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on Risk Management |
| By: | Friederke Niepmann; Leslie Sheng Shen; Friederike Niepmann |
| Abstract: | How do banks respond to geopolitical risk, and is this response distinct from other macroeconomic risks? Using U.S. supervisory data and new geopolitical risk indices, we show that banks reduce cross-border lending to countries with elevated geopolitical risk but continue lending to those markets through foreign affiliates---unlike their response to other macro risks. Furthermore, banks reduce domestic lending when geopolitical risk rises abroad, especially when they operate foreign affiliates. A simple banking model in which geopolitical shocks feature expropriation risk can explain these findings: Foreign funding through affiliates limits downside losses, making affiliate divestment less attractive and amplifying domestic spillovers. |
| Keywords: | geopolitical risk, bank lending, credit risk, international spillovers |
| JEL: | F34 F36 G21 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12145 |
| By: | Yuming Ma |
| Abstract: | Extending Buehler et al.'s 2019 Deep Hedging paradigm, we innovatively employ deep neural networks to parameterize convex-risk minimization (CVaR/ES) for the portfolio tail-risk hedging problem. Through comprehensive numerical experiments on crisis-era bootstrap market simulators -- customizable with transaction costs, risk budgets, liquidity constraints, and market impact -- our end-to-end framework not only achieves significant one-day 99% CVaR reduction but also yields practical insights into friction-aware strategy adaptation, demonstrating robustness and operational viability in realistic markets. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.22611 |
| By: | Eduardo Abi Jaber (CMAP - Centre de Mathématiques Appliquées de l'Ecole polytechnique - Inria - Institut National de Recherche en Informatique et en Automatique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique); Shaun Xiaoyuan Li (UP1 - Université Paris 1 Panthéon-Sorbonne) |
| Abstract: | An extensive empirical study of the class of Volterra Bergomi models using SPX options data between 2011 and 2022 reveals the following fact-check on two fundamental claims echoed in the rough volatility literature: Do rough volatility models with Hurst index H ∈ (0, 1/2) really capture well SPX implied volatility surface with very few parameters? No, rough volatility models are inconsistent with the global shape of SPX smiles. They suffer from severe structural limitations imposed by the roughness component, with the Hurst parameter H ∈ (0, 1/2) controlling the smile in a poor way. In particular, the SPX at-the-money skew is incompatible with the power-law shape generated by rough volatility models. The skew of rough volatility models increases too fast on the short end, and decays too slow on the longer end where "negative" H is sometimes needed. Do rough volatility models really outperform consistently their classical Markovian counterparts? No, for short maturities they underperform their one-factor Markovian counterpart with the same number of parameters. For longer maturities, they do not systematically outperform the one-factor model and significantly underperform when compared to an under-parametrized two-factor Markovian model with only one additional calibratable parameter. On the positive side: our study identifies a (non-rough) path-dependent Bergomi model and an under-parametrized two-factor Markovian Bergomi model that consistently outperform their rough counterpart in capturing SPX smiles between one week and three years with only 3 to 4 calibratable parameters. |
| Keywords: | Neural Networks, Calibration, Pricing, Stochastic volatility, SPX options |
| Date: | 2025–05–07 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04372797 |
| By: | Fekria Belhouichet; Guglielmo Maria Caporale; Luis Alberiko Gil-Alana |
| Abstract: | This paper examines tail connectedness between various exchange-traded funds (ETFs) focused on artificial intelligence (AI) and some traditional assets such as bonds, equities, Bitcoin, and oil, as well as the VIX uncertainty index, using US daily data over the period from 1 January 2023 to 23 June 2025. The investigation is carried out following the QVAR (Quantile VAR) approach introduced by Ando et al. (2022); this is an extension of the connectedness measure of Diebold and Yilmaz (2012, 2014) which captures the dynamic relationships between assets under different market conditions. The results show that AI and robotics ETFs, along with the S&P 500 Index, act as net transmitters of shocks, while other assets and the VIX serve as net receivers. Furthermore, connectedness intensifies under extreme market conditions. These findings suggest that technology ETFs play a central role in shock transmission and could be effectively employed for hedging purposes. Our findings provide valuable information to investors for diversification and hedging purposes, and to policy makers for maintaining financial stability, particularly during periods of market turbulence. |
| Keywords: | exchange-traded funds (ETFs), artificial intelligence (AI), connectedness, quantile VAR (QVAR) |
| JEL: | C32 G11 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12143 |
