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...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Hoboken, New Jersey :
Wiley,
[2022]
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| Series: | Wiley finance series.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
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 sensitivities on the market space
- Dynamic sensitivities and IM via Jacobian Projection technique
- MVA optimisation - further computational enhancement.