no code implementations • 19 Feb 2024 • Sebastian Jaimungal, Xiaofei Shi
When an investor is faced with the option to purchase additional information regarding an asset price, how much should she pay?
no code implementations • 2 Jan 2024 • Liam Welsh, Sebastian Jaimungal
In response to the global climate crisis, governments worldwide are introducing legislation to reduce greenhouse gas (GHG) emissions to help mitigate environmental catastrophes.
no code implementations • 22 Aug 2023 • Emma Kroell, Sebastian Jaimungal, Silvana M. Pesenti
Insurers meanwhile seek to maximise their expected utility without ambiguity.
no code implementations • 16 Aug 2023 • Ziteng Cheng, Anthony Coache, Sebastian Jaimungal
Specifically, we prove that the agent's risk aversion can be identified as the number of questions tends to infinity, and the questions are randomly designed.
no code implementations • 18 May 2023 • Sebastian Jaimungal, Silvana M. Pesenti, Yuri F. Saporito, Rodrigo S. Targino
We define and develop an approach for risk budgeting allocation -- a risk diversification portfolio strategy -- where risk is measured using a dynamic time-consistent risk measure.
no code implementations • 5 Apr 2023 • Sebastian Jaimungal, Yuri F. Saporito, Max O. Souza, Yuri Thamsten
This article explores the optimisation of trading strategies in Constant Function Market Makers (CFMMs) and centralised exchanges.
no code implementations • 27 Mar 2023 • David Wu, Sebastian Jaimungal
The objectives of option hedging/trading extend beyond mere protection against downside risks, with a desire to seek gains also driving agent's strategies.
no code implementations • 1 Mar 2023 • Vedant Choudhary, Sebastian Jaimungal, Maxime Bergeron
We introduce a new approach for generating sequences of implied volatility (IV) surfaces across multiple assets that is faithful to historical prices.
no code implementations • 27 Feb 2023 • Ziteng Cheng, Sebastian Jaimungal, Nick Martin
We introduce a distributional method for learning the optimal policy in risk averse Markov decision process with finite state action spaces, latent costs, and stationary dynamics.
no code implementations • 14 Nov 2022 • Ryan Donnelly, Sebastian Jaimungal
To encourage exploration of the state space, we reward exploration with Tsallis Entropy and derive the optimal distribution over states - which we prove is $q$-Gaussian distributed with location characterized through the solution of an FBS$\Delta$E and FBSDE in discrete and continuous time, respectively.
1 code implementation • 6 Nov 2022 • Emma Kroell, Silvana M. Pesenti, Sebastian Jaimungal
We find that under the stressed measure, the intensity and the severity distribution of the process depend on time and state.
1 code implementation • 29 Jun 2022 • Anthony Coache, Sebastian Jaimungal, Álvaro Cartea
We propose a novel framework to solve risk-sensitive reinforcement learning (RL) problems where the agent optimises time-consistent dynamic spectral risk measures.
1 code implementation • 26 Dec 2021 • Anthony Coache, Sebastian Jaimungal
We develop an approach for solving time-consistent risk-sensitive stochastic optimization problems using model-free reinforcement learning (RL).
no code implementations • 22 Dec 2021 • Dena Firoozi, Arvind V Shrivats, Sebastian Jaimungal
We find through these techniques that the optimal penalty function is linear in the agents' state, suggesting the optimal emissions regulation market is more akin to a tax or rebate, regardless of the principal's utility function.
no code implementations • 3 Oct 2021 • Steven Campbell, Yichao Chen, Arvind Shrivats, Sebastian Jaimungal
Here, we develop a deep learning algorithm for solving Principal-Agent (PA) mean field games with market-clearing conditions -- a class of problems that have thus far not been studied and one that poses difficulties for standard numerical methods.
