60 citations · 101 across the 8 of their papers we have counts for
9 papers
Modeling Loss-Versus-Rebalancing in Automated Market Makers via Continuous-Installment Options
Srisht Fateh Singh, Reina Ke Xin Li, Samuel Gaskin +5
This paper mathematically models a constant-function automated market maker (CFAMM) position as a portfolio of exotic options, known as perpetual American continuous-installment (C…
EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning
Parvin Malekzadeh, Zissis Poulos, Jacky Chen +2
Recent advancements in Distributional Reinforcement Learning (DRL) for modeling loss distributions have shown promise in developing hedging strategies in derivatives markets. A com…
A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement Learning
Parvin Malekzadeh, Konstantinos N. Plataniotis, Zissis Poulos +1
Distributional Reinforcement Learning (RL) estimates return distribution mainly by learning quantile values via minimizing the quantile Huber loss function, entailing a threshold p…
Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning
Jay Cao, Jacky Chen, Soroush Farghadani +4
We show how D4PG can be used in conjunction with quantile regression to develop a hedging strategy for a trader responsible for derivatives that arrive stochastically and depend on…
Deep Hedging of Derivatives Using Reinforcement Learning
Jay Cao, Jacky Chen, John Hull +1
This paper shows how reinforcement learning can be used to derive optimal hedging strategies for derivatives when there are transaction costs. The paper illustrates the approach by…
Variational Autoencoders: A Hands-Off Approach to Volatility
Maxime Bergeron, Nicholas Fung, John Hull +1
A volatility surface is an important tool for pricing and hedging derivatives. The surface shows the volatility that is implied by the market price of an option on an asset as a fu…
Training CNNs faster with Dynamic Input and Kernel Downsampling
Zissis Poulos, Ali Nouri, Andreas Moshovos
We reduce training time in convolutional networks (CNNs) with a method that, for some of the mini-batches: a) scales down the resolution of input images via downsampling, and b) re…
Astraea: A Decentralized Blockchain Oracle
John Adler, Ryan Berryhill, Andreas Veneris +3
The public blockchain was originally conceived to process monetary transactions in a peer-to-peer network while preventing double-spending. It has since been extended to numerous o…
Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How
Alberto Delmas, Patrick Judd, Dylan Malone Stuart +5
We show that, during inference with Convolutional Neural Networks (CNNs), more than 2x to $8x ineffectual work can be exposed if instead of targeting those weights and activations…