Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Godin, Frederic"'
Autor:
Veroutis, Peter, Godin, Frédéric
The Multiarmed Bandits (MAB) problem has been extensively studied and has seen many practical applications in a variety of fields. The Survival Multiarmed Bandits (S-MAB) open problem is an extension which constrains an agent to a budget that is dire
Externí odkaz:
http://arxiv.org/abs/2410.16486
We present a dynamic hedging scheme for S&P 500 options, where rebalancing decisions are enhanced by integrating information about the implied volatility surface dynamics. The optimal hedging strategy is obtained through a deep policy gradient-type r
Externí odkaz:
http://arxiv.org/abs/2407.21138
The recent work of Horikawa and Nakagawa (2024) claims that under a complete market admitting statistical arbitrage, the difference between the hedging position provided by deep hedging and that of the replicating portfolio is a statistical arbitrage
Externí odkaz:
http://arxiv.org/abs/2407.14736
This paper tackles the problem of mitigating catastrophic risk (which is risk with very low frequency but very high severity) in the context of a sequential decision making process. This problem is particularly challenging due to the scarcity of obse
Externí odkaz:
http://arxiv.org/abs/2406.15612
Dynamic hedging is the practice of periodically transacting financial instruments to offset the risk caused by an investment or a liability. Dynamic hedging optimization can be framed as a sequential decision problem; thus, Reinforcement Learning (RL
Externí odkaz:
http://arxiv.org/abs/2402.13326
The brittleness of finetuned language model performance on out-of-distribution (OOD) test samples in unseen domains has been well-studied for English, yet is unexplored for multi-lingual models. Therefore, we study generalization to OOD test data spe
Externí odkaz:
http://arxiv.org/abs/2311.06549
Intent discovery is the task of inferring latent intents from a set of unlabeled utterances, and is a useful step towards the efficient creation of new conversational agents. We show that recent competitive methods in intent discovery can be outperfo
Externí odkaz:
http://arxiv.org/abs/2305.19783
We present a method for pretraining a recurrent mixture density network (RMDN). We also propose a slight modification to the architecture of the RMDN-GARCH proposed by Nikolaev et al. [2012]. The pretraining method helps the RMDN avoid bad local mini
Externí odkaz:
http://arxiv.org/abs/2302.14141
For text classification tasks, finetuned language models perform remarkably well. Yet, they tend to rely on spurious patterns in training data, thus limiting their performance on out-of-distribution (OOD) test data. Among recent models aiming to avoi
Externí odkaz:
http://arxiv.org/abs/2210.11805
Publikováno v:
In Finance Research Letters March 2025 73
