Tag Archives: Seminar

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

NYU FRE Lecture Series: Matthias Heymann

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NYU Tandon School of Engineering

Dear All,

You are cordially invited to attend the NYU FRE Lecture Series on Thursday, October 3rd in the Event MakerSpace
(6 MetroTech Center – 1st Floor) at 6 p.m.

Dr. Matthias Heymann will present a talk on the following topic:

Title:

The Adaptive Curve Evolution Model for Interest Rates

Abstract:

In this talk, the speaker presents the key results from his recent book of the same title. The ACE model—in its original form developed by Gregory Pelts and now carefully rephrased, refined, and made more accessible by Matthias Heymann—is the first to combine all of the most desirable analytical properties in one interest rate model: It is low-dimensional (with any dimension n ∈ ℕ\{2}), complete (i.e., it models all tenors), arbitrage-free, highly flexible (it provides 2n+1 discrete parameters, plus the functional noise parameter σ(x,t)), and time homogeneous if desired, and it imposes a lower bound on rates; moreover, it has the rare feat of being unspanned (i.e., its bond price function does not depend on σ), which simplifies calibration. While its original derivation relied on an arsenal of compelling tools borrowed from theoretical physics (in particular, Einstein’s Special Theory of Relativity), the model’s form presented in this talk will only require basic mathematical skills.

Bio:

Matthias Heymann has a Ph.D. in mathematics (2002–07, Courant Institute of Mathematical Sciences, NYU) and did a postdoc in the Duke University Mathematics Department (2007–10). Specializing in probability theory, during his academic career he made contributions related to Wentzell–Freidlin theory, i.e., the study of maximum likelihood transition curves in stochastic dynamical systems with small noise. One of his most notable publications is his monograph “Minimum Action Curves in Degenerate Finsler Metrics — Existence and Properties,” published in Springer’s “Lecture Notes in Mathematics” series.

In 2010 he started working as a quantitative analyst at Goldman Sachs. During this time he began his work on the ACE model, which eventually turned into his second book, whose results are presented in this talk.

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

Seminar

October 2, 2019: FinTech Seminar Series

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Join us on October 2nd for a discussion about Dynamic Replication and Hedging: A Reinforcement Learning Approach presented by Petter Kolm.

About this Event

In this talk we address the problem of how to optimally hedge an options book in a practical setting, where trading decisions are discrete and trading costs can be nonlinear and difficult to model.

Based on reinforcement learning (RL), a well-established machine learning technique we propose a model that is flexible, accurate and very promising for real-world applications. A key strength of the RL approach is that it does not make any assumptions about the form of trading cost. RL learns the minimum variance hedge subject to whatever transaction cost function one provides. All that it needs is a good simulator, in which transaction costs and options prices are simulated accurately.

This is joint work with Gordon Ritter.

Published Paper:
https://jfds.iijournals.com/content/1/1/159

View Peter Kolm’s Profile.

 

Location

Manhattan Institute of Management
2 Washington Street
17th Floor
New York, NY 10004