Re-cap IAQF’s “How I became a Quant” at NC State

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.

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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.

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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.

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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.

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Meet our Financial Math Alumni- Brandon Blevins

Meet Brandon Blevins, Product Controller at Credit Suisse in New York City. Brandon graduated from the Financial Math program in 2009. We were glad to catch up with him in Manhattan and learn about his job and life in the city.

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Part I: Education & Job Background

1) How did the program prepare you for your job?

Brandon: The Financial Mathematics program gave me the background about quantitative finance. It provided me with the basics on how financial assets work, how models applied to assets, and how interest rate curves work.

2) Describe your job.

Brandon: Currently, my job is Product Controller at Credit Suisse. The main point of Product Control is to ensure that the Profit and Loss (PL) generated by portfolios reviewed gets to the general ledger of the bank, which then is reported to shareholders and board members who make financial decisions based on Credit Suisse earnings.

My day involves reviewing risks on books, making sure risks are within tolerances, and P/L are in line with the risks. For example, suppose you have net Vega on a single position of $100K. The volatility moved on the position by 100 basis points and you did not make or lose any money on that position. Did that make sense? It is the Product Controller’s job to make sure it does makes sense. If something is wrong, we flag it. We make comments on any big moves, big losses, and provide reasons why money is lost.

Part II: Analytic techniques

3) Does your company use stochastic models? If it does, what kind of models are used? Is there any reason for choosing these models?

Brandon: We use Black-Scholes formula to build up implied volatility curve. Options with the same underlyings and maturity but different strike prices have different implied volatility. The same options with different maturities may also have different implied volatility. Thus, implied volatility is a function of strike price and maturity, and we can define a volatility surface. We also use jump-diffusion models to model certain protocols. For example, is there a big court case coming up in the next few years for a specific company, or does this specific company have any big products coming out in a few years? That is where jump-diffusion comes into play.

Part III: Risk management

4) How does the crisis and the regulation policies enacted afterwards affect the behavior of your company?

Brandon: Radically. Since then, many parts of businesses have been shut down. Interest rate products have been drastically cut by 90%, because the Feds have kept rates low. There is a huge push to move everything onto exchange and standardize all products. Any flow business has been hit hard such as the credit default swap (CDS) market. Junk bond market has been on fire lately. But it did excite the mortgage back portion.

5) The goal of risk management is to achieve a balance between returns and risks. Thus, with lots of capitals 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?

Brandon: Lead traders will listen to risk management and work in conjunction to set risk limits and VAR measures. If the limits get breached, everyone will look at it.

6) You used to be an interest rate derivative analyst, but now you are focusing on equity derivatives. So in your opinion, what is the difference between the interest rate derivative market and the equity derivative market?

Brandon: Interest rate market and equity derivative market start to look alike with low volatility. The bond market did very well in the past, but now it hover sideways because of the flat yield curve environment. Equity market is experiencing the same problem. Rates are not moving and are low.

Part IV: Suggestion & Advice

7) What skills set are important to succeed in your field? And what kind of courses will you recommend for current students to take.

Brandon: Networking! Make sure people like you, so they will recommend you. I landed all of my jobs because I knew people who worked for companies that I wanted to join as well. I got the interviews because people recommended me. Therefore, students should get out there, meet people, talk to them, learn from them, make relationship with them, and then they will recommend you for jobs. Just get connected! People can put you in positions to succeed, and give you opportunities to help you succeed. If they like you, they want to you succeed.

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Thank you Brandon!

IAQF’s “How I became a Quant” comes to NC State!

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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

How I Became a Quant: North Carolina State University's Financial Math Program

Friday, November 14
5:00pm Registration
5:30pm Program Begins
6:30pm Reception & Networking

North Carolina State University- SAS Hall
2311 Stinson Drive- Room 2203

Panelists:

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

Meet our Financial Math Alumni- Coffee chat with Albert Hopping- Part 2

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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.

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Thank you Albert for inviting us to SAS for coffee and taking the time for this fun and informative interview!

 

Meet our Financial Math Alumni- Coffee Chat with Albert Hopping- Part 1

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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”.

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Stay tuned for Part 2 & follow Albert on twitter @SASQuant 

Join our new workshop- “Introduction to Financial Risk”

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:

Risk Workshop Overview

Presenter information:

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

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Those interested- please contact Leslie Bowman, Director of Career Services- leslie_bowman@ncsu.edu 

Workshop begins Friday, September 5th 2014- registration deadline, August 28th

“Meet our Financial Math Alumni”- Emmanuel Sanchez

 

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Meet Emmanuel Sanchez. He is currently an Associate with Allianz Risk Transfer in New York City. He is one of the first students to graduate from NC State's Financial Mathematics program in 2004. We were able to chat with Emmanuel in New York City, and catch him when he visits Raleigh, North Carolina.

