Wagner Math Finance has worked in the area of developing value-at-risk
models for credit risk management for several years, helping our clients
reserve capital for losses and counterparty defaults, as well as for making
risk aware allocation of capital to multiple enterprises. In joint
projects with KPMG, the methods have been applied to the loan and equity
investment portfolio of the International Finance Corporation (IFC), an arm
of the World Bank, and to the loan portfolio of Hanvit Bank of Korea.
An essential feature of these models is that they include both a stochastic
systematic risk Z(t) factor affecting all borrowers whose sample paths are
generated by Monte Carlo methods, as well as borrower specific risk factors which,
conditioned on Z(t), are independent of one another. This hybrid approach
allows the model to retain much of the computational efficiency of purely
analytic methods and does not restrict the value-at-risk distribution to be
to inquire about our consulting services in credit risk management.