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on Risk Management |
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Issue of 2024‒06‒10
seven papers chosen by |
| By: | Bingzhen Geng; Yang Liu; Yimiao Zhao |
| Abstract: | The systemic risk measure plays a crucial role in analyzing individual losses conditioned on extreme system-wide disasters. In this paper, we provide a unified asymptotic treatment for systemic risk measures. First, we classify them into two families of Value-at-Risk- (VaR-) and expectile-based systemic risk measures. While VaR has been extensively studied, in the latter family, we propose two new systemic risk measures named the Individual Conditional Expectile (ICE) and the Systemic Individual Conditional Expectile (SICE), as alternatives to Marginal Expected Shortfall (MES) and Systemic Expected Shortfall (SES). Second, to characterize general mutually dependent and heavy-tailed risks, we adopt a modeling framework where the system, represented by a vector of random loss variables, follows a multivariate Sarmanov distribution with a common marginal exhibiting second-order regular variation. Third, we provide second-order asymptotic results for both families of systemic risk measures. This analytical framework offers a more accurate estimate compared to traditional first-order asymptotics. Through numerical and analytical examples, we demonstrate the superiority of second-order asymptotics in accurately assessing systemic risk. Further, we conduct a comprehensive comparison between VaR-based and expectile-based systemic risk measures. Expectile-based measures output higher risk evaluation than VaR-based ones, emphasizing the former's potential advantages in reporting extreme events and tail risk. As a financial application, we use the asymptotic treatment to discuss the diversification benefits associated with systemic risk measures. The expectile-based diversification benefits consistently deduce an underestimation and suggest a conservative approximation, while the VaR-based diversification benefits consistently deduce an overestimation and suggest behaving optimistically. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.18029&r= |
| By: | Kolos Csaba \'Agoston; Veronika Varga |
| Abstract: | Since 2016 the operation of insurance companies in the European Union is regulated by the Solvency II directive. According to the EU directive the capital requirement should be calculated as a 99.5\% of Value at Risk. In this study, we examine the impact of this capital requirement constraint on equilibrium premiums and capitals. We discuss the case of the oligopoly insurance market using Bertrand's model, assuming profit maximizing insurance companies facing Value at Risk constraints. First we analyze companies' decision on premium level. The companies strategic behavior can result positive as well as negative expected profit for companies. The desired situation where competition eliminate positive profit and lead the market to zero-profit state is rare. Later we examine ex post and ax ante capital adjustments. Capital adjustment does not rule out market anomalies, although somehow changes them. Possibility of capital adjustment can lead the market to a situation where all of the companies suffer loss. Allowing capital adjustment results monopolistic premium level or market failure with positive probabilities. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.17915&r= |
| By: | Mengshuo Zhao; Narayanaswamy Balakrishnan; Chuancun Yin |
| Abstract: | This paper considers the best- and worst-case of a general class of distortion risk measures when only partial information regarding the underlying distributions is available. Specifically, explicit sharp lower and upper bounds for a general class of distortion risk measures are derived based on the first two moments along with some shape information, such as symmetry/unimodality property of the underlying distributions. The proposed approach provides a unified framework for extremal problems of distortion risk measures. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.13637&r= |
| By: | Shuochen Bi; Wenqing Bao |
| Abstract: | With the rapid growth of technology, especially the widespread application of artificial intelligence (AI) technology, the risk management level of commercial banks is constantly reaching new heights. In the current wave of digitalization, AI has become a key driving force for the strategic transformation of financial institutions, especially the banking industry. For commercial banks, the stability and safety of asset quality are crucial, which directly relates to the long-term stable growth of the bank. Among them, credit risk management is particularly core because it involves the flow of a large amount of funds and the accuracy of credit decisions. Therefore, establishing a scientific and effective credit risk decision-making mechanism is of great strategic significance for commercial banks. In this context, the innovative application of AI technology has brought revolutionary changes to bank credit risk management. Through deep learning and big data analysis, AI can accurately evaluate the credit status of borrowers, timely identify potential risks, and provide banks with more accurate and comprehensive credit decision support. At the same time, AI can also achieve realtime monitoring and early warning, helping banks intervene before risks occur and reduce losses. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.18183&r= |
| By: | Emmanuelle Augeraud-Véron (BSE - Bordeaux sciences économiques - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Marc Leandri (SOURCE - SOUtenabilité et RésilienCE - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines - IRD [France-Nord] - Institut de Recherche pour le Développement, EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | Health Economics published by John Wiley & Sons Ltd.In this contribution to the longstanding risk theory debate on optimal self-protection, we aim to enrich the microeconomic modeling of self-protection, in the wake of Ehrlich and Becker (1972), by exploring the representation of risk perception at the core of the Health Belief Model (HBM), a conceptual framework extremely influential in Public Health studies (Janz and Becker, 1984). In our two-period model, we highlight the crucial role of risk perception in the individual decision to adopt a preventive behavior toward a generic health risk. We discuss the optimal prevention effort engaged by an agent displaying either imperfect knowledge of the susceptibility (probability of occurrence) or the severity (magnitude of the loss) of a health hazard, or facing uncertainty on these risk components. We assess the impact of risk aversion and prudence on the optimal level of self-protection, a critical issue in the risk and insurance economic literature, yet often overlooked in HBM studies. Our results pave the way for the design of efficient information instruments to improve health prevention when risk perceptions are biased. |
| Keywords: | Health Belief Model, Prudence, Risk aversion, Risk perception, Self-protection, Uncertainty |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04557076&r= |
| By: | Xu Cheng (University of Pennsylvania); Eric Renault (University of Warwick); Paul Sangrey? (Noom Inc) |
| Abstract: | In asset pricing models with stochastic volatility, uncertainty about volatility affects risk premia through two channels: aversion to decreasing returns and aversion to increasing volatility. We analyze the identification of and robust inference for structural parameters measuring investors' aversions to these risks: the return risk price and the volatility risk price. In the presence of a leverage effect (instantaneous causality between the asset return and its volatility), we study the identification of both structural parameters with the price data only, without relying on additional option pricing models or option data. We analyze this identification challenge in a nonparametric discrete-time exponentially affine model, complementing the continuous-time approach of Bandi and Renò (2016). We then specialize to a parametric model and derive the implied minimum distance criterion relating the risk prices to the asset return and volatility's joint distribution. This criterion is almost flat when the leverage effect is small, and we introduce identification-robust confidence sets for both risk prices regardless of the magnitude of the leverage effect. |
| Keywords: | leverage effect, nonparametric identication, stochastic volatility, volatility factor, volatility risk price, weak identication |
| JEL: | C12 C14 C38 C58 G12 |
| Date: | 2024–04–23 |
| URL: | http://d.repec.org/n?u=RePEc:pen:papers:24-013&r= |
| By: | Daniel Bartl; Stephan Eckstein |
| Abstract: | We address the problem of estimating the expected shortfall risk of a financial loss using a finite number of i.i.d. data. It is well known that the classical plug-in estimator suffers from poor statistical performance when faced with (heavy-tailed) distributions that are commonly used in financial contexts. Further, it lacks robustness, as the modification of even a single data point can cause a significant distortion. We propose a novel procedure for the estimation of the expected shortfall and prove that it recovers the best possible statistical properties (dictated by the central limit theorem) under minimal assumptions and for all finite numbers of data. Further, this estimator is adversarially robust: even if a (small) proportion of the data is maliciously modified, the procedure continuous to optimally estimate the true expected shortfall risk. We demonstrate that our estimator outperforms the classical plug-in estimator through a variety of numerical experiments across a range of standard loss distributions. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.00357&r= |