The credit risk(+) model with general sector correlations

We consider an enhancement of the credit risk+ model to incorporate correlations between sectors. We model the sector default rates as linear combinations of a common set of independent variables that represent macro-economic variables or risk factors. We also derive the formula for exact VaR contributions at the obligor level.

Total risk rating and stability analysis of embankment dams in the Kachchh Region, Gujarat, India

A method for total risk analysis of embankment dams under earthquake conditions is discussed and applied to the selected embankment dams, i.e., Chang, Tapar, Rudramata, and Kaswati located in the Kachchh region of Gujarat, India, to obtain the seismic hazard rating of the dam site and the risk rating of the structures. Based on the results of the total risk analysis of the dams, coupled non-linear dynamic numerical analyses of the dam sections are performed using acceleration time history record of the Bhuj (India) earthquake as well as five other major earthquakes recorded worldwide. The objective of doing so is to perform the numerical analysis of the dams for the range of amplitude, frequency content and time duration of input motions. The deformations calculated from the numerical analyses are also compared with other approaches available in literature, viz, Makdisi and Seed (1978) approach, Jansen's approach (1990), Swaisgood's method (1995), Bureau's method (1997). Singh et al. approach (2007), and Saygili and Rathje approach (2008) and the results are utilized to foresee the stability of dams in future earthquake scenario. (C) 2010 Elsevier B.V. All rights reserved.

Application of nonlinear filtering to credit risk

Merton's model views equity as a call option on the asset of the firm. Thus the asset is partially observed through the equity. Then using nonlinear filtering an explicit expression for likelihood ratio for underlying parameters in terms of the nonlinear filter is obtained. As the evolution of the filter itself depends on the parameters in question, this does not permit direct maximum likelihood estimation, but does pave the way for the `Expectation-Maximization' method for estimating parameters. (C) 2010 Elsevier B.V. All rights reserved.

Pricing credit derivatives

The financial crisis set off by the default of Lehman Brothers in 2008 leading to disastrous consequences for the global economy has focused attention on regulation and pricing issues related to credit derivatives. Credit risk refers to the potential losses that can arise due to the changes in the credit quality of financial instruments. These changes could be due to changes in the ratings, market price (spread) or default on contractual obligations. Credit derivatives are financial instruments designed to mitigate the adverse impact that may arise due to credit risks. However, they also allow the investors to take up purely speculative positions. In this article we provide a succinct introduction to the notions of credit risk, the credit derivatives market and describe some of the important credit derivative products. There are two approaches to pricing credit derivatives, namely the structural and the reduced form or intensity-based models. A crucial aspect of the modelling that we touch upon briefly in this article is the problem of calibration of these models. We hope to convey through this article the challenges that are inherent in credit risk modelling, the elegant mathematics and concepts that underlie some of the models and the importance of understanding the limitations of the models.