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Seminars

The "Practitioner Seminar Series" invites experienced  professionals in risk management and quantitative finance to the campus to give talks on the applications, practice and issues in these fields that are important in the day-to-day operations or business models of their institutions. These seminars and discussions give the students in the program a complementary perspective to what they learn in the classroom and form a component of their overall practical training experience. Prospective students, students and faculty in other Rutgers programs and interested third parties may also attend selected seminars, depending on seating availability and the IP protection policy of the particular speaker and his/her institution: please contact Prof Lee Dicker at This email address is being protected from spambots. You need JavaScript enabled to view it. for more information.  Information on upcoming seminars follows:

FALL 2016

Friday, November 11 2016, 3:00 pm – 4:30 pm

Title:  "FSRM Boot Camp: Understanding the Market Basics"

Abstract: Graduate students will strengthen their understanding of the market, gain industry insight, discuss topics specific to Financial Services, and build students’ business acumen. This workshop is designed to review concepts and terminology that lay the groundwork for gaining industry knowledge as well as provide an opportunity for participants to showcase their understanding of practical knowledge to an industry practitioner for feedback and support. Resources on how to increase industry knowledge will be provided.

Speaker: Erika Green, Global Staffing - Global Risk Management, Bank of America

 

Wedensday, November 9 2016, 4:00 pm – 5:30 pm in Room 552; Refreshments 3:30 in Room 502

Title:  Guggenheim Partners Overview, What the Risk Management Team does, how the FSRM program has helped me, Insights on Liquidity Risk Measurement and its Regulation

Speaker: Balazs Khoor, FRM, Senior Associate, Risk Analyst – Market and Credit Risk. Guggenheim Partners and FSRM alumnus

BalazsKhoor BIO: Balazs Khoor, FRM, Senior Associate Risk Analyst – Market and Credit Risk. Guggenheim Partners
Balazs joined Guggenheim Partners Investment Management as an Analyst for the Market and Credit Risk team. His current role includes integrating third-party vendor data with internal portfolio risk reporting/analytics frameworks and developing proprietary quantitative models using MATLAB. Prior to joining Guggenheim, Balazs was a member of the Investment Risk team for K2 Advisors, Franklin Templeton Investments’ Alternatives/Fund of Hedge Funds arm. He holds a M.S. in Financial Statistics and Risk Management from Rutgers University and a M.S. in Applied Economics from Florida State University. Balazs is also a member of the Global Association of Risk Professionals (GARP) and a holder of the Financial Risk Manager (FRM) certification.

 

 

Friday Nov 4th. 12:00pm - 1:00 pm , in Room 552; pizza and refreshments in Room 502

Title: Statistics and Risk Management in Banking

vnn2Speaker: Vijay Nair is Donald A. Darling Professor of Statistics and Professor of Industrial and Operations Engineering, University of Michigan and statistical consultant at Wells Fargo. Professor Vijay Nair has made many outstanding contributions over the course of his career, to the statistics community in general and to the Department of Statistics at the University of Michigan in particular. He has played a major role in promoting a broad and outward-looking emphasis in statistics research, in developing ties between academic research and industry, and in promoting the internationalization of statistical research and advanced practices. As a long time (former) chair of the U-M Department of Statistics, Professor Nair led the formation of an Informatics concentration for undergraduates, and more recently has been instrumental in the creation of the Michigan Institute for Data Science on the U-M campus. A lasting legacy of his vision and efforts in promoting ties between industrial and academic research dating from his time as a member of the technical staff at Bell Labs is the successful annual Spring Research Conference series in industrial statistics. As current president of the International Statistical Institute, Vijay Nair has played a highly visible role in efforts to build the capacity of statistical expertise worldwide, including in Latin America, Africa, and Asia.

 

Friday, October 28, 2016, 3:30 pm – 4:30 pm :  (Light Refreshments and "Meet/Greet" 3:00 pm)

Speaker: Yong MaManaging Director Risk Management, COO for Risk Analytics, Morgan Stanley

MaYong 20141120 AFS smallBio: Yong Ma is a Managing Director of Morgan Stanley and currently serves as Chief Operating Officer ("COO") for Risk Analytics. His key management responsibilities include risk model development to meet regulatory and internal requirements, stress testing (such as CCAR), governance and project process, global organizational structure and resource efficiency. Prior to his current role, he was Global Head of Risk Infrastructure covering the Basel capital processes, project and data management, risk reporting, credit policy and regulatory relations. He also served as COO for Credit Risk Management, Market Risk Management, and CAO (Chief Administrative Officer) for Firm Risk Management. Prior to joining Morgan Stanley, Mr. Ma served in a variety of Risk and Technology roles within Barclays Capital/Lehman Brothers, Merrill Lynch, Goldman Sachs and IBM.

Among his civic activities, Mr. Ma serves as a NY Chapter Board Member of International Leadership Foundation, a Board member of New York Young Entrepreneurs Roundtable, and on the Advisory Board of the Asian Financial Society. He is an active member of Huaxia Chinese Schools; formerly served as the Chair of the Board of Trustees at Livingston Branch and a Board member of the Headquarter.

Mr. Ma holds a PhD in Computer Science from the University of Illinois, Urbana-Champaign, and a M.S. in Computer Science from University of California, Davis. Prior to that, he attended the Computer Science Graduate Program at Tsinghua University and earned a B.S. in Computer Engineering from Dalian University of Technology.

 

Friday, October 21 from2:00 to 4:45 in Core Auditorium

Title: Munich Re Company Information Session and Practitioner Seminar

Agenda:

2:00 – 2:30 Munich Re Information Session and Q&A (Ariana Mastroianni who is a HR Business Partner at Munich Re will be going over a brief background of Munich Re and then discuss our International Graduate Trainee Program.  Q&A

2:30 – 3:00   Randy Coppola who is a Client Manager at Munich Re will discuss (Re)Insurance Industry Risks.  Q&A

3:00 - 3:30 Refreshment break and meet students 

3:30 – 4:45  Munich Re Practitioner Seminar (Andy Kirtland who is the Corporate Predictive Analytics Lead here at Munich Re will speak about Predictive Analytics)

 

Thursday, October 20 from1:00 to 3:30 in Hill 552

Title: Imagine Software Company Information Session and Practitioner Seminar

Agenda:

1:00 - 1:30 Light lunch and meet and greet students

1:30 - 2:30 A) Current Financial Industry Climate and Trends - Trever Evans, Director, Consulting at Imagine Software
- Smart Beta ETFs
- Risk Culture
- Emergence of Global Macro
B) Quick Demo of work done by FSRM interns at Imagine Software this past summer - Mike Cannon, Imagine Software

2:30 - 3:00 Transitions from School to the Workplace ... Brian DeLuca, Imagine Software and Rutgers Alumnus

3:00 - 3:30 Q&A

 

Friday, October 7, 2016, 3:00 pm – 4:30 pm 

Title:   "Professional Branding : A Tool for Success"

Abstract: FSRM and MSDS Graduate students will learn how to represent themselves professionally in the job market at interviews, networking opportunities and in the workplace. This workshop is focused on providing participants with tools and strategies to build a professional brand and expand their professional network. Participants will learn how to prepare for professional interactions and articulate their brand effectively to others in a professional manner. (Interactive activity

Speaker: Erika Green, Global Staffing - Global Risk Management, Bank of America

 

Tuesday, September 20, 2016, 2:00 - 3:30 pm, 552 Hill Center

Title: Quantitative Investment Strategies at Goldman Sachs - Alpha, Liquid Alternatives, Advanced Beta, Traditional Beta

Speaker: Gary W. Chropuvka, Head of Customized Beta Strategies, Alternative Investment Strategies and Tax-Advantaged Core Strategies businesses within Goldman Sachs Asset Management’s (GSAM) Quantitative Investment Strategies (QIS) team.

