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Author: Alexandre Carbonneau Publisher: 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.
Author: Alexandre Carbonneau Publisher: 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.
Author: Leonardo Marroni Publisher: John Wiley & Sons ISBN: 1119954584 Category : Business & Economics Languages : en Pages : 277
Book Description
The only guide focusing entirely on practical approaches to pricing and hedging derivatives One valuable lesson of the financial crisis was that derivatives and risk practitioners don't really understand the products they're dealing with. Written by a practitioner for practitioners, this book delivers the kind of knowledge and skills traders and finance professionals need to fully understand derivatives and price and hedge them effectively. Most derivatives books are written by academics and are long on theory and short on the day-to-day realities of derivatives trading. Of the few practical guides available, very few of those cover pricing and hedging—two critical topics for traders. What matters to practitioners is what happens on the trading floor—information only seasoned practitioners such as authors Marroni and Perdomo can impart. Lays out proven derivatives pricing and hedging strategies and techniques for equities, FX, fixed income and commodities, as well as multi-assets and cross-assets Provides expert guidance on the development of structured products, supplemented with a range of practical examples Packed with real-life examples covering everything from option payout with delta hedging, to Monte Carlo procedures to common structured products payoffs The Companion Website features all of the examples from the book in Excel complete with source code
Author: Samson Qian Publisher: 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.
Author: Irene Perdomo Publisher: ISBN: Category : Business enterprises Languages : en Pages : 264
Book Description
The only guide focusing entirely on practical approaches to pricing and hedging derivatives One valuable lesson of the financial crisis was that derivatives and risk practitioners don't really understand the products they're dealing with. Written by a practitioner for practitioners, this book delivers the kind of knowledge and skills traders and finance professionals need to fully understand derivatives and price and hedge them effectively. Most derivatives books are written by academics and are long on theory and short on the day-to-day realities of derivatives trading. Of the few practical guides available, very few of those cover pricing and hedging-two critical topics for traders. What matters to practitioners is what happens on the trading floor-information only seasoned practitioners such as authors Marroni and Perdomo can impart. Lays out proven derivatives pricing and hedging strategies and techniques for equities, FX, fixed income and commodities, as well as multi-assets and cross-assets Provides expert guidance on the development of structured products, supplemented with a range of practical examples Packed with real-life examples covering everything from option payout with delta hedging, to Monte Carlo procedures to common structured products payoffs The Companion Website features all of the examples from the book in Excel complete with source code.
Author: Tingting Ye Publisher: ISBN: Category : Languages : en Pages : 40
Book Description
In this paper, we combine the theory of stochastic process and techniques of machine learning with the regression analysis, first proposed by Longstaff and Schwartz 2001 and apply the new methodologies on financial derivatives pricing. Rigorous convergence proofs are provided for some of the methods we propose. Numerical examples show good applicability of the algorithms.
Author: Jerome Detemple Publisher: CRC Press ISBN: 1420034863 Category : Business & Economics Languages : en Pages : 247
Book Description
Focusing on recent developments in the field, American-Style Derivatives provides an extensive treatment of option pricing with emphasis on the valuation of American options on dividend-paying assets. This book reviews valuation principles for European contingent claims and extends the analysis to American contingent claims. It presents basic valuation principles for American options including barrier, capped, and multi-asset options. It also reviews numerical methods for option pricing and compares their relative performance. Ideal for students and researchers in quantitative finance, this material is accessible to those with a background in stochastic processes or derivative securities.
Author: Wayne Ferson Publisher: MIT Press ISBN: 0262039370 Category : Business & Economics Languages : en Pages : 497
Book Description
An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
Author: Ashwin Rao Publisher: CRC Press ISBN: 1000801101 Category : Mathematics Languages : en Pages : 658
Book Description
Foundations of Reinforcement Learning with Applications in Finance aims to demystify Reinforcement Learning, and to make it a practically useful tool for those studying and working in applied areas — especially finance. Reinforcement Learning is emerging as a powerful technique for solving a variety of complex problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Its penetration in high-profile problems like self-driving cars, robotics, and strategy games points to a future where Reinforcement Learning algorithms will have decisioning abilities far superior to humans. But when it comes getting educated in this area, there seems to be a reluctance to jump right in, because Reinforcement Learning appears to have acquired a reputation for being mysterious and technically challenging. This book strives to impart a lucid and insightful understanding of the topic by emphasizing the foundational mathematics and implementing models and algorithms in well-designed Python code, along with robust coverage of several financial trading problems that can be solved with Reinforcement Learning. This book has been created after years of iterative experimentation on the pedagogy of these topics while being taught to university students as well as industry practitioners. Features Focus on the foundational theory underpinning Reinforcement Learning and software design of the corresponding models and algorithms Suitable as a primary text for courses in Reinforcement Learning, but also as supplementary reading for applied/financial mathematics, programming, and other related courses Suitable for a professional audience of quantitative analysts or data scientists Blends theory/mathematics, programming/algorithms and real-world financial nuances while always striving to maintain simplicity and to build intuitive understanding To access the code base for this book, please go to: https://github.com/TikhonJelvis/RL-book
Author: James M. Hutchinson Publisher: ISBN: Category : Derivative securities Languages : en Pages : 68
Book Description
We propose a nonparametric method for estimating derivative financial asset pricing formulae using learning networks. To demonstrate feasibility, we first simulate Black-Scholes option prices and show that learning networks can recover the Black-Scholes formula from a two-year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta-hedge options out-of-sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis functions, multilayer perceptrons, and projection pursuit. To illustrate practical relevance, we also apply our approach to S & P 500 futures options data from 1987 to 1991. Option pricing, Learning, Finance, Black-Scholes, Hedging.
Author: Alexandre Carbonneau
Author: Alexandre Carbonneau
Author: Leonardo Marroni
Author: Samson Qian
Author: Irene Perdomo
Author: Tingting Ye
Author: Jerome Detemple
Author: Wayne Ferson
Author: Ashwin Rao
Author: James M. Hutchinson
Author: Nicholas H. Bingham