The Industrial Companies Corporate Debt Mathematical Inverse Distribution Evaluation Models
Moscow State Technological University “STANKIN”, chair “ The Applied Mathematics Vadkovsky lane 1a, Moscow, 127055, Russia, +7(499)972-95-20, E-mail: firstname.lastname@example.org pp. (accepted)
Mass defaults are possible at any influencing default condition national measures, such, as industrial sector condition, the branch or regional factor. The credit derivatives such the collateralized debt obligation (CDO) and the credit default swaps (CDS) being the developed countries economy condition changes indicators are investigated in this work . Currently credit risks are associated with default assets. А new adequate estimation model construction tasks complex of the industrial sector companies are necessary . The credit derivative popularity has increased recently, that considerably well-known synthetic indexes imposition has affected. For an estimation CDO task solution the synthetic CDO estimation is considered. In this work the premium part estimation takes into account the saved payments, default time distribution is calibrated under the CDS rates and for the basic portofolio CDS valuation the single-name default probability model is used. For the basic portofolio CDO valuation the multiple-name default probability model is used. The value of protection is determined by the size of the expected tranche default losses and the value of tranche premium part is calculated as the current price of all expected payments on spread . A series of computing experiments on market products value factors modeling of the industrial sector companies credit default derivatives are carried out both with the generated samples and with the real data, the results verification is done.