on a daily panel of multiple ETFs augmented with VIX and yield-curve information under a rolling walk-forward design. The empirical results suggest that the proposed framework improves tail-risk reliability during stressed periods while preserving full evaluable coverage. Under deliberate input corruption, the quality layer enhances service robustness. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.08765 |
| By: | Songrun He |
| Abstract: | In this paper, I present the first comprehensive, around-the-clock analysis of systematic jump risk by combining high-frequency market data with contemporaneous news narratives identified as the underlying causes of market jumps. These narratives are retrieved and classified using a state-of-the-art open-source reasoning LLM. Decomposing market risk into interpretable jump categories reveals significant heterogeneity in risk premia, with macroeconomic news commanding the largest and most persistent premium. Leveraging this insight, I construct an annually rebalanced real-time Fama-MacBeth factor-mimicking portfolio that isolates the most strongly priced jump risk, achieving a high out-of-sample Sharpe ratio and delivering significant alphas relative to standard factor models. The results highlight the value of around-the-clock analysis and LLM-based narrative understanding for identifying and managing priced risks in real time. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.13458 |
| By: | Zhangying Li (Economics and Management School, Wuhan University); O-Chia Chuang (School of Digital Economics, Hubei University of Economics); Rangan Gupta (Department of Economics, University of Pretoria) |
| Abstract: | The onset of the Russia-Ukraine war in 2022 caused significant fluctuations in global energy markets, particularly in natural gas prices, highlighting the growing importance of natural gas for financial market stability. Using a structural econometric framework, we analyze the dynamic effects of natural gas supply shocks compared to three distinct oil shocks popularly used in the energy economics literature using constant and time-varying parameter local projections model, and associated historical decomposition. Our findings reveal that supply shocks of natural gas has replaced oil as the primary driver of stock market volatility, particularly during the 2022 energy crisis. Additionally, natural gas supply shocks are found to perform better in an out-of-sample forecasting exercise compared to oil supply shocks. These results suggest the need for policymakers and investors to incorporate natural gas price dynamics into financial risk management frameworks for Europe. |
| Keywords: | Natural Gas Price Supply Shocks, Oil price Supply Shocks, Stock Price Volatility, Local Projection, Forecasting |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202609 |
| By: | Yiqing Wang |
| Abstract: | The Kolmogorov-Smirnov (KS) statistic is widely used in credit risk model monitoring and validation to assess discriminatory power. In practice, a material decline in KS often triggers governance review and requires validation teams to identify the breach source and the potential business risk. However, such diagnosis is frequently conducted on an ad hoc basis, relying on the judgment of individual validators rather than a standardized analytical framework. This paper proposes a counterfactual diagnostic framework for explaining KS deterioration in credit risk model validation. The framework sequentially attributes observed KS decline to sampling variability, portfolio composition change, covariate shift, and residual deterioration consistent with model drift, with explicit gateway conditions governing escalation at each stage. Simulation experiments demonstrate that the proposed approach provides more interpretable and governance-relevant explanations than threshold-based review alone, and contributes to more consistent, transparent, and defensible performance-breach assessment in credit risk model validation. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.11561 |
| By: | Dhruvin Dungrani; Disha Dungrani |
| Abstract: | In computational paralinguistics, detecting cognitive load and deception from speech signals is a heavily researched domain. Recent efforts have attempted to apply these acoustic frameworks to corporate earnings calls to predict catastrophic stock market volatility. In this study, we empirically investigate the limits of acoustic feature extraction (pitch, jitter, and hesitation) when applied to highly trained speakers in in-the-wild teleconference environments. Utilizing a two-stream late-fusion architecture, we contrast an acoustic-based stream with a baseline Natural Language Processing (NLP) stream. The isolated NLP model achieved a recall of 66.25% for tail-risk downside events. Surprisingly, integrating acoustic features via late fusion significantly degraded performance, reducing recall to 47.08%. We identify this degradation as Acoustic Camouflage, where media-trained vocal regulation introduces contradictory noise that disrupts multimodal meta-learners. We present these findings as a boundary condition for speech processing applications in high-stakes financial forecasting. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.14619 |
| By: | Tenghan Zhong |
| Abstract: | Volatility forecasting becomes challenging when market conditions change and model performance varies across regimes. Motivated by this instability, we develop a regime-aware specialist routing framework for ETF volatility forecasting. The framework uses online risk-sensitive evaluation and state-dependent gating to combine different forecasting specialists across calm and stressed market states. Using a daily panel of six ETFs under a rolling walk-forward design, we find that the strongest forecaster is regime-dependent rather than global. Relative to the rolling-best baseline, the proposed routing framework reduces high-volatility forecast loss by about 24\% and underprediction loss by about 22\%. These results suggest that specialist routing provides a practical adaptive forecasting architecture for changing market conditions. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.10402 |
| By: | Onur Polat (Institute of Informatics, Hacettepe University, Beytepe Campus, 06800 Cankaya, Ankara, Turkiye); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Dhanashree Somani (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA); Sayar Karmakar (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA) |
| Abstract: | This study examines the predictive power of multi-scale positive and negative speculative bubbles in equity and energy markets for S&P 500 realized variance across horizons from 1 to 24 months. Using a hierarchical modeling framework and machine learning estimators, the analysis evaluates whether stock and oil bubbles provide incremental information beyond macroeconomic variables and financial uncertainty. Applying Clark and West's (2007) tests for nested model comparisons, the results reveal a hierarchy in predictive content that varies by forecast horizon. At the 1-month horizon, neither stock nor oil bubbles improves forecast accuracy. At the 3-month horizon, oil bubbles emerge as the dominant predictor; the Bayesian Regularized Neural Network (BRNN) estimator achieves a statistically significant improvement when oil bubbles are included with stock bubbles, resulting in a 30.7 percent reduction in mean squared error (MSE). At the 6-month horizon, stock bubbles become more important, with both the Gradient Boosting Machine (GBM) and BRNN estimators showing significant improvements. For longer horizons, oil bubbles remain relevant, but their predictive value depends on the estimator: BRNN captures oil bubble effects at 12 months, while GBM does so at 24 months. These findings highlight the importance of horizonspecific model selection and indicate a complex transmission of speculative shocks across asset classes. |
| Keywords: | Stock Market Realized Variance, Stock and Oil Bubbles, Machine Learning, Forecasting |
| JEL: | C22 C53 G10 Q51 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202611 |