Category Archives: Seminar

Brooklyn Quant Experience Lecture Series: Harvey Stein

Brooklyn Quant Experience Lecture Series, NYU TandonDear All,

You are cordially invited to the Brooklyn Quant Experience Lecture Series (BQE) on Thursday, February 13th at 6PM in LC 400, Dibner Building, 5 MetroTech Center – 4th Floor.

Dr. Harvey Stein will present a talk on the following topic:

Title:

A Unified Framework for Default Modeling

Abstract

Credit risk models largely bifurcate into two classes — the structural models and the reduced form models. Attempts have been made to reconcile the two approaches by adjusting filtrations to restrict information, but they are technically complicated and tend to approach filtration modification in an ad-hoc fashion.

Here we propose a reconciliation inspired by actuarial science’s approach to survival analysis. We model the survival curve and hazard rate curve as stochastic processes. This puts default models in a form resembling the HJM framework for interest rates, yielding a unified framework for default modeling.

Predictability of default has a simple interpretation in this framework. The framework enables us to disentangle predictability and the distribution of the default time from calibration decisions such as whether to use market prices or balance sheet information. It supplies a formal framework for combining models, yielding a simple way to define new default models.

Bio:

Dr. Harvey J. Stein is Head of the Quantitative Risk Analytics Group at Bloomberg, responsible for Bloomberg’s market risk and credit risk models. Dr. Stein is well known in the industry, having published and lectured on mortgage backed security valuation, CVA calculations, interest rate and FX modeling, credit exposure calculations, financial regulation, and other subjects. Dr. Stein is also on the board of directors of the IAQF, an adjunct professor at Columbia University, a board member of the Rutgers University Mathematical Finance program and of the NYU Enterprise Learning program, and organizer of the IAQF/Thalesians financial seminar series. He received his BA in mathematics from WPI in 1982 and his PhD in mathematics from UC Berkeley in 1991.

We look forward to having you join us for the talk and refreshments.

Brooklyn Quant Experience Lecture Series: Dhruv Madeka

Brooklyn Quant Experience Lecture Series, NYU TandonDear All,

You are cordially invited to the Brooklyn Quant Experience Lecture Series (BQE) on Thursday, February 6th at 6PM in LC 400, Dibner Building, 5 Metrotech Center – 4th Floor.

Dr. Dhruv Madeka who will present a talk on the following topic:

Title:

Practical Deep Reinforcement Learning

Abstract

We present a Deep Reinforcement Learning approach to solving a dynamic periodic review inventory system with stochastic vendor lead times, lost sales, correlated demand, and price matching. While this dynamic program has historically been considered intractable, we show that different policy learning approaches are competitive or outperform classical baseline policies. In order to train these algorithms, we develop techniques to convert historical data into off-policy data for a simulator.

Bio:

Dhruv Madeka is a Senior Machine Learning Scientist at Amazon. His current research focuses on applying Deep Reinforcement Learning to inventory management problems. Dhruv has also worked on developing generative and supervised deep learning models for probabilistic time series forecasting. In the past – Dhruv worked in the Quantitative Research team at Bloomberg LP, developing open source tools for the Jupyter Notebook and conducting advanced mathematical research in derivatives pricing, quantitative finance and election forecasting.

We look forward to having you join us for the talk and refreshments.

Brooklyn Quant Experience Lecture Series: Milind Sharma

Brooklyn Quant Experience Lecture Series, NYU Tandon

Dear All,

We are delighted to announce the new Brooklyn Quant Experience (BQE) Lecture Series (formerly known as the FRE Lecture Series), which will begin Thursday, January 30th at 6PM in the Event MakerSpace, 6 Metrotech Center, 1st Floor.

To kickoff our first lecture this spring, we have invited Dr. Milind Sharma who will present a talk on the following topic:

Title:

From Smart Betas to Smart Alphas

Bio:

Milind Sharma’s 24 years of market experience span running prop desks at RBC & Deutsche Bank (Saba unit) as well as hedge funds (QuantZ) & mutual funds (MLIM) not to mention his fintech venture QMIT. His funds have won many awards over the years including those from Morningstar, Lipper, WSJ, Battle of the Quants & BattleFin. He was also a co-founder of Quant Strategies at MLIM (now BlackRock) & Manager of the Risk Analytics and Research Group at Ernst & Young where he was co-architect of Raven TM. His publications have appeared in Risk, JoIM, Elsevier, World Scientific, Wiley etc. In addition to dual MS degrees he was also in the Logic/ AI PhD program at Carnegie Mellon. Other education includes Oxford, Vassar & Wharton.

