Uncertainty quantified as probability is the rock upon which Bayesian inference is built. The instability of sample covariance matrices leads to major problems in Markowitz portfolio optimization. In this webinar, Max Margenot, Academia & Data Science Lead at Quantopian, will use probabilistic programming to compute probability distributions on the covariance of a set of assets. This yields a more robust estimate of their variation and adds uncertainty into how we calculate weights for a portfolio of assets.
This webinar is sponsored by DataCamp: https://www.datacamp.com