 Chropuvka Gary 200x200Bio: Gary is head of the Customized Beta Strategies, Alternative Investment Strategies and Tax-Advantaged Core Strategies businesses within Goldman Sachs Asset Management’s (GSAM) Quantitative Investment Strategies (QIS) team. In this capacity, he oversees the team’s rules-based and customized index strategies, alternative risk premia and liquid hedge fund replication strategies, as well as tax-efficient investment strategies. 

Additionally, Gary oversees global client portfolio management for the entire QIS franchise across the alpha and beta spectrum, including the team’s Equity Alpha, Macro Alpha and smart beta offerings.

Previously, Gary was a portfolio manager for GSAM’s QIS Equity Alpha business, managing the portfolio implementation efforts for both tax-exempt and taxable clients, a position he held for
seven years. Gary has been a member of the QIS team since 1999, having joined to manage the team’s tax-efficient investment strategies. He joined Goldman Sachs in 1998 as an analyst in GSAM’s Private Equity Group and was named Managing Director in 2006 and partner in 2014. 

Gary earned a BA in Mathematics from Rutgers University and a master’s degree in financial engineering from Columbia University. He is a CFA charterholder.

SPRING 2015

Friday, April 30 from 1:0 to 2:00 in Room 552; Refreshments 12:30 in Room 502

Title: Do Statistics Have Predictive Power for Financial and Environmental Extreme Events

Abstract: The occurrence of financial and environmental extreme events can lead to substantial human and economic losses. Academics and practitioners are concerned about how to do risk assessment and to develop precautionary measures. An interesting question is whether extreme events are predictable. In this talk, we will review some historical statistics which may give us some ideas on catastrophe risk management. Specifically, we may be able to identify some statistics which are related to the occurrence of catastrophic incident..

Speaker: Mike K.P. So, The Hong Kong University of Science and Technology

MikeSophoto2BIO: Prof. Mike SO is an Associate Professor of the Department of Information Systems, Business Statistics and Operations Management of HKUST. He devotes to excellence in research on nonlinear time series analysis, dynamic modeling of economic & financial data, Bayesian analysis, risk management and data analytics. His research findings have been published in more than 50 scholarly articles in international journals. Active in university and industry collaborations, he has served as an advisor in numerous collaborative projects with mutual funds, stock exchange and international companies in financial and data analytics areas. Currently, he is a co-regional director of the Hong Kong Chapter of Professional Risk Managers’ International Association (PRMIA). Prof. So is also a recognized professor who demonstrates outstanding dedication on teaching and learning development.

He is the founding director of the Risk Management and Business Intelligence Program of HKUST, a nine-time recipient of the Best Ten Lecturers, a winner of the Franklin Prize for Teaching Excellence, a winner of the Awards for Excellence in Teaching Innovation as well as a 2009 Michael G. Gale Medalist for Distinguished Teaching.

 

Friday, April 17 from 3:30 to 4:30 in Room 552; Refreshments 3:00 in Room 502

Title: Generating Alpha with Equity  P/E Factor Models 

Abstract: Adapting familiar multi-factor modeling techniques to a new framework, Equity P/E Factor Models explain equity prices based on how firms generate profits (or not), delivering useful insights such as factor valuations that signal “alpha periods” and market premiums that reflect market expectations for firm-specific growth. This presentation provides an overview of the methodology and illustrates example usage for asset selection, buy/sell timing decisions, and portfolio analysis.

Speaker: Nathan Tidd, Tidd Laboratories

2b7a3f3Bio: Nathan is the founder of Tidd Laboratories, Inc., a quantitative research firm that helps active equity managers outperform using the unique insights from the firm’s innovative P/E Factor Models.
Prior to founding Tidd Labs, Nathan served on the operating committee of MSCI Inc., where he headed the Barra Portfolio Analytics division.  Nathan has previously held executive and management positions at Morgan Stanley, Barra International, hedge fund Horizon Management Ltd, and software provider Corel Inc.  He holds an MBA from INSEAD, an undergraduate degree in Finance from Brigham Young University, and is a CFA Charterholder.

 

Friday, March 27 from 3:30 to 4:30 in Room 552; Refreshments 3:00 in Room 502

Title: Quantitative Practices in Credit Risk for CCAR/DFAST Stress Testing

Abstract: Comprehensive Capital Analysis and Review (CCAR) and Dodd Frank Annual Stress Testing (DFAST) is the most quantitative and the most comprehensive exercise of modeling and analysis to assess aggregate risk and to ensure capital sufficiency for large banks operating in United States. Talents in CCAR and DFAST are among the most demanded in the current job market. 

After a briefly introduction of CCAR and DFAST in the beginning, this short talk will focus on the quantitative aspect of credit risk forecasting in the stress testing for CCAR and DAFST.  The following topics will be discussed in some details: (1) the Basel default-based construct of expected credit losses PD × LGD × EAD;  (2) current practices in modeling and estimating PD, LGD, EAD and credit losses over future time horizon; (3) model risk assessment and capital buffer

Speaker: Dr. Hengzhong Liu, Fifth Third Bancorp

hlphotoBio: Dr. Hengzhong Liu earned a PhD in Financial Economics, Dr. Hengzhong Liu is a veteran risk quant professional in financial and banking industry. 
In his industrial career of 20 plus years after his early years of academic life, he headed various quant and strategy groups as SVP and/or MD for consumer, commercial and wholesale banking and lending in Citi Group, and CIT Group. He is currently heading the CCAR program for commercial/wholesale portfolio in Fifth Third Bancorp

 

 

Friday, March 6 from 3:30 to 4:30 in Room 552; Refreshments 3:00 in Room 502

Title: SEVEN SINS OF QUANTITATIVE INVESTING 

Abstract: We discuss the seven common biases in model building. We compare various data normalization techniques; address the issues of signal decay/turnover/transaction costs; illustrate the asymmetric payoff patterns and the impact of short availability; show the optimal rebalancing frequency; and compare active management via multi-factor models versus smart beta investing via factor portfolios.

Speaker: YIN LUO, MANAGING DIRECTOR AND GLOBAL HEAD OF QUANTITATIVE STRATEGY, DEUTSCHE BANK

176a142Bio: Yin Luo is a Managing Director and Global Head of Quantitative Strategy at Deutsche BankPrior to Deutsche Bank, Yin spent over 12 years in investment banking and at a management consulting firm with various roles in quantitative research, fundamental research, portfolio management, investment banking and consulting. Yin was ranked #1 in the Institutional Investor's II-All America equity research survey in quantitative research in four consecutive years (2011-2014). Yin and the global quant strategy team were also ranked #1 in II-Europe and II-Asia surveys. Yin has a Bachelor of Economics degree from Renmin University of China, a MBA in Finance from University of Windsor, a Master of Management and Professional Accounting from University of Toronto. He is a CFA charterholder, a US CPA, a CGMA (Chartered Global Management Accountant), and a PStat (Professional Statistician).

 

FALL 2014

Friday, December 5 from 3:30 to 4:30 in Room 552; Refreshments 3:00 in Room 502

Title: Is There Alpha in Stock Buybacks? A Case Study 
Abstract: Stock buybacks programs are a hot topic, as listed companies among the largest net purchasers in today's market. We will explore the mechanics of stock buyback programs, and consider their role in the context of management activities. But what are the performance implications for investors? How might we build an investment strategy to capture the alpha (if any)? What are the processes for collecting relevant market data, and what are the barriers to high-quality output?

