Schedule for Fall 2024

9/12/2024

Florian Bourgey (Bloomberg)

Title: Smile Dynamics and Rough Volatility

Abstract: We investigate the dynamic properties of various stochastic and notably rough volatility models, focusing on the dynamics of implied volatilities. While recent literature has extensively analyzed static properties, such as a model's calibration power or the term structure of ATM skews, dynamic features have received less attention. We focus on the Skew-Stickiness Ratio (SSR), an industry-standard indicator of joint spot price and implied volatility dynamics, pursuing the analysis of [Bergomi, Smile dynamics IV, Risk 2009] and extending it to rough volatility models. Using different numerical estimators, we compare the behavior of the model SSR for several models (not limited to the affine framework) with the empirical market SSR for the SPX Index; this comparison sheds light on the suitability of certain modeling choices. Notably, we observe that Bergomi's original intuition—that a forward variance model with a power-law kernel should generate an SSR with a constant term structure—turns out to be accurate, but only for small volatilities of volatilities. On the contrary, the typical parameter sets required for the calibration of fractional models to the SPX options surface (with high levels of volatilities of volatilities) generate a term structure of the SSR that displays important deviations with respect to the market, leading to preliminary conclusions not in favor of models such as rough Bergomi or rough Heston.
This is joint work with Stefano De Marco and Jules Delemotte from Ecole Polytechnique, France.

9/19/2024

Zoltan Eisler (Gardening) [ VIRTUAL]

Title: Enhanced measurement of broker performance via explicit models of execution cost

Abstract:  Trading costs are an integral part of the PnL of a strategy. Most portfolio construction algorithms use some formula to predict the expected cost of trades. The average cost level, described by the parameters of the formula, are often fitted to one’s own trading data. Such data, however, is very noisy. We present two simple tricks to reduce the noise level in linear and impact costs. These can be applied with only data readily available from a broker, and do not require any expensive resources. The improvement in precision is demonstrated via a novel application of the Obizhaeva-Wang model, but the final results hold regardless of the choice of model.

9/26/2024

NO SEMINAR

EASTERN CONFERENCE ON MATHEMATICAL FINANCE

10/3/2024

Eduardo Abi Jaber (Ecole Polytechnique)

Title:

Abstract: 

10/10/2024

10/17/2024

10/24/2024

10/31/2024

11/7/2024

11/14/2024

11/21/2024

11/28/2024

12/5/2024