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on Risk Management |
| By: | Prashanth Bhaskara; Aadit Jerfy |
| Abstract: | This paper proposes an investment strategy through resource allocation into Kalshi Crypto Event Contracts in order to effectively hedge exposure to spot asset volatility. Using Bitcoin as a proof of concept, we treat corresponding Kalshi markets on the asset's future price as option contracts, and through construction of different portfolio allocations present a framework for which event contracts can be effectively utilized by retail investors in order to control risk framework without reducing underlying exposure to spot volatility. We showcase this information through analysis of three separate Kalshi event contracts, all relating to a different underlying market regime (i.e bullish, bearish, flat), and provide a comprehensive risk profile for each regime. Results show that a dynamic Kalshi/Bitcoin hedging strategy outperforms more simplistic portfolios across all regimes, supporting our initial hypothesis. We discuss further considerations which can erode profits in the future (i.e. fees), and provide direction for future research. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.14267 |
| By: | Vemareddy, Manas |
| Abstract: | Investors commonly use historical risk-adjusted performance measures to guide stock choice, yet it remains unclear whether limiting risk analysis to downside volatility only can reliably identify stocks that will deliver increased future returns. This study examined whether the Sortino ratio, a measure of downside risk, can be used to construct a diversified, profit-oriented portfolio that outperforms lower-ranked stocks and market benchmarks. We hypothesized that portfolios comprising the five stocks with the highest Sortino ratios out of a particular sectoral stock pool would generate higher returns from 2015 to 2025 than portfolios comprising of the five stocks with the lowest Sortino ratios of the same. From an initial sample of 179 large, publicly traded U.S companies across six sectors, 141 stocks meeting market capitalization and historical-data requirements were picked and analyzed. Sortino ratios were calculated using monthly returns from the 2011-2014 formation period and the resulting high and low Sortino portfolios were evaluated over ten years. The high-sortino portfolio outperformed low-sortino portfolio by 78.65% at the 10 year mark and both surpassed the S&P 500 and our 141-stock pool. The findings suggest that the Sortino ratio can support long-term stock selection and portfolio construction to an extent. The effectiveness varies by sector and should be utilized in combination with broader company and market specific analysis. |
| Date: | 2026–09–10 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:z8t4j_v1 |
| By: | Andrzej Tokajuk; Jaros{\l}aw A. Chudziak |
| Abstract: | Volatility forecasts play a central role in financial risk management because their overall level and day-to-day movements affect downstream decisions. Most studies compare forecasting models while keeping the training loss fixed. Yet losses emphasise different errors and can target different properties of future volatility, so raw comparisons may combine persistent forecast-level differences with differences in daily forecast movements. This leaves unresolved whether the importance of loss choice comes mainly from the forecast level it targets or from differences that remain after level adjustment. We address this gap through a comparison of seven losses and five models across major cryptocurrencies. Validation-based alignment adjusts the forecast level before the raw and aligned forecasts are evaluated using statistical scores and one-day Value-at-Risk. Before alignment, marginal score variation is greater across losses. After alignment, model choice becomes the larger source of variation in the full five-model comparison, while cross-loss differences in VaR breach rates narrow substantially. Our contribution is a comprehensive evaluation of loss and model choice that shows why losses can appear so influential in raw comparisons and how this interpretation changes when forecast level and downstream risk are considered explicitly. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.27024 |
| By: | Mihaela Nistor |
| Abstract: | Remarks at the Risk Live North America Conference, New York City. |
| Keywords: | operational risk; artificial intelligence (AI); Human Capital Risk; resilience |
| Date: | 2026–09–24 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednsp:103817 |
| By: | Shuping Shi (Macquarie University); Jun Yu (University of Macau); Chen Zhang (Sun Yat-sen University) |
| Abstract: | Continuous time models provide a stronger benchmark for realized volatility forecasting than the discrete time models standard in empirical finance. Using 111 U.S. equities and ETFs, we compare continuous time fractional processes, ARFIMA, and HAR models on a common footing. Three families perform similarly at the daily horizon but separate at longer horizons, where continuous time models lead statistically and economically. The ranking reflects how each model balances recent volatility against its long run level. The continuous time advantage grows with volatility persistence and diminishes as jumps account for more variation. |
| Keywords: | Realized volatility, Continuous time models, Discrete time models, Forecasting, Economic utility |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:boa:wpaper:202648 |
