Brooklyn Quant Experience Lecture Series: Luca Capriotti
Upon the request of the speaker, no recording is available for this lecture.
The Department of Finance and Risk Engineering welcomes Luca Capriotti from Credit Suisse to the BQE Lecture Series.
Title
A Gentle Introduction to Adjoint Algorithmic Differentiation (AAD):
(How to Better Hedge Financial Risk, Crack Some Puzzles of Condensed Matters and Much More with Upside-Down Derivatives)
Abstract
Adjoint Algorithmic Differentiation (AAD) is one of the principal innovations in risk management of recent times, revolutionizing the way practitioners compute price sensitivities (the so-called `Greeks') of derivatives portfolios and reducing by orders of magnitude the cost associated with their computation.
Despite being a well-established mathematical approach, the potential of Algorithmic Differentiation (AD) has remained untapped until recently in many areas of natural and applied sciences. In particular, it has been only recently rediscovered in both Financial Engineering and Physics. In Physics, it has made possible the study of exotic states of condensed matter via Quantum Monte Carlo simulations. Financial Engineering has opened a new important chapter in risk management, by making possible the calculation of the risk borne by large portfolios of securities accurately, in real-time, and with limited computational costs, ultimately boosting profitability through better risk management practices.
Bio
Luca works in the Quantitative Analysis and Technology (QAT) Department in New York where he is the Global Head of Quantitative Strategies Credit, and he is responsible for both front office (pricing models, and eTrading) and capital models (including Var/IRC/FRTB SA, IMA and DRC).
Luca is also visiting professor at the Department of Mathematics at University College London, and an Adjunct Professor at NYU, Tandon School of Engineering, and at Columbia University, at the Department of Industrial Engineering and Operations Research. His current research interests are in Credit Models, Computational Finance, and Machine Learning, with a focus on efficient numerical techniques for Derivatives Pricing and Risk Management, and applications of Adjoint Algorithmic Differentiation (AAD), which he has helped introduce to Finance and Physics, and for which he holds a US Patent. Luca has published over 70 scientific papers, with the top 3 papers collecting to date over 900 citations.
Prior to working in Finance, Luca was a researcher at the Kavli Institute for Theoretical Physics, Santa Barbara, California, working in High-Temperature Superconductivity and Quantum Monte Carlo methods for Condensed Matter systems. He has been awarded the Director's fellowship at Los Alamos National Laboratory, and the Wigner Fellowship at Oak Ridge National Laboratory.
Luca holds an M.S. cum laude in Physics from the University of Florence, and an M.Phil. and a Ph.D. cum laude in Condensed Matter Theory, from the International School for Advanced Studies, Trieste.