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on Risk Management |
| By: | Cyril Bénézet (LaMME - Laboratoire de Mathématiques et Modélisation d'Evry - ENSIIE - Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, ENSIIE - Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise); Stéphane Crépey (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité, UPCité - Université Paris Cité); Dounia Essaket (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité, UPCité - Université Paris Cité) |
| Abstract: | The dynamic hedging theory only makes sense in the setup of one given model, whereas the practice of dynamic hedging is just the opposite, with models fleeing after the data through daily recalibration. This is quite of a quantitative finance paradox. In this paper we revisit Burnett (2021) & Burnett and Williams (2021)'s notion of hedging valuation adjustment (HVA), originally intended to deal with dynamic hedging frictions, in the direction of recalibration and model risks. Specifically, we extend to callable assets the HVA model risk approach of Bénézet and Crépey (2024). The classical way to deal with model risk is to reserve the differences between the valuations in reference models and in the local models used by traders. However, while traders' prices are thus corrected, their hedging strategies and their exercise decisions are still wrong, which necessitates a risk-adjusted reserve. We illustrate our approach on a stylized callable range accrual representative of huge amounts of structured products on the market. We show that a model risk reserve adjusted for the risk of wrong exercise decisions may largely exceed a basic reserve only accounting for valuation differences. |
| Keywords: | Pricing models, Cross Valuation Adjustments XVAs, Callable assets, Model risk, Model calibration, Early Exercise |
| Date: | 2026–01–20 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04057045 |
| By: | Einmahl, John (Tilburg University, School of Economics and Management); Peng, Liang |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:tiu:tiutis:5c4a6edb-7914-4ac2-81b8-168c4f668db4 |
| By: | Muhammad Abro; Hassan Jaleel |
| Abstract: | We attempt to mitigate the persistent tradeoff between risk and return in medium- to long-term portfolio management. This paper proposes a novel LLM-guided no-regret portfolio allocation framework that integrates online learning dynamics, market sentiment indicators, and large language model (LLM)-based hedging to construct high-Sharpe ratio portfolios tailored for risk-averse investors and institutional fund managers. Our approach builds on a follow-the-leader approach, enriched with sentiment-based trade filtering and LLM-driven downside protection. Empirical results demonstrate that our method outperforms a SPY buy-and-hold baseline by 69% in annualized returns and 119% in Sharpe ratio. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.17021 |
| By: | Paulus, Alexandra |
| Abstract: | Today's armed forces are highly dependent on software. Software products are built by complex networks of software components, software vendors, service providers, and other companies that, together, form the software supply chain. In "conventional" cybersecurity incidents, threat actors usually gain direct access to their target. But in the case of the software supply chain, the risks originate upstream in the supply chain itself and have an impact on entities downstream - often the end users. The armed forces are particularly vulnerable to these risks. Software supply chain incidents in the military sector have caused disruption and allowed malicious actors to engage in industrial espionage, political espionage, and sabotage. Policymakers and the Bundeswehr can manage software supply chain risk in the military sector through a set of measures. First, decision-makers should determine the requisite level of protection for the various areas of software use to strike a balance between risk management, on the one hand, and the functionality, cost, and speed of deployment, on the other. Thereafter, the Bundeswehr should establish effective risk management. Further, the federal government and the Bundeswehr should ensure that software suppliers reduce the software supply chain risk posed by their products. By doing so, the armed forces can be given adequate protection. |
| Keywords: | armed forces, software supply chain, software supply chain risks, complex networks, cybersecurity incidents, industrial espionage, political espionage, sabotage, Bundeswehr |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:swprps:335863 |
| By: | Stefano Corti; Roberto Daluiso; Andrea Pallavicini |
| Abstract: | In recent decades, companies have frequently adopted share repurchase programs to return capital to shareholders or for other strategic purposes, instructing investment banks to rapidly buy back shares on their behalf. When the executing institution is allowed to hedge its exposure, it encounters several challenges due to the intrinsic features of the product. Moreover, contractual clauses or market regulations on trading activity may make it infeasible to rely on Greeks. In this work, we address the hedging of these products by developing a machine-learning framework that determines the optimal execution of the buyback while explicitly accounting for the bank's actual trading capabilities. This unified treatment of execution and hedging yields substantial performance improvements, resulting in an optimized policy that provides a feasible and realistic hedging approach. The pricing of these programs can be framed in terms of the discount that banks offer to the client on the price at which the shares are delivered. Since, in our framework, risk measures serve as objective functions, we exploit the concept of indifference pricing to compute this discount, thereby capturing the actual execution performance. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.18686 |
| By: | Kim Christensen; Mathias Siggaard; Bezirgen Veliyev |
