“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!

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.