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on Risk Management |
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Issue of 2024‒05‒06
nine papers chosen by |
| By: | Eduardo Abi Jaber (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Camille Illand (AXA Investment Managers, Multi Asset Client Solutions, Quantitative Research - AXA); Shaun Xiaoyuan Li (UP1 - Université Paris 1 Panthéon-Sorbonne, AXA Investment Managers, Multi Asset Client Solutions, Quantitative Research - AXA) |
| Abstract: | The quintic Ornstein-Uhlenbeck volatility model is a stochastic volatility model where the volatility process is a polynomial function of degree five of a single Ornstein-Uhlenbeck process with fast mean reversion and large vol-of-vol. The model is able to achieve remarkable joint fits of the SPX-VIX smiles with only 6 effective parameters and an input curve that allows to match certain term structures. Even better, the model remains very simple and tractable for pricing and calibration: the VIX squared is again polynomial in the Ornstein-Uhlenbeck process, leading to efficient VIX derivative pricing by a simple integration against a Gaussian density; simulation of the volatility process is exact; and pricing SPX products can be done efficiently and accurately by standard Monte Carlo techniques with suitable antithetic and control variates. |
| Keywords: | SPX and VIX modeling, Stochastic volatility, Pricing, Calibration |
| Date: | 2023–06–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03909334&r=rmg |
| By: | Issam Samiri |
| Abstract: | I propose a general equilibrium model with endogenous defaults and a banking sector operating under a Value-at-Risk constraint. Analytical examination reveals that (a) the Value-at-Risk rule introduces a risk premium on bank lending, (b) this risk premium fluctuates with the business cycle, amplifying the impact of real shocks, and (c) bank leverage also fluctuates with real shocks, but its cyclical behaviour depends on the shocks' effects on default expectations, credit demand, and the bank's balance sheet. Assuming TFP shocks as the sole exogenous source of fluctuation, the model quantitatively replicates realistic fluctuations in banks' leverage, equity, lending, and credit spreads. |
| Keywords: | RBC, Value-at-Risk, bank leverage, Credit Spreads, Financial Frictions |
| JEL: | E13 E32 E44 G21 G32 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:nsr:niesrd:555&r=rmg |
| By: | Hoang Hiep Nguyen (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique); Jean-Laurent Viviani (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique); Sami Ben Jabeur (ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University), UCLy - UCLy (Lyon Catholic University)) |
| Abstract: | Recently, ensemble-based machine learning models have been widely used and have demonstrated their efficiency in bankruptcy prediction. However, these algorithms are black box models and people cannot understand why they make their forecasts. This explains why interpretability methods in machine learning attract attention from many artificial intelligence researchers. In this paper, we evaluate the prediction performance of Random Forest, LightGBM, XGBoost, and NGBoost (Natural Gradient Boosting for probabilistic prediction) for French firms from different industries with the horizon of 1-5 years. We then use Shapley Additive Explanations (SHAP), a model-agnostic method to explain XGBoost, one of the best models for our data. SHAP can show how each feature impacts the output from XGBoost. Furthermore, single prediction can also be explained, thus allowing black box models to be used in credit risk management. |
| Keywords: | Shapley additive explanations, Explainable machine learning, Bankruptcy prediction, Ensemble-based model, XGBoost |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04223161&r=rmg |
| By: | François Pannequin (CEPS, ENS Paris-Saclay, Université Paris-Saclay); Anne Corcos (CURAPP-ESS UMR 7319, CNRS, Université de Picardie Jules Verne) |
| Abstract: | Using a principal-agent framework, we extend the insurance monopoly model (Stiglitz, 1977) to self-insurance opportunities. Relying on a two-part tariff contract as an analytical tool, we show that an insurance monopoly can achieve the same equilibrium as a competitive insurer. However, in the monopoly situation, the insurer captures all the insurance market surplus. Yet, compared to a monopoly market with insurance only, self-insurance opportunities act as a threat to the insurer, resulting in a cut of the insurer's market power and an increase in the policyholders' welfare. Moreover, within our principal-agent framework, we show that while insurance and self-insurance are substitutes, compulsory self-insurance, and compulsory insurance have non-equivalent effects. Although compulsory self-insurance reduces the market size of the insurer, it has no impact on the policyholder's well-being. On the other hand, mandatory insurance favors the insurer and makes policyholders worse off. The implications of these public policies are discussed. |
| Keywords: | self-insurance, insurance, monopoly, compulsory insurance, public regulation |
| JEL: | D86 D42 G22 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:eve:wpaper:23-02&r=rmg |
| By: | Eva L\"utkebohmert; Julian Sester |
| Abstract: | We propose a new deep learning approach for the quantification of name concentration risk in loan portfolios. Our approach is tailored for small portfolios and allows for both an actuarial as well as a mark-to-market definition of loss. The training of our neural network relies on Monte Carlo simulations with importance sampling which we explicitly formulate for the CreditRisk${+}$ and the ratings-based CreditMetrics model. Numerical results based on simulated as well as real data demonstrate the accuracy of our new approach and its superior performance compared to existing analytical methods for assessing name concentration risk in small and concentrated portfolios. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.16525&r=rmg |
| By: | Yang, Fan; Havranek, Tomas; Irsova, Zuzana; Novak, Jiri |
