Latest Practical CVA & Counterparty Risk Advancements
09.00 - 10.30: Marco Bianchetti: Head of Financial Engineering & Validation, Intesa Sanpaolo, Stefano Scoleri: Lason Ltd and Sergei Kucherenko: Imperial College London
Better Pricing and Risk Management with High Dimensional Quasi Monte Carlo
- Quasi Monte Carlo in finance: state of the art
- Why QMC is so good ? Effective dimension and global sensitivity analysis
- Computation of prices, greeks, and risk measures
- Performance analysis: convergence, error, stability.
Break: 10.30 - 10.50
10.50 - 11.40: Matthew Dixon: Professor of Analytics, University of San Francisco
A Fast, Portable and Robust Calibration Approach for Stochastic Volatility Models
Frequent and robust recalibration to intraday market data reduces error in CVA pricing. However, the calibration of stochastic volatility (SV) models with global solvers is computationally intensive and ill-suited to latency sensitive execution platforms used for on-demand CVA pricing. Moreover, the use of multi-core and many-core CPUs and GPUs to accelerate the computation requires architecture dependent low level programming environments, such as CUDA, which complicate the code-base and reduce the portability. This talk presents a robust, fast and portable implementation approach to market implied calibration of SV models. By using the Xcelerit platform we show how to efficiently deploy C++ written SV models on multi-core and many-core CPU and GPUs, without the use of complex low level programming environments.
11.40 - 12.30: Fabrizio Anfuso: Head of IB CCR Backtesting, Credit Suisse
Backtesting Counterparty Credit Risk Models
- Historical vs Market Implied approach
- Test-Statistic properties
- Capital Buffer
- Real Examples
Lunch: 12.30 - 13.30
13.30 - 15.00: Norbert Hari: Head of Quantitative Development Team, ING Bank
Challenges in Counterparty Exposure Modeling
Counterparty Exposure Management System running on GPUs
GPU Computing – What and Why?
- Wrong-way / right-way risk
- Additional Termination Events
- Benefits of combining Numerical Methods: MC, PDE, LS, SBGM, COS etc.
- Efficient sensitivity calculation
- Impact of single- vs multi-factor models
- Impact of smile
Break: 15.00 - 15.15
15.15 - 16.45: Luis Manuel García Muñoz: Head of Interest Rates, Credit and CVA Quant Teams, BBVA
CVA for Credit Derivatives
We will deal with the calculation of CVA of portfolios of credit derivatives, putting special focus in the way in which default times of the different credit references are correlated.
- Weaknesses of traditional copula approaches.
- The Marshall-Olkin copula: a feasible approach for a reduced number of credit references.
- Nested Archimedean copulae: a way to reduce the number of parameters for multidimensional Marshall-Olkin copulae.
- Relationship with the Lévy-frailty model.
- Correlation skew produced by canonical Lévy subordinators (constant drift plus Poisson process).
- Correlation skew produced by a constant drift plus a compound Poisson process with exponential jumps.
- Impact of stochastic default intensities in the correlation skew implied by the model.
- Calibration of the model to credit index tranches.
- Impact of the different parameters on the CVA of a portfolio of CDSs.
End of Conference
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