This book is written for quantitative finance professionals, students, educators, and mathematically inclined individual investors. It is about some of the latest developments in pricing, hedging, and investing in incomplete markets. With regard to pricing, two frameworks are fully elaborated: neutral and indifference pricing. With regard to hedging, the most conservative and relaxed hedging formulas are derived. With regard to investing, the neutral pricing methodology is also considered as a tool for connecting market asset prices with optimal positions in such assets. Srdjan D. Stojanovic is Professor in the Department of Mathematical Sciences at University of Cincinnati (USA) and Professor in the Center for Financial Engineering at Suzhou University (China).
This book is written for quantitative finance professionals, students, educators, and mathematically inclined individual investors. It is about some of the latest developments in pricing, hedging, and investing in incomplete markets. With regard to pricing, two frameworks are fully elaborated: neutral and indifference pricing. With regard to hedging, the most conservative and relaxed hedging formulas are derived. With regard to investing, the neutral pricing methodology is also considered as a tool for connecting market asset prices with optimal positions in such assets. Srdjan D. Stojanovic is Professor in the Department of Mathematical Sciences at University of Cincinnati (USA) and Professor in the Center for Financial Engineering at Suzhou University (China).
This book is written for quantitative finance professionals, students, educators, and mathematically inclined individual investors. It is about some of the latest developments in pricing, hedging, and investing in incomplete markets. With regard to pricing, two frameworks are fully elaborated: neutral and indifference pricing. With regard to hedging, the most conservative and relaxed hedging formulas are derived. With regard to investing, the neutral pricing methodology is also considered as a tool for connecting market asset prices with optimal positions in such assets. Srdjan D. Stojanovic is Professor in the Department of Mathematical Sciences at University of Cincinnati (USA) and Professor in the Center for Financial Engineering at Suzhou University (China).
This is the first book about the emerging field of utility indifference pricing for valuing derivatives in incomplete markets. René Carmona brings together a who's who of leading experts in the field to provide the definitive introduction for students, scholars, and researchers. Until recently, financial mathematicians and engineers developed pricing and hedging procedures that assumed complete markets. But markets are generally incomplete, and it may be impossible to hedge against all sources of randomness. Indifference Pricing offers cutting-edge procedures developed under more realistic market assumptions. The book begins by introducing the concept of indifference pricing in the simplest possible models of discrete time and finite state spaces where duality theory can be exploited readily. It moves into a more technical discussion of utility indifference pricing for diffusion models, and then addresses problems of optimal design of derivatives by extending the indifference pricing paradigm beyond the realm of utility functions into the realm of dynamic risk measures. Focus then turns to the applications, including portfolio optimization, the pricing of defaultable securities, and weather and commodity derivatives. The book features original mathematical results and an extensive bibliography and indexes. In addition to the editor, the contributors are Pauline Barrieu, Tomasz R. Bielecki, Nicole El Karoui, Robert J. Elliott, Said Hamadène, Vicky Henderson, David Hobson, Aytac Ilhan, Monique Jeanblanc, Mattias Jonsson, Anis Matoussi, Marek Musiela, Ronnie Sircar, John van der Hoek, and Thaleia Zariphopoulou. The first book on utility indifference pricing Explains the fundamentals of indifference pricing, from simple models to the most technical ones Goes beyond utility functions to analyze optimal risk transfer and the theory of dynamic risk measures Covers non-Markovian and partially observed models and applications to portfolio optimization, defaultable securities, static and quadratic hedging, weather derivatives, and commodities Includes extensive bibliography and indexes Provides essential reading for PhD students, researchers, and professionals
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.
We are pleased to present this Global Edition, which has been developed specifically to meet the needs of internationalInvestment students. A market leader in the field, this text introduces major issues of concern to all investors and placesemphasis on asset allocation. It gives students the skills to conduct a sophisticated assessment of watershed current issuesand debates. Bodie Investments' blend of practical and theoretical coverage combines with a complete digital solution tohelp your students achieve higher outcomes in the course.
This book contains the proceedings of the KIER-TMU International Workshop on Financial Engineering 2010, which was held in Tokyo, in order to exchange new ideas in financial engineering among industry professionals and researchers from various countries. It has been held for two consecutive years since 2009, as a successor to the Daiwa International Workshop, which was held from 2004 to 2008, and is organized by the Institute of Economic Research of Kyoto University (KIER) and the Graduate School of Social Sciences of Tokyo Metropolitan University (TMU).The workshop serves as a bridge between academic researchers and practitioners. This book consists of eleven papers — all refereed — representing or related to the presentations at the workshop. The papers address state-of-the-art techniques in financial engineering. The Proceedings of the 2009 workshop was also published by World Scientific Publishing.
The second edition of this authoritative textbook continues the tradition of providing clear and concise descriptions of the new and classic concepts in financial theory. The authors keep the theory accessible by requiring very little mathematical background. First edition published by Prentice-Hall in 2001- ISBN 0130174467.The second edition includes new structure emphasizing the distinction between the equilibrium and the arbitrage perspectives on valuation and pricing, as well as a new chapter on asset management for the long term investor."This book does admirably what it sets out to do - provide a bridge between MBA-level finance texts and PhD-level texts....many books claim to require little prior mathematical training, but this one actually does so. This book may be a good one for Ph.D students outside finance who need some basic training in financial theory or for those looking for a more user-friendly introduction to advanced theory. The exercises are very good." --Ian Gow, Student, Graduate School of Business, Stanford University - Completely updated edition of classic textbook that fills a gap between MBA level texts and PHD level texts - Focuses on clear explanations of key concepts and requires limited mathematical prerequisites - Updates includes new structure emphasizing the distinction between the equilibrium and the arbitrage perspectives on valuation and pricing, as well as a new chapter on asset management for the long term investor
Efficiently Inefficient describes the key trading strategies used by hedge funds and demystifies the secret world of active investing. Leading financial economist Lasse Heje Pedersen combines the latest research with real-world examples and interviews with top hedge fund managers to show how certain trading strategies make money - and why they sometimes don't. -- from back cover.