Speaker: David Krein, Head of Research, MarketAxcess

DAVIDKREINBio: David Krein is Head of Research for MarketAxess, the leading electronic bond trading platform. He plays a leading role in evaluating market microstructure, developing new trading capabilities and data products, and understanding client transaction costs and best execution metrics.Previously, he was Head of Research for Nasdaq Global Indexes and Senior Director of Product Development and Analytics for Dow Jones Indexes. In those roles, David led the research and development of new and existing indexes across asset classes, which were used as benchmarks for a wide array of active and passive investment funds globally.Prior to Dow Jones Indexes, Mr. Krein was president of DTB Capital, a firm he founded in 2006 to develop indexes and structured investment products for derivatives exchanges and the over-the-counter marketplace. Before establishing DTB Capital, Mr. Krein spent more than 10 years in various trading, structuring and technology positions at leading investment banks, including UBS and Merrill Lynch. ​​Mr. Krein earned an MBA with Honors from The University of Chicago and a bachelor’s degree in mechanical engineering with Distinction from Cornell University. In 2013, Mr. Krein wrote and taught “Indexing and ETFs,” the first-of-its-kind class focused on these markets, at Rutgers Business School where it was cross-listed for both MBA and Master of Quantitative Finance programs.

 

Friday, November 14  from 3:30 to 4:30 in Room 552; Refreshments 3:00 in Room 502

Title: Notes on the Role of Transaction Costs in Portfolio Analysis 
Abstract; In traditional portfolio theory, following Markowitz, it is assumed that an investor can trade immediately and costlessly to obtain a portfolio that optimally expresses her views on future security returns (i.e. her forecasts) while controlling risk exposures. This assumption results in a particular approach to the sort of analysis routinely done on trading strategies: statistical evaluation of forecasts, attribution of portfolio returns to individual forecasts, examination of risk exposures, and sizing of positions. Over the last fifteen years, however, it has become incresingly evident that transaction costs must be considered in studying any trading strategy in which the forecasts are not essentially constant over very long horizons. The transaction costs introduce a dynamical component to the analysis, as the investor must balance her eagerness to adapt to the changing forecasts against the costs of trading rapidly. I will discuss how the traditional approaches to risk analysis, forecast evaluation and attribution, and capacity need to be modified in the light of these considerations. 

Speaker: Jerome Benveniste 

Bio: Jerome Benveniste was a member of the Quantitative Trading Group at Highbridge Capital Management, LLC for twelve years, the last six as Managing Director and Portfolio Manager. He was involved in nearly every aspect of Highbridge's quantitative business, including forecast generation, risk modeling, transaction cost modeling, and optimization. Before joining Highbridge, he was a mathematician working in the areas of differential geometry, Lie theory, and ergodic theory and was on the faculty of Case Western Reserve and Stanford Universities. Jerome holds a Ph.D. from the University of Chicago and an A. B. from Harvard University, both in mathematics

 

Thursday, November 13 from 3:30 to 4:30 in Room 552; Refreshments 3:00 in Room 502

Title: Utilizing  'Big Data' to Generate Alpha in Portfolios of Consumer Equities

Abstract: The presentation will explore several kinds of new quantitative "fundamentals" that can be extracted daily from "Big Data", along with the challenges encountered while collecting and processing the vast amounts of data involved. The presentation will show how significant and consistent alpha can result from utilizing at least four of these new metrics. And the presentation will also demonstrate how to use "Big Data" tactically to anticipate revenue surprises -- before earnings reports, before guidance and in some cases even before corporate insiders fully grasp what is happening at the far end of their distribution channels

Speaker: Richard C. Davis, President & CEO, Consumer Metrics Institute, Inc.

RichardDavisBio: Richard C. Davis is the founder and President of the Consumer Metrics Institute. The Consumer Metrics Institute grew out of Mr. Davis’ frustration with the lack of timeliness and poor quality of information available to investors about the consumer economy in the United States. “It became clear to me that nearly all of the so-called ‘Leading Indicators’ available to investors were in fact no more timely or leading than last month’s account statements,” he says. “I also knew that data was already being collected that could allow investors to know how consumers are acting day-to-day. The only problem was developing analytical techniques capable of delivering that information several times per week.” 

Mr. Davis’ interest in the development of technologies that empower investors started over 30 years ago. One consistent strategy utilized by Mr. Davis during his entire career has been to push the data collection process as far up-stream as possible, developing technologies that capture critical data as close to the original source as conceivable. The internet has finally made the capture of vast amounts of data about consumer economic activities on a real-time basis practical. Mr. Davis created the Consumer Metrics Institute to acquire and channel that information to investors in a timely manner. 

A secondary theme throughout Mr. Davis’s career has been the development of tools to measure and analyze risks. “One of the broadest kinds of risk encountered by an investor is systemic risk, where the entire market, economy or financial system is under stress,” he says. “At the beginning of a downturn the investor may be encountering either a ‘market correction’, a cyclical bear market or a once-in-a-lifetime event – all of which look the same at the onset. The first should be attacked, looking for buying opportunities. But clearly the latter should be fled. How does the investor know which course to take?” 

Under Mr. Davis’ direction the Consumer Metrics Institute has focused on turning upstream economic data into information that an investor can use to assess the scope of the systemic risks associated with the equity markets. “We’ve developed our indexes to help investors identify the possible severity of the economic events that are unfolding in front of them”, Mr. Davis says.

Mr. Davis graduated from college with a B.S. Cum Laude in Physics. Before founding the Consumer Metrics Institute, he held a number of positions, including founding principal of a NASD broker/dealer and registered investment advisor, and senior IT management positions with Fortune 500 firms. He even took a 5 year non-profit sabbatical, which included a stint as the CEO of an American orchestra. The results of the Consumer Metrics Institute’s research and development is available free of charge on the Consumer Leading Indicators website: http://www.consumerindexes.com. 

 

Friday, September 12 from 3:00 to 4:30 in Room 552; Refreshments 2:30 in Room 502

Title: Multiperiod Portfolio Selection and Bayesian Dynamic Models 

Speaker: Dr, Gordon Ritter. Buy-side Statistical Arbitrage Alpha Research and Management

Abstract: We present joint work with Petter Kolm on a new theoretical framework for multiperiod optimization with transaction costs which recasts the problem as estimation of a hidden state sequence in a Markov chain. This framework is general enough to encompass the vast majority of the multiperiod portfolio choice and portfolio tracking problems that have thus far appeared in the literature. Constraints are incorporated gracefully with no change to the fundamental theory. The framework leads naturally to practical optimization methods which are shown to converge for a large class of cost functions.

0FD27BE76263C12F.18704753Bio: Gordon Ritter is an Adjunct Professor at the Courant Institute (NYU), where he teaches graduate-level courses in the Mathematics in Finance program, and at Rutgers where he teaches in the Financial Statistics and Risk Management (FSRM) program. Concurrently with his academic roles he has held several prestigious buy-side positions in the area of statistical arbitrage alpha research and portfolio management. He completed his PhD at Harvard University in mathematical physics, where he published original research in top international journals across several fields including quantum field theory, quantum computation, and abstract algebra. He is a recipient of Harvard's award for excellence in teaching. He also holds an Honors BA from the University of Chicago in Mathematics. LinkedIn: https://www.linkedin.com/profile/view?id=8456749

 

Thursday, September 4 from 1:00 to 2:30 in Room 552; Refreshments 2:30 in Room 502

Title: Models and Algorithms for Understanding the US Equity Derivative Markets

Speaker: Dr. Xiang Wu, Senior Trader - Equity Options Automated Market Making, Bank of America - Merrill Lynch

Abstract: I will be talking about from my experience, the general structure of US equity derivative markets. I will cover basic products and particularly VIX and VXX that make volatility more easily tradable by investors from all walks. I will then go into more technical details about how models and algorithms function the US electronic options markets. The last part of my talk will be a few words about starting a career in quantitative finance.

