Machine learning for risk calculations: a practitioner's view
"The computational demand of risk calculations in financial institutions has ballooned. Traditionally, this has led to the acquisition of more and more computer power -- some banks have farms in the order of 50,000 CPUs, with running costs in the multimillions of dollars -- but this path is no longer economically or operationally viable. Algorithmic solutions represent a viable way to reduce costs while simultaneously increasing risk calculation capabilities."--
Read More ↓Title Statements
- Full title :Machine learning for risk calculations : a practitioner's view / Ignacio Ruiz, Mariano Zeron Medina Laris.
Summary
"The computational demand of risk calculations in financial institutions has ballooned. Traditionally, this has led to the acquisition of more and more computer power -- some banks have farms in the order of 50,000 CPUs, with running costs in the multimillions of dollars -- but this path is no longer economically or operationally viable. Algorithmic solutions represent a viable way to reduce costs while simultaneously increasing risk calculation capabilities."--
Read More ↓Edition Statements
- Publication Date:2022
Publication Statements
- Publisher(s):Wiley
- Place of Publication:Hoboken, New Jersey
- Language:eng
- Publication Date:2022
Notes
Includes index.
The Physical Object
- Description:1 online resource
Edition Identifiers
- ISBN:9781119791416, 1119791413, 1119791405, 9781119791393, 1119791391, 9781119791409
- DOI:10.1002/9781119791416
- oclc:on1264725414
- LC Classification:Q325.5
- Dewey Decimal:332.10285/631
Table of Contents
Fundamental Approximation Methods. Machine Learning -- Deep Neural Nets -- Chebyshev Tensors -- The toolkit - plugging in approximation methods. Introduction: why is a toolkit needed -- Composition techniques -- Tensors in TT format and Tensor Extension Algorithms -- Sliding Technique -- The Jacobian projection technique -- Hybrid solutions - approximation methods and the toolkit. Introduction -- The Toolkit and Deep Neural Nets -- The Toolkit and Chebyshev Tensors -- Hybrid Deep Neural Nets and Chebyshev Tensors Frameworks -- Applications. The aim -- When to use Chebyshev Tensors and when to use Deep Neural Nets -- Counterparty credit risk -- Market Risk -- Dynamic sensitivities -- Pricing model calibration -- Approximation of the implied volatility function -- Optimisation Problems -- Pricing Cloning -- XVA sensitivities -- Sensitivities of exotic derivatives -- Software libraries relevant to the book -- Appendices. Families of orthogonal polynomials -- Exponential convergence of Chebyshev Tensors -- Chebyshev Splines on functions with no singularity points -- Computational savings details for CCR -- Computational savings details for dynamic sensitivities -- Dynamic sensitivitie…
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