| By: | Nektarios Aslanidis; Aurelio Bariviera; George Kapetanios; Vasilis Sarafidis; Alexia Ventouri |
| Abstract: | We study the anatomy of commodity risk by distinguishing micro, market-level, and economy-wide sources. We develop a two-stage "divide-and-conquer" framework that allows sensitivities to these risk sources to vary across commodities while treating economy-wide risk as latent. The first stage uses defactored instrumental-variable estimation to recover commodity-specific sensitivities to micro and market conditions. The second combines principal components with high-dimensional variable selection to identify an observable representation of macro-financial risk. We then construct Risk Intensity Indices (RIIs), which combine estimated sensitivities with prevailing risk conditions to quantify the relative importance of each risk source on a common scale. Market risk is the largest component on average, accounting for about two fifths of total risk intensity and more than half for energy commodities. Risk intensity is also highly concentrated across individual commodities: the top 20% account for approximately half of micro and market risk intensity, whereas macro risk is more broadly dispersed. The composition of risk varies substantially across sectors and over time, with market risk becoming particularly prominent during episodes of commodity-market stress. Micro and market RIIs also contain information about future volatility and absolute returns. These findings provide investors, risk managers, and policymakers with a diagnostic of where commodity risk is concentrated, which risk layers are most important, and how their importance changes over time. More broadly, our divide-and-conquer framework provides a flexible approach to decomposing layered risk in settings where common risk is latent. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.00581 |
| By: | Masaaki Fukasawa; Jun Maeda; Tatsuya Ogiwara |
| Abstract: | We study the short-maturity implied volatility and the skew stickiness ratio for baskets of assets with continuous, possibly rough, stochastic volatility. The fluctuation of the instantaneous basket variance has two sources: fluctuations of the constituent variances and fluctuations of the basket weights. We derive a near-the-money implied volatility expansion that separates these contributions. We then specialize the result to volatility models given by general functions of Gaussian Volterra factors and obtain an explicit basket skew coefficient in terms of the short-time kernel asymptotics, the factor sensitivities, and the return-factor correlations. A density expansion justifies differentiation of the near-the-money expansion at the money. Finally, using a Malliavin representation of the dynamics of total implied variance, we prove that the short-maturity skew stickiness ratio converges to the universal limit $H + 3/2$ for Gaussian factor basket models with $H \in (0, 1/2]$. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.38230 |
| By: | Haroon Mumtaz; Sofia Velasco |
| Abstract: | The tails of macroeconomic outcomes can respond differently from the centre of their distribution: shocks with modest effects on median growth or inflation can shift downside growth or upside inflation risk. We develop a threshold stochastic-volatility-in-mean VAR with regime-dependent leverage to study their structural drivers. The model allows endogenous interactions between outcomes and volatility, contemporaneous level-volatility dependence, and regime-specific propagation. In nearly 150 years of U.S. data, predictive model selection supports three inflation-defined regimes. We identify business-cycle, financial, macroeconomic-uncertainty, and financial-uncertainty shocks and decompose their contributions to growth- and inflation-at-risk. The structural composition of tail risk differs from that of the predictive median. Business-cycle shocks dominate the median response of GNP growth but account for a substantially smaller share of growth-at-risk. Macroeconomic uncertainty makes a material contribution to both growth- and inflation-at-risk, with its share of growth-at-risk increasing with the magnitude of a positive macroeconomic-uncertainty impulse, despite its limited role at the median. In high-inflation states, the contribution of financial uncertainty to inflation-at-risk rises with the magnitude of positive financial-uncertainty impulses. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.26994 |
| By: | Xinyu Song |
| Abstract: | We introduce a network realized GARCH-It\^o model in which volatility transmission is a dynamic relation among the latent daily integrated volatilities of multiple assets. An unknown directed and signed network is embedded in a continuous-time variance process and appears in the resulting exponential daily recursion. Intraday returns identify integrated volatility and hence the network that governs its propagation. For a fixed network template, feasible estimation based on realized volatility is first-order equivalent to estimation based on latent integrated volatility. For an unknown network, regularization selects the relevant structure, and, under oracle conditions, a fixed-rank local refit provides conditional inference for model-implied response-based connectedness. In an application to nine U.S. sector ETFs from 2007 to 2025, the model attains the lowest average out-of-sample QLIKE among the reported recursive forecasts, although its advantage over HAR is not statistically significant. In the full sample the weighted LASSO selects an empty sparse support, so inference concerns the rank-one projected factor component; this conditional analysis identifies Energy as a net volatility transmitter. Aggregate connectedness is more stable across volatility measures than individual sparse channels. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.29515 |
