Machine Learning for Risk Calculations : A Practitioner's View /: A Practitioner's View. (2021)
- Record Type:
- Book
- Title:
- Machine Learning for Risk Calculations : A Practitioner's View /: A Practitioner's View. (2021)
- Main Title:
- Machine Learning for Risk Calculations : A Practitioner's View
- Further Information:
- Note: Ignacio Ruiz, Mariano Zeron.
- Authors:
- Ruiz, Ignacio
Zeron, Mariano - Contents:
- I Fundamental Approximation Methods 31 1 Machine Learning 33 2 Deep Neural Nets 77 3 Chebyshev Tensors 109 II The toolkit | plugging in approximation methods155 4 Introduction: why is a toolkit needed 157 5 Composition techniques 165 6 Tensors in TT format and Tensor Extension Algorithms 177 7 Sliding technique 197 8 The Jacobian projection technique 203 III Hybrid solutions | approximation methods and the toolkit 215 9 Introduction 217 10 The Toolkit and Deep Neural Nets 221 11 The Toolkit and Chebyshev Tensors 225 12 Hybrid Deep Neural Nets and Chebyshev Tensors Frameworks 229 13 The Aim 247 14 When to use Chebyshev Tensors and when Deep Neural Nets253 15 Counterparty Credit Risk 271 16 Market Risk 323 17 Dynamic sensitivities 363 18 Pricing model calibration 385 19 Approximation of the implied volatility function 407 20 Optimisation Problems 435 21 Pricing Cloning 451 22 XVA sensitivities 461 23 Sensitivities of exotic derivatives 467 24 Software libraries relevant to the book 475 Appendix A Families of orthogonal polynomials 501 Appendices Appendix B Exponential convergence of Chebyshev Tensors 503 Appendix C Chebyshev Splines on functions with no singularity points 507 Appendix D Computational savings details for CCR 511 Appendix E computational savings details for dynamic sensitivi-ties 519 Appendix F Dynamic sensitivities on the market space 523 Appendix G Dynamic sensitivities and IM via Jacobian Projec-tion technique 53 Appendix H MVA optimisation|furtherI Fundamental Approximation Methods 31 1 Machine Learning 33 2 Deep Neural Nets 77 3 Chebyshev Tensors 109 II The toolkit | plugging in approximation methods155 4 Introduction: why is a toolkit needed 157 5 Composition techniques 165 6 Tensors in TT format and Tensor Extension Algorithms 177 7 Sliding technique 197 8 The Jacobian projection technique 203 III Hybrid solutions | approximation methods and the toolkit 215 9 Introduction 217 10 The Toolkit and Deep Neural Nets 221 11 The Toolkit and Chebyshev Tensors 225 12 Hybrid Deep Neural Nets and Chebyshev Tensors Frameworks 229 13 The Aim 247 14 When to use Chebyshev Tensors and when Deep Neural Nets253 15 Counterparty Credit Risk 271 16 Market Risk 323 17 Dynamic sensitivities 363 18 Pricing model calibration 385 19 Approximation of the implied volatility function 407 20 Optimisation Problems 435 21 Pricing Cloning 451 22 XVA sensitivities 461 23 Sensitivities of exotic derivatives 467 24 Software libraries relevant to the book 475 Appendix A Families of orthogonal polynomials 501 Appendices Appendix B Exponential convergence of Chebyshev Tensors 503 Appendix C Chebyshev Splines on functions with no singularity points 507 Appendix D Computational savings details for CCR 511 Appendix E computational savings details for dynamic sensitivi-ties 519 Appendix F Dynamic sensitivities on the market space 523 Appendix G Dynamic sensitivities and IM via Jacobian Projec-tion technique 53 Appendix H MVA optimisation|further computational enhance-ment 537 … (more)
- Edition:
- 1st
- Publisher Details:
- Wiley
- Publication Date:
- 2021
- Extent:
- 1 online resource (464 pages)
- Languages:
- English
- ISBNs:
- 9781119791409
- Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.657399
- Ingest File:
- 07_030.xml