We look forward to having you join us for the talk and refreshments.

FinTech Seminar Series

FinTech Applications of AI & Machine Learning in B2B space

With its formidable datasets within domain sets like credit default, economic, security ratings, firmographic & people data, Moody’s is well positioned to develop new products to serve the needs of its clients. The Moody’s Analytics Accelerator group aims to identify, research and develop new business products for new clients and new markets using sophisticated AI & Machine learning techniques that are now ubiquitous in the Consumer space. This talk will focus on how we have brought to market products in the Compliance, Loan Origination, Financial Spreading and Commercial Real Estate domains.

Bio

Rakesh Parameshwar is a Senior Director for Strategy & Innovation in the Moody’s Analytics Accelerator (MAA) group. The MAA group aims to identify, research, and develop new business opportunities, with a special focus on emerging technologies like AI & ML. Before joining Moody’s Analytics in 2018, he was in charge of business strategy and product development for Bloomberg’s Desktop API, Alerts and Quant Solutions group. Prior to this, he had extensive experience working for global banks where he advised clients with risk management solutions and built technology solutions. Rakesh has an MBA from NYU Stern, a Bachelor’s degree in Electrical Engineering from IIT Kharagpur in India and a certificate in Quantitative Finance (CQF)

When

Wednesday January 29, 2020
6 PM – 7PM

Where

University of Waterloo @ Manhattan Institute of Management
2 Washington Street, 17th floor
New York, New York

NYU FRE Lecture Series: Yuewu Xu

NYU Tandon School of Engineering

Dear All,

You are cordially invited to attend the last FRE Lecture Series for the Fall 2019 semester on Thursday, December 5th in the Event MakerSpace (6 MetroTech Center, Brooklyn, NY) at 6:00 p.m.

Dr. Yuewu Xu will present a talk on the following topic:

Title:

A New Approach to Recover Risk-Neutral Distributions From Options

Abstract

This paper develops a novel model-free representation of the risk-neutral density in terms of market observed options prices by combining an exact series representation of the Dirac Delta function and the Carr-Madan spanning formula. Compared to the widely used method for obtaining the risk-neutral densities via the Breeden-Litzenberger device, our method yields risk-neutral densities that are model-free, automatically smooth, in closed-form, and do not involve operations such as interpolation of the implied volatilities. The closed-form feature of our new representation makes it ideal for many potential applications, including a new model-free representation of the local volatility function in the Dupire’s local volatility model. The validity of our method is demonstrated through simulation studies as well as an empirical application using real options data. Extension of the method to higher dimensions is also established by extending the Carr-Madan spanning formula.

JEL Classification: G12, G13, G14, C58

Keywords: Risk-neutral distribution, option-implied information, Carr-Madan formula, Dirac Delta function.

Bio:

Dr. Yuewu Xu is currently an associate professor of finance at the Gabelli School of Business, Fordham University. His research interests are in the areas of theoretical and empirical asset pricing, and financial econometrics. Dr. Xu’s research articles have appeared in leading academic journals such as the Journal of Finance, Journal of Financial Economics, Journal of Financial and Quantitative Analysis, and the Journal of Econometrics, and his works have been presented in the conference of American Finance Association and the Western Finance Association. Prior to joining Fordham, Dr. Xu was director of investment strategy and research at a major asset-management firm in New York where he worked for five years.

Prof. Xu holds a PhD in finance and a PhD in statistics from Yale University.

We look forward to having you join us for the talk and refreshments.

NYU FRE Lecture Series: Ben Steiner

NYU Tandon School of Engineering

Dear All,

You are cordially invited to attend the FRE Lecture Series on Thursday, November 21st in LC 400 Dibner Library – 4th Floor (5 MetroTech Center, Brooklyn, NY) at 6:00 p.m.

Dr. Ben Steiner will present a talk on the following topic:

Title:

Model Risk Management for Trading Strategies Built with Deep Learning

Abstract

Deep Learning has demonstrated spectacular success in domains outside finance and offers tantalizing potential for developing trading strategies.

This presentation reviews the basics of Deep Learning and highlights when it should (or should not) be used.