| By: | José Martínez |
| Abstract: | What explains differences in the composition of households' portfolios? Recent literature has focused on the effects of heterogeneous portfolios. This paper provides an explanation of differences in households' savings patterns that is consistent with micro-level data for the United States. I focus on the effect of heterogeneity in earnings risk to explain differences in the type of deposits. First, I find micro-evidence that relates differences in the maturity structure of households savings with earnings risk, where high idiosyncratic risk results in high demand for short-term liquid deposits, while low earnings risk allows agents to allocate a larger fraction of their assets in long-term illiquid deposits. Second, I present a continuous-time macroeconomic model with incomplete markets, heterogeneous agents, and two assets, where agents have heterogeneous demand for liquidity, driven by differences in earnings risk. The model reproduces differences in the composition of optimal portfolios, consistent with the empirical findings. |
| Keywords: | Macroeconomics;Heterogeneous Agents;Optimal Portfolios;Household Finance |
| JEL: | E00 E21 G11 G50 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:bdm:wpaper:2026-05 |
| By: | Peng Liu; Tiantian Mao |
| Abstract: | In this paper, we study the diversification properties of convex combinations of iid random variables with infinitely divisible distributions. We characterize, in terms of subadditivity and concavity of the transformed L\'{e}vy measures, L\'{e}vy processes that exhibit the non-diversification phenomenon or the reverse diversification order with respect to the majorization order uniformly over all time horizons. In particular, we show that the symmetric 1-stable L\'{e}vy process is the only symmetric L\'{e}vy process exhibiting the non-diversification phenomenon. We further investigate convex combinations of components of multivariate infinitely divisible distributions, allowing for dependent and heterogeneously distributed risks, and characterize the L\'{e}vy measures of the corresponding multidimensional L\'{e}vy processes exhibiting the two diversification phenomena uniformly over time. Explicit characterizations are obtained for the multidimensional symmetric L\'{e}vy processes, multidimensional $\alpha$-stable processes and multidimensional compound Poisson processes. Finally, we show that the non-diversification phenomenon and the reverse diversification order extend beyond L\'{e}vy processes to several sample-path-dependent processes including running maxima and L\'{e}vy-driven stochastic integrals, with L\'{e}vy-driven Ornstein-Uhlenbeck processes as an important special case. Applications to ruin theory, storage processes and stochastic volatility are also discussed. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.25452 |
| By: | Masaaki Fukasawa |
| Abstract: | We derive a first-order representation of Black-Scholes implied variance in a continuous local martingale model. Total implied variance is the conditional expectation of the quadratic variation of the log price given its terminal value, up to a smaller-order term, for bounded standardized log-strikes. The framework incorporates small volatility-of-volatility, fast mean-reverting, and short-maturity asymptotics. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.13961 |
| By: | Florian Gach; Simon Hochgerner |
| Abstract: | This article is concerned with the asymptotic shape of quantile surfaces, defined as the set of quantiles at a given level $\alpha$ generated by a controlled one-dimensional distribution. Specifically, when the distribution arises as a linear combination of log-normal random variables and the control is a vector of positive coefficients, we prove that quantile surfaces are globally concave in the left tail ($\alpha\to0$) and globally convex in the right tail ($\alpha\to1$). Moreover, these surfaces exhibit asymptotic separation of scale and shape. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.31345 |
| By: | Schröder, Maximilian |
| Abstract: | Macroeconomic policy increasingly requires assessments of risk rather than point forecasts alone. This paper develops a framework to compute impulse responses for scenario probabilities and the full predictive distribution of macroeconomic outcomes. The approach combines a regime-dependent mixture VAR with endogenous state probabilities and heteroskedastic shock identification, complemented by sparse sign and narrative restrictions. Applied to the euro area and U.S., the framework addresses a central policy question following the outbreak of the 2026 Iran war: do geopolitical shocks imply a return to the risk profile observed after Russia’s invasion of Ukraine, or is this time different? Results show that shocks differ not only in their effects on expected outcomes, but also in how they reshape scenario probabilities, tail risks, and transitions into high-volatility states. JEL Classification: C11, C32, C34, C53, E37 |
