The Presentation From Trading Algorithms With Learning In Latent Alpha Models, and IAQF/Thalesians Talk by Sebastian Jaimungal
May 15th, 2017
IAQF/Thalesians Seminar Series
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. We provide a verification theorem, and provide a methodology for calibrating the model by deriving a variation of the expectation-maximization algorithm. To illustrate the efficacy of the optimal strategy, we demonstrate its performance through simulations and compare it to strategies which ignore learning in the latent factors. We also provide calibration results for a particular model using INTC as an example.
Dr. Sebastian Jaimungal is a Full Professor in the Department of Statistical Sciences at the University of Toronto, where he is the director of the Masters of Financial Insurance program, teaches in the Masters of Mathematical Finance program, and the PhD program. Sebastian is the current Chair (and former Vice Chair; Program Director) for SIAM Financial Mathematics and Engineering (SIAG/FM&E), he is a co-author of the book titled “High-Frequency and Algorithmic Trading” published by Cambridge University Press (2015), and acts on the editorial board for a number of academic and industry journals including: SIAM Journal on Financial Mathematics (SIFIN), The International Journal of Theoretical and Applied Finance (IJTAF), High Frequency, Journal of Risks and Argo. Sebastian is also a founding board member of the Commodities and Energy Markets Association.