Author: Saeed MarzbanPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Deep Reinforcement Learning for Option Pricing and Hedging Under Dynamic Expectile Risk Measures
Author: Saeed MarzbanDeep Reinforcement Learning for Dynamic Expectile Risk Measures
Author: Saeed MarzbanEqua-risk Pricing, Hedging, and Portfolio Management Using Dynamic Risk Measures and Deep Reinforcement Learning Methods
Author: Saeed MarzbanPricing and Hedging Financial Derivatives with Reinforcement Learning Methods
Author: Alexandre CarbonneauPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
This thesis studies the problem of pricing and hedging financial derivatives with reinforcement learning. Throughout all four papers, the underlying global hedging problems are solved using the deep hedging algorithm with the representation of global hedging policies as neural networks. The first paper, "Equal Risk Pricing of Derivatives with Deep Hedging'', shows how the deep hedging algorithm can be applied to solve the two underlying global hedging problems of the equal risk pricing framework for the valuation of European financial derivatives. The second paper, "Deep Hedging of Long-Term Financial Derivatives'', studies the problem of global hedging very long-term financial derivatives which are analogous, under some assumptions, to options embedded in guarantees of variable annuities. The third paper, "Deep Equal Risk Pricing of Financial Derivatives with Multiple Hedging Instruments'', studies derivative prices generated by the equal risk pricing framework for long-term options when shorter-term options are used as hedging instruments. The fourth paper, "Deep equal risk pricing of financial derivatives with non-translation invariant risk measures'', investigates the use of non-translation invariant risk measures within the equal risk pricing framework.
Multi-agent Deep Reinforcement Learning and GAN-based Market Simulation for Derivatives Pricing and Dynamic Hedging
Author: Samson QianPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Advancements in computing capabilities have enabled machine learning algorithms to learn directly from large amounts of data. Deep reinforcement learning is a particularly powerful method that uses agents to learn by interacting with an environment of data. Although many traders and investment managers rely on traditional statistical and stochastic methods to price assets and develop trading and hedging strategies, deep reinforcement learning has proven to be an effective method to learn optimal policies for pricing and hedging. Machine learning removes the need for various parametric assumptions about underlying market dynamics by learning directly from data. This research examines the use of machine learning methods to develop a data-driven method of derivatives pricing and dynamic hedging. Nevertheless, machine learning methods like reinforcement learning require an abundance of data to learn. We explore the implementation of a generative adversarial network-based approach to generate realistic market data from past historical data. This data is used to train the reinforcement learning framework and evaluate its robustness. The results demonstrate the efficacy of deep reinforcement learning methods to price derivatives and hedge positions in the proposed systematic GAN-based market simulation framework.
Risk-averse Deep Distributional Reinforcement Learning for Option Hedging Under Market Frictions
Author: 林鼎鈞Deep Hedging
Author: Hans BuehlerPublisher:
ISBN:
Category :
Languages : en
Pages : 32
Book Description
We present a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, market impact, liquidity constraints or risk limits using modern deep reinforcement machine learning methods.We discuss how standard reinforcement learning methods can be applied to non-linear reward structures, i.e. in our case convex risk measures. As a general contribution to the use of deep learning for stochastic processes, we also show in section 4 that the set of constrained trading strategies used by our algorithm is large enough to ∈-approximate any optimal solution.Our algorithm can be implemented efficiently even in high-dimensional situations using modern machine learning tools. Its structure does not depend on specific market dynamics, and generalizes across hedging instruments including the use of liquid derivatives. Its computational performance is largely invariant in the size of the portfolio as it depends mainly on the number of hedging instruments available.We illustrate our approach by showing the effect on hedging under transaction costs in a synthetic market driven by the Heston model, where we outperform the standard “complete market” solution.This is the "stochastic analysis" version of the paper. A version in machine learning notation is available here "https://ssrn.com/abstract=3355706" https://ssrn.com/abstract=3355706.