| Keywords: | Bayesian econometrics, density forecasting, macroeconomic risks, scenario analysis, state-dependent transmission |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263290 |
| By: | Onur Polat (Hacettepe University, Informatics Institute 06800 Beytepe, Ankara, Turkiye); Hardik A. Marfatia (Department of Economics, Northeastern Illinois University, 5500 N. St. Louis Ave, Chicago 60625, USA); Christophe Andre (Economics Department, Organisation for Economic Co-operation and Development (OECD), 75775 Paris, Cedex 16, France); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | This paper examines time-varying connectedness and volatility spillovers between housing markets and macroeconomic policy conditions, with particular emphasis on the role of housing deregulation. Using daily data from September 2007 to May 2026, we estimate a 15-node TVP-VAR network comprising housing volatility series for ten major U.S. metropolitan areas alongside five policy and financial indicators: the housing deregulation index, economic policy uncertainty (EPU), a spliced monetary policy proxy (Effective Federal Funds Rate/Krippner Shadow Short Rate), the Aruoba-Diebold-Scotti (ADS) business conditions index, and the 5-year breakeven inflation rate (T5YIE). Volatility inputs are filtered using a multivariate GJR-GARCH model augmented with time-varying skewness and kurtosis (GJRSK), and parameters are estimated within a Bayesian prior (BayesPrior) framework. The Total Connectedness Index reveals a counter-cyclical network topology, peaking during the 2008 financial crisis, the 2020 pandemic, and the 2024 monetary pivot. Pairwise decompositions reveal pronounced heterogeneity across cities: supply-inelastic coastal market, New York (peak spillover 45.5%), San Diego (48.8%), and San Francisco (39.8%), absorb the largest regulatory shocks, while deregulation also transmits persistently to monetary policy conditions (Shadow Short Rate positive in 87.7% of observations), real activity, and inflation expectations. These findings carry direct implications for macroprudential policy design and institutional portfolio risk management in the U.S. housing sector. |
| Keywords: | Housing Volatility Networks, Housing Deregulation; TVP-VAR, Net Pairwise Spillovers, GJRSK Volatility, BayesPrior |
| JEL: | C32 G10 R31 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202630 |
| By: | Walter Kurz |
| Abstract: | This paper proposes a formal multi-agent architecture for implementing enterprise AI in regulated insurance firms, integrating economic theory with institutional design. The framework synthesises three core theoretical perspectives: Arrow's risk pooling theory to formalise risk transformation under uncertainty, Nash equilibrium to model strategic interactions between decision agents, and Principal-Agent theory to address incentive alignment under information asymmetry. The insurer is modelled as a constrained optimisation entity operating under solvency, legal, ESG, and operational boundaries, with specific focus on the regulatory contexts of Austria and Germany. The architecture decomposes the firm into multiple specialised agents, each representing distinct functional domains such as capital management, underwriting, claims processing, compliance, fraud detection, and client interaction. Human-in-the-loop agents are integrated through a tiered access control system, ensuring differentiated data visibility and decision influence based on user roles. An orchestrator agent supervises inter-agent coordination, enforcing regulatory admissibility and institutional coherence under frameworks such as Solvency II, the AI Act, and the Insurance Distribution Directive. Protocol integration is based on asynchronous execution and dual-layer communication infrastructures, specifically the Model Context Protocol (MCP) and Agent-to-Agent (A2A) messaging. This structure enables the systematic design of compliant, auditable multi-agent systems aligned with the institutional logic of financial firms in Austria and Germany. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.27636 |
| By: | Mathias Beiglb\"ock; Manuel Hasenbichler; Gudmund Pammer |
| Abstract: | European option smiles determine the risk-neutral marginal laws of an asset, but not their intertemporal coupling, which is decisive for many applications. The Bass martingale construction selects, among all calibrated martingales, the one closest to Bachelier dynamics; it permits fast calibration at discrete maturities and recovers the Dupire local-volatility (LV) model as the maturity grid is refined. This article develops a modular calibration overlay for existing stochastic and path-dependent volatility models. We recursively construct a martingale that matches all prescribed marginals exactly while remaining as close as possible, in an adapted Knothe-Rosenblatt sense, to the reference dynamics. As with the Bass LV model, each calibration step is amenable to an efficient Martingale Sinkhorn algorithm. We develop the theoretical foundations, numerical implementation and consider convergence to the SLV model. We also benchmark the method for Heston and Bergomi finite-factor path-dependent volatility dynamics and develop the multi-asset extension. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.39256 |
