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Master of Financial Mathematics
Class of 2016

Model risk management was my favorite area when I was a student in the MFM.  I was interested because of the challenging and level of mathematical and analytical thinking it involves.  Also it so happened that we have some top tier professors in this area in our program.

Financial Mathematics Modeling for Graduate Students: Winter 2019

Winter 2019 Workshop: January 10-19, 2019

Description
The School of Mathematics at the University of Minnesota holds a 10-day workshop on Financial Mathematics Modeling every winter between fall and Spring Semesters.
Below are the details for the winter 2019 Modeling Workshop.

Format
Students will work in teams of up to 6 students under the guidance of a mentor from the field of quantitative finance. The mentor will help guide the students in the modeling process, analysis and computational work associated with a real-world quantitative finance modeling problem. A progress report from each team will be scheduled during the period. In addition, each team will be expected to make a final oral presentation and submit a written report at the end of the 10-day period.

Projects and Industry Mentors
There will be 5 teams participating in the workshop:

Team 1 - Industry Mentor - Matt Abroe
Dr. Abroe is a portfolio manager focused on interest rates in developed economies, specializing in inflation linked securities and derivatives. Prior to working as a portfolio manager, he was an quantitative analyst at Black River Asset Management. He holds a PhD in physics from the University of Minnesota.

Yield Curve Construction: Calculating the present value of future cash flows is a crucial task for any trading desk. We will explore yield curve bootstrapping and interpolation techniques used to value financial instruments in a consistent framework. The pros and cons of each method will become apparent, and we will converge on an optimal technique that is consistent with major trading desks. Finally, we will see how the financial crisis changed yield curve construction, and fundamentally altered the way we think of the valuing future cash flows.

Team 2 - Industry Mentor - Dr. Chris Bemis

Dr. Bemis is Head of Quantitative Analysis and Research for Whitebox Advisors, focusing on cross-asset alpha drivers for a variety of asset classes. He is an active researcher for the Whitebox quantitative group, where he works on varied problems in the context of equity, derivative, and fixed income strategies; of particular interest is the interpretation of constraints in an optimal portfolio setting in a Bayesian context. Dr. Bemis earned his PhD in applied mathematics from the University of Minnesota, where his work involved both modeling and optimization for portfolios of risky assets.

Machine Learning in Equity Classification: In this module, we will work with various machine learning classification models with the goal of classifying equities via well-known quantitative factors such as Value and Momentum. The classification will be supervised, utilizing a novel ETF dataset which we will supplement extensively. Participants will not be required to have extensive background in Python, although that will be the language we use; especially the Scikit Learn module.

Team 3 - Industry Mentor - Roman Borisov
Roman Borisov is a quantitative analyst at the Hedge Strategy Group for Allianz Investment Management. He is responsible for maintenance and development of general hedging models, analysis of hedging strategies, and portfolio management of exotics. He is a graduate of the MFM program.

Valuation and Replication Strategies for Variance Derivatives: Variance derivatives have played a major role in the financial markets since the 1990s, as they provide pure exposure to volatility without other added effects. A primary instrument is variance swaps. In this project the participants will explore traditional theory of valuing variance swaps, and their continuous and discrete replication with vanilla derivatives. The connections among variance, volatility swaps, and VIX derivatives will be drawn, alongside examining non-parametric variance swap replication approaches.

Team 4 - Industry Mentor - Perry Li
Mr. Li is responsible for trading and assisting with day-to-day management of Parametric’s options-based Volatility Risk Premium strategies, including Defensive Equity and other proprietary strategies. Prior to joining Parametric in 2014, Perry worked for CHS Inc., where he managed commodity futures and options portfolios and conducted research on macro economy and derivative strategies. He earned a B.S. in Statistics from the Sun Yat-Sen University and a M.S. in Financial Mathematics from the University Of Minnesota Twin Cities. He is a Certified FRM®, as well as a CFA® charter holder and a member of the CFA Society of Minnesota.

