Machine Learning for Risk Calculations: A Practitioner’s View (The Wiley Finance Series) 1st Edition Ignacio Ruiz

Machine Learning for Risk Calculations: A Practitioner’s View (The Wiley Finance Series) – Ebook Instant Download/Delivery ISBN(s): 9781119791386,1119791383

Product details:

• ISBN-10 ‏ : ‎ 1119791383
• ISBN-13 ‏ : ‎ 978-1119791386
• Author(s): Ignacio Ruiz, Mariano Zeron

The computational demand of risk calculations in financial institutions has ballooned and shows no sign of stopping. It is no longer viable to simply add more computing power to deal with this increased demand. The solution? Algorithmic solutions based on deep learning and Chebyshev tensors represent a practical way to reduce costs while simultaneously increasing risk calculation capabilities. Machine Learning for Risk Calculations: A Practitioner’s View provides an in-depth review of a number of algorithmic solutions and demonstrates how they can be used to overcome the massive computational burden of risk calculations in financial institutions.

This book will get you started by reviewing fundamental techniques, including deep learning and Chebyshev tensors. You’ll then discover algorithmic tools that, in combination with the fundamentals, deliver actual solutions to the real problems financial institutions encounter on a regular basis. Numerical tests and examples demonstrate how these solutions can be applied to practical problems, including XVA and Counterparty Credit Risk, IMM capital, PFE, VaR, FRTB, Dynamic Initial Margin, pricing function calibration, volatility surface parametrisation, portfolio optimisation and others. Finally, you’ll uncover the benefits these techniques provide, the practicalities of implementing them, and the software which can be used.

Table contents:

Part One Fundamental Approximation Methods

Part Two The toolkit — plugging in approximation methods

Part Three Hybrid solutions — approximation methods and the toolkit

Part Four Applications

Part Five Pricing model calibration

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