Fantastic Diverse Class of Students Starting Fall 2016 in the Financial Mathematics Program

Message from the Director...

Fantastic Diverse Class of Students Starting Fall 2016 in the Financial Mathematics Program

Class of 2016-2017

I am excited to welcome the class of 2017-2018 to the Masters of Financial Mathematics. The largest reward for me comes from working with talented and motivated candidates. Students always come from a variety of backgrounds and countries. This year, the main countries of origin are China, US, and India, but Korea, Kyrgyzstan, Iran, and Malaysia are also represented.

While in NC State’s Financial Mathematics Program, you’ll learn synthesize theory with practice to analyze, quantify, manage and predict risks associated with financial instruments and price options, derivatives, and futures. Joining the MFM program means you’ll have a holistic educational experience, developing skills that enable a lifetime of personal and professional growth.

Huanying – Welcome – Swagat

Prof. Scroggs

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!

Re-cap of Hedge Fund Challenge 2014

By Dendi Suhudby, May 2016 graduate

A couple of weeks ago a few students in the Financial Math program decided to attend the local Hedge Fund Challenge held nearby at the Washington Duke Inn in Durham. Once we heard about this event from local alumni and current students, we immediately became interested. I researched the event to learn that it was a challenge to pitch an investment strategy for a new startup hedge fund. This really sparked my interest, because hedge fund structures allows us to find strategies in broad range of asset classes compared to, for instance, finding investment strategies for mutual funds or traditional investment vehicles.

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I attended the event with our Program Director, Dr. Scroggs and current student, Bingxi Du (both pictured above). The other participating teams were from Duke, UNC Chapel Hill, Eastern Carolina University, University of Richmond, and Elon University. There were 5 scheduled speakers to gave lectures about 5 main points of an investment strategy buildup:

1. Idea generation

2. Valuation

3. Macroeconomic Analysis

4. Risk Management

5. Trade Structuring

Here are the summaries of the speakers lecture.

1. Idea generation

Idea generation is all about the brainstorming and finding inefficiencies in several asset classes for an opportunity to take an advantage for convergence of mis-pricing. For an example, in an idea generation there may be due diligence from a researcher, portfolio manager and the investor relations on the company’s on going corporate strategies. There might be more indirect idea generation by looking at data from Bloomberg and making a quantitative analysis pattern on data.

2. Valuation

The part of the valuation is where you pull up a spreadsheet and try to predict the outcome of the investment strategy in todays time. In finance lingo, it is the process to project future cash-flows and discount back to todays present value. The highest valuation of the investment opportunities are then chosen.

3. Macroeconomic Analysis

Macroeconomic analysis is done to find the possible directions of the environment say for example of interest rates, or business policies that are favorable for specific industries.

4. Risk Management

The portfolio manager and the buy side researcher then finds possible deviations of outcomes through risk analysis. For example using scenario analysis, they would scenario the investment strategy if there is a deviation of parameters within their models that change the outcome of investments. Within this process, the portfolio managers also find ways to hedge the investment if something goes in an adverse way or how to completely liquidate the investment if it is on loss.

5. Trade Structuring

After doing all those above, then portfolio managers make the decision to structure their trade either 1) buying the underlying asset or 2) enter a derivative contract. For example, if a portfolio manager has the view that the interest rates would increase he/she might want to short bonds or has the possibility to enter in interest rate future contracts in the Chicago Mercantile Exchange or even enter an OTC Credit Default Swap. Even these three investment strategy are betting on an increase in interest rate; there are several things that needs to be decided such as liquidity of each asset (futures liquidity > CDS liquidity > bond liquidity) where the future asset class might be more favorable than shorting the bond.

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The main conclusion of the kickoff event and the hedge fund lectures are that to conduct a trade takes a lot of effort, brainstorming, quantitative analysis, and due diligence, differs far from the public opinion that a hedge fund bets on reckless investments. Hedge funds also are much more flexible than other fund structures because of its unregulated nature, and it can also invest in exotic investment structures that mutual funds cannot invest in.- Dendi Suhudby.

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 

New Series- “Meet our Financial Math Alumni”- Coffee Chat with Jonathan Leonardelli

"Meet our Financial Math Alumni" is an up-close interview series with select Financial Math alumni to learn more about their career, experience and knowledge after receiving their Masters in Financial Math degree from NC State University. Alumni are important part of our program for many reasons. They provide support, vision and strategy to ensure the success of our program. They are role models and mentors for current students. They strengthen the reputation of the Financial Math program. They provide job leads and recruitment activity for students. Our alumni are intelligent and awesome! Thank you to those who participant in this series"- Leslie Bowman, Director of Career Services

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Meet Jonathan Leonardelli, FRM, Risk Consultant (Graduated 2004)

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Interview conducted and summarized by Yi Chao and Xiaohong Chen, Financial Math Interns, May 2015 Graduates

Jonathan is currently a Risk Consultant at Financial Risk Group. He is also one of the first students to graduate from NCSU’s Financial Mathematics program. We were honored to have the opportunity to interview him.

