Résumé: We show that the integration by parts formula based on Malliavin-Skorohod calculus techniquesfor additive processes helps us to compute quantities like E(LTh(LT)),or more generally E(H(LT)),fordifferent suitable functionshorHand different models for the cumulative loss processL.These quantities areimportant in Insurance and Finance. For example they appearin computing expected shortfall risk measuresor prices of stop-loss contracts. The formulas given in the present paper generalize the formulas given in arecent paper by Hillairet, Jiao and R ́eveillac (HJR). In theHJR paper, despite the use of advanced models,including the Cox process, the treatment of the formulas is based only on Malliavin calculus techniques forthe standard Poisson process, a particular case of additiveprocess. In the present paper, Malliavin calculustechniques for additive processes are used, more general results are obtained and proofs appears to be shorter.

Résumé: Managing and controlling risk is a topic research in the world of finance. Before a risky situation, the stakeholders need to do comparison according to the positions and actions, and financial institutions must take measures of a particular market risk and credit. In this work, we study a model of risk measure in finance: Value at Risk (VaR), which is a new tool for measuring an entity's exposure risk. We explain the concept of value at risk, your average, tail, and describe the three methods for computing: Parametric method, Historical method, and numerical method of Monte Carlo. Finally, we briefly describe advantages and disadvantages of the three methods for computing value at risk.

Résumé: Managing and controlling risk is a topic research in the world of nance. Before a risky situation, the stakeholders needs to do comparison according to the positions and actions, nancial institutions must take measures of a particular market risk and credit. In this work, we study a model of risk measure in nance : Value at Risk (VaR), which is a new tool for measuring an entity's exposure risk. We explain the value at risk for continuous distribution.