Recent research on portfolio optimization featured in interview of Dr Cristin Buescu by Faculti.Net

A comparison of the effectiveness of Machine Learning vs Financial Mathematics techniques when optimally managing a portfolio of cointelated assets was presented in the recent research output of Dr Cristin Buescu, a member of the Financial Mathematics group at King’s.

• Portfolio Optimization for Cointelated Pairs SDEs vs Machine Learning

The Practitioners’ Lecture Series Presents: Antoine Savine

Workshop: Introduction to back-propagation and automatic adjoint differentiation (AAD) in machine learning and finance, 28-29 March 2019. This six-hour workshop included: Introduction to Artificial Neural Networks and Deep Learning; Back-propagation through ANNs; Applications of Deep Learning in Finance; Hands-on examples with Excel/C++ and Python/Tensor Flow; Extract computation graphs and differentiate arbitrary calculations; Fast (reverse mode) AAD with operator overloading in C++ Application to financial risk management. The Practitioners' Lecture Series at King's College London is organised by Dr Blanka Horvath and sponsored by Simudyne.

FIPS 2018

The eighth Workshop on Finance, Insurance, Probability and Statistics (FIPS 2018) brought together a global cast of leading academic experts, practitioners and junior researchers. Attendees shared research that underscored the contributions of probability and statistics to the development of quantitative models, methods, techniques and technologies in the fields of finance and insurance.

Risk Management for Whales

Thursday, 1 February 2018 Speaker: Lakshithe Wagalath (IESEG School of Management) Title: Risk Management for Whales Abstract: We propose a portfolio risk model which integrates market risk with liquidation costs. The model provides a framework for computing liquidation-adjusted risk measures such as Liquidation-adjusted VaR (LVaR). Calculation of liquidation-adjusted Value-at-Risk (LVaR) for simulated and real-life examples reveals a substantial impact of liquidation costs on portfolio risk for portfolios with large concentrated positions. This is joint work with Rama Cont.

Optimisation in energy markets

Optimization in energy markets, 3 April 2017 Speaker 1: Michel De Lara, École des Ponts ParisTech Title: Game Theory with Information: a Framework for the New Energy Systems Speaker 2: Pierre Carpentier, École des Ponts ParisTech Title: Optimization of Energy Production and Transport: Approaches by Decomposition Under Stochasticity Speaker 3: Jean-Philippe Chancelier, École des Ponts ParisTech Title: Stochastic Spatial Decomposition for Large Scale Hydro Valleys Speaker 4: Teemu Pennanen, King's College London Title: Mathematical models for pricing and hedging of delivery contracts

Convex Analysis Seminar

Convex Analysis Seminar, 30 March 2017 Speaker 1: Ari-Pekka Perkkiö, LMU Mathematics Institute Title: Regular processes and duality Abstract: We study the optional projection on spaces of cadlag and continuous processes. Our main results sharpen those of [Bismut, 1978] in cases where the projected process has additional integrability properties. Moreover, we characterize the topological duals of optional cadlag processes and of regular processes with the given integrability properties. Our main results are derived by purely functional analytic arguments simplifying original proofs of [Bismut, 1978]. We also present results on dual representations for convex integral functionals on regular processes. These yield a maximum principle for a general class of singular stochastic control problems. The talk is based on a joint work with TeemuPennanen. Speaker 2: Christian Léonard, Université Paris Ouest Title: Some results about entropic transport Abstract: Optimal transport is a powerful tool for proving several functional inequalities such as: concentration inequalities, geometric inequalities (Prekopa-Leindler inequality) or entropy-entropy production inequalities related to rates of convergence to equilibrium of heat flows. In this talk, an alternate similar approach will be introduced. It relies on entropic transport rather than standard optimal transport. The optimal transport problem is replaced by the Schrödinger problem: an entropy minimization problem on the set of probability measures with marginal constraints. The large deviation principle leading to the Schrödinger problem will be introduced and some easy proofs of functional inequalities, based on this entropy minimization problem, will be sketched.