Part I: Education & Job Background

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

Jonathan: I had just moved here and was interested in changing careers. I wanted to find a program that combined mathematics and finance. As it happened, NC State was in the process of creating the Financial Mathematics program. Although the start of the program was still a couple years off, that time allowed me to get the necessary mathematics background I needed. Entering the program produced the exact result that I wanted: it gave the mathematics I need to do interesting work in the banking industry.

2) Interviewer: How did the program prepare you for your job?

Jonathan: The program really gave me the depth of math that I needed to work in the field of risk. It also taught me how to apply rigorous logic to a problem to help find a solution. Beyond this, though, I learned that life was filled with randomness. This randomness, as a result, causes the quantification of some metrics to be difficult.

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Part II: Analytic techniques

3) Interviewer: "Big Data" is a hot specialization in the field. Do you see this as long term trend or something that might pass as a fad?

Jonathan: It is definitely not a fad. In this day and age many actions we take, especially when using a piece of technology, is probably captured and stored in some database. Now, think about all those action and all the data that comes along with it. What is a company going to do with this data? They are going to improve their sales, improve their risk practice…the list goes on. Having skills to work with big data, to be able to find the relevant information and then integrate it into a model, is very useful.

4) Interviewer: The trend of “Big Data” implies that historical data can shed some lights on future prediction. However, this contradicts with “efficient market theory” to some degree. What are your thoughts about this?

Jonathan: I think concerns should always be involved when using historical data because the tacit assumption is that the future is going to behave like the past. That being said, there are ways to mitigate these concerns. For example, when we calibrate models one of the first things we do is test it with a different period of data to see if the model is robust. Sometimes we might use the model on data representative of a stressed scenario (i.e., a scenario that is uncommon but still possible) and see how the model performs. If it performs badly, we try to assess why that is. Are the parameter estimates wrong? Are different variables needed?

5) Interviewer: Does your company use stochastic models to predict interest rate? What kind of models are used?

Jonathan: To be honest, in my current job the main stochastic (i.e., diffusion based) models I have used are CIR (Cox-Ingersoll-Ross) and GBM (Geometric Brownian Motion) with the occasional jump-diffusion model thrown into the mix. Most of the models I have worked with recently are linear regression, logistic regression, and Markov chains.

6) Interviewer: In your area of specialization, what is your favorite method or model and why? Do you believe it is perfect?

Jonathan: Markov chains and their resulting transition matrices. This comes from the years when I worked in the banking industry. At a glance, the transition matrix tells you the behavior of different segments of accounts. Depending on how the states of the Markov chain are defined, the transition matrix can tell you: 1) The probability of going to default, 2) the probability of paying off, 3) the probability of curing, 4) the probability of moving across multiple states over a given time, etc.

The transition matrix is a great summary tool. Of course, it is not perfect. Always keep that in mind when you build a model. Even though the model looks pretty and deals well with the data – now – it is not perfect. It is an easy move from complacency, when the model is performing well during good times, to anxiety when the model is performing poorly during a financial crisis.

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Part III: Risk Management

7) Interviewer: How do the regulation policies enacted after crisis affect the behavior of your company?

Jonathan: The regulations have not impacted the behavior of our company. However as a risk consulting company, we have seen more requests from financial institutions asking us to help them comply with the regulations.

8) Interviewer: The goal of risk management is to achieve a balance between returns and risks. Thus, with a lot of capital 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 companies pay enough attention for risk management?

Jonathan: I may be biased, given my chosen career path, but I think with the recent financial crisis still fresh in our memories as well as all the regulations that were created as a result of it, businesses will continue to pay enough attention to risk management. And, I think, it will be that way for a while.

Part IV: Suggestions & Advice

9) Interviewer: Any tips for those interested in getting into the field?

Jonathan: First, be comfortable with the idea of randomness. Randomness is uncertainty and uncertainty is risk. Second, companies do not only seek candidates that are good at math and programming. They also seek those candidates who can clearly present their ideas in writing and presentations. Third, get perspective from areas outside of mathematics. I strongly suggest taking business classes because it gives you a different view of a business. A company is multifaceted and does not solely revolve around models.

10) Interviewer: What courses do you recommend?

Jonathan: The courses in the Financial Math program are very good. Among them, I definitely think the probability course is the most important one. That course takes you deep into the world of randomness and, hopefully, makes you comfortable with it. If you have an option of taking a course on risk or financial regulation that would be good. I took econometrics as an elective and, more often than not, draw upon the math tools I learned from that class more than others. Another very good course I took, and have used frequently, is time series analysis.

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"It is a great experience to interview Jonathan. He is humorous and smart. We learned a lot when we talked with him. Thanks Jonathan for participating in our interview! We are sure that your answers will shed some light for those interested in Financial Mathematics!"- Yi Chao and Xiaohong Chen

 

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

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(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.

