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


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


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.


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.


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.


"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


Meet Xiaohong Chen- Career Ambassador of the week!


"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.


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

Student’s view on Financial Math core courses at NC State

"Having taken many courses so far, Masters of Financial Math (MFM) students have discovered interesting and useful courses. Below are examples of a few core courses I have found important and useful."- Yizhou Chen, May 2015 Graduate

Statistical Theory:

Statistical Theory I & II is important in providing fundamental theory and formulas. In Statistical Theory II, we developed the probabilistic tools and language of mathematical statistics. The course describes basic probability theory, probabilistic models for a properties of random variables, common probability distributions for univariate and multivariate random variables, and sampling distributions and convergence theory. We learn description of discrete and absolutely continuous distributions, expected values, moments, moment generating functions, transformation of random variables, marginal and conditional distributions, independence, order-statistics, multivariate distributions, and concept of random sample.

The Statistical Theory classes are designed to provide the basic tools of statistical inference and prepare us to understand the foundations behind statistical inference. Thus, the knowledge enables us to formulate appropriate statistical procedures. Additionally we learn sufficient, ancillary, and complete statistics; Methods of finding estimators, including maximum likelihood; Mean squared error and unbiasedness; Hypothesis testing, including maximum likelihood; Mean squared error and unbiasedness; Hypothesis testing, including likelihood ratio; Power functions; Neyman-Pearson Lemma; Uniformly most powerful tests; Confidence intervals; Asymptotic properties of estimators and tests.

Asset Pricing:

Asset Pricing is a core course in the first semester of the Financial Math program. We gained a lot knowledge about finance from this course, especially for the students who have little knowledge about finance.  This course is an introduction to the pricing of assets. The emphasis is on the mathematical methods used to derive pricing formulas, and there is additional time devoted to explaining the major types of paper assets that can be priced with those methods. Real assets, such as factories and machines, also can be priced with the same methods. The goal of this course is to introduce us to the major types of asset prices and give us an understanding at an intuitive level of the relation between asset prices and the mathematics that governs their evolution.

The content in this course: Introduction to major fundamental assets (stocks and bonds), interest rates, and derivative assets, such as put and call options. Arbitrage theorem, present value, risk aversion, hedging, duration, properties of derivative assets, binomial trees, elementary stochastic calculus, Black-Scholes option pricing formula, implied volatility, capital asset pricing model. Emphasis on mathematical methods used to price derivative assets.

Probability and Stochastic Processes:

Probability and Stochastic Processes I: This course is set as an alternative course to Statistical Theory II. This course is more theoretical and the key point of this course is different from Statistical Theory I. In Statistical Theory I, we developed the probabilistic tools and language of mathematical statistics. Probability and Stochastic Processes describes basic probability theory, probabilistic models for and properties of random variables, common probability distributions for univariate and multivariate random variables, and sampling distributions and convergence theory. It is a modern introduction to Probability Theory and Stochastic Processes. The choice of material is motivated by applications to problems such as queueing networks, filtering and financial mathematics. Topics include: review of discrete probability and continuous random variables, random walks, markov chains, martingales, stopping times, erodicity, conditional expectations, continuous-time Markov chains, laws of large numbers, central limit theorem and large deviations.

Financial Mathematics:

Financial Mathematics- This is a core course in second semester, and challenging; some say difficult! Probability and Stochastic Processes and Asset Pricing courses are necessary to prepare us for Financial Mathematics class. Understanding the history of mathematics evolving over time as they are subjected to random shocks and knowledge of the mathematics of asset pricing are essential tools for this course.

Financial Mathematics course focuses on the basic mathematical tools for finance. In particular, we cover time value of the money, simple interest rate, bank discount rates, compound interest, ordinary annuities, extending ordinary annuities, amortization, sinking funds, perpetuities and capitalized costs.

Content of this course: Stochastic models of financial markets, No-arbitrage derivative pricing, discrete to continuous time models, Brownian motion, stochastic calculus, Feynman-Kac formula and tools for European options and equivalent martingale measures. We also learn about Black-Scholes formula, Hedging strategies and management of risk, Optimal stopping and American options, Term structure models and interest rate derivatives, and Stochastic volatility.

Monte Carlo Methods:

Monte Carlo Methods with Application to Financial Mathematics- This course requires some knowledge of programming. We use Matlab to write functions, apply appropriate control structures, and import and export data. We implement the methods mentioned in the other learning outcomes in Matlab. Matlab is utilized to visualize the results. The homework of this course may not be so difficult, but it takes a lot of time. Because of plenty use Matlab, we need take some pre-courses to prepare for it.

In this course we learn Monte Carlo (MC) methods for accurate option pricing, hedging and risk management. Modeling using stochastic asset models (e.g. geometric Brownian motion) and parameter estimation. Stochastic models, including use of random number generators, random paths and discretization methods (e.g. Euler-Maruyama method), and variance reduction."

By- Yizhou Chen, May 2015 Graduate, Career Ambassador & Financial Math Intern

Learn more about NC State Masters of Financial Math.

CME Group Trading Challenge

"Financial Mathematics students at North Carolina State University will attend the CME Group's Trading Challenge.

CME trading challenge is an annual competition and the number of participates grows each year. There were 320 teams from 26 countries in 2013. The challenge will last four weeks in a simulated trading environment on a real-time professional trading platform, and it will provide resources and opportunities for students to get hands on experience of trading derivatives in the financial market. Graduate students will trade a variety of CME Group products from multiple classes. The trading contracts include futures of agriculture, energy, metal, equity index, interest rate and foreign exchange rate. The team who accumulates the highest trading account balance in the final round can get bonus at the end.

There will be two teams participating the trading competition this year. Each team can choose one advisor to provide professional suggestions in the process of the challenge.

Taking an active role in trading competition is a kind of practical trainings in NCSU financial mathematics program. Different from mathematical modeling in usual classes, students in the program will utilize the trading challenge opportunity to practice beating the market combined the knowledge that they learned in usual classes. 

The upcoming practice competition round is around the corner. We all look forward to impressive performance of two teams in the trading challenge!”- Xun Ma 

To know more about the CME trading challenge, please go to the official website:

Xun Ma- May 2015 Graduate, Financial Math Intern