Search Results for author: Sebastian Jaimungal

Found 23 papers, 8 papers with code

Exploratory Control with Tsallis Entropy for Latent Factor Models

no code implementations14 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.

Q-Learning

Stressing Dynamic Loss Models

1 code implementation6 Nov 2022 Emma Kroell, Silvana M. Pesenti, Sebastian Jaimungal

We illustrate the applicability of our framework by considering "what if" scenarios, where we answer the question: What is the severity of a stress on a portfolio component at an earlier time such that the aggregate portfolio exceeds a risk threshold at the terminal time?

Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement Learning

1 code implementation29 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.

reinforcement-learning

Reinforcement Learning with Dynamic Convex Risk Measures

1 code implementation26 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).

reinforcement-learning Stochastic Optimization

Principal agent mean field games in REC markets

no code implementations22 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.

Navigate

Deep Learning for Principal-Agent Mean Field Games

no code implementations3 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.

Navigate

Robust Risk-Aware Reinforcement Learning

1 code implementation23 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.

reinforcement-learning

Arbitrage-Free Implied Volatility Surface Generation with Variational Autoencoders

1 code implementation10 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.

Portfolio Optimisation within a Wasserstein Ball

1 code implementation8 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.

Management

Exploratory LQG Mean Field Games with Entropy Regularization

no code implementations25 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.

Trading Foreign Exchange Triplets

no code implementations24 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.

A Variational Analysis Approach to Solving the Merton Problem

no code implementations18 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.

A Mean-Field Game Approach to Equilibrium Pricing in Solar Renewable Energy Certificate Markets

no code implementations10 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.

Navigate

Latency and Liquidity Risk

no code implementations8 Aug 2019 Álvaro Cartea, Sebastian Jaimungal, Leandro Sánchez-Betancourt

The interaction between the LOB and MLOs is modelled as a marked point process.

Hedging Non-Tradable Risks with Transaction Costs and Price Impact

no code implementations31 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.

Lévy-Ito Models in Finance

no code implementations19 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.

Deep Q-Learning for Nash Equilibria: Nash-DQN

1 code implementation23 Apr 2019 Philippe Casgrain, Brian Ning, Sebastian Jaimungal

Model-free learning for multi-agent stochastic games is an active area of research.

Q-Learning

Optimal Behaviour in Solar Renewable Energy Certificate (SREC) Markets

1 code implementation12 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.

Active and Passive Portfolio Management with Latent Factors

no code implementations16 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.

Management

Double Deep Q-Learning for Optimal Execution

no code implementations17 Dec 2018 Brian Ning, Franco Ho Ting Lin, Sebastian Jaimungal

Optimal trade execution is an important problem faced by essentially all traders.

Q-Learning

Trading algorithms with learning in latent alpha models

no code implementations12 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.

Mixing LSMC and PDE Methods to Price Bermudan Options

no code implementations20 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.

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