| By: | Abbas Fathiazar (DICCA - Dipartimento di Ingegneria Civile, Chimica e Ambientale [Genova] - UniGe - Università degli studi di Genova = University of Genoa = Université de Gênes); Silvia De Angeli (LOTERR - Centre de Recherche en Géographie - UL - Université de Lorraine, LIEC - Laboratoire Interdisciplinaire des Environnements Continentaux - INSU - CNRS - Institut national des sciences de l'Univers - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique, DICCA - Dipartimento di Ingegneria Civile, Chimica e Ambientale [Genova] - UniGe - Università degli studi di Genova = University of Genoa = Université de Gênes); Serena Cattari (DICCA - Dipartimento di Ingegneria Civile, Chimica e Ambientale [Genova] - UniGe - Università degli studi di Genova = University of Genoa = Université de Gênes) |
| Abstract: | Multi-hazard risk reduction is increasingly recognized as essential for effective disaster risk management, yet its operationalization remains constrained by decision-making frameworks that are not designed for cross-hazard comparison. This paper presents a unified microscale framework for harmonized flood and earthquake risk assessment and multi-hazard intervention appraisal in residential buildings. Here, microscale refers to building-specific assessment in which hazard intensity, exposure attributes, vulnerability functions, and intervention appraisal are represented at the level of individual residential buildings rather than aggregated spatial units. The framework establishes a common structure for hazard representation, exposure characterization, vulnerability modelling, and decision variables across flood and seismic domains while retaining hazard-specific physical models, enabling consistent evaluation of single-hazard as well as multi-hazard intervention strategies.The methodology combines building-level flood and seismic risk assessment with multidimensional performance metrics, including expected losses, life-safety outcomes, habitability, and environmental impacts. Flood and earthquake risks are evaluated using hazard-specific models, while their annualized consequences and intervention outcomes are brought together at the multi-hazard appraisal stage. Intervention appraisal is supported through feasibility screening based on payback-period constraints and Pareto-efficient analysis to identify nondominated retrofit options prior to a stakeholder-driven decision. The framework is demonstrated through an application to a portfolio of residential buildings in Pesaro (Italy) exposed to riverine flooding and seismic hazard under multiple hazard and vulnerability scenarios.Results show that changes in seismic demand, building vulnerability, and payback-period thresholds reshape the intervention decision space, shifting the portfolio from a feasibilitylimited regime dominated by no action or flood-only measures toward broader adoption of integrated strategies. In the investigated case study, under lower seismic demand, adoption of interventions is limited, and integration emerges primarily as an extension of economically viable single-hazard measures. As seismic demand and vulnerability increase, integrated strategies become structurally competitive, expand the actionable portfolio, and substantially shorten payback periods through cost sharing and synergy effects, although benefits remain typologydependent. More generally, the proposed framework provides an operational pathway for translating multi-hazard principles into building-scale decision support and offers practical |
| Keywords: | Multi-risk decision making; Earthquake and flood risk mitigation; Building portfolio analysis; Property-level mitigation; Payback period analysis; Risk governance |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05751280 |
| By: | Micha{\l} Balcerek; Micha{\l} Wronka |
| Abstract: | We examine the conditional volatility dynamics of the USD 1Yx10Y forward swap rate using GARCH(1, 1), GJR-GARCH(1, 1), and a two-regime Markov-switching GARCH (MSGARCH) model. The analysis uses daily data from 2007 to 2023 and incorporates market-implied measures (ATM swaption volatility and the SRVIX in- dex) together with a broad set of diagnostic tests. Standard GARCH and GJR- GARCH models show stable short-run parameters, but the intercept {\omega} varies markedly across rolling windows, causing instability in the implied long-run vari- ance. This pattern, confirmed by the Nyblom test, motivates adopting a regime- switching specification. MSGARCH mitigates this issue by keeping regime-specific parameters stable and capturing time variation through filtered regime probabili- ties. It delivers the highest log-likelihood and lowest AIC, whereas BIC favours the more parsimonious GJR-GARCH. One-step-ahead backtesting indicates comparable short-horizon accuracy across models, but MSGARCH offers a clearer structural in- terpretation by isolating high- and low-volatility regimes aligned with major market events. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.25965 |
| By: | Drew Fudenberg; Andrew Koh |
| Abstract: | We study a continuous-time game in which two firms choose how quickly to advance their capabilities while an exogenous safety threshold advances at a fixed rate. When the capability frontier (max of firms' capabilities) exceeds the safety threshold, all firms are exposed to common disaster that arrives at a hazard rate increasing in the capability-safety difference. We characterize Markov perfect equilibria in terms of the disaster risk, how quickly safety advances, and flow payoffs: when they are low, only racing is an equilibrium; when they are intermediate, racing and pacing coexist; when they are high, only pacing survives. Across all subgame perfect equilibria, low risk induces perpetual racing while high risk rules it out and we bound the probability of disaster across all SPE. If internal capabilities are hidden with a fixed lag until deployment, it is harder to sustain pacing which highlights the importance of transparency about internal capabilities. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.28291 |
