Congratulations to all of our December 2016 graduates! We are so proud of your hard work!
Our graduates were placed at Bank of America, TD Bank, Financial Risk Group, EY, SAS & First Citizens Bank. We wish you all success!
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
Congratulations to everyone who graduated this May 2015! We are proud of your hard work and wish you many years of success.
We our proud to announce that our recent graduates received offers and have started working at Bank of America, Genworth, SAS, BB&T, Credit Suisse, Deutsche Bank, Maxpoint, & Aohey, LLC.
On Friday evening, Nov 14th 2014, NC State’s Financial Math program and IAQF (International Association of Quantitative Finance) hosted the event “How I Became a Quant”. The panel included Financial Math alumni, Jared Bogacki with BB&T and Albert Hopping with SAS, as well as Altrius Capital Founder, Jim Russo and current student Jeff High with Captrust. They each took turns sharing their career path stories with the audience and answered questions about quantitative careers. Dr. Jeff Scroggs, Director of the Financial Math program, acted as the moderator for the event.
To start, Jim Russo talked about his background and starting his company, Altrius Capital in 1997. He enjoys quantitative finance and visited investment firms to learn more about the field, which included networking with his best friend who got an MBA from Princeton and worked at Bernstein (Alliance Bernstein). This inspired him to open his own investment management and financial consulting business in New Bern, North Carolina. Altrius Capital also has an office in downtown Raleigh, North Carolina and is growing fast.
Next, FM Alumnus, Albert Hopping shared his career path story. He got his Bachelors in Physics at NCSU and then worked in the energy industry as a Risk Analyst. Several years later, he enrolled in the Financial Math program at NCSU and learned more about quantitative analyzing. He found it be interesting and amazing. Thus, this led to his current role at SAS where he applies quantitative analysis to his daily work. You can read his personal interview here.
Jeff High is finishing up his Financial Math degree at NC State. He did his undergraduate studies in Finance and Financial Economics, and then got a job at Wells Fargo. In 2006, he noticed his job became more and more quantitative. During 2007 to 2009, he worked at another investment firm and managed a team in Valparaiso, Chile while supporting New York, London, and Hong Kong trading services. Due to the 2008 financial crisis, he came back to US and and started his Masters in Financial Math at NC State while working at other investment firms. He realized technology skills are very important, which he is enhancing through the Financial Math program.
Lastly, FM Alumnus, Jared Bogacki shared his career path and has worked at BB&T for more than 10 years. He is currently a manager about shared his expertise and advice to current students on getting a job in the field. Jared and Albert both emphasized the importance of communication as a top soft skill to sharpen as it is required to be successful in the industry.
After they shared their stories, Dr. Scroggs asked them about careers and salaries in the Financial Math industry, and work and life balance. Mr. Hopping said there is a high correlation between working hard and receiving high rewards and benefits. Thus, the harder you work, the more you are rewarded. But that comes with longer hours and stress. Mr. Russo made the point that if you enjoy what you are doing, the long hours and hard work will pay off and the stress is worth it because you are doing something you value. Mr. Bogacki agreed and mentioned the importance of having passion in what you do. Being overly stressed and not enjoying your job is not ideal and students should choose a career path that closely aligns with their interests, talents and passion.
They also talked about specific courses and types of technical skills students need to gain to be successful. All panelists stressed to not only focus on academics but to enhance business and soft-skills such as communication, interpersonal and problem solving skills. Being able to clearly articulate ideas, processes and models to clients and business colleagues is very important. Mr. High gave personal examples of his own experiences to emphasize the importance of gaining and improving technical and soft skills as significant factors in succeeding with one's own career path.
The audience had an opportunity to ask several questions about interview tips, types of interview questions expected in interviews, and other tips to succeed in the Financial Math industry. Thank you Jim, Albert, Jeff and Jared! Everyone enjoyed hearing your career stories and expert advice. The evening ended with a reception held in SAS Hall.
We are pleased to announce this event below:
Financial Engineers Give a Personal View of their Careers in Quantitative Finance
A Series of Panel Discussions For Students Interested in a Career in Quantitative Finance
Friday, November 14
5:30pm Program Begins
6:30pm Reception & Networking
North Carolina State University- SAS Hall
2311 Stinson Drive- Room 2203
Jared Bogacki- BB&T
Jeff Rockwell High- Captrust
Albert Hopping- SAS
James Russo- Altrius Capital
Moderator- Jeff Scroggs
Registration is Complimentary!
Please Click Here to Register
Part II: Analytic techniques continued...