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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.

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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.

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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.

Networking and the Financial Math Board – a student’s perspective

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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.

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“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!”

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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.”

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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.”

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The board meeting and reception was successful, and the students were happy to meet and converse with the board members.

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“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! 

Meet Xiaohong Chen- Career Ambassador of the week!

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"Hello, My name is  Xiaohong Chen, a current student in the Masters of Financial Mathematics Program of NCSU. I have been in the United States for over nine months now and I feel this is one of the greatest times in my life!

To me, ‘quant’ was once a mysterious but exciting word, which captured my imagination. I can still remember the first time I learned about the binomial tree option pricing model; I became instantly fascinated with learning more. Since then, becoming a 'quant’ was my dream. Thus, I decided to come to NC State to chase this dream!

During my time here, I have realized that being a quant is challenging. Through the Financial Math’s career development services, I attended a job shadowing program to a local financial institution. Through this learning experience I got the chance to communicate with employees (in risk management and investment departments) to understand their job responsibilities. The job shadowing event was a great opportunity and I realized which career path I did and did not want to pursue. (Tip- it is just as important to know what you do and do not want to do in life) 

After carefully consideration, I have decided to pursue a Ph.D. in math after graduation. I know there is long way to go, and I will never give up my dream. If possible, I wish to be a Quantitative developer one day. I have talents and gifts in programming and I want to make full use of this skill in my future position. For next several years in academia, I plan to build a solid foundation for math and complement my background in computer science in order to make myself qualified for this job. This is a long way off, but it is a laudable dream. And I believe I can make it one day!

Life here is not tedious. I have enjoyed some of the greatest moments of my life and I made best friends in the past year. NC State’s Financial Math program offers several professional events during the year, and I am honored to be a Career Ambassador and actively participate in these activities. These experiences have already helped me improve my social skills and professionalism, which will help me network to land my dream job one day. I appreciate all these opportunities I have within this program and I quite enjoy my life here."

Xiaohong Chen, May 2015 Graduate, Financial Math Intern & Career Ambassador

A student’s interview experience- the benefits of planning and preparing for the big day!

Internship Interview Experience at a Financial Security Company

(Disclosure- this is a true interview story. This student received an offer and is currently working at this company. Her interview experience and interview format is typical but may not reflect all interview experiences. For privacy reason, the interviewee and company are made anonymous.)

I had an interview with the risk management model group of a financial security company. The main financial products of the company are mortgage loans insurance and long term care insurance.

The interview lasted two rounds, and each round covered two parts with two interviewers from the risk management model group. In the first round interview, the interviewer mainly asked about some questions ranging from mortgage loans, basic statistical knowledge of data mining to regression techniques and models. (Tip- Researching the employer was an important part of this round.)

Questions about mortgages loans referred to concept of premium, prepayment and interest rate. The interviewer introduced their mortgage product to me and I expressed some of my points of view about the product combined with financial terms that I learned in some related classes. (Tip- show employers your educational knowledge through related courses.)

In the basic statistical question part, I was asked about questions of distribution of certain scatter plots that were prepared by the interviewer. Besides, the interviewer asked me to come up with some fast data mining methods that can help a modeler quickly deal with data in an efficient way. (Tip- be prepared to show examples of your quantitative skills)

Most technical questions were focus on regression. The interviewer discussed with me two econometrics models that I had implemented before. Based on the discussion of certain regression models, the interviewer thoroughly asked questions about data cleaning, explanatory variables choosing and model modification. It was a nice progression since the interviewer guided me to take more practical problems into consideration in the process of model construction. (Tip- actively listen to the interviewer and be aware of the direction their conversation is heading, including tips they may give you.)

The second round of interview was an on-site visit to the company. During the second part of first round interview, the interviewer was interested in my interpretation of Logistic model application. I was also asked about basic SAS programming skills. Three interviewers interviewed me and gave me have a tour of the office. The SVP (Senior Vice President) of Risk Management introduced the team corporation of different groups in the division.

During the first part of the second round of interview, I had the opportunity to gather more details about the company’s business and culture, and had a nice communication with the interviewer about team contribution. All the questions were focused on the type of role that I could participate in the risk modeling team, and why I chose this company.

The Director and Modeler from the Risk Modeling team asked me to talk about some opinions on default probability model construction. Furthermore, they gave me concrete examples to show what kind of data that is usually dealt with and gave me chances to discuss practical techniques of data processing implemented by SAS programming. (Tip- be prepared to give  examples of your programming skills.)

The whole interview process was nice. The interviewers were willing to guide candidates to answer questions through practical problems solving techniques, come up with different solutions and opportunities for further discussion. The interview was a great way to communicate with professional people in the finance industry.

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Have you had an interview yet? If so, was your experience similar or different? Share your thoughts and comments.