| By: | Anup Basnet (UNIS - University of Surrey); Magnus Blomkvist (EDHEC - EDHEC Business School - UCL - Université catholique de Lille); Karl Felixson (Hanken School of Economics [Helsinki]); Eva Liljeblom; Hitesh Vyas (Audencia Business School) |
| Abstract: | The Dodd-Frank Act in 2010 increased ex ante downgrade threats without changing credit-rated firms' underlying credit quality. We show that the act had negative impacts on credit-rated firms' acquisition activities, especially among speculative-grade firms as they face greater downgrade-induced costs. The more selective acquisition strategies led to higher announcement returns and greater postacquisition upgrade probabilities. Consistent with firms refraining from taking on overall acquisition risk rather than financial risk, we show significant reductions in both cash-and stock-settled dealmaking after the Dodd-Frank Act. In sum, our study highlights that increased legal stringency on credit rating agencies has important spillover effects on firms' mergers and acquisitions activities. |
| Keywords: | Mergers and Acquisitions JEL Classification: G30, G32, G34, Credit Ratings, Credit Ratings Dodd-Frank Act Mergers and Acquisitions JEL Classification: G30, G32, G34, Mergers and Acquisitions, Dodd-Frank Act |
| Date: | 2026–05–01 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05741138 |
| By: | Jarrod Burgh; Emerson Melo |
| Abstract: | This paper studies risk-averse treatment allocation when individuals self-select into treatment based on unobserved characteristics. We develop a framework that combines the marginal treatment effect approach to endogenous selection with a general class of coherent risk measures that capture distributional preferences over welfare outcomes. We show that the planner's problem admits equivalent interpretations in terms of uncertainty aversion, distributional robustness, and worst-case welfare. For law-invariant coherent risk measures, we derive a Kusuoka representation that expresses the planner's objective as a weighted evaluation of different regions of the welfare distribution and characterize the resulting optimal allocation rule. We further establish finite-sample regret guarantees for empirical risk-averse policy learning, showing how the statistical difficulty of learning a policy depends on the planner's sensitivity to adverse welfare outcomes. The framework nests the risk-neutral policy learning model of \cite{Kiatagawa_Tetenov_2018} as a special case. An application to the \cite{Card1995} college proximity data demonstrates that incorporating risk aversion can lead to economically meaningful changes in optimal college admission policies. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.30617 |
| By: | Alejandro Rodriguez Dominguez |
| Abstract: | We study whether causal risk mandates constructed from overlapping information blocks can be implemented by one self-financing portfolio that is optimal under pooled information. In an incomplete continuous Brownian market, signal-responsive exposures are projected onto traded Brownian directions and embedded as closed subspaces of a predictable Hilbert space. Each causal mandate is the attainable projection of an identified, baseline-centered intervention surface and is therefore fixed upstream of the allocator. A first exact decomposition separates non-traded structural response, incompatibility across local mandates, and distortion of the common traded book. A fusion operator corrects duplication of common risk directions. The main theorem shows that exact causal decentralization holds if and only if the pooled enlargement preserves reference martingales, the pooled optimum has no component outside the additive desk span, the local causal shadows are compatible with one common book, and that book equals the additive pooled optimum. Under immersion, log-growth regret separates into pooled-interaction loss and common-book distortion, while raw causal implementation error also contains non-attainability and incompatibility. No common benchmark can reduce incompatibility. We derive the unique allocation under a quadratic causal-mandate penalty and exact comparative statics in a two-desk model with shared, desk-specific, and pooled-interaction risk. Additional results cover joint interventions, representation stability, admissible coalitions, and a covariance-weighted continuous-semimartingale formulation. The conclusions are partial-equilibrium statements for continuous markets. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.00158 |
| By: | Mathias Drehmann; Sonya Zhu |
| Abstract: | Near record-high government debt levels and the growing role of non-banks have reshaped government debt markets in recent years.High levels of government sector debt are expected to worsen future government debt market liquidity conditions in general, including by increasing the risk of market dysfunction. A large non-bank financial institution footprint heightens the risk of market dysfunction while also making very favourable liquidity conditions more likely. |
| Date: | 2026–09–29 |
| URL: | https://d.repec.org/n?u=RePEc:bis:bisblt:136 |