Bio: Dr. Wu is currently a senior trader on the Equity Options Automated Market Making team at Bank of America, Merrill Lynch. He received his Ph.D. in Computer Engineering from the University of Texas at Austin. His dissertation research was dedicated to scheduling algorithms for high performance on-chip networks. His innovative work on applying data mining techniques to the modern semiconductor manufacturing process earned him a share of the best student paper award from the International  Conference on Integrated Circuit Design and Technology (ICICDT).After graduation, Dr. Wu joined J.P. Morgan Chase as a quantitative researcher. He designed and developed pricing libraries for a wide range of markets and products, achieving significant performance and inter-operability improvement. He was also part of the team that built the holistic risk management framework to meet the new and much stricter regulation requirements. Seeking more challenge, he then moved to Bank of America to become a risk runner. He is now managing a portfolio of thousands of listed derivatives across all major exchanges. Outside of trading in the market, he spends his time  on discovering novel approaches to harvest risk premiums across the equity universe, robust risk management methods to maximizing risk adjusted returns and efficient algorithms that exploit market microstructure patterns.

  

SPRING 2014

Friday, April 18 from 3:00 to 4:30 in Room 552; Refreshments 2:30 in Room 502

Title: Experiments in Conditioning Risk Estimates with Quantified News

Speaker: Dan diBartolomeo, President Northfield Information Services, Boston, MA

Abstract: Since 1997, Northfield has used various forms of “contemporaneously observable” information to improve estimates of the future risk of securities and investment portfolios.  Our first effort was to include changes in “option implied” stock volatility in our short horizon risk models.  In 2007, we began to expand this concept use of information sets outside the regular data inputs to our models to adjust risk forecasts to current market conditions. Our “near horizon” family of models was commercially introduced in 2009 that used observable broad market information (e.g. VIX level) inform our models how things now are different from the way they usually are.  Recently we conducted a joint research project with a group of MIT graduate students to explore how quantified news text can used to further improve risk estimates of over short time horizon, as first suggested in diBartolomeo, Mitra and Mitra (Quantitative Finance, 2009).   This presentation will review the research done to date on “conditional” risk  modeling, describe the various forms of quantified news feeds that are now available to investment professionals, and provide empirical data from the MIT study on the extent to which quantified has been demonstrated to be a useful ingredient to stock level risk assessment.

homedanbBio: Mr. diBartolomeo is President and founder of Northfield Information Services, Inc.  Based in Boston since 1986, Northfield develops quantitative models of financial markets.   Additional Northfield staff members are located in London, Moscow, Toronto, Tokyo and Chicago. The firm’s clients include more than two hundred financial institutions in a dozen countries.
He serves on the Board of Directors of the Chicago Quantitative Alliance and is an active member of the Financial Management Association, and “QWAFAFEW”.  Mr. diBartolomeo is a director of the American Computer Foundation, a former member of the Board of Directors of The Boston Computer Society, and formerly served on the industry liaison committee of the Department of Statistics and Actuarial Sciences at New Jersey Institute of Technology.  Dan is a Trustee of Woodbury College, Montpelier, VT and continues his several years of service as a judge in the Moscowitz Prize competition, given for excellence in academic research on socially responsible investing. Dan has been admitted as an expert witness in federal court for litigation matters regarding investment management practices and derivatives.  
Mr. diBartolomeo has written extensively for the CFA Research Foundation.  This work includes “The Risk of Equity Securities and Portfolios” published in Equity Specialization Program Readings 1997 and a new wealth management monograph “Investment Management for Private, Taxable Wealth” (with Jarrod Wilcox and Jeffrey Horvitz). 
Other writings include chapters in four other textbooks (The Handbook of Municipal Bonds, Advances in Portfolio Construction and Implementation, Portfolio Analysis and Linear Factor Models in Finance).  His journal publications include "Socially Screened Portfolios: An Attribution of Relative Performance" (with Lloyd Kurtz) that appeared in the Fall 1996 Journal of Investing;  “Investment Performance Measurement and the Probability Distribution of Pension, Assets, Liabilities and Surplus” that appeared in the Spring 1997 Journal of Performance Measurement; and two papers in Financial Analysts Journal, “Approximating the Confidence Interval on Sharpe Style Weights” (with Angelo Lobosco, July 1997) and “Mutual Fund Misclassification” (with Erik Witkowski, September 1997).  His most recent publications are “Just Because We Can Doesn’t Mean We Should: Use of Daily Data in Performance Attribution” published in the Spring, 2003 Journal of Performance Measurement, and the “DSI Catholic Values 400” (with Lloyd Kurtz in Journal of Investing 2005

 

Friday, April 11 from 3:00 to 4:30 in Room 552; Refreshments 2:30 in Room 502

Title: Measuring Systemic Risk - An International Framework

Speaker: Dr. Giuseppe Corvasce

Abstract: A time and cross-sectional methodology for estimating systemic risk in case of a Global (Regional) financial crisis. This quantity, denoted Global (Regional) Systemic Expected Shortfall (SES), is defined as the aggregate amount of capital that financial institutions need, in order to offset a certain fraction of liabilities, when in aggregate the Global (Regional) financial system is undercapitalized. Each financial institution’s SES is a combination of three main components: the market capitalization, the total amount of liabilities and the appropriate capital adequacy ratios. We empirically study the joint combination of these components for a sample of 1981 financial institutions that belong to the main G- 20 economies, screened at the beginning of December 2011. The findings of the paper are many: (i) We derive a parametric and a non-parametric generalized version of the Marginal Expected Shortfall (MES) able to take into account asynchronicity issues that arise in international analysis. (ii) We study the relationship between percentage variation of market capitalization and liabilities with leverage (LVG) and the (generalized) non-parametric MES, during a “demo” period of crisis (July 2007 to December 2008). (iii) We derive appropriate capital adequacy ratios for financial industries/subindustries able to take into account divergences across accounting standards. (iv) We compute each financial institution’s SES in case of a Global (Regional) financial crisis, at the beginning of December 2011. (v) We compare several specifications of SES with an extended version of SRISK.

G.Corvasce pictureBio: Prof. Dr. Giuseppe CORVASCE is a research scientist in financial economics and affiliated with the Sociey for Financial Studies, although the views expressed in his presentation are his own. He graduated from the Swiss Finance Institute with a Ph.D inEconomics with specialization in Finance, after his studies undertaken at Bocconi University. During his doctorate, he was a research and teaching assistant for the project RISK  MANAGEMENT  (NCCR-FINRISK).  Giuseppe  is  or  has  been  a  visiting research scholar at the University of Calgary – Haskayne School of Business, University of Alberta, Luxembourg School of Finance, Fordham University and New York University – Stern School of Business where he was also appointed as a research scientist in The Salomon Center and Volatility Institute. He has been a visiting researcher at the Bank of Italy, Banque de France, Office of Financial Research and member of several academic and professional associations and societies. He is also a reviewer of several journals in finance, economics and operational research. His research interests are: financial economics, systemic risk, financial intermediation, risk management, asset allocation, international finance, volatility, financial regulation, financial stability, corporate governance and special situations.

