2015 Summer projects- SAS credit risk project summary

SAS Credit Risk Project

Designed & led by Financial Math Alumnus- Jonathan Leonardelli

For students to apply credit risk concepts while developing SAS programming skills

1. Become Base SAS certified
2. Have an understanding of CCAR and Basel II calculations
3. Learn how to model PD / LGD / EAD
4. Use equations to calculate Expected Loss (EL), RWA (Risk Weighted Assets), and capital ratios

By Aisha Barnes & Preethi Kankanala- The purpose of the summer SAS case study was to develop an understanding of the different steps that are involved in calculating the loss portion of CCAR (Comprehensive Credit Analysis & Review). CCAR is a regulatory framework that ensures Bank Holding Companies (BHCs) have enough capital under the worst scenarios. This is tested under various stress-testing scenarios.

In our case study, we analyzed a portfolio of different products and estimated the capital that is required to hold the portfolio under three different scenarios. For this, we have estimated the historic Probability of Default (PD)*, Loss Given Default (LGD)* and Exposure at Default (EAD) and forecasted the future values using a variety of techniques (e.g., regression models) in SAS. Then we used these values to estimate risk weighted assets and capital.

This exercise helps BHCs ensure that they have enough capital if there is any change in the economic conditions. If the capital plan does not pass regulatory review, then the company has to change it to ensure there is adequate regulatory capital.


"Throughout the project, Financial Math Alumnus and Board Member, Jonathan Leonardelli, directed and mentored our team. We gained knowledge and enhanced our technical and business skills under his guidance. The project provided us hands-on experience on estimating the credit metrics and how to apply them with real world problems."- Preethi Kankanala, December 2015 Graduate

"This summer I experienced real application of how I will use my Financial Mathematics degree. I learned how to program in SAS and plan to gain certification. I used SAS to find the amount of capital a bank reserves to meet the Basel II requirements. I feel confident in having these skills."- Aisha Barnes, December 2015 Graduate

*PD (Probability of Default) = likelihood that a loan will default in the future
*LGD (Loss Given Default) = amount a bank will lose if a customer defaults on their loans

(All the project groups presented their summaries at the end of the summer session. More info to come soon from the other groups).

Financial Math Alumni Panel Discussion on Big Data, High Frequency Trading and more…


(Above left to right- Jeff Scroggs, Jonathan Leonardelli, Jared Bogacki, Ryan Wesslen, Albert Hopping, Emmanuel Sanchez)

Jeff Scroggs, Director of the Financial Mathematics Program, conducted a panel of industry experts on trends in financial mathematics and quantitative risk.  Three of the practitioners,  Jared Bogacki (BB&T),  Jonathan Leonardelli (Financial Risk Group), and Emmanuel Sanchez (Allianz), were from the class of 2004 – the first class to graduate with a Masters of Financial Mathematics.  Two of the panelists,  Albert Hopping (SAS Institute, class of 2007) and Ryan Wesslen (Bank of America, class of 2009), were more recent graduates.

The panel offered the audience an opportunity to see what role quants play in optimal business practices.

All the panelists agreed that their Masters of Financial Mathematics from NC State opened opportunities for career advancement, ranging from a entry-level quant positions to promotions to lead quant.


Jared Bogacki started with the topic of 'Big Data'.  'Big Data' is the use of data sets so large and complex that it has become difficult to process them using traditional tools or data processing applications.  It is currently a hot specialization for quants, and will likely remain a sector of the job market that is hungry for well-qualified people. 'Big Data' is a broad term that covers several topics including analytics used to glean market sentiment as well as some aspects of high frequency trading.

High-Frequency Trading (HFT) is algorithmic trading that uses algorithms to rapidly trade securities. The methods involve proprietary trading strategies carried out by computers to move in and out of positions in fractions of a second.  Of course, there are many different approaches to HFT that range from geometric observations (Golden Cross) to taking advantage of arbitrage opportunities across markets (e.g. New York vs London).  Albert Hopping was asked, “Do you think HFT is good or bad for markets?”  He pointed out that HFT is really a response to the way electronic trading in markets such as the New York Stock Exchange and Chicago Mercantile Exchange function are regulated.  HFT reduces friction by providing liquidity, but it can also cause flash crashes that force markets to temporarily halt trading.  There was consensus that such trading is impossible to control – regulations always lag advances in technology.


Effective deployment of quantitative risk management is a challenge for all businesses, large and small. Jonathan Leonardelli led this discussion.  There are many aspects to risk management, ranging from data mining for parameter estimation to the creation of dashboards in the context of Enterprise Risk Management. The push to use more quantitative risk measures can come from inside the business or from outside.  For example, regulations like the Dodd-Frank act require more transparency from banks and reliable quantitative measures for stress testing.


Emmanuel Sanchez was asked to lead the discussion on how weather-related risks can be controlled. Climate change has brought the spotlight on some of the impacts of short-term and long-term weather. Catastrophe bonds can provide protection against large-impact short-time events such as hurricanes and floods; whereas, weather insurance provides coverage based on measures such as annual rainfall and heating degree days (when it is cold enough to need a furnace).  The availability of these securities and derivatives allows the sharing of risk inherent in sectors like farming and homeowners insurance.

Ryan Wesslen was asked to share his favorite/best model in the area of consumer credit risk and/or counterpart credit risk.  Of course, quants do not share the best model to predict recent trends, but many share their wisdom in hindsight after a particular model offers no significant competitive advantage.  No model is likely to produce a clear crystal ball with excellent valuations, and all models provide some level of insight. For example an individual’s credit scores, like many holistic indicators, are good at the extreme high and low ends. But scores in the middle aren’t good predictors of default risk.

Thank you Jared, Albert, Jonathan, Emmanuel and Ryan!

Keep a lookout for an upcoming new blog series- interviews with a featured alumnus to learn even more about his or her experience and expertise.