| By: | Vasily Melnikov |
| Abstract: | We consider the optimal risk sharing problem with a continuum of agents, modeled via a non-atomic measure space. Individual preferences are not assumed to be convex. We show the multiplicity of agents induces the value function to be convex, allowing for the application of convex duality techniques to risk sharing without preference convexity. The proof in the finite-dimensional case is based on aggregate convexity principles emanating from Lyapunov convexity, while the infinite-dimensional case uses the finite-dimensional results conjoined with approximation arguments particular to a class of law invariant risk measures, although the reference measure is allowed to vary between agents. Finally, we derive a computationally tractable formula for the conjugate of the value function, yielding an explicit dual representation of the value function. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.08832 |
| By: | Leonardo Baggiani; Martin Herdegen; Nazem Khan |
| Abstract: | We revisit the problem of portfolio selection, where an investor maximizes utility subject to a risk constraint. Our framework is very general and accommodates a wide range of utility and risk functionals, including non-concave utilities such as S-shaped utilities from prospect theory and non-convex risk measures such as Value at Risk. Our main contribution is a novel and complete characterization of well-posedness for utility-risk portfolio selection in one period that takes the interplay between the utility and the risk objectives fully into account. We show that under mild regularity conditions the minimal necessary and sufficient condition for well-posedness is given by a very simple either-or criterion: either the utility functional or the risk functional need to satisfy the axiom of sensitivity to large losses. This allows to easily describe well-posedness or ill-posedness for many utility-risk pairs, which we illustrate by a large number of examples. In the special case of expected utility maximization without a risk constraint (but including non-concave utilities), we show that well-posedness is fully characterised by the asymptotic loss-gain ratio, a simple and interpretable quantity that describes the investor's asymptotic relative weighting of large losses versus large gains. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.10351 |
| By: | Mulabdic, Alen; Yotov, Yoto V. |
| Abstract: | This paper studies the impact of geopolitical risks on international trade, using the Geopolitical Risk (GPR) index of Caldara and Iacoviello (2022) and an empirical gravity model. The impact of spikes in geopolitical risk on trade is negative, strong, and heterogeneous across sectors. The findings show that increases in geopolitical risk reduce trade by about 30 to 40 percent. These effects are equivalent to an increase of global tariffs of up to 14 percent. Services trade is most vulnerable to geopolitical risks, followed by agriculture, and the impact on manufacturing trade is moderate. These negative effects are partially mitigated by cultural and geographic proximity, as well as by the presence of trade agreements. |
| Date: | 2025–09–23 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:11219 |
| By: | Yi Lu; Aifan Ling; Chaoqun Wang; Yaxin Xu |
| Abstract: | In recent years, China's bond market has seen a surge in defaults amid regulatory reforms and macroeconomic volatility. Traditional machine learning models struggle to capture financial data's irregularity and temporal dependencies, while most deep learning models lack interpretability-critical for financial decision-making. To tackle these issues, we propose EMDLOT (Explainable Multimodal Deep Learning for Time-series), a novel framework for multi-class bond default prediction. EMDLOT integrates numerical time-series (financial/macroeconomic indicators) and unstructured textual data (bond prospectuses), uses Time-Aware LSTM to handle irregular sequences, and adopts soft clustering and multi-level attention to boost interpretability. Experiments on 1994 Chinese firms (2015-2024) show EMDLOT outperforms traditional (e.g., XGBoost) and deep learning (e.g., LSTM) benchmarks in recall, F1-score, and mAP, especially in identifying default/extended firms. Ablation studies validate each component's value, and attention analyses reveal economically intuitive default drivers. This work provides a practical tool and a trustworthy framework for transparent financial risk modeling. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.10802 |
| By: | Stéphane Lhuissier |
| Abstract: | I propose a dynamic factor model with time-varying skewness to assess asymmetric risk around the economic outlook across a set of macroeconomic aggregates. Applied to U.S. data, the model shows that macroeconomic skewness is procyclical, displays significant independent variations from GDP growth skewness, and does not require conditioning on financial variables to manifest. Compared to univariate benchmarks, the model improves the detection of downside risk to growth and delivers more accurate predictive distributions, especially during downturns. These findings underscore the value of using a richer information set to quantify the balance of macroeconomic risks. |
