SAS code for LGD distribution simulation Loss given default (LGD) is usually modelled using beta distribution. The following is quoted from a discussion on pinggu.org on how to simulate the loss given default (LGD) distribution with SAS. The original post is in Chinese. The data is as follows, [0,0.1] (0.1,0.2] (0.2,0.3] (0.3,0.4] (0.4,0.5] 18.56 20.36 8.38 8.98 11.98 (0.5,0.6] (0.6,0.7] (0.7,0.8] (0.8,0.9] (0.9,1.0] 7.19 5.39 5.99 9.58 3.59 Based on the empirical loss distribution above to simulate 50 ...
VaR code in SAS Value at Risk (VaR) is the maximum amount of money that may be lost on a portfolio on average over a given period of time, with a given confidence interval under normal circumstances. For traders this period of time, or holding period, can be a day or less. Normally, VaR is ...
SAS code to compute distribution of default Douglas W. Dwyer, from Moody's KMV published "The distribution of defaults and Bayesian model validation" at Journal of Risk Model Validation (23–53) Volume 1/Number 1, Spring 2007. The article can be downloaded from here. In the Appendix A of this wonderful article, he presents the SAS code to compute distribution ...
Code collection of KMV Merton model Probability of Default (PD) is one of the most important metrics in modern banking industry to manage their economic capital. The so-called KMV default forecasting model is one of these models based on Merton's bond pricing concept and is developed by the KMV Corporation. Nowadays, this model has been intensively ...
Predictive modeling on probability of default: a logistic regression model using Microsoft Excel and VBA Logistic regression is often used to estimate the probability of default in commercial loans against different types of borrowers (obligor’s PD model). Microsoft Excel is probably the most popular used software in daily banking business work. However, the logistic regression model function is not captured by Excel. In this case ...
Quantminds.com was launched in 2009 when I moved back to financial risk management area. Over the past two years, the website has been supported by many friends from all over the world. It was an amazing journey with them to share ideas and research either on this little wordpress community or via emails. I [ Read More ]
Sick of interpreting all those credit derivatives – CDO tranches and complicated structure, which has been the devil of 2008 financial crisis. but in my leisure time, it turns out it is even more complicated to understand the following picture from China.
It has been an ugly day in US markets. It is even more ugly when President Obama made an unscheduled statement: US will always be a triple-A country despite what rating agencies say I might speak and write broken English, but everytime I sit in a English-based exam such as GMAT, GRE [ Read More ]
Although Do Gong does have a lot qualified quants and experts in terms of rating business, I would rather choose Moody, S&P, and Fitch simply becasue Da Gong’s rating is by far highly influenced by the power of ‘party’, which is used to manipulate stats to the public. But when one read [ Read More ]
This article is originally posted on PRMIA website. The author is Manoj Kumar who is working for Commercial Bank of Kuwait, Risk Management Division as a Senior Risk Officer where he is involved in Credit Risk, Market Risk and Stress testing. His main areas of responsibilities are implementation of Internal Ratings Based approach, periodic [ Read More ]
This is quote from Professional risk professional assocaition (PRMIA) case study on Long Term Capital Management. I don’t know what this means – basically no one was hurt! even Merton and Scholes were more legendary from this case. Among the PRMIA case studies, LTCM has some unique characteristics: No US [ Read More ]
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