About
Andrea Bortolini is a Master of Financial Engineering candidate at UCLA Anderson and a Fink Center Quantitative Finance Fellow. Before UCLA, he worked in Corporate Treasury at Goldman Sachs and previously interned in structured derivatives at Société Générale. His current work sits at the intersection of derivatives, market microstructure, prediction markets, and digital assets, with a strong interest in research-driven quantitative finance.
Research & Projects
All workWhy So Serious?
Decomposing the Belief Volatility Smile in Prediction Markets
Prediction market prices are bounded between zero and one, but they are often interpreted with tools borrowed from standard asset pricing. This paper develops a logit-based framework for measuring belief volatility and shows that the apparent smile in implied belief volatility is largely a geometric effect rather than a true information pattern. Using Kalshi FOMC rate-target contracts, the paper also studies how entropy evolves into event resolution and how non-Gaussian belief dynamics can generate meaningful pricing distortions.
Structural Credit Model with Time-Varying Default Barriers
A Disclosure-Based Calibration
This note extends structural credit models by linking the shape of the default barrier to information from firms' 10-K maturity disclosures. Rather than treating the barrier as fixed or purely exogenous, the model allows it to reflect the underlying debt profile and rollover structure of the firm. The result is a more flexible framework for interpreting default risk and distance to default in a way that is closer to the firm's actual financing structure.