| Keywords: | Dynamic Factor Models, Markov-Switching, Skewness |
| JEL: | C34 C38 C53 E37 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:bfr:banfra:1004 |
| By: | Jirong Zhuang; Xuan Wu |
| Abstract: | Constructing the implied volatility surface (IVS) is reframed as a meta-learning problem training across trading days to learn a general process that reconstructs a full IVS from few quotes, eliminating daily recalibration. We introduce the Volatility Neural Process, an attention-based model that uses a two-stage training: pre-training on SABR-generated surfaces to encode a financial prior, followed by fine-tuning on market data. On S&P 500 options (2006-2023; out-of-sample 2019-2023), our model outperforms SABR, SSVI, Gaussian Process, and an ablation trained only on real data. Relative to the ablation, the SABR-induced prior reduces RMSE by about 40% and dominates in mid- and long-maturity regions where quotes are sparse. The learned prior suppresses large errors, providing a practical, data-efficient route to stable IVS construction with a single deployable model. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.11928 |
| By: | Todd Prono |
| Abstract: | In heavy-tailed cases, variance targeting the Student's-t estimator proposed in Bollerslev (1987) for the linear GARCH model is shown to be robust to density misspecification, just like the popular Quasi-Maximum Likelihood Estimator (QMLE). The resulting Variance-Targeted, Non-Gaussian, Quasi-Maximum Likelihood Estimator (VTNGQMLE) is shown to possess a stable limit, albeit one that is highly non-Gaussian, with an ill-defined variance. The rate of convergence to this non-standard limit is slow relative √n and dependent upon unknown parameters. Fortunately, the sub-sample bootstrap is applicable, given a carefully constructed normalization. Surprisingly, both Monte Carlo experiments and empirical applications reveal VTNGQMLE to sizably outperform QMLE and other performance-enhancing (relative to QMLE) alternatives. In an empirical application, VTNGQMLE is applied to VIX (option-implied volatility of the S&P 500 Index). The resulting GARCH variance estimates are then used to forecast option-implied volatility of volatility (VVIX), thus demonstrating a link between historical volatility of VIX and risk-neutral volatility-of-volatility. |
| Keywords: | GARCH; VIX; VVIX; Heavy tails; Robust estimation; Variance forecasting; Volatility; Volatility-of-volatility |
| JEL: | C13 C22 C58 |
| Date: | 2025–08–27 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-75 |
| By: | Alexander Gairat; Vyacheslav Gorovoy; Vadim Shcherbakov |
| Abstract: | We derive an explicit analytical approximation for the local volatility function in the Cheyette interest rate model, extending the classical Dupire framework to fixed-income markets. The result expresses local volatility in terms of time and strike derivatives of the Bachelier implied variance, naturally generalizes to multi-factor Cheyette models, and provides a practical tool for model calibration. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.23876 |
| By: | Sagi Schwartz; Qinling Wang; Fang Fang |
| Abstract: | Predicting default is essential for banks to ensure profitability and financial stability. While modern machine learning methods often outperform traditional regression techniques, their lack of transparency limits their use in regulated environments. Explainable artificial intelligence (XAI) has emerged as a solution in domains like credit scoring. However, most XAI research focuses on post-hoc interpretation of black-box models, which does not produce models lightweight or transparent enough to meet regulatory requirements, such as those for Internal Ratings-Based (IRB) models. This paper proposes a hybrid approach: post-hoc interpretations of black-box models guide feature selection, followed by training glass-box models that maintain both predictive power and transparency. Using the Lending Club dataset, we demonstrate that this approach achieves performance comparable to a benchmark black-box model while using only 10 features - an 88.5% reduction. In our example, SHapley Additive exPlanations (SHAP) is used for feature selection, eXtreme Gradient Boosting (XGBoost) serves as the benchmark and the base black-box model, and Explainable Boosting Machine (EBM) and Penalized Logistic Tree Regression (PLTR) are the investigated glass-box models. We also show that model refinement using feature interaction analysis, correlation checks, and expert input can further enhance model interpretability and robustness. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.11389 |
| By: | Wenbin Sun; Rahul Govind (UNSW - University of New South Wales [Sydney]); Mahabubur Rahman (ESC [Rennes] - ESC Rennes School of Business) |
