Monthly Archives: June 2014

Luca Capriotti, “Real Time Risk Management with Adjoint Algorithmic Differentiaton”

Friday June 20  2014
11.00 – 12.30, 14.30 – 16.00
Scuola Normale Superiore
Aula Bianchi

Luca Capriotti
Credit Suisse London

Real Time Risk Management with Adjoint Algorithmic Differentiaton

Abstract
Adjoint Algorithmic Differentiation (AAD) is one of the principal innovations in risk management of the recent times. In this minicourse I will introduce AAD and show how it can be used to implement the calculation of price sensitivities in complete generality and with minimal analytical effort. The focus will be the application to Monte Carlo methods – generally the most challenging from the computational point of view. With several examples I will illustrate the workings of AAD and demonstrate how it can be straightforwardly implemented to reduce the computation time of the risk of any portfolio by order of magnitudes. 

Download flyer here and slides here.

All interested people are kindly invited.

Taranto, D. E., Bormetti, G., and Lillo, F. (2014) The adaptive nature of liquidity taking in limit order books. Journal of Statistical Mechanics: Theory and Experiment 2014.6: P06002

Abstract

In financial markets, the order flow, defined as the process assuming value one for buy market orders and minus one for sell market orders, displays a very slowly decaying autocorrelation function. Since orders impact prices, reconciling the persistence of the order flow with market efficiency is a subtle issue. A possible solution is provided by asymmetric liquidity, which states that the impact of a buy or sell order is inversely related to the probability of its occurrence. We empirically find that when the order flow predictability increases in one direction, the liquidity in the opposite side decreases, but the probability that a trade moves the price decreases significantly. While the last mechanism is able to counterbalance the persistence of order flow and restore efficiency and diffusivity, the first acts in the opposite direction. We introduce a statistical order book model where the persistence of the order flow is mitigated by adjusting the market order volume to the predictability of the order flow. The model reproduces the diffusive behaviour of prices at all time scales without fine-tuning the values of parameters, as well as the behaviour of most order book quantities as a function of the local predictability of the order flow.

http://dx.doi.org/10.1088/1742-5468/2014/06/P06002