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Simulating the Credit Loss Distribution

Monte Carlo methods have become a valuable computational tool in modern finance as the increased availability of powerful computers has enhanced their efficiency. A particularly useful feature of Monte Carlo methods is that their computational complexity increases linearly with the number of variables. Moreover, they are flexible and easy to implement for a range of distributional assumptions for the underlying variables that influence the outcomes of interest. Monte Carlo methods are particularly effective for simulating credit loss distribution and for evaluating tail risk measures, and they are computationally less intensive than analytical methods. 
 
 
Keywords: 
 
credit risk; 
tail risk; 
corporate bonds; 
Monte Carlo methods; 
expected shortfall; 
Student's t distribution