Friday, March 28 from 3:00 to 4:30 in Room 552; Refreshments 2:30 in Room 502

Title: "Optimal Hedging Monte Carlo"

Abstract: The  Optimal  Hedging  Monte  Carlo  (OHMC)  method  will  be  discussed  in  terms  of derivative pricing and risk management. The OHMC approach is a methodology well suited for discrete time hedging problems where measures of hedge slippage and tail risk are required. The pricing of derivatives within the OHMC approach includes the allocation of risk capital, the estimation of residual risks, and the determination of optimal hedge ratios. It can be used to unravel the risk premiums associated with derivative trading in terms of hedge slippage, realistic complexity of asset returns, transaction  costs,  and  risk-­‐return  characteristics.  

Speaker:  Dr. Rupak Chatterjee, Ph.D, Industry Professor at Stevens Institute of Technology

R ChatterjeeBio: Rupak Chatterjee, Ph.D., is an Industry Professor and Deputy  Director  of  Financial Engineering  at  the Stevens  Institute  of  Technology.  Dr.  Chatterjee  has  over fifteen years experience as a quantitative analyst working for various top-­‐tier Wall Street firms. His last role before returning to academia was as Director of the Multi-­‐ Asset Hybrid Derivatives Quantitative Research group at Citigroup in New York. He was also the Global Basel III coordinator for all modeling efforts needed to satisfy the new regulatory risk requirements imposed on banks. Previously, he was a quantitative analyst at Barclays Capital, a vice president at Credit Suisse, and a senior vice president at HSBC. His educational background is in theoretical physics where he studied at Stony Brook University and the University of Chicago. His research interests have included discrete time hedging problems using the Optimal Hedging Monte Carlo (OHMC) method and the design and execution of systematic trading  strategies  that  embody  the  hallmarks  of  capital  preservation  and  measured risk taking. 

 

March 14, 3:00 - 4:30, Hill Center 552

Title: "Deconstructiong Black-Litterman - Or How to get the Portfolio You Always Knew You Wanted"

Abstract:  The Markowitz (1952, 1959) mean-variance (MV) efficient frontier has been the theoretical standard for defining portfolio optimality for more than a half century. However, MV optimized portfolios are highly susceptible to estimation error and difficult to manage in practice (Jobson and Korkie 1980, 1981; Michaud 1989). The Black and Litterman (BL) (1992) proposal to solve MV optimization limitations produces a single maximum Sharpe ratio (MSR) optimal portfolio on the unconstrained MV efficient frontier based on an assumed MSR optimal benchmark portfolio and active views. The BL portfolio is often uninvestable in applications due to large leveraged or short allocations. BL use an input tuning process for computing acceptable sign constrained solutions. We compare constrained BL to MV and Michaud (1998) optimization for a simple data set. We show that constrained BL is identical to Markowitz and that Michaud portfolios are better diversified under identical inputs and optimality criteria. The attractiveness of the BL procedure is due to convenience rather than effective asset management and not recommendable relative to alternatives.

Speaker:  Dr. David Esch, Ph.D; Director of Research at New Frontier Advisors

New Frontier Advisors***

david eschBiography:  Dr. Esch is an  applied statistician with experience in multiple fields. I specialize in solving difficult numerical problems and am proficient in mathematical analysis and computation. I hope to continue doing research and tackling interesting quantitative problems. Specialties:Quantitative and Data analysis problem solving using compute-intensive methods; Mathematical statistics with applications in finance and econometrics, health care research, and astrophysics; technical writing and computer programming in several languages.

** Refreshments will be served at @ 2:30pm in Room 503 Hill Center **

***New Frontier is a Boston-based research and registered investment advisory firm specializing in the development and application of state-of-the-art investment technology. Founded in 1999 by the inventors of the world's first broad spectrum, patented, provably effective portfolio optimization process, the firm continues to pioneer new developments in asset management. New Frontier's services help institutional investors worldwide select and maintain more effective portfolios. A globally-recognized independent research authority, New Frontier continues to contribute to the cutting-edge of investment theory in asset allocation, investment strategies, global investment management, optimization, stock valuation, portfolio analysis, and trading costs.

 

February 28, 3:00 - 4:30, Hill Center 552

Title: "Quant Trading: Who does it and  What is it?"

Abstract: I begin by giving an overview of hedge funds and what they do.   I then discuss the idea of “quant trading” and discuss several problems that require statistical thinking to address.  I will finish by discussing some things that you should consider if you want to work in this area.

Speaker:  Dr. Anthony Brockwell

Two Sigma Investments and Carnegie Mellon University

Anthony Brockwell HeadshotBiography:  Dr. Brockwell's research interests include stochastic differential equations, Markov chains, time series, and control theory. He is particularly interested in the study of stochastic control problems in which system descriptions are incomplete and/or inaccurate, and in the development of new models explaining market price behavior. Dr. Brockwell has published articles in such journals as the Annals of Statistics, Journal of Time Series Analysis, SIAM Journal on Control and Optimization, and Journal of Computational and Graphical Statistics. In 2007 he left academia to join a small startup hedge fund as a quant in New York city, where he developed and managed a portfolio of quantitative trading algorithms. In 2010 he joined Two Sigma Investments LLC, where he currently works. He plans to continue teaching time series in the MSCF program concurrently with his employment at Two Sigma. 

** Refreshments will be served at @ 2:30pm in Room 503 Hill Center **

 

January 31 , 3:00 - 4:30, Hill Center 552

Title: Statistical Quirks, Subtleties, and Surprises in Financial Data

Speaker: Dr. Martin Goldberg, Lead Consultant at ValidationQuant.com
Martin Goldberg presents a few statistical techniques where financial data demands more sophisticated treatment than one finds in elementary textbooks.

31f10b4Biography: Martin Goldberg has worked as a quantitative analyst since 1988, first as a desk quant in fixed income and commodities, then developing market risk and VaR models, and Head of Model Validation at Citi, then at Standard and Poor's.  His main research interests are copulas and Extreme Value Theory as applied to risk management.  He got a Bachelor's in Chemistry from CalTech and his Ph.D. in Theoretical Quantum Chemistry from CUNY.  His thesis centered on a non-linear optimization where the parameters are calibrated to the Fourier Transform of the data.

** Refreshments will be served at @2:30pm in Room 452 Hill Center **

 

November 22 , 3:00 - 4:30, Hill Center 552

Title: Applications of Statistics in the Credit Card Industry

Speakers: Dr. Lana Song and Dr. Jun Bai, JP Morgan Chase

Did you know the credit card industry is a $1 trillion+ industry in the USA?
Have you ever heard of the FICO score?
Do you know how to build a credit score to predict future profitability and losses?
Do you want to know how statistics is being used in one of the biggest banks in the USA?
Do you know the difference between raw data and information?
This presentation intends to give a high level overview of the American credit card industry and provides insights on how statistics is being applied in this industry, with an emphasis on credit scoring - its history, objectives, methodologies, and processes. A case study is provided to illustrate why credit scoring is one of the most successful applications of statistical and operations research modeling in finance and banking.

Bio of Speakers:
Lana at WorkDr. Lana [Xiaolan] Song is a Director at JP Morgan Chase leading Core Risk Modeling group for Chase Card Services. Lana obtained her Ph.D. in Statistics from Rutgers University in 2003. She also has an MS in Industrial & Systems Engineering from Rutgers and a Master in Statistics from Renmin University of China. Lana joined JP Morgan Chase (its predecessor Bank One) in 2002. She began her career by building Bank One's first customer level credit risk model. Lana is responsible for core risk scores and decision support tools for all aspects of credit risk decision points across life-cycle of Chase's 60 million credit card customers. Examples of core risk scores include proprietary credit bureau attributing system and models to support both underwriting and customer management decisions, internal behavior based risk assessment models with 360 degree of Chase customer view, and ground breaking proprietary models in fraud detection on both transaction authorization and account protection.  Prior to joining Chase, Xiaolan has worked for United Technologies Research Center (UTRC) and Educational Testing Service (ETS) as a summer consultant.

