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

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!

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.