Publications

Publications of the Author


Stochastic Receding Horizon Control for Short-term Risk Management in Foreign Exchange

Farzad Noorian, Philip H.W. LeongBarry Flower

The Journal of Risk, Incisive Media, May 12, 2016

Foreign exchange (FX) dealers are exposed to currency risk through both market
and counterparty activities. Research in FX risk management has mainly focused
on long-term risks, yet trading costs associated with long-term strategies make them
undesirable for short-term risk hedging. In this paper, a short-term risk management
system for FX dealers is described, in which the optimal risk–cost profiles are obtained
through dynamic control of the dealer’s positions on the spot market. This approach
is formulated as a stochastic receding horizon control (SRHC) problem, incorporating
elements that model client flow, transaction cost, market impact, exchange rate
volatility and fluctuations caused by macroeconomic announcements. The proposed
technique is backtested using both synthetic and historical client trade data. The
results obtained outperform three benchmark hedging strategies on a risk–cost Pareto
frontier, achieving up to a 47.6% cost improvement over benchmark strategies. A
flexible scenario generation oracle is also introduced and used to quantify the effects
of predictive model quality on risk management.

Keywords: foreign exchange (FX) hedging; short-term risk management; stochastic models;
receding horizon control; scenario generation.


Adaptive analogue VLSI neural systems

Springer, 1996

Barry Flower , M. Jabri, R. Coggins

Since the pioneering work of Carver Mead and his group at Caltech analog neural computing has considerably matured to become a technology that can provide superior solutions to many real world problems.
Many books and technical papers have been written over the last ten years on analog implementations of artificial neural networks in silicon. This book presents chips and systems that have been developed and applied to real world problems. A strong emphasis is placed on micro power trainable artificial neural network systems and the book demonstrated how such systems provide adaptive signal processing and classification capabilities.


For more publications from Barry Flower see here