Monday, May 15th
5:45 PM Registration
6:00 PM Seminar Begins
7:30 PM Reception
Alpha signals for statistical arbitrage strategies are often driven by latent factors. This paper analyses how to optimally trade with latent factors that cause prices to jump and diffuse. Moreover, we account for the effect of the trader's actions on quoted prices and the prices they receive from trading. Under fairly general assumptions, we demonstrate how the trader can learn the posterior distribution over the latent states, and explicitly solve the latent optimal trading problem in an online fashion. Furthermore, we develop a forward-backward algorithm based on expectation-maximization to calibrate a pure-jump model to historical data, illustrate the efficacy of the optimal strategy through simulations, and compare to strategies which ignore learning in the latent factors.
(Joint work with Philippe Casgrain, U. Toronto)
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.