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Fri Sep 14

MCFAM Seminar

5:30pm - Vincent Hall 16
How to Get the Most Out of the MFM
MFM 2nd Yr. Student/Alumni Panel, U of M - School of Mathematics - MCFAM
Fri Sep 21

MCFAM Seminar

5:30pm - Vincent Hall 16
Topological Data applied to Finance
Kaisa Taipale - 2018 MCFAM Summer Seminar Students, University of Minnesota
Fri Oct 05

MCFAM Seminar

5:30pm - Vincent Hall 16
CCAR (Comprehensive Capital Analysis and Review) and Basel Framework - Risk Management Modeling
Dr. Xu Li

Dr.Xu Li will give an overview of what he is does in Market Risk Analytics as a SVP of Risk Analytics at Citi. He will then focus on a default model that is useful for both CCAR, the stress testing framework set out by the Federal Reserve (IDR) and Basel framework which is the international regulatory framework for banks (IRC, DRC). He will show the general ideas on modeling the default risks and discuss some options on the modeling choices.

Fri Oct 12

MCFAM Seminar

5:30pm - Vincent Hall 16
Interpreting Constraints in Mean Variance Optimization
Chris Bemis, Head of Quantitative Analysis and Research, Whitebox Advisors, UMN Math Dpt. Affiliated Faculty

We study the effect linear constraints have on risk in the context of mean variance optimization (MVO). Jagannathan and Ma (2003) establish an equivalence between certain constrained and unconstrained MVO problems via a modification of the covariance matrix. We extend their results to arbitrary linear constraints and provide alternative interpretations for the effect of constraints on both the input parameters to the problems at hand and why ex-post performance is improved in the constrained setting.

Fri Oct 26

MCFAM Seminar

5:30pm - Vincent Hall 16
Dynamic Linear Models
Katy Micek, 3M Finance

Dynamic linear models (DLMs), a subset of state space models, describe the output of a dynamic system as a function of a non-observable state process affected by random errors. Because DLMs can be used either for traditional time series analysis tasks (making inferences on observed states or prediction future observations) or for feature generation in machine learning tasks, they are a very useful tool for any data scientist who works with time series data. As a data scientist on the Data Analytics team for 3M Finance, I work primarily with time series data from the general ledger. Our team both leads data science projects and assists in organizational development of internal capabilities around data science.
In this talk, I will first provide an overview of the Finance organization and describe the technical tasks our team addresses. Next, I will give an introduction on the mathematics of DLMs. Finally, I will conclude showing examples of how DLMs can be used on time series data in a Jupyter notebook demo.

Fri Nov 09

MCFAM Seminar

5:30pm - Vincent Hall 16
Macroecomic Analysis and Insight - Steepness of the Yield Curve As of September 2018
Ujae Kang, UnitedHealth Group

Ujae Kang will present on the Federal Reserve over the years and its influence on the yield curve. Then, he will cover what to expect from the Federal Reserve in the coming years.

Bio: Ujae Kang is Director of Enterprise Risk Management at UnitedHealth Group. He is an Associate of the Society of Actuaries and has an Master of Financial Mathematics from the University of Minnesota's School of Mathematics. He also provides economic research and insights to UnitedHealth Group's Asset Liability Management Committee as well as to other investors. For more information on Ujae go to linkedin.com/in/ujaeaugustinekang

Fri Nov 30

MCFAM Seminar

5:30pm - Vincent Hall 16
The Effect of the Risk Corridors Program on Marketplace Premiums and Participation
Pinar Karaca Mandic, MILI Director/Carlson Finance Professor

We investigate the effect of the Risk Corridors (RC) program
on premiums and insurer participation in the Affordable Care Act
(ACA)’s Health Insurance Marketplaces. The RC program, which was
defunded ahead of coverage year 2016, and ended in 2017, is a risk
sharing mechanism: it makes payments to insurers whose costs are high
relative to their revenue, and collects payments from insurers whose
costs are relatively low. We show theoretically that the RC program
creates strong incentives to lower premiums for some insurers.
Empirically, we find that insurers who claimed RC payments in 2015,
before defunding, had greater premium increases in 2017, after the
program ended. Insurance markets in which more insurers made RC claims
experienced larger premium increases after the program ended,
reflecting equilibrium effects. We do not find robust evidence that
insurers with larger RC claims in 2015 were less likely to participate
in the ACA Marketplaces in 2016 and 2017. Overall we find that the end
of the RC program significantly contributed to premium growth.

