Perturbation Methods in Credit Derivatives (PDF)
Strategies for Efficient Risk Management
(Sprache: Englisch)
Stress-test financial models and price credit instruments with confidence and efficiency using the perturbation approach taught in this expert volume
Perturbation Methods in Credit Derivatives: Strategies for Efficient Risk Management offers an incisive...
Perturbation Methods in Credit Derivatives: Strategies for Efficient Risk Management offers an incisive...
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Stress-test financial models and price credit instruments with confidence and efficiency using the perturbation approach taught in this expert volume
Perturbation Methods in Credit Derivatives: Strategies for Efficient Risk Management offers an incisive examination of a new approach to pricing credit-contingent financial instruments. Author and experienced financial engineer Dr. Colin Turfus has created an approach that allows model validators to perform rapid benchmarking of risk and pricing models while making the most efficient use possible of computing resources.
The book provides innumerable benefits to a wide range of quantitative financial experts attempting to comply with increasingly burdensome regulatory stress-testing requirements, including:
* Replacing time-consuming Monte Carlo simulations with faster, simpler pricing algorithms for front-office quants
* Allowing CVA quants to quantify the impact of counterparty risk, including wrong-way correlation risk, more efficiently
* Developing more efficient algorithms for generating stress scenarios for market risk quants
* Obtaining more intuitive analytic pricing formulae which offer a clearer intuition of the important relationships among market parameters, modelling assumptions and trade/portfolio characteristics for traders
The methods comprehensively taught in Perturbation Methods in Credit Derivatives also apply to CVA/DVA calculations and contingent credit default swap pricing.
Perturbation Methods in Credit Derivatives: Strategies for Efficient Risk Management offers an incisive examination of a new approach to pricing credit-contingent financial instruments. Author and experienced financial engineer Dr. Colin Turfus has created an approach that allows model validators to perform rapid benchmarking of risk and pricing models while making the most efficient use possible of computing resources.
The book provides innumerable benefits to a wide range of quantitative financial experts attempting to comply with increasingly burdensome regulatory stress-testing requirements, including:
* Replacing time-consuming Monte Carlo simulations with faster, simpler pricing algorithms for front-office quants
* Allowing CVA quants to quantify the impact of counterparty risk, including wrong-way correlation risk, more efficiently
* Developing more efficient algorithms for generating stress scenarios for market risk quants
* Obtaining more intuitive analytic pricing formulae which offer a clearer intuition of the important relationships among market parameters, modelling assumptions and trade/portfolio characteristics for traders
The methods comprehensively taught in Perturbation Methods in Credit Derivatives also apply to CVA/DVA calculations and contingent credit default swap pricing.
Autoren-Porträt von Colin Turfus
COLIN TURFUS, PHD., works in Global Model Validation and Governance at Deutsche Bank. For the last fifteen years, he has been a financial engineer, mainly analysing model risk for credit derivatives and hybrids. He specialises in the application of perturbation methods to risk management, finding efficient analytic methods for computing prices and risk measures. He also taught courses on C++ and Financial Engineering at City, University of London for seven years. Prior to that, Colin worked as a developer consultant in the mobile phone industry after an extended period in academia, teaching applied mathematics and researching in fluid dynamics and turbulent dispersion.??
Bibliographische Angaben
- Autor: Colin Turfus
- 2020, 1. Auflage, 256 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 1119609623
- ISBN-13: 9781119609629
- Erscheinungsdatum: 17.12.2020
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- Größe: 2.29 MB
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Englisch
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