Part I: Education & Job Background

1) Why did you decide to get a Masters in Financial Math at NC State?

Emmanuel- I was studying at NC State to get a Masters in Computer Science at the time. It was in 1999 or 2000 before the Financial Math program had started. I was in Harrelson Hall for a Math class and a professor had pinned outside his office a description of a course he was going to teach the following semester: the course was Financial Mathematics and the professor was Jean-Pierre Fouque. I got very interested, took the class and when the program started a few years later, I enrolled full-time.

 (side note - Harrelson Hall is an iconic round building at NC State's brickyard, and after many, many years of controversy it will be torn down!)

2) Briefly describe your job, and if possible list some financial products you are dealing with.

Emmanuel- I work for a company called Allianz Risk Transfer where I price and structure weather insurance. Here are a couple of examples of weather products:

  • Utilities with hydro plants are dependent on rainfall. To protect them against low rainfall they could purchase a put option on cumulative rainfall.
  • In the North-East and Midwest in winter, snow removal can be very expensive for municipalities. They could purchase a call option on cumulative snowfall.

Part II: Analytic techniques

3)  The trend of “Big Data” implies that people do believe historical data can shed some lights on future prediction. And interestingly, such predictions may sometimes affect the future movement of the market. Is this also true in your area?

Emmanuel- In the weather business it is critical to have enough historical data. Unlike other markets like equities, it won’t be hotter or colder because people buy or sell more temperature contracts. Similarly weather forecast will have no impact on the actual weather: the fact that tomorrow’s maximum temperature in New York is forecast to be 80F won’t change the value of the actual temperature. There is no concept of implied volatility in weather.

4) In your area of specialization, what kinds of models or methods are used? Please briefly describe the basic process for applying them.

Emmanuel- For a lot of the deals I price, I do not use models. For example, if I am pricing a structure where the payout depends on cumulative rain in New York from July to September, I will first get historical daily rain data and compute the cumulative amount for the specified period. In a lot of locations (like New York) there will be at least 50 or 60 years of data available. This gives me 50 or 60 data points (1 per year). I will then try to find a probability distribution that best fits the data and use that distribution to simulate rainfall data.

Part III: Risk management

5)  How do the regulation policies enacted after crisis affect the behavior of your company? Do you see this as long term trend or fad?

Emmanuel- Regulation policies enacted after the crisis (e.g. Dodd-Frank) made my company pay more attention to compliance and regulatory requirements. I don’t see this going away any time soon.

6)  The goal of risk management is to achieve a balance between returns and risks. Thus, with lots of capitals and human resource spent, risk management may, to some extent, reduce a company’s profits. Driven by the motivation of maximizing the profits, will the leaders of your company pay enough attention for risk management?

Emmanuel- I work for an insurance company. Generally insurance companies have a tendency to be conservative (and definitely much more conservative than investment banks). I find my company to pay a lot of attention to risk management in general (not just financial risk management). The legal and compliance departments are very important.

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Part IV: Suggestions & Advice

7) Any suggestions for current FM students to get a job?
Emmanuel- My suggestion to students is to take full advantage of their internship to learn as much as they can about the company and the problems/challenges it faces.
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Big thanks to Emmanuel for participating in our alumni interview series from New York City.

Database trends in financial services that quants should know

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Recent trip to New York City included a small alumni meet-up and Data Summit 2014. At Data Summit 2014 we learned about several database trends in financial services well beyond the popular RDBMS (relational databases) including Hadoop Big Data Platforms, NoSQL, NewSQL, and in-memory databases.

Quants know SQL, and it's important for them to be aware of the above database trends and what's driving them in financial services - such as risk analytics and reporting, market data feeds, high frequency trading, regulation, among other use cases driving demand for high volume and scalable, specialized databases.  While many quants are proficient in programming, it's not reasonable to expect them to learn each programming language driving these technologies to access data (Erlang, Javascript, C#, Java, etc).  This is not unique to quants as we're seeing SQL enable wider adoption of the Hadoop Big Data Ecosystems.

Sumit Sarkar of Progress Software (Gold sponsor of our program) talks about how professionals such as those in quantitative finance can easily work with data in the growing landscape of highly specialized database technologies, MongoDB for example, using standard based SQL interfaces such as ODBC and JDBC.

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(Alumni Left to Right- Emmanuel Sanchez with Allianz; Director of Career Services, Leslie Bowman; Yoshi Funabashi with Credit Suisse; Brandon Blevins with Credit Suisse)

Keep a lookout for their  "Meet our Financial Math Alumni" interviews.

We will be back again in October, 2014- so all NYC alumni, plan for another fun gathering!