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Pricing And Hedging Financial Derivatives With Reinforcement Learning Methods
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Book Synopsis Pricing and Hedging Financial Derivatives with Reinforcement Learning Methods by : Alexandre Carbonneau
Download or read book Pricing and Hedging Financial Derivatives with Reinforcement Learning Methods written by Alexandre Carbonneau and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Multi-agent Deep Reinforcement Learning and GAN-based Market Simulation for Derivatives Pricing and Dynamic Hedging by : Samson Qian
Download or read book Multi-agent Deep Reinforcement Learning and GAN-based Market Simulation for Derivatives Pricing and Dynamic Hedging written by Samson Qian and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Pricing and Hedging Financial Derivatives by : Leonardo Marroni
Download or read book Pricing and Hedging Financial Derivatives written by Leonardo Marroni and published by John Wiley & Sons. This book was released on 2014-06-19 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Pricing and Hedging Financial Derivatives by : Irene Perdomo
Download or read book Pricing and Hedging Financial Derivatives written by Irene Perdomo and published by . This book was released on 2013 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Derivatives Pricing Via Machine Learning by : Tingting Ye
Download or read book Derivatives Pricing Via Machine Learning written by Tingting Ye and published by . This book was released on 2019 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Foundations of Reinforcement Learning with Applications in Finance by : Ashwin Rao
Download or read book Foundations of Reinforcement Learning with Applications in Finance written by Ashwin Rao and published by CRC Press. This book was released on 2022-12-16 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks by : James M. Hutchinson
Download or read book A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks written by James M. Hutchinson and published by . This book was released on 1994 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Risk-neutral Valuation by : Nicholas H. Bingham
Download or read book Risk-neutral Valuation written by Nicholas H. Bingham and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis American-Style Derivatives by : Jerome Detemple
Download or read book American-Style Derivatives written by Jerome Detemple and published by CRC Press. This book was released on 2005-12-09 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Empirical Asset Pricing by : Wayne Ferson
Download or read book Empirical Asset Pricing written by Wayne Ferson and published by MIT Press. This book was released on 2019-03-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Risk-neutral Valuation by : N. H. Bingham
Download or read book Risk-neutral Valuation written by N. H. Bingham and published by Springer Verlag. This book was released on 1998 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: With a simple approach accessible to a wide audience, this book aims for the heart of mathematical finance: the fundamental formula of arbitrage pricing theory. This method of pricing discounts everything and takes expected values under the equivalent martingale measure. The authors approach is simple and excludes unnecessary proofs of measure-theoretic probability, instead, it favors techniques and examples of proven interest to financial practitioners.
Download or read book Deep Hedging written by Hans Buehler and published by . This book was released on 2019 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Applications of Least-Squares Regressions to Pricing and Hedging of Financial Derivatives by :
Download or read book Applications of Least-Squares Regressions to Pricing and Hedging of Financial Derivatives written by and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Option pricing with Monte Carlo (MC) simulation: This thesis presents three new ideas for the application Least-Squares regressions. The first idea separates the pricing process into two parts, where one is often known analytically and the other can be estimated by MC simulation. The combination has better convergence than a usual MC estimate. The second idea is the application of sparse grids to Least-Squares Monte Carlo (LSMC). This technique allows the solution of high-dimensional option pricing problems. Examples with Moving Window Asian options and soft-call constraints of convertible bonds demonstrate the efficiency of the approach. The third and main contribution of this thesis is the Simulation-Based Hedging method which connects realistic models for the underlying with suitable pricing and hedging in complete and incomplete markets. The resulting method converges an order of magnitude faster to the Black-Scholes prices than the comparable LSMC, while the algorithm computes the optimal hedging strategy and thus obtains realistic risk-adjusted prices and hedges.
Book Synopsis Financial Derivatives Pricing and Hedging by : 黃士峰
Download or read book Financial Derivatives Pricing and Hedging written by 黃士峰 and published by . This book was released on 2008 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A nonparametric approach to pricing and hedging derivative securities via learning networks by : James M. Hutchinson
Download or read book A nonparametric approach to pricing and hedging derivative securities via learning networks written by James M. Hutchinson and published by . This book was released on 1994 with total page 49 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Machine Learning in Finance by : Matthew F. Dixon
Download or read book Machine Learning in Finance written by Matthew F. Dixon and published by Springer Nature. This book was released on 2020-07-01 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
Book Synopsis Advanced Derivatives Pricing and Risk Management by : Claudio Albanese
Download or read book Advanced Derivatives Pricing and Risk Management written by Claudio Albanese and published by Elsevier. This book was released on 2005-09-08 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Derivatives Pricing and Risk Management covers the most important and cutting-edge topics in financial derivatives pricing and risk management, striking a fine balance between theory and practice. The book contains a wide spectrum of problems, worked-out solutions, detailed methodologies, and applied mathematical techniques for which anyone planning to make a serious career in quantitative finance must master. In fact, core portions of the book’s material originated and evolved after years of classroom lectures and computer laboratory courses taught in a world-renowned professional Master’s program in mathematical finance. The book is designed for students in finance programs, particularly financial engineering. *Includes easy-to-implement VB/VBA numerical software libraries*Proceeds from simple to complex in approaching pricing and risk management problems*Provides analytical methods to derive cutting-edge pricing formulas for equity derivatives