Smart Beta Investing in Commodities: Ever since the first stocks and bonds were issued by the Dutch East India Company (VOC), investors have tried to understand what drives returns. Smart Beta strategies have gained popularity lately by offering the potential for better-than-market returns with better-defined risks, especially after the recognition in 2008 that multi-asset classes can experience severe losses at the same time despite their apparent intrinsic differences. Smart beta strategies can take many different forms, with a variety of objectives. They can simply aim at reducing risks (the “risk-based approach”) or enhancing return through exposure to systematic factors (the “factor-based” approach). In commodities investing, alternative index movement was born from frustration with the inherent biases of conventional indices. For example, negative commodity “roll yields” can erode returns by as much as 50%.

Participants in this project will explore the opportunities of constructing a commodity investment portfolio that uses different smart beta approaches to seek enhancing returns and risk reduction. Factors like curve, value, and momentum will be examined in the back-test.

Team 5 - Industry Mentor – Dr. Jing Wang
Dr. Jing Wang is Managing Director of Quantitative Research at Pine River Capital Management. He is responsible for developing quantitative models/strategies for supporting the global investment activities, and as part of the risk management, managing the daily/monthly risk reports. Prior to joining Pine River in February 2008, he was a Scientist at Vital Image from 2005 to 2008. And prior to that, he worked as a Senior Scientist for Johnson & Johnson in 2004. Jing received a PhD in Mathematics in 2002 and an MS in Computer Science in 2004, both from the University of Minnesota. From 2002 to 2004, he was a Postdoctoral Associate at the Institute for Mathematics and its Application (IMA) of the University of Minnesota.

Quantitative Strategies on Quantopian: Participants in this module will utilize the online platform, Quantopian, to explore quantitative strategies in equities or futures; the Quantopian platform provides free historical data that can be used to design, back-test and analyze quantitative investment strategies. This model will be practically useful for the participants as it involves getting familiar with fundamental data, programming in Python, and back-testing quantitative strategies.

Schedule
Team break-out rooms are available from 8 am to 4 pm, rooms are the same for all workshop days (* except Saturday, January 12 and Sunday, January 13):
Team 1 - Vincent Hall 364
Team 2 - Vincent Hall 301
Team 3 - Vincent Hall 20
Team 4 - Vincent Hall 6
Team 5 - Vincent Hall 113

* Breakout rooms for Saturday, January 12 and Sunday, January 13, 2019 are: Vincent Hall 502 and 570.

Thursday, January 10 - Vincent Hall 16: All Day Workshop Outline:

Posing of workshop projects by the 5 industry mentors through half-hour introductory talks in the morning followed by a welcoming lunch. In the afternoon, the teams work with the mentors. The goal at the end of the day is for students to start working on the projects.

9:00am-9:30am - Check In & Coffee (120 Vincent Hall)
9:30am-9:40am - Welcome — Dr. Chris Bemis (University of Minnesota)
9:40am-10:00am - Mentor 1
10:00am-10:20am - Mentor 2
10:20am-10:40am - Mentor 3
10:40am-11:00am - Mentor 4
11:00am-11:20am - Mentor 5
12:00pm - Lunch (120 Vincent Hall)
1:30pm-4:30pm - Afternoon - start work on projects

Friday, January 11 to Sunday, January 13: All Day Students work on the projects. Mentors will be available by phone, email, evening meetings, to guide their groups through the modeling process, leading discussion sessions, suggesting references, and assigning work.

Monday, January 14 - Vincent Hall 16

9:00am-9:30am - Coffee (120 Vincent Hall)
9:30am-9:50am - Team 1 progress report
9:50am-10:10am - Team 2 progress report
10:10am-10:30am - Team 3 progress report
10:30am - 10:50am - Team 4 progress report
10:50am-11:10am - Team 5 progress report
12:00pm-1:30pm Lunch (on your own)
2:00pm-5:00pm - Remainder of the day students work on projects in breakout rooms. Mentors available for consultation.

Tuesday, January 15 to Friday, January 18: All Day Students work on the projects in their breakout rooms. Mentors available for consultation.

Saturday, January 19 - Vincent Hall 16

9:00am-9:30am - Coffee (120 Vincent Hall)
9:30am - Team 1 Final Report
10:00am - Team 2 Final Report
10:30am -Team 3 Final Report
11:00am - Team 4 Final Report
11:30am -Team 5 Final Report
12:30pm-2:00pm - Lunch (120 Vincent Hall)

Past Workshops