Financial Mathematics Modeling for Graduate Students
Winter 2014 Workshop: January 09-18, 2014)
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 2013 Modeling Workshop.
Students will work in teams of up to 6 students under the guidance of a mentor from the financial modeling/trading sector. The mentor will help guide the students in the modeling process, analysis and computational work associated with a real-world financial modeling/trading problem. A progress report from each team will be scheduled during the period. In addition, each team will be expected to make an oral final presentation and submit a written report at the end of the 10-day period.
Projects and Industry Mentors
There will be 4 teams participating in the workshop.
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.
Nelson Neale, Market Strategist - Corporate Risk Management
Cash Flow at Risk for Non-Financial Corporations
Value-at-risk (VaR) represents the standard risk measurement and modeling framework for banks, trading firms and other financial corporations. While the metric is particularly useful for those entities holding highly liquid assets like equities, the unique properties and operations of non-financial corporations demand a risk framework that focuses on cash flow or earnings uncertainty over a time horizon that matches the view of management and shareholders. Cash flow-at-risk (CFaR) or earnings-at-risk (EaR) represent extensions of VaR that may be used to effectively evaluate both price and non-price risks associated with these firms.
Participants in this module will develop a functional Monte Carlo simulation-based CFaR framework for a hypothetical agricultural commodity-based corporation. The team will evaluate a variety of spot price processes to describe each cost or revenue component (e.g., geometric Brownian motion, mean reversion, etc.), parameterize models, and identify key risk drivers for the corporation. Based on the identified risk profile, the team will consider risk management options under different management objective scenarios
Dr. Lourenco Miranda, Vice President of Quantitative Analytics
US Bank Treasury Department
Detecting, Predicting and Preventing Malicious Behavior using Artificial Intelligence for Operational Risk Modeling
In this two-week project we will focus on a vital Operational Risk event type that can be totally (and it was in 2008) disruptive to the financial system and the real economy - External Fraud (Malicious Behavior). It is in the spotlight for National Regulators like the OCC and the White House itself . This modeling area becomes even more interesting and intriguing because the technique we will apply to detect these events is Artificial Intelligence, which is a very sought after mechanism in the industry these days. We will develop together a simple case of pattern classification and detection mechanism.
Unlike the other risk classes in financial services, for example market and credit risk, the understanding of operational risk, and how to identify, measure and manage it, is far from complete. Yet operational risk has the biggest potential to damage the financial system of the United States with direct impact in the real economy and in the general public. As an example, many of the root causes of the 2008/09 financial crisis were related to operational risk, and major financial institutions continue to experience operational risk events, such as rogue trading, whose impact potentially threatens the ability of the organization to remain in business.
Operational risk is multi-dimensional, encompassing such areas as fraudulent investment scheme (Ponzi schemes), other types of fraud, and money laundering, as well as cyber crime, terrorism and extreme weather. Whether related to internally controllable aspects, such as people, systems and business processes, or to events outside an organization's control, managing operational risk is about identification and causes of potential events that (1) could harm the organization's financial viability and ability to meet its business objectives, and (2) about reducing the likelihood of an event occurring, or mitigating the impact of an event when it does happen. The further study of operational risk, and building the widest possible understanding of it, is therefore in the vital interest of worldwide financial stability. This makes the relevance of this topic one of the most important ones when talking about Systemic Risk.
In terms of reducing the likelihood of occurrence of fraud (or any other malicious behavior) and being proactive before the event becomes a loss or even an issue of national security, the number one course of action to be applied is preventative control. In order to be considered an efficient control, there must be mechanisms in place to detect the malicious behavior and react fast.
As stated before, the most promising mechanism to detect and prevent malicious behavior is the application of Artificial Intelligence for pattern recognition . Here we will review the current methodologies and we will apply Artificial Neural Networks to the detection of such patterns. The final deliverable will be a position presentation showing the relevance and the applicability of this topic and a simple code that illustrates the applicability to a simple problem.
Therefore, with no shade of doubt, you will be involved in one of the most relevant, exciting and critical problems in the financial industry these days that has a direct and vivid impact in people's lives, the real economy and systemic risks.
All Day Workshop Outline: Posing of problems by the 4 industry mentors. 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 - Coffee and Registration
9:30am-9:40am - Welcome Rina Ashkenazi (University of Minnesota)
9:40am-10:00am - Team 1: TBA
10:00am-10:20am - Team 2: TBA
10:20am-10:40am -Team 3: TBA
10:40am-11:00am -Team 4: TBA
1:30pm-4:30pm - Afternoon - start work on projects
Friday, January 10 to Sunday, January 12
All Day Students work on the projects. Mentors guide their groups through the modeling process, leading discussion sessions, suggesting references, and assigning work.
Dr. Nelson Neale is Market Strategist for Land O'Lakes, a $13 billion food and agricultural cooperative in Arden Hills, MN with over $4 billion in commodity spend. He is responsible for developing programs to identify commodity risk and volatility across the Land O'Lakes portfolio of businesses (cost, revenues, margin management) and to identify and implement solutions to mitigate these risks. Prior to Land O'Lakes, Nelson served as a member of the Global Risk and Trading practice of Oliver Wyman in New York where he directed North American and European engagements focused on trading, hedging and risk management strategies and programs for oil and gas companies. Nelson has also previously served as head of the market and commodity analysis group for Tyson Foods and as a market risk officer for UBS Investment Bank. He began his career in the quantitative research group at Enron in Houston.
Nelson holds a PhD in engineering from Rice University and a BS in mathematics from Davidson College.
Dr. Lourenco Miranda has 16 years of progressive experience in risk management including capital adequacy and quantitative finance and for twenty five years he has done quantitative modeling. Dr. Miranda has had hands-on experience in international banking systems, global regulatory practices and capital adequacy processes in a variety of regions across the globe - the Americas, the Middle East, East and South Asia, Western Africa, Western and Eastern Europe. Before he joined US Bank he was a Senior Risk Officer for the International Finance Corporation within the World Bank Group, Head of Integrated Risk Management and Modeling at ABN AMRO and a Senior Derivatives Specialist at Santander Investment Bank. Dr. Miranda received his PhD in Statistical Physics from Pontificia Universidade Catolica do Rio de Janeiro, Brazil and has contributed to different academic institutions in the US, Brazil, Russian Federation and the Netherlands.
Christopher Prouty serves as the instructor for FM 5091/5092: Programming and Presentation in Finance and works for Cargill.
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.
Dr. Carlos Tolmasky is a derivatives trader at Cargill Petroleum. He joined Cargill in 1996 as a member of their Research Group focusing on the development and implementation of derivatives models for fixed income and commodities markets. He later joined the petroleum group as a "desk quant" and, more recently, as a derivatives/relative value trader. The author of various papers in financial journals, Dr. Tolmasky holds an undergraduate degree (Licenciado) from the University of Buenos Aires and a PhD in mathematics from the University of Washington.
Dr. Chris Bemis is a Senior Portfolio Manager for Whitebox Advisors, working primarily on equity market modeling outside of the United States. He is also an active researcher for the Whitebox quantitative group, where he works on varied problems in the context of equity, derivative, and fixed income strategies. Dr. Bemis earned his PhD in applied mathematics from the University of Minnesota. His thesis work involved both modeling and optimization for portfolios of risky assets.