2 citations · 4 across the 11 of their papers we have counts for
11 papers
From Deep Filtering to Deep Econometrics
Robert Stok, Paul Bilokon
Calculating true volatility is an essential task for option pricing and risk management. However, it is made difficult by market microstructure noise. Particle filtering has been p…
Combining Deep Learning on Order Books with Reinforcement Learning for Profitable Trading
Koti S. Jaddu, Paul A. Bilokon
High-frequency trading is prevalent, where automated decisions must be made quickly to take advantage of price imbalances and patterns in price action that forecast near-future mov…
Implementing portfolio risk management and hedging in practice
Paul Alexander Bilokon
In academic literature portfolio risk management and hedging are often versed in the language of stochastic control and Hamilton--Jacobi--Bellman~(HJB) equations in continuous time…
Applying Deep Learning to Calibrate Stochastic Volatility Models
Abir Sridi, Paul Bilokon
Stochastic volatility models, where the volatility is a stochastic process, can capture most of the essential stylized facts of implied volatility surfaces and give more realistic…
Transformers versus LSTMs for electronic trading
Paul Bilokon, Yitao Qiu
With the rapid development of artificial intelligence, long short term memory (LSTM), one kind of recurrent neural network (RNN), has been widely applied in time series prediction.…
Derivatives Sensitivities Computation under Heston Model on GPU
Pierre-Antoine Arsaguet, Paul Bilokon
This report investigates the computation of option Greeks for European and Asian options under the Heston stochastic volatility model on GPU. We first implemented the exact simulat…