| Abstract: | This study explores the under-researched area of vertical coopetition in business-to-business markets. Drawing on the resource-based view of the firm and signaling theory, we develop a conceptual model linking vertical coopetition to a supplier firm's systematic risk (SR) and idiosyncratic risk (IR) profile and incorporating coopetitionspecific attributes as the boundary conditions. Using a dataset of over 20, 000 observations from more than 4000 firms spanning 29 years, employing a novel measure of vertical coopetition and a robust analytical method, we document that vertical coopetition with customers reduces a firm's SR. Additionally, we uncover an inverted Ushaped relationship between vertical coopetition and IR, suggesting that moderate levels of coopetition heighten firm-specific risks due to competitive tensions, while higher levels mitigate risk through improved resource coordination. We also identify that the length of the coopetitive relationship amplifies the risk-reducing effects on SR. In contrast, competition intensity within the relationship increases SR but has a non-monotonic effect on IR. The support for the results is further validated with several additional measures of the key variables, ensuring the robustness of our results. These insights contribute to the theoretical understanding of vertical coopetition and offer practical implications for B2B managers in strategic risk management, emphasizing the importance of balancing cooperation and competition to achieve long-term stability and competitive advantage. |
| Keywords: | Vertical coopetition, Firm performance, Relationship length |
| Date: | 2025–07–24 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05224431 |
| By: | Domenech Palacios, Mar |
| Abstract: | This paper examines whether firm-specific cyclical and idiosyncratic risk profiles influence corporate bond spreads and the transmission of monetary policy. I extend the standard excess bond premium (EBP) framework of Gilchrist & Zakrajšek (2012) to allow investors’ required compensation for default risk to vary with firm-level risks. Incorporating these effects reveals that a significantly larger share of a monetary policy shock’s impact on credit spreads is driven by changes in default risk compensation (as opposed to the EBP). In particular, for firms with more cyclical risk, up to one-fourth of the additional spread widening following a contractionary monetary policy shock reflects higher expected default compensation, substantially more than implied by the traditional EBP. By contrast, firms with high idiosyncratic risk show no strong differential response to monetary policy shocks relative to other firms. JEL Classification: D22, E43, E44, E52, G12 |
| Keywords: | cyclicality, excess bond premium, monetary policy, risk, sentiment |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253118 |
| By: | Carol Bertaut; Ester Faia; Ṣebnem Kalemli-Özcan; Camilo Marchesini; Simon Paetzold; Martin Schmitz |
| Abstract: | We use administrative security-level data from the U.S. and Euro Area (EA) portfolios to estimate asset demand and supply elasticities by exploiting exogenous variation in bond-specific currency wedges. Employing a Bartik-style shift-share identification approach, we document extensive heterogeneity in investor demand responsiveness to exogenous changes in the price of currency risk, conditional on the issuer characteristics. Demand for AE-bonds is always inelastic, whereas for EM-bonds, elasticity depends on investor type and currency: insurance/pension, nonbanks and banks have finite-elastic demand for EM-bonds that are issued in their own (investor) currency. For EM-issuer-currency bonds, only EA non-bank investors increase the share of these bonds in their portfolio when currency wedges widen, suggesting they accept higher currency risk for higher returns. In response, issuers adjust their supply endogenously: an exogenous increase of 8 basis point in currency wedges leads to a 0.26% decline in local currency bond issuance relative to GDP. We develop a theoretical framework where debt issuance decisions take into account heterogenous demand of investors in terms of their response to changes in the price of currency risk. |
| JEL: | F30 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34275 |
| By: | Shawn Kimble; Matthew P. Seay |
| Abstract: | Did bank equity prices reflect growing sector imbalances before the 2023 failure of Silicon Valley Bank? We find that banks with higher reliance on uninsured deposits, or with higher marked-to-market leverage, had lower equity returns prior to SVB's collapse. Although markets priced uninsured deposits and high leverage individually, their interaction was not reflected in market prices prior to SVB’s failure. Post-SVB, banks with less ability to meet outflows without severely depleting capital, and banks with too little useable liquidity relative to runnable funding, experienced larger stock price declines, beyond what other fundamentals and business model risks explain. In addition, we highlight evidence of feedback between equity prices and balance sheet management: banks with lower returns in 2023:Q1 were more likely to rely heavily on reciprocal deposits by 2023:Q2. |
| Keywords: | Financial Institutions; Bank Capital; Interest Rate Risk; Liquidity |
| JEL: | G20 G21 G28 |
| Date: | 2025–09–05 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-78 |
| By: | Omar Abdelrahman; David Chen; Cameron MacDonald; Adi Mordel; Guillaume Ouellet Leblanc |
| Abstract: | We present a technical description of the Top-Down Solvency Assessment (TDSA) tool. As a solvency stress-testing tool, TDSA is used to assess the banking sector’s capital resilience to hypothetical future risk scenarios. |