Fri Dec 07

MCFAM Seminar

5:30pm - Vincent Hall 16
Data Visualization in R
Chen Zhang, Sr. Consultant, Analytics & Research at Travelers; Ph.D. in Statistics from UConn; UMN MFM Alumnus

Data visualization is often overlooked by people working with data and/or modeling but can in fact reveal very useful insight into problems at hand. R is an open-source programming language for statistical computing and graphics with increasing popularity among practitioners in data science and machine learning in recent years. The "ggplot2" package in R, in particular, provides very powerful, intuitive and versatile tools for data visualization. An overview of these tools will be presented in this talk accompanied by a live demo.

Fri Feb 01

MCFAM Seminar

5:30pm - Vincent Hall 16
Computational Issues in Making Math Models Operational in Insurance
Scott Monitor, VP & Financial Engineer - MFM alumnus, FSA

Many insurance companies offer a wide array of investment guarantees, and some of these are complicated and have no clear analytical solution. In order to manage these contracts and along with increased scrutiny from regulatory bodies, companies are having to value these contracts more frequently and in a greater number of runs. We will demonstrate methodologies and computational approaches to be able to perform analysis on these contract that can be actionable and timely.

Fri Feb 08

MCFAM Seminar

5:30pm - Vincent Hall 16
Today's Seminar - Canceled
Jie Ding, School of Statistics - University of Minnesota

In the era of “big data”, analysts usually explore various statistical models or machine learning methods for observed data in order to facilitate scientific discoveries or gain predictive power. Whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model or method from a set of candidates. Model selection is a key ingredient in data analysis for reliable and reproducible statistical inference or prediction, and thus central to scientific studies in fields such as ecology, economics, engineering, finance, political science, biology, and epidemiology. There has been a long history of model selection techniques that arise from researches in statistics, information theory, and signal processing. A considerable number of methods have been proposed, following different philosophies and exhibiting varying performances. The purpose of this talk is to bring an overview of them, in terms of their motivation, large sample performance, and applicability. I will provide practically relevant discussions on theoretical properties of state-of- the-art model selection approaches, and share some thoughts on controversial views on the practice of model selection.

Bio for Jie Ding
https://cla.umn.edu/statistics/news-events/story/new-faculty-member-char...

Fri Feb 15

MCFAM Seminar

5:30pm - Vincent Hall 16
Simulating the Greeks of American Options
P.A. (Phuong Anh) Nguyen, University of Minnesota

Abstract: In this paper, we implement an efficient simulation-based method for estimating the Greeks of American options. We perform a least square regression to determine the optimal stopping rule that is applied to calculate the Greeks, which are derived via a path-wise derivative approach. We prove that this method provides asymptotically unbiased simulation estimators for the Greeks. In addition, we propose a boundary integral technique as a faster way to approximate gamma. This technique can also be used to calculate delta and theta. Our paper is the first to provide complete simulation-based approximations for all of the Greeks (delta, gamma, theta, rho, and vega) of American options. To make the computational process more efficient, we incorporate a Brownian Bridge into the numerical simulations. We then extend the application to American basket options.

Bio: P.A. Nguyen is a PhD candidate in the University of Minnesota's Industrial Systems Engineering (ISyE) Doctoral Program. She is working with Dr. Dan Mitchell whose focus is in the area of financial engineering, specifically applying stochastic control to problems in finance and quantitative risk management. P.A. is also an alumna of the Master of Financial Mathematics (MFM) at the University of Minnesota (2014) and is currently a teaching assistant for the MFM. She worked in enterprise risk management, primarily in credit risk and interest rate risk areas, for a few years before joining UMN’s ISyE PhD program.

Fri Feb 22

MCFAM Seminar

5:30pm - Vincent Hall 112
The Prospect of a Forgivable Premium Insurance Policy
Kyle Jore, University of Minnesota

Despite low premiums and high subsidies, farmers view crop insurance programs as a gamble. One explanation, in a revenue protection program, is that farmers exhibit loss aversion when premiums are just above coverage. Introducing a model for conditional loss aversion (CLA), in the context of cumulative prospect theory, it can be shown that the introduction of a forgivable premium can remove the producers loss aversion. This would result in producers being willing to spend more on an insurance program and thus, allow for a reduction in the implied subsidy.