| By: | Francisco Blasques (Vrije Universiteit Amsterdam); Nepomuk Dunz (World Bank); Erik Feyen (World Bank); Charl Jooste (World Bank); Siem Jan Koopman (Vrije Universiteit Amsterdam); Gabriele Mingoli (Aarhus University) |
| Abstract: | Predicting macro-financial crises in developing economies is a critical but difficult task, complicated by significant data challenges. Machine learning techniques have shown improved predictive performance relative to traditional parametric models, yet this often comes at the cost of lower model explainability, limiting their use for policy makers. To strike a balance between predictive performance and explainability, this paper proposes a Mixture-of-Experts framework that combines ensemble models and traditional logistic regression to generate country-level crisis risk scores which can be decomposed into sub-risks, such as fiscal, financial, and balance of payments, to aid understanding of the drivers of risk within and across countries. Leveraging a global panel dataset spanning from 1990 to 2025 for 161 countries, the Mixture-of-Experts framework is shown to meaningfully outperform standard crisis prediction models in the literature. This paper therefore offers a practical and flexible framework for technical audiences developing early-warning systems in the presence of data gaps. |
| JEL: | C45 C82 G18 O11 |
| Date: | 2026–09–27 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260074 |
| By: | Hartmut Egger; Peter Egger; Katharina Erhardt; Leandro Navarro |
| Abstract: | This paper studies how international firms hedge against trade-cost uncertainty. We analyze two margins of adjustment: foreign market entry (exporting versus foreign investment) and invoicing in free-on-board (FOB) or cost-insurance-freight-inclusive (CIF) prices. Under oligopolistic competition and price rigidity, trade-cost volatility jointly shapes entry and invoicing choices. Firms with small market shares prefer exporting and FOB-price invoicing, while firms with large market shares favor foreign investment and CIF-price invoicing. Using a novel measure of trade-cost volatility constructed from French customs data, we provide empirical support for these mechanisms and document the joint determination of entry and invoicing decisions. Counterfactual analyses based on the structurally estimated and calibrated model indicate that trade-cost uncertainty has sizable effects on these decisions. |
| Keywords: | trade-cost uncertainty, Oligopoly, Invoicing, Foreign market entry |
| JEL: | F12 F23 F31 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_13009 |
| By: | Alfred M\"uller |
| Abstract: | The concept of weighted universal Value-at-Risk superadditivity (WUVS) was recently introduced by Chen et al. (2026) as a generalization of the question whether for some infinite mean distributions convex combinations of i.i.d. random variables can stochastically dominate the parent distribution. In this short note we prove that the property WUVS can basically never hold for discrete distributions except for the case of comonotonicity. This implies as a corollary that for discrete distributions with infinite mean it can also never hold that convex combinations of i.i.d. random variables can stochastically dominate the parent distribution. This settles an open problem mentioned in M\"uller (2025). |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.26398 |
| By: | Enrique Bátiz-Zuk; Anuar Bechara; Fabrizio López-Gallo; Corrado Macchiarelli |
| Abstract: | This paper examines the effect of alternative digital lending—defined as digital credit provision by non‑bank financial intermediaries (NBFIs)—on bank and systemic risk taking and competition. The paper focuses on Sociedades Financieras Populares (SOFIPOs) which constitute a regulated and well‑documented NBFI segment allowing an empirical assessment of the relationship between digital non-bank lending expansion and bank outcomes. Using a quarterly sample of 53 Mexican banks and 8 of these non-banks for the period 2018Q1–2024Q4, we document that growth in alternative digital lending is associated with lower individual bank risk-taking and stronger competitive pressures, with the latter effect being stronger for small banks. Reflecting the moderate size of digital lenders in Mexico and the compositional reallocation between bank and non‑bank credit, overall systemic risk appears broadly unaltered by the entry of the new players. |
| Keywords: | Digital finance; alternative lending; non-bank financial intermediaries; bank risk‑taking; systemic risk; competition; financial inclusion; Mexico |
| Date: | 2026–09–18 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2026/198 |
| By: | Giovanni Bonaccolto (Department of Economics and Law, ``Kore" University of Enna, Piazza dell'Universita, 94100 Enna, Italy); Massimiliano Caporin (Department of Statistical Sciences, University of Padova, Via Cesare Battisti 241/243, Padova, Italy); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | We revisit Quantile-on-Quantile connectedness and argue that estimating the equations of a Quantile Vector AutoRegressive model at different quantile levels does not fully capture the conventional Quantile-on-Quantile mechanism. We propose a local, kernel-weighted QVAR approach that explicitly accounts for the quantile location of the conditioning variable. An empirical comparison using U.S. Treasury yields shows that this modification substantially affects estimated connectedness, producing a more homogeneous pattern across quantile pairs and markedly attenuating the differences between directly and reversely related quantiles. |