| Keywords: | Economic model; Financial institutions; Financial stability; Financial system regulation and policies |
| JEL: | C C2 C22 C5 C52 C53 G G1 G17 G2 G21 G28 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocatr:128 |
| By: | Yufei Sun (Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | This study evaluates the performance of pairs trading strategies in the Chinese stock market across extreme market environments, including the Financial Crisis, Bull and Bear phases, and the COVID-19 period. Using a comprehensive stock dataset and incorporating transaction costs, we find that most portfolios deliver near-zero excess returns after costs. However, in volatile conditions—especially during the Financial Crisis—top-performing portfolios achieved monthly returns up to 156 basis points. The strategy underperforms in stable or bullish markets with fewer mean-reversion opportunities. COVID-19 introduced challenges that further reduced profitability. Results highlight the critical role of transaction costs and the importance of advanced pair selection methods, such as combining the Sum of Squared Deviations (SSD), Hurst exponent, and the Number of Zero Crossings (NZC), which consistently outperform traditional approaches. While generally unprofitable, pairs trading can succeed under specific market regimes, offering insights into risk management and strategy adaptation. |
| Keywords: | Pairs Trading, Statistical Arbitrage, Sum of Squared Deviations, Hurst Exponent, Chinese Stock Market |
| JEL: | C58 C63 G11 G14 G17 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:war:wpaper:2025-23 |
| By: | Vaishali Garga; Edward P. Herbst; Alisdair McKay; Giovanni Nicolo; Matthias Paustian |
| Abstract: | We review the design and communication of monetary policy strategies that take into account risks and uncertainty. A key element in a robust monetary strategy is the concept of risk management, which is the weighing of key risks when setting policy. When risks to the outlook are balanced, the baseline outlook may be sufficient to guide policy decisions. However, risk-management considerations become important when risks are asymmetric. We discuss how robust simple interest rate rules and optimal control policy can incorporate risk-management considerations into the design of a monetary policy strategy. Alternative scenarios can illustrate salient risks and how monetary policy might respond if those risks were to materialize. However, using alternative scenarios in policy deliberations and communications requires important implementation choices. |
| Keywords: | Uncertainty; Risk management; Robust monetary policy strategies; Scenario analysis; Monetary policy communication |
| JEL: | E31 E32 E52 E58 |
| Date: | 2025–08–22 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-74 |
| By: | van Ewijk, Casper (Tilburg University, Center For Economic Research); Meijdam, Lex (Tilburg University, Center For Economic Research) |
| Keywords: | pension; payg; NDC; intergenerational risk sharing |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:tiu:tiucen:4c768d0a-7f76-4fd1-88e1-56656843198c |
| By: | Scarlett Tannous (LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique); Terje Aven (University of Stavanger); Myriam Merad (INERIS - Institut National de l'Environnement Industriel et des Risques, LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | Today most public authorities have implemented some type of risk governance framework or system, which provides structure, approaches, and methods for how to handle societal risks. One main challenge of risk-related policies and frameworks is to adequately and effectively take into account risks. To meet this challenge there is a need for knowledge about the performance of the various policies on risk. However, such knowledge is not easily derived since the performance is subject to uncertainties and is difficult to measure -especially before the occurrence of impacts, which can be significant when focusing on high-risk sites. The present paper discusses this issue of risk policy performance or "effectiveness". The main aim is to establish a set of suitable criteria for assessing the performance of such risk policies by relying on both (1) foundational theoretical and methodological studies and (2) empirical and methodological case studies. Results provide new insights into risk policy assessment by relating the discussion to current risk science knowledge and multi-criteria decision analysis. Consequently, a novel multi-criteria framework consisting of 16 assessment criteria is proposed to cover the spectrum of various improvements, degradations, or stagnations of the effectiveness of risk policies. The discussion is illustrated by a case study on a risk policy linked to a high-risk site in the chemical and petrochemical industry. Based on 25 interviews with risk actors, the study findings reveal how perceived policy improvements or degradations vary under the proposed set of criteria including efficiency, efficacy, and reputational effects. The main contribution demonstrates the practicability of the proposed risk policy assessment framework consisting of 16 criteria. Further research can include the investigation of the Multiple-Criteria Decision Aiding (MCDA) techniques to support the application of this framework. |
