Machine learning for risk calculations a practitioner's view - Ruiz, Ignacio 1972-; Laris, Mariano Zeron Medina (Hoboken, New Jersey Wiley, 2022)
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."--
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
Includes index.
Keywords: Financial risk management; Machine learning; Algorithms -- BUSINESS & ECONOMICS -- COMPUTERS -- Electronic books -- Finance -- Financial Engineering -- Financial risk management -- Machine learning -- Programming