| Abstract: | We inspect how accurate machine learning (ML) is at forecasting realized variance of the Dow Jones Industrial Average index constituents. We compare several ML algorithms, including regularization, regression trees, and neural networks, to multiple Heterogeneous AutoRegressive (HAR) models. ML is implemented with minimal hyperparameter tuning. In spite of this, ML is competitive and beats the HAR lineage, even when the only predictors are the daily, weekly, and monthly lags of realized variance. The forecast gains are more pronounced at longer horizons. We attribute this to higher persistence in the ML models, which helps to approximate the long-memory of realized variance. ML also excels at locating incremental information about future volatility from additional predictors. Lastly, we propose a ML measure of variable importance based on accumulated local effects. This shows that while there is agreement about the most important predictors, there is disagreement on their ranking, helping to reconcile our results. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.13014 |
| By: | Alexander Barzykin |
| Abstract: | Dealers in foreign exchange markets provide bid and ask prices to their clients at which they are happy to buy and sell, respectively. To manage risk, dealers can skew their quotes and hedge in the interbank market. Hedging offers certainty but comes with transaction costs and market impact. Optimal market making with execution has previously been addressed within the Almgren-Chriss market impact model, which includes instantaneous and permanent components. However, there is overwhelming empirical evidence of the transient nature of market impact, with instantaneous and permanent impacts arising as the two limiting cases. In this note, we consider an intermediate scenario and study the interplay between risk management and impact resilience. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.13421 |
| By: | Aya Ouchene (ESCA Ecole de Management, Morocco); Azzeddine Allioui (ESCA Ecole de Management, Morocco) |
| Abstract: | This paper discussеs thе tоpic оf tаx frаud аnd prеsеnts sоlutiоns tо hеlp businеssеs cоmply with rеgulаtiоns. Tаx frаud cаn tаkе vаriоus fоrms, such аs undеrrеpоrting incоmе, mаnipulаting invоicеs, VРT frаud, аnd intеrnаtiоnаl tаx еvаsiоn. Thеsе illеgаl prаcticеs cаn hаvе sеvеrе cоnsеquеncеs, bоth fоr businеssеs аnd fоr public finаncеs. By еxplоring thеsе typеs оf frаud, thе study highlights thе criticаl rоlе оf tаx аdvisоrs in idеntifying incоnsistеnciеs аnd аssеssing risks. It аlsо prоpоsеs rеcоmmеndаtiоns tо strеngthеn cоmpliаncе, such аs еnhаncing intеrnаl cоntrоls, imprоving thе prоcеss fоr vеrifying suppliеrs, аnd using аdvаncеd tеchnоlоgiеs tо dеtеct suspiciоus аctivitiеs. Thе paper thеn еxаminеs thе impаct оf VРT withhоlding, а mеаsurе thаt cаn hеlp prеvеnt tаx frаud. By аdоpting thеsе аpprоаchеs, businеssеs cаn rеducе thе risk оf cоstly pеnаltiеs аnd еstаblish strоng rеlаtiоnships with tаx аuthоritiеs. |
| Keywords: | tax fraud, compliance, VAT fraud, internal controls, tax advisors, advanced technologies |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:smo:raiswp:0557 |
| By: | Steven E. Pav |
| Abstract: | We show that the Markowitz portfolio is a scalar multiple of another portfolio which replaces the covariance with the second moment matrix, via simple application of the Sherman-Morrison identity. Moreover it is shown that when using conditional estimates of the first two moments, this "Sherman-Morrison-Markowitz" portfolio solves the standard unconditional portfolio optimization problems. We argue that in multi-period portfolio optimization problems it is more natural to replace variance and covariance with their uncentered counterparts. We extend the theory to deal with constraints in expectation, where we find a decomposition of squared effects into spanned and orthogonal components. Compared to the Markowitz portfolio, the Sherman-Morrison-Markowitz portfolio downlevers by a small amount that depends on the conditional squared maximal Sharpe ratio; the practical impact will be fairly small, however. We present some example use cases for the theory. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.18124 |
| By: | Tjeerd De Vries |
| Abstract: | We propose a projection method to estimate risk-neutral moments from option prices. We derive a finite-sample bound implying that the projection estimator attains (up to a constant) the smallest pricing error within the span of traded option payoffs. This finite-sample optimality is not available for the widely used Carr--Madan approximation. Simulations show sizable accuracy gains for key quantities such as VIX and SVIX. We then extend the framework to multiple underlyings, deriving necessary and sufficient conditions under which simple options complete the market in higher dimensions, and providing estimators for joint moments. In our empirical application, we recover risk-neutral correlations and joint tail risk from FX options alone, addressing a longstanding measurement problem raised by Ross (1976). Our joint tail-risk measure predicts future joint currency crashes and identifies periods in which currency portfolios are particularly useful for hedging. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.14852 |
| By: | Housseni Wague (ESCA Ecole de Management, Morocco); Azzeddine Allioui (ESCA Ecole de Management, Morocco) |
| Abstract: | The financial audit landscape is changing rapidly, driven by globalization, technological innovation, and the growth of financial data. Traditional auditing methods are struggling to keep pace with this growing complexity, driving the need for a more efficient and accurate approach. Artificial intelligence (AI) plays a crucial role in addressing these challenges. This analysis explores the varied impact of AI on financial auditing, highlighting trends, challenges, and opportunities. By examining academic studies, empirical research and real-life cases, this article reveals significant transformations brought about by AI in financial auditing. Its integration does not simply replace human expertise but offers synergistic collaboration. AI enables auditors to extract deep insights from financial data, highlighting risks and promoting transparency in organizations. As the financial world moves towards digitalization, the partnership between human auditors and AI promises to profoundly reshape the future of financial auditing. |
| Keywords: | artificial intelligence, financial auditing, financial data analysis, audit risk, data management, financial regulation |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:smo:raiswp:0572 |