| Keywords: | Risk management, Risk governance, Multi-criteria decision analysis, Industrial high-risk sites, Policy assessment, Risk policy |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05252430 |
| By: | H\'ector Jasso-Fuentes; Alejandra Quintos; Xinta Yang |
| Abstract: | In this paper we apply a probabilistic approach to analyze the impact of contagious default among investment group members. A general formula is given to compute the group survival probability with the presence of contagion effect. Special cases of this probability model are examined. In particular, we show that if the investment group is homogeneous, defined in the paper, then including more members into the group will eventually lead to default with probability 1, contrasting with the non-contagious scenario, where the default probability increases monotonically with respect to the group size. Also, we provide an upper bound of the optimal group size under the homogeneous setup; so, one can run a linear search with finite time to locate this optimizer. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.11579 |
| By: | Kemper, Annika (Center for Mathematical Economics, Bielefeld University); Schmeck, Maren Diane (Center for Mathematical Economics, Bielefeld University) |
| Abstract: | In this paper, we extend the market price of risk for delivery periods (MPDP) of electricity swap contracts by introducing a dimension for jump risk. As introduced by Kemper et al. (2022), the MPDP arises through the use of geometric averaging while pricing electricity swaps in a geometric framework. We adjust the work by Kemper et al. (2022) in two directions: First, we examine a Merton type model taking jumps into account. Second, we transfer the model to the physical measure by implementing mean-reverting behavior. We compare swap prices resulting from the classical arithmetic (approximated) average to the geometric weighted average. Under the physical measure, we discover a decomposition of the swap’s market price of risk into the classical one and the MPDP. |
| Keywords: | Electricity Swaps, Delivery Period, MPDP for Diffusion and Jump Risk, Mean-Reversion, Jumps, Samuelson Effect, Seasonality |
| Date: | 2025–08–14 |
| URL: | https://d.repec.org/n?u=RePEc:bie:wpaper:726 |
| By: | Lara Loewenstein; Paul S. Willen; Yuxi Yao; David Hao Zhang |
| Abstract: | A recurring question in the mortgage default literature is why underwater default is rare relative to model predictions. We find that one answer is miscalibration of flow payoffs. We build a novel, detailed quantitative model of mortgage default and find that realistic rent dynamics plus mild levels of default costs are sufficient to eliminate negative-equity strategic default. We present further empirical results supporting our model's focus on flow payoffs. Our model addresses the underwater mortgage default puzzle, offers more realistic interpretations of policy consequences, and reinforces the theoretical effectiveness of cash-flow-based interventions. |
| Keywords: | mortgage default; strategic default; household balance sheets; household decision making |
| JEL: | D15 G51 R30 |
| Date: | 2025–09–25 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedcwq:101796 |
| By: | MENG, WEI |
| Abstract: | In open-source intelligence (OSINT) research, traditional risk identification methods reliant on expert scoring face growing challenges due to their high subjectivity, cost, and lack of scalability. This study aims to propose and validate an algorithmic framework that transcends expert judgment. Centered on truth discovery, weakly supervised learning, and learning-based ranking, it enables automated, explainable risk identification within complex, multi-source heterogeneous data. The study first constructs a hierarchical-quota sampling system, acquiring and deduplicating data from four source categories: institutional authorities, official statements, mainstream and international reports, and visual materials. Subsequently, a truth discovery algorithm estimates source credibility to replace expert weighting. Weakly supervised labeling functions generate initial annotations, which are then aggregated by generative models to form robust labels. Finally, a learning ranking model dynamically prioritizes risk trajectories, with explainability ensured through Explainable AI techniques (e.g., SHAP, Grad-CAM). Results demonstrate that this framework reliably identifies risk signals across multiple time windows and control conditions. The classifier achieves PR-AUC improvements exceeding expert baselines, with average absolute error in inflection point localization maintained below 1 hour. It exhibits high consistency and robustness across cross-domain datasets. The study concludes that algorithmic expert-scoring replacement not only excels in accuracy and efficiency but also significantly outperforms traditional models in transparency and reproducibility, offering a systematic, scalable, and cutting-edge approach for OSINT risk research. |
| Date: | 2025–09–14 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:5642u_v1 |