Jun Bai at workDr. Jun Bai is a Senior Manager at JP Morgan Chase Card Services (CCS) responsible for CCS balance transfer analytics and strategy development. He obtained his Ph.D. in Industrial & System Engineering from Rutgers University in 2004. He also holds an MA in Economics from American University-Washington DC and an MS in Statistics from Rutgers University. Before joining Chase, he has worked in Agricultural Bank of China as a statistician after graduating from Renmin University of China (RUC) with a BS in Statistics. He also worked for Merrill Lynch and Unilever as a summer associate. His research interests include statistical modeling, financial analytics, warranty design and analysis. He is a referee for various journals such as European Journal of Operational Research, IEEE Trans. on Systems, Man, and Cybernetics-Part A, and IEEE Transactions on Reliability.

 

 October 25 , 3:00 - 4:30, Hill Center 552

Title: A Structural Model of Sovereign Credit and Bank Risk

Speaker: Emilian Belev, CFA, Northfield Information Services

In the recent past, sovereign credit took center stage in the market's perception of risk.  This occurred not only because certain countries emerged as potential debtors in default, but also because some major economies showed signs of fundamental strain in their public finances.  The financial market effects of potential sovereign default and ensuing bank weakness and contagion, and the undermined capability of governments to sustain the Keynesian support for their vulnerable economies demonstrate the paramount importance of accurate and rigorous risk measurement of sovereign entities and financial institutions.

In this presentation we make an overview of a model designed for this task.  It builds on established credit risk methodology for corporate entities known from the seminal work of Merton, and takes a new direction to adapt the default option argument to a sovereign debt setting. The proposed model caters to economic intuition and resorts to explicit measures of macro economic activity as its buildings blocks.  We differentiate sovereign entities into three distinct categories, and adapt our general arguments to each of these groups. Furthermore, we pursue the connection between the viability of sovereign entities and the viability of the jurisdictional financial institution.  Finally, we juxtapose the model results with metrics of credit risk from the financial markets, as a reality check of the success of approach.  

headshot1Bio: Emilian heads the development of Northfield's Enterprise Risk analytics for the last 13 years.  His domain of responsibilities include modeling equity and fixed income, currency, equity, interest rate, and credit derivatives, structured products, directly owned real estate, private equity, and infrastructure, and developing an integrated framework for these asset classes to be analyzed side-by-side in a coherent, accurate, and economically logical fashion.  He has introduced various innovative methodologies in the areas of convertible bonds modeling, MBS path dependency, efficiency of numerical derivative pricing algorithms, credit risk among tranches of seniority, infrastructure investments, and directly owned real estate.  Emilian has presented on some of these topics at various industry events in North America and Europe.  Prior to joining Northfield, Emilian has been with State Street Global Advisors.   Emilian is an actively involved CFA charter holder, holder of the Certificate in Advanced Risk and Portfolio Management, a member and founding member of respectively QWAFAFEW Boston and QWAFAFEW Toronto, a member of the PRMIA expert advisory group for Market Risk, and a winner of the 2013 PRMIA award for New Frontiers in Risk Management.

 

October 21 , 3:30 - 4:30, Hill Center 124

Title: IMPACT OF REGULATION (CCAR, D-F, Basel) AND CURRENT STATE AND TRENDS IN JOB MARKET FOR QUANTITATIVE RISK MANAGEMENT

Speaker: Ken Abbott, Morgan Stanley

Come to hear Ken Abbott, Managing Director and Chief Operation Officer of Firm Wide Risk at Morgan Stanley,  talk about the impact of recent regulation and regulatory trends on the financial services industry  and on the current state and future of the job market for quantitative financial analysts and risk managers.

Ken Abbott is a managing director at Morgan Stanley in New York, where he is chief operating officer for the firm risk management.  In addition, he is the senior risk officer assigned to buy-side activity.
Previously, Mr. Abbott ran market risk management for Bank of America's Investment Bank. He has over 25 years of banking experience, including 14 years at Bankers Trust as an analyst, trader and risk manager.
Mr. Abbott is an adjunct faculty member at New York University, Rutgers University and Baruch College, and sits on the GARP Board of Directors

Ken Abbott has a B.A. from Harvard in economics, an M.A. from New York University in economics and an M.S. from New York University's Stern School of Business in statistics and operations research.

 

October 18, 3:00 - 4:30, Hill Center 552,

Title: Stock Market Inefficiencies Over the Last Six Decades

Speaker: C. Michael Carty, Principal and Chief Investment Officer, New Millennium Advisors, LLC

Abstract: A sizable body of empirical research suggests that securities’ prices follow a random walk; i.e., that successive price changes can be treated as identically distributed independent random variables. Much effort has been expended in identifying the probably distribution to which stock prices changes confirm. We explore the evolution of these efforts and test new evidence concerning the nature of these distributions and their implication for stock market efficiency over the last six decades. The results suggest the Gaussian representation of the model is inadequate in explaining stock price changes, the parameters of the distributions are time dependent; the importance of their third and fourth moments has been largely ignored; and simple specification of the model omits significant explanatory variables.

cartyBrief Bigraphical History: Michael supervises the construction and management of portfolios to achieve specific client objectives. He also designs, develops and evaluates indexes for use as ETFs. In addition he serves as an expert witness in securities cases. Prior to founding New Millennium Advisors in 1995 he was the Director of Closed-end Funds Strategy and Research at Prudential Securities and, before that, the Associate Research Chairman and Senior Portfolio Manager at Value Line where he managed the flagship Value Line Fund and the Centurion Fund, then the best performing equity growth fund in Lipper’s variable annuities universe.

He currently serves as president of the New York chapter of the Quantitative Work Alliance for Applied Finance, Education and Wisdom, is a member of the Financial Analysts – Money Manager Society, and Alpha Pi Mu (the Industrial Engineering Honor Society).

His academic background includes a BS degree in Industrial Engineering from the New Jersey Institute of Technology and a MBA from Columbia University where he also pursued doctoral studies.

 

October 4, 3:00 - 4:30, Hill Center 552 

Title: Filtering Noise from Correlation/Covariance Matrices. Implications for Trading, Asset Allocation and Risk Management

Speaker: Dr. Alexander Izmailov, Market Memory Trading LLC

Abstract: Correlation/Covariance matrices inevitably contain noise due to finite sampling.  It is the typical case that content of the noise component in correlation matrix is more than 90%. Therefore, removal of this noise is of paramount importance since it discriminates “noisy” from “signal” trading, asset allocation, risk management, etc.  A very effective and robust procedure to remove noise from correlation/covariance matrices has been developed.  This procedure exhibits dramatic improvements in (A) various unconstrained and constrained optimization schemes and (B) out-of-sample stability.  The ability of this procedure to detect “regime change” is illustrated on real portfolios.  A few portfolios with obvious underlying correlation structure are presented in order to demonstrate “proof of concept”, i.e. efficacy of the noise filtering procedure. Noise associated with the standard approach to correlation estimation conceals these underlying correlation structures, while the noise filtering procedure exposes them.

alexlBio: Alexander Izmailov has a Ph.D. in Physics (Theoretical and Mathematical Physics, 1986, Lebedev Physics Institute of the former USSR Academy of Sciences). Previously, he had won the Silver Award in the “All Russia” USSR Physics competition among undergraduates. He has attracted $2M in grant money from NASA and NSF (1991-1999, his success rate in grant applications was 1/3). He published more than 40 papers in prestigious scientific journals.  He has held positions as a quantitative trader on vice-president and senior vice-president levels at several major investment banks and hedge funds.  He has developed several successful trading strategies based on innovations in probability theory and statistics.  These strategies were the basis of completely automated trading systems licensed to leading investment banks and hedge funds for trading equities, futures and currency pairs.  He is currently a member of Market Memory Trading, L.L.C.