| Abstract: | We examine the factors influencing published estimates of hedge fund performance. Using a sample of 1, 019 intercept terms from regressions of hedge fund returns on risk factors (the alphas) collected from 74 studies, we document a strong downward trend in the reported alphas. The trend persists even after controlling for heterogeneity in hedge fund characteristics and research design choices in the underlying studies. Estimates of current performance implied by best practice methodology are close to zero across all common hedge fund strategies. Additionally, our data allow us to estimate the mean management and performance fees charged by hedge funds. We also document how reported performance estimates vary with hedge fund and study characteristics. Overall, our findings indicate that, while hedge funds historically generated positive value for investors, their ability to do so has diminished substantially. |
| Keywords: | Hedge funds, alpha, performance, fees, meta-analysis, model uncertainty |
| JEL: | J23 J24 J31 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:289497&r=rmg |
| By: | Natalia Fischl-Lanzoni; Martin Hiti; Nathan Kaplan; Asani Sarkar |
| Abstract: | We examine how investors’ perception of bank balance sheet risk evolved before and during the March-April 2023 bank run. To do so, we estimate the covariance (“beta”) of bank excess stock returns with returns on factors constructed from long-short portfolios sorted on shares of uninsured deposits and unrealized losses on securities. We find that the market’s perception of bank risk shifted in both the time series and the cross-section. From January 2022 to February 2023, both factor betas were mostly insignificant, but after the bank run started, they became positive and significant for all banks on average. However, in the cross-section, only the factor betas of banks put on downgrade watch on March 13 were significant, consistent with our finding that this announcement was informative. When additional banks were downgraded in April, their factor betas also became significant, even though we find the April announcements to be noninformative for these banks. We suggest that investors with limited attention focused on the banks included in the April announcements to update their priors on balance sheet risk. |
| Keywords: | bank run; information sensitivity; limited attention; balance sheet beta; uninsured deposits; unrealized losses |
| JEL: | G01 G12 G14 G21 |
| Date: | 2024–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:98045&r=rmg |
| By: | Maxime Rigaud (UGA - Université Grenoble Alpes); Jurgen Buekers (VITO - Flemish Institute for Technological Research); Jos Bessems (VITO - Flemish Institute for Technological Research); Xavier Basagaña (ISGlobal - Instituto de Salud Global - Institute For Global Health [Barcelona], University Pompeu Fabra, CIBERESP - Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública = Consortium for Biomedical Research of Epidemiology and Public Health); Sandrine Mathy (GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes); Mark Nieuwenhuijsen (ISGlobal - Instituto de Salud Global - Institute For Global Health [Barcelona], University Pompeu Fabra, CIBERESP - Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública = Consortium for Biomedical Research of Epidemiology and Public Health); Rémy Slama (UGA - Université Grenoble Alpes) |
| Abstract: | Once an external factor has been deemed likely to influence human health and a dose response function is available, an assessment of its health impact or that of policies aimed at influencing this and possibly other factors in a specific population can be obtained through a quantitative risk assessment, or health impact assessment (HIA) study. The health impact is usually expressed as a number of disease cases or disability-adjusted life-years (DALYs) attributable to or expected from the exposure or policy. We review the methodology of quantitative risk assessment studies based on human data. The main steps of such studies include definition of counterfactual scenarios related to the exposure or policy, exposure(s) assessment, quantification of risks (usually relying on literature-based dose response functions), possibly economic assessment, followed by uncertainty analyses. We discuss issues and make recommendations relative to the accuracy and geographic scale at which factors are assessed, which can strongly influence the study results. If several factors are considered simultaneously, then correlation, mutual influences and possibly synergy between them should be taken into account. Gaps or issues in the methodology of quantitative risk assessment studies include 1) proposing a formal approach to the quantitative handling of the level of evidence regarding each exposure-health pair (essential to consider emerging factors); 2) contrasting risk assessment based on human dose–response functions with that relying on toxicological data; 3) clarification of terminology of health impact assessment and human-based risk assessment studies, which are actually very similar, and 4) other technical issues related to the simultaneous consideration of several factors, in particular when they are causally linked. |
| Keywords: | Dose-response, Environment, Hazard, Health impact, Policy, Risk |
| Date: | 2024–01–27 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04523440&r=rmg |
| By: | Stijn De Backer; Luis E. C. Rocha; Jan Ryckebusch; Koen Schoors |
| Abstract: | Accurate modeling of the temporal evolution of asset prices is crucial for understanding financial markets. We explore the potential of discrete-time quantum walks to model the evolution of asset prices. Return distributions obtained from a model based on the quantum walk algorithm are compared with those obtained from classical methodologies. We focus on specific limitations of the classical models, and illustrate that the quantum walk model possesses great flexibility in overcoming these. This includes the potential to generate asymmetric return distributions with complex market tendencies and higher probabilities for extreme events than in some of the classical models. Furthermore, the temporal evolution in the quantum walk possesses the potential to provide asset price dynamics. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.19502&r=rmg |