Copy of Abstracts
Date: 4th February 2022
Ruiqi Liu (University of Warwick): The Optimal Control of Inventory and Production for a Hybrid Energy Producer
We study a continuous-time, infinite horizon optimal energy storage and production problem. The primary source of production is modelled as an uncontrolled one-dimensional diffusion process with general dynamics. By controlling the secondary source of production and total energy output, which are both bounded variation processes, we aim to optimize the storage level under a general running reward function and maximize the profit generated from the production. Through associating the control problem with Dykin's game, the optimal control is closely related to two free boundaries, and we show that one can be directly computed and the other is characterized via an integral equation. After establishing the smooth-pasting principle on these boundaries, a viscosity approach is used to prove the smoothness of the value function, which leads to the verification of the proposed optimal control.
Date: 25th February 2022
Huy Chau (University of Manchester): Super-replication with transaction costs under model uncertainty for continuous processes
We formulate a superhedging theorem in the presence of transaction costs and model uncertainty. Asset prices are assumed continuous and uncertainty is modelled in a parametric setting. Our proof relies on a new topological framework in which no Krein-Smulian type theorem is available. This is joint with Masaaki Fukasawa (Osaka University) and Miklos Rasonyi (Alfred Renyi Institute of Mathematics)
Date: 18th March 2022
Mikko Pakkanen (Imperial College London): Rough volatility – Re-examining empirical evidence using the generalised method of moments
In late 2014, Jim Gatheral, Thibault Jaisson and Mathieu Rosenbaum released the first preprint of their ground-breaking paper "Volatility is rough", arguing that financial market volatility should be modelled by stochastic processes with rough trajectories, such as a fractional Brownian motion with Hurst index below 0.5.
While Gatheral, Jaisson and Rosenbaum's empirical findings on the roughness of realised volatility have since been replicated across different asset classes and with thousands of assets, determining the roughness of realised volatility remains a delicate statistical problem. It is complicated by the fact that we can only observe volatility as a time integral (integrated variance) with measurement error (estimated by means of realised variance). Integration is a smoothing operation while measurement error increases the perceived roughness of the measurements, giving rise to two counteracting sources of bias whose net effect is unclear. In particular, critics have questioned to what degree roughness of volatility can be distinguished from measurement error.
In this talk, I will present a novel generalised method of moments (GMM) estimation technique for general log-normal volatility models, aiming to address this concern. The GMM estimator accommodates both the impact of integration and the presence of measurement error, the latter through a bias correction. After presenting asymptotic theory for the GMM estimator, I will demonstrate by Monte Carlo experiments that the bias correction is indispensable. Indeed, without it, non-rough volatility may be estimated as rough, but once it is incorporated, the bias problem is, for all practical purposes, resolved. Finally, by applying the GMM estimator to empirical realised volatility data on 29 stock market indices worldwide, I show that Gatheral, Jaisson and Rosenbaum's conclusion stands: realised volatility is indeed best described by a rough process.
Based on joint work with Anine Bolko, Kim Christensen and Bezirgen Veliyev.
Date: 15th October 2021
Nazem Khan (University of Warwick): Sensitivity to large losses and -arbitrage for convex risk measures
Date: 4th November 2021
Matteo Burzoni (Università degli Studi di Milano): Mean Field Games with absorption and a model of bank run
Date: 26th November 2021
Johannes Muhle-Karbe (Imperial College London): Hedging with Market and Limit Orders
Date: 3rd December 2021
Roxana Dumitrescu (King's College London): Control and optimal stopping mean-field games: a linear programming approach