 

September 20, 3:00 - 4:30, Hill Center 552

Speaker: Dr. David Li, Managing Director, AIG Investments

Title: A Transformed Copula Function Approach to Credit Portfolio Modeling

Abstract: We present a fundamental modification to the current popularly used copula function approach to the credit portfolio modeling introduced by Li (2000). The original approach simply uses a copula function to create a joint survival time distribution where individual survival time distribution is already risk neutralized, and given from a single name perspective.  Based on Buhlmann's equilibrium pricing model (1980) under some assumptions on the aggregate risk or the multivariate Esscher and Wang transforms we find that the covariance between each individual risk and the market or aggregate risk should be included in the measure change.  In the Gaussian copula model it is shown that we simply need to adjust the asset return by subtracting an item associated with the covariance risk.

This discovery allows us to theoretically link our credit portfolio modeling with our classical equity portfolio modeling in the CAPM setting.  This can help us solve some practical problems we have been encountering in the credit portfolio modeling.

david li  finalBiography: David Li is currently the head of modeling and a managing director at AIG Investments.  Previously he was the CRO at CICC, and head of credit derivative research and analytics at Barclays Capital and Citigroup. David is an early participant of credit derivative market, and his approach on credit curve construction and copula function method for credit portfolio modeling have been widely used in the industry. For background, see "The Formula that Killed Wall Street"

 

March 14, 3:00 - 4:30, Hill Center 552,

Speaker: Wei Zhu, Managing Director, Citi

Title: Basel Capital Framework and Risk Modeling

Abstract:  The presentation will first provide an overview of the Regulatory Capital requirement instituted by the Basel Committee of Banking Supervision (BCBS), covering both its historical evolution (from Basel I) and the current status (Basel III). The second part of the presentation will focus on the general modeling techniques required by market risk capital calculation under the Basel framework.  This includes VaR, Stressed VaR, Incremental Risk Charge, and Comprehensive Risk Charge.

wie zhu  finalShort Bio: Wei Zhu is a Managing Director in Citi’s ICG (Institutional Client Group) Risk Analytics group.   He heads the Market Risk Analytics team and the Counterparty Risk Analytics Modeling team globally.   After joining Citi in 2001,  he has been focusing on building quantitative models to capture market risk and counterparty credit risk, for the purpose of both internal risk management and regulatory capital calculation.  Wei received his BS in Physics from Fudan University in 1995, and his PhD in Physics from New York University in 2001.  He is a CFA charter holder since 2004.

 

Frequently Asked Questions

General

        1. What is financial statistics and risk management (FSRM)?
        2. Can I enroll in the program as a part-time student?
        3. What is the difference between the FSRM master's program and the MS in Statistics program at Rutgers?
     4. What is the differences between the FSRM master's program, the MS in Mathematical Finance (MSMF) program, and the Master's in Quantitative Finance (MQF) program at Rutgers?

Admissions and courses

1. What type of background should students have?
2. What is the prerequisite coursework for the FSRM program?
3. What are the requred GRE and GMAT scores? 
4. What is the application process? What are the deadlines?
5. Can I start the program in Spring?
6. If I enroll in the MS in Statistics program at Rutgers, can I transfer into FSRM later?
7. If I enroll in the MS in Statistics program at Rutgers, can I register for special FSRM course sections?
8. If I enroll in the FSRM program, can I transfer to the MSMF or MQF programs later?
9. I have attended or am currently attending a graduate school. Will my credits transfer?
10. Can you give some guidance about the required recommendation letters; can they be either professional or academic?
11. Is working experience necessary for entering the program?
12. I notice that my TOELF scores are below the requirement in Listening/Speaking/Reading/Writing part. Am I still eligible to apply?
13. I have attached my profile (including CV, GPA, GRE/TOELF scores). Could you please tell me whether I have any chance for admission?

Careers

        1. What types of careers are available for graduates?
        2. Does the program arrange for internships during study?
        3. What kind of placement services does the FSRM program provide?
        4. How successful has the program been at placing its students?

 


 

  

General

  What is Financial Statistics and Risk Management (FSRM)?

"Financial Statistics" as a discipline is the application of statistical methods to problems which arise in finance. It is NOT a reference to "financial statistics" as used in common parlance where the phrase can refer to the actual price data about stocks and flows. In recent years, a great need for the development and implementation of fast and sophisticated statistical methods has arisen in the financial industry. Financial statistics addresses this need. Methods from financial statistics are essential for processing the massive amount of data available throughout the industry, extracting useful information from this data, exploring arbitrage opportunities resulting from market inefficiencies, automating the asset evaluation and selection process, optimizing portfolios, and evaluating risk exposure.

Risk Management is a field that is closely related to financial statistics. Here the main objectives are the identification, evaluation, management and on-going monitoring of risk exposures. It includes credit risk, market risk, operations risk and enterprise risk management and involves learning the  techniques for identifying, estimating and managing risk e.g. thru risk hedging, risk/return optimization, and regulatory compliance in finance – all essential issues in modern finance. Effective risk management requires a deep understanding of uncertainty and probability and a high level of skill in statistical modeling, data analysis, estimation and prediction. Knowledge of financial markets and instruments and the major regulations that apply to financial institutions like the Basel Accords and the Frand-Dodd legislation and related agency rule makings is also part of the training.

  Can I enroll in the program as a part-time student?

Yes. You may enroll in the FSRM program as a full-time or part-time student. Most FSRM courses will be taught during the evening at the Rutgers Busch campus (in Piscataway), in order to accommodate part time students who work during the day. We encourage applications from those who are currently working in the financial industry and wish to further advance their careers by upgrading their statistical modeling and data analysis skills.

  What is the difference between the FSRM master's program and the MS in Statistics program at Rutgers?

The FSRM program is a professional master's degree program that is designed to train students for immediate post-graduation employment in the financial services  industry or in financially focused departements of enterprises e.g. Treasury. The  coursework and training that students receive in the FSRM program is focused on teaching statistical and related data analytical and computational  tools required for quantitative analyst positions in the financial services industry, as well as related finance,  finanical markets and regulatory content. Only financial data and applications are used for course projects. Such in-depth training is most suitable for students with a strong interest in working as quantitative analysts in the financial industry. On the other hand, the MS in Statistics program is less specialized, with coursework and training concentrating on general statistical methods. Graduates of the MS in Statistics program are prepared to continue their studies towards a Ph.D degree, or to seek employment in various industries, such the pharmaceutical industry, marketing, and consulting, as well as the financial industry. Though some of the course content required for the FSRM and MS in Statistics programs overlap, special course sections that emphasize financial applications are reserved for FSRM students. These special sections tend to be more demanding, with challenging problem sets that often involve the analysis of real financial datasets, and written projects and oral presentations, which help to hone students' skills for the financial workplace. In addition, in the FSRM program,  regular faculty are supplemented by bringing in practitioners for certain courses or course sections.

  What is the differences between the FSRM master's program, the MS in Mathematical Finance (MSMF) program, and the Master's in Quantitative Finance (MQF) program at Rutgers?