Traditionally, Model Risk Management (MRM) consists of three elements:

  1. Conceptual Soundness – assessing the quality of the model design and construction;
  2. Implementation Validation – confirming that the model is correctly implemented; and
  3. Ongoing Monitoring – ensuring that the model is performing as intended.

In the context of trading strategies, ‘Conceptual Soundness’ can be viewed as the decision to start trading a strategy while ‘Ongoing monitoring’ is a requirement to anticipate when to stop.

Using deep learning to create trading strategies presents a number of challenges. Paramount is the non-stationary nature of financial markets: out-of-sample data is most likely drawn from a different distribution to training data. The key question is recalibration frequency: recalibrating too fast results in fitting to noise, too slowly and a model is trained on stale data. Either way, trading the sub-optimal strategy results in losses. A second challenge is interpretation. Without knowing why a strategy is performing, limited information is available for risk budgeting. The third challenge is ensuring deep learning is not simply an expensive way of rediscovering well-known factors.

In the presence of these three challenges, model risk management can still be used for evaluating deep learning trading strategies. No simple test can discriminate between good and bad strategies; rather a suite of analysis can be used to understand strategy behavior and characteristics. Ongoing monitoring is then critical to understand when live trading is not performing as intended. In this respect, evaluating deep learning strategies is an evolution of how quant trading strategies have always been evaluated. However, the increased ease with which deep learning strategies can be created now prompts even greater diligence in their systematic evaluation and ongoing monitoring.

Bio:

BNP Paribas Asset Management

In his current role, Ben handles chief-of-staff and business management responsibilities within the Global Fixed Income division of BNP Paribas Asset Management

Earlier in his career, he held roles of Head of Model Development, Portfolio Manager & Quant Researcher at investment managers and quantitative hedge funds. This experience covered models & investment strategies in multiple asset classes ranging from the traditionally illiquid (Private Debt and Real Estate) to the more liquid markets (Non-traditional Bond; Managed Futures; Global Macro and Equity Long/Short).

Prior to his current role, Ben was Head of Model Development at CIT where he managed the team researching and implementing credit models. Earlier in his career, he was a Portfolio Manager and Senior Quant Researcher at BNP and, before that, Research Manager at Aspect Capital in London. Ben started his career at Deutsche Bank in quantitative research and portfolio construction.

He holds a BA in Economics from the University of Manchester and an MSc in Mathematical Finance from Imperial College, London.
In 2013, Ben was appointed to the Board of Directors of the Society of Quantitative Analysts (SQA) and has given recent lectures on machine learning and model risk management at Columbia & NYU.

We look forward to having you join us for the talk and refreshments.

Please note that Dr. Yuexu from Fordham University will present the final lecture this semester on December 5th. Mark your calendars.

NYU FRE Lecture Series: David Shimko

NYU Tandon School of Engineering

Dear All,

You are cordially invited to attend the FRE Lecture Series on Thursday, November 14th in the Event MakerSpace (6 MetroTech Center, Brooklyn, NY) at 6:00 p.m.

Dr. David Shimko will present a talk on the following topic:

Title:

A Structural Model for Capital Market Equilibrium

Bio:

Dr. David Shimko is an Industry full professor in the FRE Department at NYU Tandon. His academic history include posts at HBS, Kellogg (Northwestern), and NYU Courant. He has published extensively in both the academic and trade literature on valuation, derivatives, risk management, commodities and credit. He has worked at JPMorgan in commodity derivatives and credit research, and built Risk Capital, an award-winning independent risk management consulting enterprise. He was Chairman of the Global Association of Risk Professionals and served on private, nonprofit and public boards of directors. Most recently, he founded CreditCircle, an internet startup company focusing on consumer credit.

We look forward to having you join us for the talk and refreshments. Please mark your calendars.

NYU Courant: Mathematical Finance Seminar

The mathematical finance seminar covers a broad range of topics in mathematical and quantitative finance, including:

  • Data science and machine learning in finance
  • Big data and econometric techniques
  • Quantitative finance
  • Portfolio and risk management
  • Pricing and risk models
  • Regulation and regulatory models
  • Trading strategies and back testing

Presenters include invited visitors and NYU Courant faculty. A seminar presentation often covers original research. The seminar meets monthly on Tuesdays at 5:30 pm to 7 pm in room 1302 of Warren Weaver Hall at 251 Mercer Street, unless specified otherwise. Please make sure to check the exact schedule and room assignment. Talks generally last an hour, followed by networking.