1 code implementation • 23 Aug 2021 • Sebastian Jaimungal, Silvana Pesenti, Ye Sheng Wang, Hariom Tatsat
We present a reinforcement learning (RL) approach for robust optimisation of risk-aware performance criteria.
1 code implementation • 10 Aug 2021 • Brian Ning, Sebastian Jaimungal, Xiaorong Zhang, Maxime Bergeron
We propose a hybrid method for generating arbitrage-free implied volatility (IV) surfaces consistent with historical data by combining model-free Variational Autoencoders (VAEs) with continuous time stochastic differential equation (SDE) driven models.
no code implementations • 25 Jan 2021 • Jean-Pierre Fouque, Sebastian Jaimungal, Yuri F. Saporito
Trading frictions are stochastic.
1 code implementation • 8 Dec 2020 • Silvana Pesenti, Sebastian Jaimungal
We study the problem of active portfolio management where an investor aims to outperform a benchmark strategy's risk profile while not deviating too far from it.
no code implementations • 25 Nov 2020 • Dena Firoozi, Sebastian Jaimungal
We study a general class of entropy-regularized multi-variate LQG mean field games (MFGs) in continuous time with $K$ distinct sub-population of agents.
no code implementations • 24 Apr 2020 • Álvaro Cartea, Sebastian Jaimungal, Tianyi Jia
To maximize revenues, the broker considers trading in a currency triplet which consists of the illiquid pair and two other liquid currency pairs.
no code implementations • 18 Mar 2020 • Ali Al-Aradi, Sebastian Jaimungal
We address the Merton problem of maximizing the expected utility of terminal wealth using techniques from variational analysis.
no code implementations • 10 Mar 2020 • Arvind Shrivats, Dena Firoozi, Sebastian Jaimungal
As such, the SREC market can be viewed as a stochastic game, where agents interact through the SREC price.
no code implementations • 8 Aug 2019 • Álvaro Cartea, Sebastian Jaimungal, Leandro Sánchez-Betancourt
The interaction between the LOB and MLOs is modelled as a marked point process.
no code implementations • 31 Jul 2019 • Alvaro Cartea, Ryan Donnelly, Sebastian Jaimungal
A risk-averse agent hedges her exposure to a non-tradable risk factor $U$ using a correlated traded asset $S$ and accounts for the impact of her trades on both factors.
no code implementations • 19 Jul 2019 • George Bouzianis, Lane P. Hughston, Sebastian Jaimungal, Leandro Sánchez-Betancourt
We present an overview of the broad class of financial models in which the prices of assets are L\'evy-Ito processes driven by an $n$-dimensional Brownian motion and an independent Poisson random measure.
1 code implementation • 23 Apr 2019 • Philippe Casgrain, Brian Ning, Sebastian Jaimungal
Model-free learning for multi-agent stochastic games is an active area of research.
1 code implementation • 12 Apr 2019 • Arvind Shrivats, Sebastian Jaimungal
A regulator imposes a floor on the amount of energy each regulated firm must generate from solar power in a given period and provides them with certificates for each generated MWh.
no code implementations • 16 Mar 2019 • Ali Al-Aradi, Sebastian Jaimungal
The solution in this case requires a filtering step to obtain posterior probabilities for the state of the Markov chain from asset price information, which are subsequently used to find the optimal allocation.
no code implementations • 17 Dec 2018 • Brian Ning, Franco Ho Ting Lin, Sebastian Jaimungal
Optimal trade execution is an important problem faced by essentially all traders.
no code implementations • 12 Jun 2018 • Philippe Casgrain, Sebastian Jaimungal
Under fairly general assumptions, we demonstrate how the trader can learn the posterior distribution over the latent states, and explicitly solve the latent optimal trading problem.
no code implementations • 20 Mar 2018 • David Farahany, Kenneth Jackson, Sebastian Jaimungal
We develop a mixed least squares Monte Carlo-partial differential equation (LSMC-PDE) method for pricing Bermudan style options on assets whose volatility is stochastic.