The three programs are related in that they all aim to train students to work in the financial industry. While there is some overlap in course contend, each program has a different focus. This is reflected in substantial differences in the programs' requirements.

The MSMF program is a mathematics program. It focuses on mathematical models for financial asset pricing and the related  numerical methods required to apply these models. Typical applications of these methods include complex derivative security pricing. 

The MQF program trains students to become financial managers and professionals in corporations and enterprises, as well as financial institutions,  with expertise in corporate finance, portfolio theory, and corporate structures and re-structuring.

On the other hand, the FSRM program is a statistics and data analytics based program, tailored to train the next generation of professional financial statisticians and risk managers. The graduates from the FSRM program will be able to take immediate employment as financial statisticians and risk managers dealing with issues of uncertainty and risk identification and mitigation rather than complex derivative pricing and complex derivative product development. The FSRM program is most suitable for students who are interested in statistical and data analysis for large financial data set, identifying statistical arbitrage opportunities, proprietary trading strategies, stress testing,  simulation and risk management.

  

 

Admissions and Courses

    What type of background should students have?

Students should have completed an undergraduate degree with a major in statistics, mathematics, or a closely related discipline, such as physics, engineering, or computer science. Students from all over the world are encouraged to apply to the FSRM program. The majority of our applicants have not worked in finance and do not already have a graduate degree, though we will consider students who have related work experience (which can be very helpful in securing an internship) or those who already have a master's or PhD in other subjects.

    What is the prerequisite coursework for the FSRM program?

Successful applicants must demonstrate a high aptitude for quantitative reasoning. They must have a firm grasp of mathematics and statistics at a high undergraduate level, which includes at least multivariate calculus, linear algebra, and statistical methods. Basic skills in computer science and computer programming are also a prerequisite.  More detailed information is available at our prerequisites and requirements page.

    What are the required GRE or GMAT Scores?

The Graduate School generally expects successful applicants to have verbal and quantitative Graduate Record Exam scores of at least 500 and 600 respectively. Our requirement for the verbal score is somewhat flexible, but successful applicants are likely to have quantitative scores considerably higher than 600.  For students submitting GMAT scores, the typical requirements are a verbal score of at least 29 and a quantitative score of at least 46.  Again, the requirement for the verbal score is somewhat flexible.

Please note that, as part of the BS/MS program, GRE/GMAT score submissions are waived for Rutgers undergraduates with cumulative GPA of 3.2 or better.

    What is the application process? What are the deadlines?

Applications are made through the Graduate and Professional Schools Admission Office and not through the FSRM program directly. You will need to provide official transcripts, three letters of recommendation (professional or academic), a personal statement, GRE or GMAT test scores and TOEFL or IELTS test scores (international students). Please see our admissions page for further details on the application process, including deadlines.

    If I enroll in the MS in Statistics program at Rutgers, can I transfer into FSRM later?

Transferring is possible, but not guaranteed. A maximum of nine credit hours of course work (which typically amounts to three semester-long courses) can be transferred to the FSRM program provided they are approved and have not been used to satisfy the graduation requirements of another graduate degree. Students in the FSRM program are required to take more demanding, specialized sections of some of the courses required for the MS in Statistics program. Students who have completed non-FSRM sections of these courses and who are seeking to transfer credits to the FSRM program may be required to complete additional work, such as a written project, before receiving credit for the FSRM program.

    If I enroll in the MS in Statistics program at Rutgers, can I register for special FSRM course sections?

The special FSRM sections often have a very limited class size to achieve the desired learning experience and are reserved for FSRM students and for other Rutgers Quantitative Finance programs which have reciprocity with FSRM (include Math Finance and the MQF program

    If I enroll in the FSRM program, can I transfer to the MSMF or MQF programs later?

It is possible but not guaranteed. Evaluation of transfer applications to these other programs will be based on your performance in the FSRM program and other considerations. If you successfully transfer from the FSRM program to another program, credits earned in the FSRM program may be transferrable as well.

  Can I start the program in Spring?

Yes, you can. However, the program is designed for full-time students to graduate in three semesters. Full time students entering the program in Spring might need to stay for the fourth semester depending on students' background and course availability. Part time students usually do not have this problem due to their flexibility in course selection.

    I have attended or am currently attending a graduate school. Will my credits transfer?

There is no “transfer” from one university to Rutgers.  You must first apply and be admitted.  Discussion will only occur after an offer of admission has been made.  Permission to transfer will be granted on a case-by-case basis and will not be granted automatically. Students can apply to transfer up to 9 credits for graduate courses, provided they replace appropriate courses offered by our program, and credit for such courses was not used to earn a previous graduate degree.

    Can you please give some guidance about the required recommendation letters, can they be either professional or academic?

The recommendation letters can be either professional or academic. It is completely your choice.

    Is working experience necessary for entering the program?

No.

    I notice that my TOELF scores are below the requirement in Listening/Speaking/Reading/Writing part. Am I still eligible to apply?

You are still welcomed to apply. Your application as a whole is more important than individual scores and weaknesses can be balanced by strengths in other parts of the application. However, if your scores are much below the requirement, we suggest that you should retake the test.

    I have attached my profile (including CV, GPA, GRE/TOEFL scores). Could you please tell me whether I have any chance for admission?

Unfortunately, we cannot give any opinions about admission until we see your full application, including transcripts, test scores, and letters of recommendation.

 

 

  

Careers

  What types of careers are available for graduates?

There is considerable demand for people with advanced statistical training, like that obtained in the FSRM master's program, throughout the financial industry. A few areas which rely heavily on employees with such training include trading, risk management, and portfolio management. Additionally, many of the largest and most widely known investment banks and hedge funds employ people with an advanced statistical skill set.

  Does the program arrange for internships during study?

The program strongly encourages students to participate in summer internships with financial companies and will do its best to help students obtain internships through its connections with the financial industry.

  What kind of placement services does the FSRM program provide?

We help graduates secure employment by utilizing our connections with recruiters and the financial services industry. Students have an opportunity to attend industry career days, financial company information sessions and campus visits as well as other networking events. Student resume books are distributed in print and online to potential employers, and a designated Student Gallery on the FSRM website exposes students to recruiters and financial institutions. Additionally, career development support is available through required workshops in resume writing, interviewing skills, professional networking and other job search tools. English as a Second Language (ESL) courses are required for non-native English speakers on basis of a language skills assessment. ESL training is provided on a non-tuition basis in the entry Fall semester.

  How successful has the program been at placing its students?

ALthough our program is relatively new (our first class entered in fall 2011), most of our students have found employment either before graduation or within one to two months of graduation. We expect to continue to  place our students well.

STUDENT PROFILES & RESUMES

News & Events

RESOURCES AND ANNOUNCEMENTS: View resources and news about FSRM and check out our news letters.

FSRM is pleased to announce that, as part of the BS/MS program, GRE/GMAT score submissions are waived for admission applications by Rutgers undergraduates with cumulative GPA of 3.2 or better.

FSRM Selects Imagine Software, Inc. as Preferred Strategic Partner: Read more here

FSRM Application for PRIMIA Module 1, University Membership, Approved: Read more here.
Gordon Ritter, Senior Portfolio Manager, GSA Capital to teach FSRM "Advanced Statistics for Finance" course in Spring 2017: Read more here.

Contact Us

Dr. Neville O'Reilly, Associate Director
P 848-445-7669
E  fsrm@stat.rutgers.edu

FSRM Program
Rutgers, The State University of New Jersey
110 Frelinghuysen Road
479  Hill Center, Busch Campus
Piscataway, NJ 08854