The second talk of the Thalesians NYC seminars will be given by Peter Decrem - head of the rates group at Quantifi.
Date and Time: 6:30 p.m. on Wednesday, 9th March, 2011.
Venue: Third floor at the Playwright Tavern, Theatre District, NYC.
Ticket price: $15
You can register for this event and pay online on Meetup.com:
http://www.meetup.com/thalesians/calendar/16551679/
ABSTRACT
This talk will discuss the application of Monte Carlo methods to a GPU implementation of the LIBOR Market/BGM Model. In the process we will discuss random number generation and inverse normal distribution functions designed for execution on the GPU. We will demonstrate how some level of abstraction can be implemented to obtain hardware agnostic code. We will also briefly discuss the limitations of the GPU for the BGM model and point at opportunities for the use of GPUs in the credit analytics space. Resources, including open source code focused on BGM implementation in CUDA, will be identified and used for illustration purposes to help jumpstart the GPU development work.
SPEAKER
Peter heads the Rates Group at Quantifi. As Director, Peter is responsible for managing the product development process of all Rates and Hybrid Solutions within the Quantifi product suite. Peter started in Research and Technology at Bear Stearns and Deutsche Bank. He traded fixed income derivatives, government bonds and agencies for Lehman Brothers and Salomon Brothers. He was responsible for fixed income derivatives trading desk for a number of European banks. Most recently he refocused on technology and specifically concentrated on machine learning and high frequency trading on parallel systems prior to joining Quantifi in 2009.
Finance Focus
MoneyScience Twitter
Random
portfolios select a sample of portfolios that obey a given set of constraints.
They have many uses -- measuring performance is one of the most important.
Performance can be measured much more accurately with random portfolios
than with other methods. The measurement can be on hypothetical strategies
in backtests as well as traditional performance measurement.