Exponential inequalities and functional estimations for weak dependent datas ; applications to dynamical systems

We estimate density and regression functions for weak dependant datas. Using an exponential inequality obtained by Dedecker and Prieur and in a previous article of the author, we control the deviation between the estimator and the function itself. These results are applied to a large class of dynamical systems and lead to estimations of invariant densities and on the mapping itself

Surrender triggers in life insurance: classification and risk predictions

This paper shows that some policy features are crucial to explain the decision of the policyholder to surrender her contract. We point it out by applying two segmentation models to a life insurance portfolio: the Logistic Regression model and the Classification And Regression Trees model. Protection as well as Savings lines of business are impacted, and results clearly explicit that the profit benefit option is highly discrimi- nant. We develop the study with endowment products. First we present the models and discuss their assumptions and limits. Then we test different policy features and policyholder's characteristics to be lapse triggers so as to segment a portfolio in risk classes regarding the surrender choice : duration and profit benefit option are essential. Finally, we explore the main dfferences of both models in terms of operational results.

Self normalized central limit theorem for some mixing processes

We prove a self normalized central limit theorem for a new mixing class of processes introduced in Kacem et al. (2013). This class is larger than the classical strongly mixing processes and thus our result is more general than Peligrad and Shao's (1995) and Shi's (2000) ones. The fact that some conditionally independent processes satisfy this kind of mixing properties motivated our study. We investigate the weak consistency as well as the asymptotic normality of the estimator of the variance that we propose

Fast remote but not extreme quantiles with multiple factors. Applications to Solvency II and Enterprise Risk Management

For operational purposes, in Enterprise Risk Management or in insurance for example, it may be important to estimate remote (but not extreme) quantiles of some function ƒ of some random vector. The call to ƒ may be time- and resource-consuming so that one aims at reducing as much as possible the number of calls to ƒ. In this paper, we propose some ways to address this problem of general interest. We then numerically analyze the performance of the method on insurance and Enterprise Risk Management real-world case studies.