6) Do you believe the future movement of the market data is, to some extent, driven by the models used by major financial institutions, even though these models may not be correct?
Albert: Does the market move because the model is assumed to be correct? I would say yes. Consider the market crash in 1987. Prior to the crash, there was no “volatility smile,” but there was after the crash. The market had actually been acting correctly, according to the model, until they realized the model made terrible assumptions. The market moved much more than their model implied. One event is meaningless; however, this highlights an incorrect model driving the market.
For a more current example, look to the way mortgages were priced before the recent mortgage bubble. Banks priced mortgages with the assumption that housing prices wouldn’t fall. The entire market priced that way because that was what everybody else did; it was group think. They used this assumption because the price had never gone down in their historical data set. Models are only as good as their assumptions. Could somebody have built a better model and actually predicted the housing price collapse? Yes, it could have been done and a few people did it.
Unfortunately, in a time of high earnings, it is easy to ignore risk. Risk is especially underweighted when quarterly earnings are prioritized over long term security. I feel that compensation packages are lagging the culture shift at most companies. This disconnect leads people to act as individuals focused on their personal bonuses rather than acting as representatives of their company. Further, a system which provides bailouts for bad behavior begets that bad behavior. The “smart” companies wouldn’t receive a bailout, leading some to assume they are better off to employ the incorrect group think model. Of course, this would not work in a free market.
7) Does your company use stochastic models? If it does, what kind of models are used?
Albert: We primarily work with customers who have reasons for the models they use. Sometimes, they ask us to implement specific models in their system. Other times, they come to us for advice asking what model may be best. For instance, in regards to interest rates, I am a big fan of the Libor Market Model (also known as the BGM Model). It is a term structure model. However, the industry almost exclusively seems to use short rate models. The Hull-White Model is very common because it’s very easy to parameterize, simple, and everyone else is using it.
However, I enjoy commodities more than interest rates or equities. In commodities, we have different issues because it is such a physical market. These models can be much more complicated. If I picked a favorite model, it is one that I was lucky enough to have helped develop (I’m biased). That model takes the term structure for a commodity and relates it back to the spot price allowing them to be simulated together. I really enjoy working with that model. In general, my favorite model is the right model for the situation; a model that makes logical sense and fits the data.
Part III: Risk management
8) How do the recent financial crisis and the regulation policies enacted after that affect the behavior of your company?
Albert: As a vendor, our business is based on our customers’ business. A crisis like that causes additional regulation or at least the changing of regulation. To handling that regulation it is very logical that a third party, a vendor would make a solution and sell it to customers. Typically, that type of regulation would cause a company like SAS to make a new product and be able to provide that solution to more people. Unfortunately, the capital expended on satisfying regulations cannot be used elsewhere in the economy.
9) The goal of risk management is to achieve a balance between returns and risks. Thus, with lots of capital and human resource spent, risk management may, to some extent, reduce a company’s profits. Now suppose you are a leader of a financial institution. Driven by the motivation of maximizing the profits, will you pay enough attention for risk management?
Albert: As a risk professional my answer must be yes. There are two aspects to consider in regards to risk: monitoring and management. Consider risk monitoring first. One should spend resource and pay attention to know the rules of the game. For an example, let’s think back to mortgages. What if housing prices could go down? I may go bankrupt. Well, that would be very important to know. If you don’t know the rules of the game, you cannot play your best.
In the same way, you need risk monitoring to help you see what the possibilities are. In terms of risk management, it’s like getting an insurance policy. Let’s say I have a house and all my money is in my house. If my house burns down, I may go bankrupt. I should clearly buy fire insurance on my house. That’s how risk management can be considered as well. Yes, if I am concerned about anything other than the very short term, I would spend enough resource on risk management and pay attention to my risk team.
Part IV: Suggestions & Advice
10) What skill sets are important to succeed in your field?
Albert: I get asked that question often: by students, by some of my friends, and some of my peers. I change the details of my answer almost every time, but there are key components that remain consistent. First and foremost is communication. No matter how smart you are, no matter how brilliant your model may be, if you cannot convince others and if you cannot explain your ideas, it is not going to matter.
Another component in my list is passion. Passion is not a skill, but an ingredient to success. Is it necessary? No. But if you are not passionate about your field, why work in it? Your passion allows you to have better ideas and think outside of the box which is critical. If you just think like everyone else, you are very replaceable. This makes it more difficult to advance. Your passion may present itself in the form of problem solving. This is an important skill.
