Financial Mathematics Modeling for Graduate Students: Winter 2018
Winter 2018 Workshop: January 4-13, 2018
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 2018 Modeling Workshop.
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 6 teams participating in the workshop.
Team 1 - presentation.pdf
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.
Credit Implied Volatility
Students in this module will study an intersection of options pricing and fixed income valuation using Merton's structural model approach. Our work will analyze market data in this setting, in particular, CDS spreads. This work will necessarily lead to viewing complications that arise in several mathematical finance models; viz., non-constant implied volatilities and the poor fit of the log-normal assumption of asset returns. One interesting resolution to these issues may be found in applying recent work by Borland and Bouchaud.
Team 2 presentation.pdf
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.
Static Arbitrage Free Volatility Surfaces
Fitting an equity volatility surface is a fundamental finance problem, and while many techniques exist, they often do not guarantee the absence of static arbitrage.
Participants in this project will learn types of static arbitrage in volatility surfaces and investigate problems involved in creating a model that guarantees no riskless profits. Issues of arbitrage-free interpolation will also be explored. As a result of this project, the participants will create a functional volatility surface model by using the work of Gatheral and Jacquier. The model belongs to the SVI family and does not admit arbitrage under certain tractable conditions. Although Matlab is encouraged, the participants can use any software.
Team 3 presentation.pdf
Industry Mentor - Dr. Eric Falkenstein
Eric has worked as a portfolio manager, quant, and risk manager since getting his Ph.D. in economics from Northwestern University in 1994. He is currently incubating a fund and developing an Ethereum smart contract.
Ethereum, Smart Contracts, and Option Valuation
Cryptocurrencies are now worth about $200B. While most of this value is in Bitcoin, Ethereum presents many interesting opportunities for revolutionizing finance via its ability to implement smart contracts, programs that use a virtual machine run by each node in their blockchain. These contracts have to be very simple, and while there are many thousands of great minds creating contracts and things related to them (eg, state channels), there is still not a really useful smart contract outside of tokens. I am currently working on a smart contract and would like to introduce people on how to develop and test smart contracts using some toolkits that have been developed. In the process, we can see the limits of what these contracts can do, given the constraints on memory, cost, and the limited nature of Ethereum Virtual Machine. For example, if one has a ledger with 100, or 1000, records, how can one put this on the blockchain and access it most efficiently? Can one calculate a Black-Scholes option price using an Ethereum contract, and at what price?
Team 4 presentation.pdf
Industry Mentor - Greg Fish
Gregory Fish is a trader at Walleye Trading, where he oversees the firm’s trading and risk management of options on interest rates. Prior to Walleye, he held various trading and risk management roles at Citigroup Derivatives Markets.
Market Option Pricing and Statistical Arbitrage
This project will analyze market data in options to understand the probability distribution implied by market prices in a series of options. We’ll evaluate various methods of pricing options and the volatility smile. We’ll chose a method that seems to represent the market best, and we’ll apply that methodology to try to emulate market prices over days, weeks, or months. As we do so, we’ll look for temporary mispricings in the market that might represent opportunity.
Team 5 presentation.pdf
Industry Mentor - Chris Prouty
Chis is an Exotics Trader at Cargill and serves as the instructor for FM 5091/5092: Computation, Algorithms and Coding in Finance. Chris began his financial career as a research assistant at the Federal Reserve Bank in Minneapolis. Since graduating from the University of Minnesota with a B.S. in Applied Economics, Chris has worked in commodities and insurance, in roles focusing on trading and risk management through derivative strategies. Chris currently works for Cargill, where he is an exotic derivatives trader. During college and shortly thereafter Chris operated a small software consulting firm, CP Consulting. He has completed freelance software development projects for Twin Cities firms, including the University of Minnesota Foundation and ACR ATI, a firm which offers employee testing services to the health care industry.
Financial Information Exchange (FIX)
Over the past decade, the rise of automated or algorithmic trading has been astounding. An increasing number of firms now employ sophisticated hardware and highly optimized software to place trades and make markets in a variety of asset classes. The volume of trades on electronic exchanges has grown along with the number of firms utilizing automated trading. By some estimates, high frequency trading now accounts for 75% of all trades in US equities. One of the tools employed in automated trading is a protocol called FIX, or Financial Information eXchange. FIX is an open protocol used by banks, hedge funds, exchanges, and other market participants to transmit order and quote data between trading partners. Although FIX as a protocol is fairly simple, implementation requires solid knowledge in both programming and finance.
Participants in the FIX module will build a functioning FIX client in C# and use it to connect to a proprietary server application generating artificial market data. Using FIX, participants will capture a stream of quotes from the server and then analyze the time series to try to design a profitable trading strategy. Once a strategy is designed and implemented in the client application, trades will be placed with the server via FIX, and P&L of the strategy will be tracked. Ideally, the team will complete the module by building a fully automated profitable trading strategy.
Team 6 presentation.pdf
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 Equity Strategies
Students in this module will utilize online platforms (like Quantopian, or QuantConnect) to implement certain quantitative equity strategies; for example the value strategies covered in the “The Guru Investor” book. The work will be practically useful as it involves getting familiar with fundamental data, programming in Python, and back-testing quantitative strategies.
Team break-out rooms are the same for all workshop days (* except Saturday, January 6 and Sunday, January 7):
Team 1 - Lind Hall 216
Team 2 - Lind Hall 325
Team 3 - Lind Hall 320
Team 4 - Lind Hall 340
Team 5 - Lind Hall 315
Team 6 - Lind Hall 303
* Breakout rooms for Saturday, January 6 and Sunday, January 7, 2018 are: Vincent Hall 502 and 570.
Thursday, January 4 - Vincent Hall 16
All Day Workshop Outline: Posing of workshop projects by the 6 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 — 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
11:20am-12:00pm - Mentor 6
12:00pm - Lunch (120 Vincent Hall)
1:30pm-4:30pm - Afternoon - start work on projects
Friday, January 5 to Sunday, January 7
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 8 - 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
11:10am- 11:30pm - Team 6 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 9 to Friday, January 12
All Day Students work on the projects in their breakout rooms. Mentors available for consultation.
Saturday, January 13 - 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
12:00pm - Team 6 Final Report
12:30pm-2:00pm - Lunch (120 Vincent Hall)