Optimal excess-of-loss reinsurance contract with ambiguity aversion in the principal-agent model

ABSTRACT

We discuss an optimal excess-of-loss reinsurance contract in a continuous-time principal-agent framework where the surplus of the insurer (agent/he) is described by a classical Cramér-Lundberg (C-L) model. In addition to reinsurance, the insurer and the reinsurer (principal/she) are both allowed to invest their surpluses into a financial market containing one risk-free asset (e.g. a short-rate account) and one risky asset (e.g. a market index). In this paper, the insurer and the reinsurer are ambiguity averse and have specific modeling risk aversion preferences for the insurance claims (this relates to the jump term in the stochastic models) and the financial market's risk (this encompasses the models' diffusion term). The reinsurer designs a reinsurance contract that maximizes the exponential utility of her terminal wealth under a worst-case scenario which depends on the retention level of the insurer. By employing the dynamic programming approach, we derive the optimal robust reinsurance contract, and the value functions for the reinsurer and the insurer under this contract. In order to provide a more explicit reinsurance contract and to facilitate our quantitative analysis, we discuss the case when the claims follow an exponential distribution; it is then possible to show explicitly the impact of ambiguity aversion on the optimal reinsurance.     

Predicting credit card fraud with Sarbanes‐Oxley assessments and Fama‐French risk factors

This research developed and tested machine learning models to predict significant credit card fraud in corporate systems using Sarbanes‐Oxley (SOX) reports, news reports of breaches and Fama‐French risk factors (FF). Exploratory analysis found that SOX information predicted several types of security breaches, with the strongest performance in predicting credit card fraud. A systematic tuning of hyperparamters for a suite of machine learning models, starting with a random forest, an extremely‐randomized forest, a random grid of gradient boosting machines (GBMs), a random grid of deep neural nets, a fixed grid of general linear models where assembled into two trained stacked ensemble models optimized for F1 performance; an ensemble that contained all the models, and an ensemble containing just the best performing model from each algorithm class. Tuned GBMs performed best under all conditions. Without FF, models yielded an AUC of 99.3% and closeness of the training and validation matrices confirm that the model is robust. The most important predictors were firm specific, as would be expected, since control weaknesses vary at the firm level. Audit firm fees were the most important non‐firm‐specific predictors. Adding FF to the model rendered perfect prediction (100%) in the trained confusion matrix and AUC of 99.8%. The most important predictors of credit card fraud were the FF coefficient for the High book‐to‐market ratio Minus Low factor. The second most influential variable was the year of reporting, and third most important was the Fama‐French 3‐factor model R² – together these described most of the variance in credit card fraud occurrence. In all cases the four major SOX specific opinions rendered by auditors and the signed SOX report had little predictive influence.     

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法律风险的有效控制对企业的发展至关重要。基于当前企业法律风险的复杂性,改变以往的企业法律风险研究的视角,从动态性控制的角度进行企业法律风险的有效防范和控制。在此基础上,将法律风险周期这样一个循环理念贯穿于法律风险防范机制的构建当中,并基于这个分析视角,提出了法律风险动态性控制的流程,为企业有效控制法律风险提供一个全新的方法。     

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上市公司为投资者创造利润,使投资者获得回报,是资本市场铁的定律。因此,完善上市公司的结构治理,深化发展上市公司内部审计,保障中小投资者资本安全,已成为上市公司内部审计的重要内容。针对上市公司目前存在的状况,阐述了如何应用现代审计技术以保证中小投资者的合法权益。     

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本文重点探讨了进口大豆不同保管方式对大豆质量的影响,在降温通风中如何降低水分减量,探索控温储藏的方法以取得较好的经济效益和社会效益。     

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当前,我国企业内部控制的现状令人堪忧,内控体系存在诸多问题：内部控制的管理基础工作薄弱,会计权责控制不完善,风险评估和风险管理水平不高,监督机制弱化。加强企业内部控制,应从公司治理结构入手,建立良好的信息沟通系统,营造企业创新文化,提高内部控制的效果,增强风险管理意识,建立风险评估机制,强化预算管理,保证管理目标的实行。     

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四川省公路交通网络发展总体水平较低,使公路交通网络在服务水平和行车速度上都较差,难以满足现代旅游业对公路交通的要求。四川公路交通网络建设与旅游业要在规划上相互协调,加快四川公路交通网络建设速度和川西高原地区的公路建设,优化公路交通网络的等级结构,提高公路交通路网的路面铺装率,重视县乡村当地的公路交通网络建设,鼓励旅游专线公路的建设,实现公路交通网络与旅游业的协同发展。     

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N106白车身测点优化及其应用

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