Seminars are open to the public.

The seminar coordinator is Petter Kolm (email: petter DOT kolm AT nyu DOT edu).

Seminar Organizer(s): Petter Kolm


Tuesday, November 12, 2019
5:30PM 
251 Mercer St.
Warren Weaver Hall 1302
Model Risk Management for Alpha Strategies created with Deep Learning
Ben Steiner, Global Fixed Income, BNP Paribas Asset Management

NYU FRE Lecture Series: Aparna Gupta

NYU Tandon School of Engineering

Dear All,

You are cordially invited to attend the FRE Lecture Series on Thursday, November 7th in LC 400, Bern Dibner Library, 4th Floor (5 MetroTech Center), at 6:00PM.

Dr. Aparna Gupta will present a talk on the following topic:

Title:

Identifying the Risk Culture of Banks Using Machine Learning

Abstract:

We introduce text mining and unsupervised machine-learning algorithms to define the risk culture for U.S. bank holding companies and examine the relation between risk culture and performance. Applying principal component analysis on textually extracted features from 10-K filings identifies uncertainty, litigious and constraining sentiments among risk culture features to be significant in defining risk culture of banks. Cluster analysis of these features proposes three distinct risk culture clusters which we label as good, fair and poor. Consistent with regulatory expectations, sound risk culture in banks is characterized by high profitability ratios, bank stability, lower default risk and good governance.

Bio:

Aparna Gupta is an associate professor of quantitative finance and director of the Center for Financial Studies in the Lally School of Management at Rensselaer Polytechnic Institute. She has been the founding director of the MS program in Quantitative Finance and Risk Analytics at RPI, and holds a joint appointment in industrial and systems engineering in the School of Engineering at RPI. Dr. Gupta has been a visiting researcher at US SEC in Washington DC for two years. Her research interest is in financial decision support, risk management, and financial engineering. She applies mathematical modeling, machine learning and financial engineering techniques for risk management both in technology-enabled network services, such as, energy and renewable energy systems, communication systems, and technology-enabled service contracts, as well as risk management in the inter-connected financial institutions and financial markets. She has worked on several US National Science Foundation funded research projects in financial innovations for risk management. Dr. Gupta’s research has been published in top quantitative finance and operations research journals, and has been awarded various recognitions, including 2018 best paper award of the Financial Management Association and 2019 best paper award at the 17th FRAP Conference. She is the author of the book, Risk Management and Simulation. Dr. Gupta is a member of WFA, FMA, INFORMS, GARP and IAQF, and serves on the editorial board of several quantitative finance and analytics journals. She earned her doctorate from Stanford University and her B.Sc. and M.Sc. degrees in Mathematics from the Indian Institute of Technology, Kanpur.

We look forward to having you join us for the talk and refreshments. Please mark your calendars.

Bloomberg Quant Seminar Series

November 18, 2019, Bloomberg Quant Seminar Series

Please join us for the next installment of the Bloomberg Quant (BBQ) Seminar Series. The seminar takes place every month and covers a wide range of topics in quantitative finance.

In this session chaired by Bruno Dupire, Peter Carr will present his current research, followed by several “lightning talks” of 5 minutes each in quick succession. This format gives the audience the opportunity to be exposed to a wider variety of topics.

Register today to secure your spot at our event – walk-ins cannot be accommodated.

Keynote

Peter Carr
Peter Carr
Finance and Risk Engineering
Department Chair
NYU Tandon School of Engineering

It Was Fifty Years Ago Today

While the seminal contributions of Black Scholes and Merton to options pricing were published in 1973, much was known by them and others in 1969. In this talk, we turn back the clock exactly 50 years and try to determine what was known and not known about pricing options on November 18, 1969.

Agenda

  • 5:00pm – Check-in
  • 5:30pm – Keynote:
    Peter Carr, Finance and Risk Engineering Department Chair, NYU Tandon School of Engineering
  • 6:15pm – Lightning talks:
    A lightning talk is a very short presentation lasting only 5 minutes. Several ones will be delivered in a single session by different speakers in quick succession
  • 7:00pm – Cocktail reception

When & Where

Monday, November 18, 2019
5:00pm – 8:00pm EDT

Bloomberg L.P.
731 Lexington Avenue
7 MPR
New York, NY 10017