Bio: https://www.linkedin.com/in/kylejore/

Fri Mar 01

MCFAM Seminar

5:30pm - Vincent Hall 16
Introduction to model selection principles for data analysis in the era of Big Data
Jie Ding, School of Statistics - University of Minnesota

In the era of “big data”, analysts usually explore various statistical models or machine learning methods for observed data in order to facilitate scientific discoveries or gain predictive power. Whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model or method from a set of candidates. Model selection is a key ingredient in data analysis for reliable and reproducible statistical inference or prediction, and thus central to scientific studies in fields such as ecology, economics, engineering, finance, political science, biology, and epidemiology. There has been a long history of model selection techniques that arise from researches in statistics, information theory, and signal processing. A considerable number of methods have been proposed, following different philosophies and exhibiting varying performances. The purpose of this talk is to bring an overview of them, in terms of their motivation, large sample performance, and applicability. I will provide practically relevant discussions on theoretical properties of state-of- the-art model selection approaches, and share some thoughts on controversial views on the practice of model selection.

Bio for Jie Ding: https://cla.umn.edu/statistics/news-events/story/new-faculty-member-char...

Fri Mar 08

MCFAM Seminar

5:30pm - Vincent Hall 16
Feb 2018 Volatility Event
Yuepeng “Perry” Li, CFA, FRM, Parametric Portfolio Associates LLC

On the Monday of February 5th 2018, VIX Index (measure of expected future volatility) spiked by 116% to 37.3, and massive turbulence was observed across global financial markets. During the talk, we will review this event and discuss on the following topics:

  • How did we get there? --- Low volatility environment and the great snake of risk 
  • What happened? --- 2017’s hottest trades went wrong, and several funds and firms (!) were effectively wiped out
  • What did we learn? – Positioning, behavior, and never underestimate the risk of financial markets (convexity to volatility and VaR calcs)

 

Fri Mar 29

MCFAM Seminar

5:30pm - Vincent Hall 16
MFM Modeling Workshop Presentations - Machine Learning in Equity Classification and Smart Beta Investing in Commodities
2019 FM Modeling Workshop Student Presentations, University of Minnesota

Two Teams of MFM students from the 2019 MFM Modeling Workshop will present their work to the Seminar. Each group will take a half hour to cover their topics, including Q & A

Machine Learning in Equity Classification: This MFM modeling workshop team worked with various machine learning classification models with the goal of classifying equities via well-known quantitative factors such as Value and Momentum. The classification was supervised, utilizing a novel ETF dataset which was supplemented extensively. The team worked in Python, especially the Scikit Learn module. They will present their project to seminar attendees

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%. This team explored 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 were examined in the back-test.

Fri Apr 12

MCFAM Seminar

5:30pm - Murphy Hall 130
Model Development & Delivery in Real World Quant Finance
Florian Huchede, Director & FX Lead Quant - CME Group

Over the last 10 years, financial companies have been increasingly using quantitative models for decision making. Well performing models can provide automatic and objective decision making as well as a certain ability to synthesize complex issues. However, models expose companies to model risk, higher development cost and longer delivery time. In this MCFAM seminar, we will cover how to reduce the model risk and time to market by using a model design process. Furthermore, we will apply the process on a particular example: OTC FX option volatility calibration.

Bio: Florian Huchedé is a Director of Quantitative Risk Management at CME Group. He leads an international team of quantitative analysts that work on designing, implementing and filing quantitative algorithms on various applications (settlement, pricing, data cleansing, risk management, product creation and large optimization). Furthermore, he is the lead quant for the FX and Equity asset classes.

Florian graduated from the Financial Mathematics program at University of Chicago (2010). He completed his undergraduate studies and MS in Engineering at Ecole Francaise d’Electronique et d’Informatique in Paris, France (2007). Prior to joining CME Group, he worked at Credit Agricole Asset Management Alternative Investment and at The Option Clearing Corporation.

With more than 10 years of experience, Florian is focused on innovation, creativity and giving back to the quant community. He holds three U.S. patents on risk management and financial products. Furthermore, he initiated a joint research program between University of Chicago and CME Group in 2012. Since then, the program has expanded and is being utilized by many other companies.

Fri Apr 19

MCFAM Seminar

5:30pm - Vincent Hall 16
2019 MFM Modeling Workshop Presentations- Yield Curve Construction and Valuation and Replication Strategies for Variance Derivatives
2019 FM Modeling Workshop Student Presenters, University of Minnesota

Two Teams of MFM students from the 2019 MFM Modeling Workshp will present their work to the Seminar. Each group will take a half hour to cover their topics, including Q & A

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.

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.