Deep Hedging Hedging Derivatives Under Generic Market Frictions Using Reinforcement Learning
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Deep Hedging
| Author | : Hans Buehler |
| Publisher | : |
| Total Pages | : 32 |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
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.
Pricing and Hedging Financial Derivatives with Reinforcement Learning Methods
| Author | : Alexandre Carbonneau |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
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.
Machine Learning and Data Sciences for Financial Markets
| Author | : Agostino Capponi |
| Publisher | : Cambridge University Press |
| Total Pages | : 742 |
| Release | : 2023-04-30 |
| Genre | : Mathematics |
| ISBN | : 1316516199 |
Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.
Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
| Author | : Jerome Yen |
| Publisher | : Springer Nature |
| Total Pages | : 1595 |
| Release | : 2023-07-25 |
| Genre | : Computers |
| ISBN | : 9464631988 |
This is an open access book.With the rapid development of modern economy and Internet technology, the traditional financial industry has to develop Internet finance to provide better services and meet the needs of the times. It is against this background that the blockchain, relying on its special advantages (collective maintenance, reliable databases, and decentralization), provides the reliability to solve the credit risk of Internet finance, has an impact on institutions, trust mechanisms, risk control, etc. in the Internet finance industry, and has derived more new application scenarios, thus paving the way for the development of finance in the Internet era. Applying blockchain technology to the financial field can promote data information sharing, improve value transmission efficiency, and enhance database security. The financial market based on the decentralized system of blockchain technology can reduce the operating costs of financial institutions, improve economic efficiency, and solve problems such as information asymmetry. The new financial business model of "blockchain+finance" is conducive to improving the Internet credit reporting system, preventing and controlling Internet financial risks, and further realizing "financial disintermediation". At present, in China's financial field, blockchain technology has been applied and innovated in supply chain finance, cross-border payment, trade finance, asset securitization and other scenarios. To promote the exchange and development of blockchain, information technology and financial experts and scholars. The 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023) will be held in Hangzhou from February 17 to 19, 2023. This conference mainly focuses on the latest research on "blockchain, information technology and smart finance". This conference brings together experts, scholars, researchers and relevant practitioners in this field from all over the world to share research results, discuss hot issues, and provide participants with cutting-edge scientific and technological information, so that you can timely understand the development trends of the industry and master the latest technologies, broaden research horizons and promote academic progress.
Advanced Portfolio Management
| Author | : Giuseppe A. Paleologo |
| Publisher | : John Wiley & Sons |
| Total Pages | : 208 |
| Release | : 2021-08-03 |
| Genre | : Business & Economics |
| ISBN | : 111978980X |
You have great investment ideas. If you turn them into highly profitable portfolios, this book is for you. Advanced Portfolio Management: A Quant’s Guide for Fundamental Investors is for fundamental equity analysts and portfolio managers, present, and future. Whatever stage you are at in your career, you have valuable investment ideas but always need knowledge to turn them into money. This book will introduce you to a framework for portfolio construction and risk management that is grounded in sound theory and tested by successful fundamental portfolio managers. The emphasis is on theory relevant to fundamental portfolio managers that works in practice, enabling you to convert ideas into a strategy portfolio that is both profitable and resilient. Intuition always comes first, and this book helps to lay out simple but effective "rules of thumb" that require little effort to implement and understand. At the same time, the book shows how to implement sophisticated techniques in order to meet the challenges a successful investor faces as his or her strategy grows in size and complexity. Advanced Portfolio Management also contains more advanced material and a quantitative appendix, which benefit quantitative researchers who are members of fundamental teams. You will learn how to: Separate stock-specific return drivers from the investment environment’s return drivers Understand current investment themes Size your cash positions based on Your investment ideas Understand your performance Measure and decompose risk Hedge the risk you don’t want Use diversification to your advantage Manage losses and control tail risk Set your leverage Author Giuseppe A. Paleologo has consulted, collaborated, taught, and drank strong wine with some of the best stock-pickers in the world; he has traded tens of billions of dollars hedging and optimizing their books and has helped them navigate through big drawdowns and even bigger recoveries. Whether or not you have access to risk models or advanced mathematical background, you will benefit from the techniques and the insights contained in the book—and won't find them covered anywhere else.
Artificial Intelligence in Finance
| Author | : Yves Hilpisch |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 478 |
| Release | : 2020-10-14 |
| Genre | : Business & Economics |
| ISBN | : 1492055387 |
The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI) Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about
Hedging Derivatives
| Author | : Thorsten Rheinlander |
| Publisher | : World Scientific |
| Total Pages | : 244 |
| Release | : 2011-05-18 |
| Genre | : Business & Economics |
| ISBN | : 9814462152 |
Valuation and hedging of financial derivatives are intrinsically linked concepts. Choosing appropriate hedging techniques depends on both the type of derivative and assumptions placed on the underlying stochastic process. This volume provides a systematic treatment of hedging in incomplete markets. Mean-variance hedging under the risk-neutral measure is applied in the framework of exponential Lévy processes and for derivatives written on defaultable assets. It is discussed how to complete markets based upon stochastic volatility models via trading in both stocks and vanilla options. Exponential utility indifference pricing is explored via a duality with entropy minimization. Backward stochastic differential equations offer an alternative approach and are moreover applied to study markets with trading constraints including basis risk. A range of optimal martingale measures are discussed including the entropy, Esscher and minimal martingale measures. Quasi-symmetry properties of stochastic processes are deployed in the semi-static hedging of barrier options.This book is directed towards both graduate students and researchers in mathematical finance, and will also provide an orientation to applied mathematicians, financial economists and practitioners wishing to explore recent progress in this field.
Stochastic Finance
| Author | : Hans Föllmer |
| Publisher | : Walter de Gruyter GmbH & Co KG |
| Total Pages | : 608 |
| Release | : 2016-07-25 |
| Genre | : Mathematics |
| ISBN | : 3110463458 |
This book is an introduction to financial mathematics. It is intended for graduate students in mathematics and for researchers working in academia and industry. The focus on stochastic models in discrete time has two immediate benefits. First, the probabilistic machinery is simpler, and one can discuss right away some of the key problems in the theory of pricing and hedging of financial derivatives. Second, the paradigm of a complete financial market, where all derivatives admit a perfect hedge, becomes the exception rather than the rule. Thus, the need to confront the intrinsic risks arising from market incomleteness appears at a very early stage. The first part of the book contains a study of a simple one-period model, which also serves as a building block for later developments. Topics include the characterization of arbitrage-free markets, preferences on asset profiles, an introduction to equilibrium analysis, and monetary measures of financial risk. In the second part, the idea of dynamic hedging of contingent claims is developed in a multiperiod framework. Topics include martingale measures, pricing formulas for derivatives, American options, superhedging, and hedging strategies with minimal shortfall risk. This fourth, newly revised edition contains more than one hundred exercises. It also includes material on risk measures and the related issue of model uncertainty, in particular a chapter on dynamic risk measures and sections on robust utility maximization and on efficient hedging with convex risk measures. Contents: Part I: Mathematical finance in one period Arbitrage theory Preferences Optimality and equilibrium Monetary measures of risk Part II: Dynamic hedging Dynamic arbitrage theory American contingent claims Superhedging Efficient hedging Hedging under constraints Minimizing the hedging error Dynamic risk measures
