Wiley Finance Deep Learning in Quantitative

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A STARTLINGLY INSIGHTFUL AND HANDS-ON GUIDE TO APPLYING DEEP LEARNING TECHNIQUES TO QUANTITATIVE FINANCE Deep Learning in Quantitative Finance is a uniquely incisive and hands-on look at applying the latest advances in deep learning and machine learning technology to the rapidly evolving field of quantitative finance. Author Andrew Green walks you through basic and advanced subjects in quantitative finance deep learning, moving from the foundations of neural networks to the latest discoveries in CNNs, sequence models, autoencoders, generative AI like VAEs, GANs and diffusion models, and deep reinforcement learning. You’ll learn how to approximate derivative values, solve PDEs and BSDEs with neural networks, enhance Monte Carlo models with deep learning, use deep semi-static replication for Bermudan and American options, map credit curves and generate exposure profiles, calibrate models and volatility surfaces, generate realistic market data, apply hedging methodologies and develop predictive market models. A can’t-miss, hands-on guide—complete with complimentary access to a collection of Jupyter notebooks filled with tested, working Python code examples of the concepts discussed in the book—Deep Learning in Quantitative Finance is an essential read for quantitative finance practitioners everywhere. The complete and practical guide to one of the hottest topics in quantitative finance Deep learning, that is, the use of deep neural networks, is now one of the hottest topics amongst quantitative analysts. Deep Learning in Quantitative Finance provides a comprehensive treatment of deep learning and describes a wide range of applications in mainstream quantitative finance. Inside, you’ll find over ten chapters which apply deep learning to multiple use cases across quantitative finance. You’ll also gain access to a companion site containing a set of Jupyter notebooks, developed by the author, that use Python to illustrate the examples in the text. Readers will be able to work through these examples directly. This book is a complete resource on how deep learning is used in quantitative finance applications. It introduces the basics of neural networks, including feedforward networks, optimization, and training, before proceeding to cover more advanced topics. You’ll also learn about the most important software frameworks. The book then proceeds to cover the very latest deep learning research in quantitative finance, including approximating derivative values, volatility models, credit curve mapping, generating realistic market data, and hedging. The book concludes with a look at the potential for quantum deep learning and the broader implications deep learning has for quantitative finance and quantitative analysts. Covers the basics of deep learning and neural networks, including feedforward networks, optimization and training, and regularization techniques Offers an understanding of more advanced topics like CNNs, RNNs, autoencoders, generative models including GANs and VAEs, and deep reinforcement learning Demonstrates deep learning application in quantitative finance through case studies and hands-on applications via the companion website Introduces the most important software frameworks for applying deep learning within finance This book is perfect for anyone engaged with quantitative finance who wants to get involved in a subject that is clearly going to be hugely influential for the future of finance.

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Pagina's: 736, Editie: Eerste editie, Hardcover, Wiley