| Keywords: | Quantile-on-Quantile, Connectedness, Quantile VAR, Spillovers, Quantile Regression |
| JEL: | C22 C32 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202629 |
| By: | Sheybanivaziri, Samaneh (Dept. of Business and Management Science, Norwegian School of Economics); Kyritsis, Evangelos (Dept. of Business and Management Science, Norwegian School of Economics) |
| Abstract: | Europe’s move toward renewable energy has sped up as the need to cut emissions has become closely linked with the need to secure energy supply. The EU’s Fit for 55 package proposed a 40% renewable energy target for 2030 [16]. The REPowerEU plan later raised the ambition to 45% in May 2022 in response to the need to reduce dependence on Russian fossil fuels [17]. Besides the significant contribution of renewable energies to the generation mix, they have created a dichotomy in electricity prices. An abundance of renewables can create extremely low prices and high volatility. On the other hand, their absence or insufficiency turns the gas-fired units or some fossil fuels on, which can create extremely high power prices. This phenomenon, in combination with geopolitical factors, has generated a binary fat-tailed distribution in electricity prices, which motivated us to study it more closely. Therefore, in this paper, we analyse the determinants of extreme electricity prices by modelling the conditional tail index. Additionally, we extend the analysis by [18] to a more recent sample period. We show that observable market conditions, such as TTF prices, load, and renewable generation in Germany and Italy from 2018 to 2023, affect the heaviness of the price distribution tail, a dimension of risk that is not captured by standard approaches such as quantile regression. |
| Keywords: | Extreme electricity prices; conditional tail-index estimator |
| JEL: | C14 C22 Q41 Q42 |
| Date: | 2026–09–21 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:nhhfms:2026_011 |
| By: | Varela, Angel; Cortez, Juan; Colmenares, Alejandro |
| Abstract: | This article, developed by the Colombian Association of Artificial Intelligence (ACIA), analyzes artificial intelligence (AI) governance as an emerging form of economic infrastructure capable of transforming markets, institutions, and decision-making processes. Although AI enables the automation of complex tasks and can generate significant efficiency gains, its adoption continues to face technical, institutional, and regulatory challenges. The study examines how AI is reshaping public administration, financial markets, and business organization through the progressive transfer of decision-making capabilities to algorithmic systems. To this end, it reviews international governance frameworks, including the OECD AI Principles and the NIST AI Risk Management Framework, assessing their limitations in relation to complex economic environments and the Colombian context. Financial markets are used as a case study to analyze the benefits, risks, and dynamics of institutional adoption associated with AI, including phenomena such as strategic mimicry and inefficiencies arising from the fear of technological obsolescence. The analysis of the Colombian case, including CONPES 4144, suggests that AI adoption in critical sectors requires effective governance capabilities, such as algorithmic auditing, traceability, and interoperability. The article concludes that trust in markets and technological sovereignty will depend on institutional capacity to supervise and respond to increasingly autonomous algorithmic systems. |
| Keywords: | AI Gobernance, AI Development, Finanace, Systems Information, Economic Development, Economic Infrastructure. |
| JEL: | O32 O33 O38 |
| Date: | 2026–06–17 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:131077 |
| By: | Duong Hien Chi Kien; Thanh Trung Huynh |
| Abstract: | Reinforcement learning for trading often struggles to balance upside participation with drawdown control. Profit-only policies can collapse toward passive long exposure on upward-drifting assets, while aggressively risk-penalized rewards can become too defensive during volatile periods. This paper proposes PPO-HRAP, a hybrid regime-aware policy that combines Proximal Policy Optimization with an interpretable regime prior. The agent observes both market features and portfolio-state variables, receives a reward combining portfolio log return, VIX-conditioned drawdown-increase penalty, target-exposure deviation, and turnover cost, and executes a blended action between the PPO actor output and a regime-derived target exposure. On the held-out 2020-2022 SPY test window, PPO-HRAP achieves 27.62% total return, 8.48% annualized return, 0.6447 Sharpe ratio, 0.8588 Sortino ratio, and 0.4592 Calmar ratio, while reducing maximum drawdown from 34.10% for Buy and Hold to 18.47%. Across five SPY seeds, PPO-HRAP remains stable with mean total return $0.2725 \pm 0.0109$ and mean Sharpe ratio $0.6219 \pm 0.0565$. Single-run cross-asset tests on QQQ and DIA further show that the proposed method ranks first on total return and Sharpe ratio for all three reported assets. These results suggest that blending learned actions with a volatility-aware regime prior is a practical way to improve risk-adjusted trading behavior, although the current policy still incurs high turnover and cross-asset robustness beyond SPY remains limited to single-run evidence. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.01325 |