Another skill of importance is programming. In our field, people are often not formally trained in programming. We are more likely to be self-taught with at most one or two university courses in programming. This is very different than those people who come out of school with an entire degree in computer science. They have different level of understanding the way a machine thinks. In some parts of our field, this understanding is critical and you will need to learn it. Being skillful enough to have a computer automate you work is always important. Automation frees your time to think and add real value.
Thank you Albert for inviting us to SAS for coffee and taking the time for this fun and informative interview!
Meet Albert Hopping, ERP- Manager of Risk Consulting at SAS Institute in Cary, North Carolina. Albert graduated from the Financial Math program in 2007. He is a Board Member and an active alumnus of our program. We were happy to meet with Albert for coffee at his office at SAS.
Part I: Education & Job Background:
1) Why did you decide to get a Master’s degree in Financial Mathematics from North Carolina State University?
Albert: That I might receive more wages. That is the short answer. I finished my undergraduate degree before this program existed. When I joined the program, I was working full time. I had been out of my undergraduate program for a few years and I ended up in a risk analytics team at a diversified energy company. At that point, I didn’t have a risk background and didn’t know much about the field. I was on the risk team looking around and I saw these quant guys programming in Matlab. It looked like fun to me and I thought that it was a cool job. I wanted to be a part of what they were doing, so I started helping them with their work as much as I could.
Eventually, it got to the point where I was doing this risk work a majority of my time. I told my manager that I should be moved to the quant job family. I was told that a master’s degree in Financial Mathematics was a prerequisite for the job. Not having the degree was a roadblock for me. I applied to the Financial Mathematics program that week and I am glad I did.
2) How did the program prepare you for your job?
Albert: I was already doing risk work; I was self-taught to a certain extent. This put me in a different position compared to most students, and I got different things out of the program than other people might have, as a result. Most students learn theory first and practice second. I started the program with the perspective of a practitioner. While in the program, I learned about models I used on the job. I used these tools at work, but I didn’t really know about stochastic partial differential equations. I used Black-Scholes, but I could not derive it.
What I received from the classes is a much deeper conceptual understanding and a firmer foundation from which to see my work. What I really took from this program is a fundamental, basic understanding of financial mathematics. I also learned new models and conceived great ideas to use in practice.
3) Please briefly describe your job, your job title, and your responsibility?
Albert: At SAS Institute, I am a Manager on the Risk Solution team of Professional Services & Delivery (PSD). Let me explain from the top down. PSD is the consulting, customization, and delivery arm of SAS. Many customers of SAS software want services, consulting, or even staff augmentation. PSD provides these services. We are the ones who go to the customer site and work with the customer to help them get the most benefit from our products. Our team within PSD specializes in the risk management domain. We work with all the risk solutions and provide consulting for all risk topics. Our team has about 20 members and is growing.
Within risk, I specialize in three industries Energy, Financial Services, and Healthcare. I am responsible for leading customer projects, providing industry and risk domain expertise to the sales teams, mentoring fellow team members, and most importantly providing value to the customer. Note that the views and opinions I express today are my own.
Part II: Analytic techniques
4) “Big Data” is a hot specialization in the field. Do you see this as a long term trend or something that might pass as a fad?
Albert: Big Data is definitely a long term trend. In fact, I would go beyond that; I would say it is going to be the new norm. It will progress to the point where big data is simply the paradigm. I will even extend that to unstructured data. Companies, who are not using big data and unstructured data to their advantage, are starting to fall behind. They are tomorrow’s luddites.
5) The trend of “Big Data” implies that people believe historical data can shed light on future prediction. However, this contradicts with “efficient market hypothesis” to some degree. What are your thoughts about this?
Albert: One of the things I would like to point out in terms of the “efficient market hypothesis,” is the irrationality in the market. A simple example comes to mind: technical traders discuss how a stock index will meet resistance or break through a barrier. But what are those points where the index meets resistance or breaks through? They are numbers with lots of zeroes on the end, round numbers. Why are those numbers important? They are only important because we tend to be emotional and we have ten fingers. I propose that if we had a different number of fingers we would use a different base for our number system. The round numbers where the stock index meets resistance would be different numbers.
Clearly, these barriers are irrational, as they are based on how many fingers we have. This means I cannot be a full believer in the “efficient market hypothesis.” The question remains, is all this historical data priced into the market already? To the extent that people are doing analytics on big data, perhaps yes. Was it priced in before? No. Was the data available? Mostly, but people could not convert the data into knowledge. It was impossible - until analytics on this big data was possible.
Now, we are in the place where something can be done because of the advancements in software and the physical hardware. Data can be restructured and put into use in the market. The fact that the data is available is clearly important, but prior to these advancements one could not glean actual insights. The data must be converted into information that helps those insights that yield a better price or a better model. Acting upon those insights is what makes the market more “efficient”.