QLBS
Author: Igor HalperinPublisher:
ISBN:
Category :
Languages : en
Pages : 30
Book Description
This paper presents a discrete-time option pricing model that is rooted in Reinforcement Learning (RL), and more specifically in the famous Q-Learning method of RL. We construct a risk-adjusted Markov Decision Process for a discrete-time version of the classical Black-Scholes-Merton (BSM) model, where the option price is an optimal Q-function, while the optimal hedge is a second argument of this optimal Q-function, so that both the price and hedge are parts of the same formula. Pricing is done by learning to dynamically optimize risk-adjusted returns for an option replicating portfolio, as in the Markowitz portfolio theory. Using Q-Learning and related methods, once created in a parametric setting, the model is able to go model-free and learn to price and hedge an option directly from data generated from a dynamic replicating portfolio which is rebalanced at discrete times. If the world is according to BSM, our risk-averse Q-Learner converges, given enough training data, to the true BSM price and hedge ratio of the option in the continuous time limit, even if hedges applied at the stage of data generation are completely random (i.e. it can learn the BSM model itself, too!), because Q-Learning is an off-policy algorithm. If the world is different from a BSM world, the Q-Learner will find it out as well, because Q-Learning is a model-free algorithm. For finite time steps, the Q-Learner is able to efficiently calculate both the optimal hedge and optimal price for the option directly from trading data, and without an explicit model of the world. This suggests that RL may provide efficient data-driven and model-free methods for optimal pricing and hedging of options, once we depart from the academic continuous-time limit, and vice versa, option pricing methods developed in Mathematical Finance may be viewed as special cases of model-based Reinforcement Learning. Further, due to simplicity and tractability of our model which only needs basic linear algebra (plus Monte Carlo simulation, if we work with synthetic data), and its close relation to the original BSM model, we suggest that our model could be used for benchmarking of different RL algorithms for financial trading applications.A supplement to this paper can be found here:"http://ssrn.com/abstract=3090608" http://ssrn.com/abstract=3090608.
Option Pricing With Machine Learning
Author: Daniel Alexandre BlochPublisher:
ISBN:
Category :
Languages : en
Pages : 49
Book Description
An option pricing model is tied to its ability of capturing the dynamics of the underlying spot price process. Its misspecification will lead to pricing and hedging errors. Parametric pricing formula depends on the particular form of the dynamics of the underlying asset. For tractability reasons, some assumptions are made which are not consistent with the multifractal properties of market returns. On the other hand, non-parametric models such as neural networks use market data to estimate the implicit stochastic process driving the spot price and its relationship with contingent claims. When pricing multidimensional contingent claims, or even vanilla options with complex models, one must rely on numerical methods such as partial differential equations, numerical integration methods such as Fourier methods, or Monte Carlo simulations. Further, when calibrating financial models on market prices, a large number of model prices must be generated to fit the model parameters. Thus, one requires highly efficient computation methods which are fast and accurate. Neural networks with multiple hidden layers are universal interpolators with the ability of representing any smooth multidimentional function. As such, supervised learning is concerned with solving function estimation problems. The networks are decomposed into two separate phases, a training phase where the model is optimised off-line, and a testing phase where the model approximates the solution on-line. As a result, these methods can be used in finance in a fast and robust way for pricing exotic options as well as calibrating option prices in view of interpolating/extrapolating the volatility surface. They can also be used in risk management to fit options prices at the portfolio level in view of performing some credit risk analysis. We review some of the existing methods using neural networks for pricing market and model prices, present calibration, and introduce exotic option pricing. We discuss the feasibility of these methods, highlight problems, and propose alternative solutions.
Financial Modeling Under Non-Gaussian Distributions
Author: Eric JondeauPublisher: Springer Science & Business Media
ISBN: 1846286964
Category : Mathematics
Languages : en
Pages : 541
Book Description
This book examines non-Gaussian distributions. It addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series.