MCFAM Seminar Series: “Kelly Criterion: From a Simple Random Walk to Levy Processes"
Please join us for our next seminar of fall semester! This seminar will feature Austin Pollok from the University of Southern California who will discuss the Kelly criterion.
About the lecture
The original Kelly criterion provides a strategy to maximize the long-term growth of winnings in a sequence of simple Bernoulli bets with an edge—that is, when the expected return on each bet is positive. The objective of this work is to consider more general models of returns and the continuous time, or high-frequency, limits of those models. The results include an explicit expression for the optimal strategy in several models with continuous time compounding. Given that we know how to optimally bet, we seek to find an edge in the financial markets by investigating the volatility risk premium in option returns. With the aid of high frequency volatility forecasts, we are able to capture an economically significant increase in risk premium compared to competing models.
About the speaker
Austin Pollok is a Ph.D. student in applied mathematics at the University of Southern California (USC), set to graduate this year. His areas of research are in optimal growth strategies, such as the Kelly criterion, under heavy-tailed processes; high frequency volatility forecasting using machine learning methods; and empirical option pricing. He has worked at Capital Group Companies as a quantitative research engineer while completing his Ph.D.