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Machine Learning for Risk Calculations - A Practitioner's View (Hardcover)
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Machine Learning for Risk Calculations - A Practitioner's View (Hardcover)
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State-of-the-art algorithmic deep learning and tensoring techniques
for financial institutions 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. Review
the fundamentals of deep learning and Chebyshev tensors Discover
pioneering algorithmic techniques that can create new opportunities
in complex risk calculation Learn how to apply the solutions to a
wide range of real-life risk calculations. Download sample code
used in the book, so you can follow along and experiment with your
own calculations Realize improved risk management whilst overcoming
the burden of limited computational power Quants, IT professionals,
and financial risk managers will benefit from this
practitioner-oriented approach to state-of-the-art risk
calculation.
General
Imprint: |
John Wiley & Sons
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Country of origin: |
United States |
Release date: |
2022 |
Authors: |
I Ruiz
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Dimensions: |
255 x 177 x 32mm (L x W x T) |
Format: |
Hardcover
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Pages: |
464 |
ISBN-13: |
978-1-119-79138-6 |
Categories: |
Books >
Business & Economics >
Finance & accounting >
General
|
LSN: |
1-119-79138-3 |
Barcode: |
9781119791386 |
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