Stay tuned for Part 2 & follow Albert on twitter @SASQuant
NC State's Financial Math program has partnered with 2004 Alumnus, Jonathan Leonardelli, to create a new workshop series "Introduction to Financial Risk" for all NC State students and faculty. The workshop is also opened to the public.
Students and faculty in Mathematics, Statistics, Economics, Finance, Operations Research, MBA and other related programs are welcome to join!
Here is what you will learn:
Jonathan Leonardelli, FRM, MFM
Jonathan Leonardelli, Risk Consultant at the Financial Risk Group, specializes in credit and market risk management. Over the course of his career he developed a diverse knowledge of retail banking risk as well as the technical skills needed to integrate risk assessment processes into a company’s business and technology infrastructure.
Jonathan’s career started with positions in the credit risk groups at Wells Fargo (Wachovia) and BB&T. In these positions, he developed expertise in acquisitions and portfolio risk management. In his current position, Jonathan develops and implements processes that provide quantitative risk assessment and reporting capabilities for clients that include banks, hedge funds, and asset management companies.
Jonathan is an experienced presenter and author. He is a certified SAS® Risk Dimensions Instructor. His papers in financial risk management covered topics such as the Dodd-Frank Act and its implications for risk professionals, as well as techniques for handling missing data. He has also authored a Webinar for the Insurance & Finance SAS® Users Group (IFSUG) regarding loss estimation using roll rate matrices.
Jonathan holds an Masters of Financial Mathematics from North Carolina State University and is a member of the Global Association of Risk Professionals (GARP).
FRM designation since 2010
SAS Certified Advanced Programmer for SAS 9
Those interested- please contact Leslie Bowman, Director of Career Services- email@example.com
Workshop begins Friday, September 5th 2014- registration deadline, August 28th.
(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.
Every year, NC State’s Master of Financial Math (MFM) program holds Fall and Spring Board meetings. (Background- “MFM’s Board consists of alumni, faculty and professionals from industry. They meet to support and discuss the future direction of the program. The Board advocates for the MFM program within NC State and with external constituents. Board members may mentor students and provide related work opportunities such as internships and job shadowing experiences,” Leslie Bowman, Director of Career Services)
This past fall semester 2013, was my first opportunity to engage with board members from SAS, Genworth, Duke Energy and Local Government Federal Credit Union. The Career Ambassadors, including myself, helped the Director of Career Services ensure the board meeting events occurred smoothly. Their appropriate behavior and professional dress received compliments and left employers a wonderful impression.
“I am excited that it is my first time to exercise my elevator pitch”, Yi Chao (May 2015 Graduate) “My task was greeting the employers upon arrival and showing them to the meeting room. I was still a little shy and nervous when leading the first employer; however, I turned out to be more brave and natural the following time”.
“I talked with Mr. Jeffrey Lovern from Genworth with my elevator pitch”, said Xun Ma(May 2015 Graduate), “The employers are much nicer than I imagined. Just be brave and talk to them; you’ll find any anxiety you have will decrease as the conversation continues. Just be yourself!”
After the Board meeting, there was a reception where all MFM students could engage with the Board members. The discussion topics covered specific skill sets required for internships, future job opportunities and the future development of course selection.
Catherine (May 2015 Graduate) felt very beneficial from talking to Mrs. Megan Jennings from Duke Energy (MFM Alumni). “Our talk initiated my passion to work in the energy industry”, she said, “I think it is suitable for me. However, the job will demand more statistical knowledge. So I will consider taking more statistic courses.”
Yi Chao, Jason Massey (May 2015 Graduate) and Priya Padher (May 2015 Graduate) had an interesting conversation with Mr. Albert Hopping from SAS (MFM Alumni). Mr. Hopping’s success and experience in risk management was inspiring. Yi Chao asked technical questions about catastrophe risk controlling which created the group's interest in learning more. Priya asked the qualifications SAS requires in potential interns. “It is never too late to improve our technical, communicational and problem solving skills to become qualified candidates.”
The board meeting and reception was successful, and the students were happy to meet and converse with the board members.
“I am glad I was an active participant in this event. I really learned a lot and improved my conversational and networking skills. We appreciate our time spent with the Financial Math board members, and are thankful for their knowledge and support with our program.”- Yi Chao
By Yi Chao, May 2015 Graduate, Financial Math Intern & Career Ambassador
We look forward to the Spring Financial Math Board Meeting to take place this Friday, April 25th, 2014 along with a celebration to mark 10 years!