خرید و دانلود نسخه کامل کتاب Derivative Pricing and Credit Exposures Modelling: Python Prototype of XVA for Practitioners (2.0) – + Pdf

WHAT IS NEW IN THE SECOND EDITION
Credit Derivatives trading, in particular Correlation products (CDS, CLN and CDO) are introduced in this new edition. Desk level risk management including hedging and client facing tactics are also included in this edition. I covered this during my time at JP Morgan and Barclays Credit Hybrids derivatives Trading & Structuring around 2008-2012. Markets have changed a lot since the financial crisis and most of these products now have a very thin market with a small number of active players. This is the reason why I only touched lightly on products like FTD/NTD and Recovery Swaps, but I wanted to preserve the product knowledge and hope that my readers will enjoy it.
Many thanks to Dr. Günter Umlauf for his proof reading and inspiring discussions which have helped me to retrieve those old golden memories.

ABOUT THE BOOK
It is a practical guide to introduce Derivative pricing models and Counterparty Credit Exposure modelling. XVA has been a hot topic in the industry for the last decade and probably more years to come. It has gathered many powerful brains into these subjects within Quantitative Finance. This book opens up the subject for non-mathematical savvy people. Many simple examples are used to explain concepts like Randomness of asset behavior, Derivatives , Counterparty Credit Risk, Monte Carlo method and most importantly, XVAs. With the help of graphs and Python prototypes, the technical complexity will be lower and the subject much easier to understand.

TABLE OF CONTENT
Preface
Structure of the Book
Acknowledgements
1 Introduction to Quantitative Finance
1.1 Basic Economic and Pricing Concepts
1.2 Counterparty Credit Risk (CCR) and XVA.
1.3 Random Behavior of the Asset
1.4 Stochastic Dynamics
2 Risk Factor Modelling and Methodology
2.1 Modelling Framework & Architecture
2.2 Interest Rate Swap (IRS)
2.3 Random Number
2.4 Risk factor Modelling
2.5 Correlation between Risk Factors
2.6 Conclusion
3 Monte Carlo Simulation
3.1 History
3.2 Monte Carlo in Finance
3.3 Monte Carlo Algorithm Step by Step
3.4 Python Prototype
3.5 Issues & challenges
3.6 Conclusion
4 Counterparty Credit Exposure
4.1 Quantify the Exposure
4.2 Counterparty Exposure in Python Prototype
4.3 Different Exposure Measurements
4.4 Graphic analysis of the Results
4.5 Conclusion
5 XVAs family
5.1 Credit Derivatives – CDS
5.2 Credit Valuation Adjustment – CVA
5.3 Debit Valuation Adjustment – DVA
5.4 Funding and FVA
5.5 Other XVA Topics
5.6 Conclusion
6 Assumptions
Closing Remark
About the Author and Motivations
Acronyms
Appendix A : Python Prototype – Full Implementation
Appendix B: Collateral Modelling Implementation
Appendix C: Credit Correlation products
Appendix D – Hazard rate, Default time, PD and CDS
References