Tag Archives: Finance Seminar

Seminar

NYU Courant: Mathematical Finance Seminar

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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, October 29, 2019
5:30PM, Warren Weaver Hall 1302
Relearning the Lessons of the Global Financial Crisis
David M. Rowe, President of David M. Rowe Risk Advisory

Seminar

NYU FRE Lecture Series: John Crosby

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NYU Tandon School of Engineering

Dear All,

You are cordially invited to attend the FRE Lecture Series on Thursday, October 17th in the Pfizer Auditorium  on the 1st Floor of the Dibner Library (5 MetroTech Center) at 6PM.

Dr. John Crosby will present a talk on the following topic:

Title:

Unspanned Risks, Negative Local Time Risk Premiums, and Empirical Consistency of Models of Interest-Rate Claims

Abstract:

We formalize the notion of local time risk premium in the context of a theory in which the pricing kernel is a general diffusion process with spanned and unspanned components. We derive results on the expected excess return of options on bond futures. These results are organized around our new empirical finding that the average returns of out-of-the-money puts and calls on Treasury bond futures are both negative. Our theoretical reconciliation warrants a negative local time risk premium, and our treatment considers models with market incompleteness and sources of volatility uncertainty. Our results provide a way to differentiate between the myriad of term-structure models.

Bio:

John gained a first class honours degree in Mathematics at Girton College, Cambridge University before going on to study Electrical Engineering at University College, Oxford University. He was in investment banking (Barclays, Lloyds, UBS) for some 20 years working as a quant, heading quant teams and trading foreign exchange options.
More recently, John has moved into academia. He is at the Smith School of Business, University of Maryland. He is best known for publishing a number of papers on the theme of international risk sharing, exchange rates, term-structure modelling and incomplete markets (for example, one paper recently published at the Review of Financial Studies).

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

Seminar

October 16, 2019: IAQF/Thalesians – Systematic Strategies and Machine Learning

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IAQF Upcoming Event

Systematic Strategies and Machine Learning

 

Kevin Noel

A Talk by
Kevin Noel

Wednesday, October 16

5:45 PM Registration
6:00 PM Seminar Begins
7:30 PM Reception

Abstract

Systematic strategies have a long history in the field of investment area, encompassing the high-frequency ones as well as low-frequency strategies. Over the last decade, the rise of ETF, Robo-allocator made them a popular choice compared to discretionary strategies. More recently, progresses in machine learning renew the theoretical development in that field as well as highlight new perspectives.

Here, we focus on low-frequency strategies and first recall briefly the history of such strategies through a common statistical framework (dynamic basket allocation): Markowitz, CPPI, Buy-Write, Vol. Control, Risk Budgeting, Factor-based, Arbitrage based,… We illustrate those strategies through actual use cases and highlight the importance of underlying risk framework.

In the second part, we focus on the various machine learning methods available to develop or optimize systematic strategies. Especially, we underline the paradigm difference with traditional statistical/stochastic methods by deepening on the fundamental concept of learning vs calibration, as well as the role of prior knowledge.

In the final part, we will evoke some potential future research to go beyond the paradigm of covariance matrix: neural control, graph representation learning.

Biography

Kevin Noel is graduated from Ecole Centrale, in financial mathematics and data mining. From 2007, He worked at BNP Paribas and then at US bank Merrill Lynch on developing advanced statistical framework and risk solutions for Institutional Investor systematic strategies in Asia/Japan. Among those solutions: volatility based, arbitrage Premium, dynamic replication of mutual/ hedge funds, long short… Then, at ING Japan, he co-leads in Re-Insurance hedging/valuation of large scale Japanese Variable Annuities, modeling complex insurance derivatives product, as well as complex modeling of optimal end-user decision process. For the latter, he started to develop machine learning and data analytics for semi-structured, unstructured data, decided to pursue research in Machine Learning/Deep learning applied to optimality or in information processing. He joined Rakuten as Principal Data Scientist and is working on solutions for unstructured or semi-structured Big Data.

Acknowledgments
Special thanks to the Fordham University Gabelli School of Business for hosting and sponsoring the seminar.

About the Series
The IAQF’s Thalesians Seminar Series is a joint effort on the part of the IAQF (www.iaqf.org) and the Thalesians (www.thalesians.com). The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion.

Registration Fees:

Complimentary for IAQF members
Login and Register

Thalesians Members can register for $25

Non-Members: $25.00 by registering

Seminar

October 8, 2019: Cornell-Citi Financial Data Science Seminars

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Featuring Machine Learning experts from Cornell, Citi, and more…

***For those of you who missed Tuesday night’s seminar and wish to see Dr. Miquel Noguer i Alonso’s presentation, the recording is now available.

You and your colleagues are invited to attend the Cornell – Citi Financial Data Science Seminars at the Bloomberg Center at Cornell Tech, Room 061/071. Through the talks this semester, we are excited to collaborate with Citi in highlighting machine learning applications in finance.

2 West Loop Road
New York, NY 10044

All seminars are from 6:10 pm to 7:25 pm. This seminar will NOT be recorded.

Seminars are free. However, registration is required for NYC attendees as seating is limited.

 

Date: Tuesday, October 8, 2019
Time: 6:10pm – 7:25pm
Speaker: Puneet Singhvi | Citi
Title: “What’s Happening with Blockchain in Financial Markets?”

Abstract

Over the past few years, the financial industry has been actively exploring blockchain and distributed ledger technology (DLT) to assess their impact in various use-cases, identify benefits, and separate the hype from reality. Citi has been an active participant and strategic investor in blockchain initiatives across the ecosystem for nearly 5 years now.

In this presentation, we will discuss real use-cases in active implementation across the financial ecosystem and review key drivers for adoption. These emerging use-cases span product lines and geographies – from the digitization of post-trade activities to transformed market exchanges, and from digitized securities to cash-on-chain models, from collateral mobility to trade finance – across North America, Europe, and Asia. We will discuss areas with tangible benefits, and what have been learnings from failed initiatives. We will also review key emerging issues with the technology and potential areas of opportunity going forward.

Speaker Bio

Puneet is Managing Director and Financial Markets Infrastructure (FMI) head for Citi Institutional Client Group. He is responsible for relationship and key initiatives with FMIs such as Exchanges, Payment Systems, Clearing Houses, and Settlement venues. He also leads Blockchain/DLT and Digital Assets initiatives for the Markets and Securities Services business working actively with FMIs, FinTechs and institutional clients on identifying and delivering solutions.

Puneet has worked at Citi across the developed and emerging markets in various management roles within Citi Markets & Securities Services and Citi Trade & Transaction Services businesses. His roles included leading Citi Global Clearing Payments Product, Citi Foreign Exchange & Derivative Clearing Product Management, Trade Finance and Asset Backed Finance.

He has a Bachelor’s degree in Electronics and Communications Engineering and has completed his post-graduation in management from the Indian Institute of Management.

We hope to see you there!

The Cornell-Citi Team

Directions to CFEM & Citi @CornellTech on Roosevelt Island: Take the Tram or the F train to Roosevelt Island; walk to the left along the East River until you see a modern, bronze building, which is the Bloomberg Center. Check in at the front desk and go downstairs to the basement, where Room 061/071 will be straight ahead on your left.

**Please excuse any duplication of this announcement

Past CFEM Events

September 24, 2019
Speaker: Dr. Miquel Noguer I Alonso I Artificial Intelligence Finance Institute

Title: “Latest Developments in Deep Learning in Finance”

November 5, 2019
Speaker: Adam Grealish (Betterment)
Title: TBD

November 12, 2019
Quant Finance Forum