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Machine learning for risk calculations = a practitioner's view /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning for risk calculations/ I. Ruiz, M. Zeron ; foreword by P. Karasinski.
其他題名:	a practitioner's view /
作者:	Ruiz, Ignacio,
其他作者:	Laris, Mariano Zeron Medina.
出版者:	West Sussex, UK :Wiley, : 2021, c2022.,
面頁冊數:	1 online resource.
附註:	Includes index.
內容註:	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.
標題:	Machine learning. -
電子資源:	https://onlinelibrary.wiley.com/doi/book/10.1002/9781119791416
ISBN:	9781119791416

Machine learning for risk calculations = a practitioner's view /
Ruiz, Ignacio,1972-

Machine learning for risk calculationsa practitioner's view /[electronic resource] :I. Ruiz, M. Zeron ; foreword by P. Karasinski. - West Sussex, UK :Wiley,2021, c2022. - 1 online resource.

Includes index.

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.

"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."--

ISBN: 9781119791416

Standard No.: 10.1002/9781119791416doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 332.10285/631

Machine learning for risk calculations = a practitioner's view /
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100 1 $a Ruiz, Ignacio, $d 1972- $3 3674575
245 1 0 $a Machine learning for risk calculations $h [electronic resource] : $b a practitioner's view / $c I. Ruiz, M. Zeron ; foreword by P. Karasinski.
260 $a West Sussex, UK : $b Wiley, $c 2021, c2022.
300 $a 1 online resource.
500 $a Includes index.
505 0 $a 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.
520 $a "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."-- $c Provided by publisher.
588 $a Description based on print version record.
650 0 $a Machine learning. $3 533906
650 0 $a Financial risk management. $3 781346
700 1 $a Laris, Mariano Zeron Medina. $3 3674576
856 4 0 $u https://onlinelibrary.wiley.com/doi/book/10.1002/9781119791416