webinar

Real World Algorithmic Exposure: An In-Depth Exploration with Case Study Examples

On March 2, 2016 featured speaker Dr. Ping Sun, built on his previous presentation introducing Real World Algorithmic Exposure and took a deep dive into the innovative new resampling approach, outlining the theory behind it along with showcasing examples of resampling in action and a comparison of the Algorithmic Exposure and Brute Force approaches.

Real World risk metrics, such as Potential Future Exposure (PFE) or Expected Exposure profiles, are heavily used in counterparty credit risk functions within banks and feature in regulatory capital requirements. The market standard for these calculations has been the computationally intensive nested Monte Carlo approach, in which the Brute Force simulations can require billions of simulation paths and substantial computing power.

In order to handle real world PFE and other counterparty exposure calculations in a single framework, which requires risk neutral pricing on top of real world scenarios, an innovative decoupling approach relaxes the requirement that the same model be used to both generate scenarios and simulate the future values. This method, based on Las Vegas Monte Carlo simulations with new resampling techniques, provides a much more efficient alternative to Real World nested Monte Carlo exposure calculations, decreasing computation time by several orders of magnitude.

On March 2, 2016 featured speaker Dr. Ping Sun, Executive Director of Financial Engineering at Numerix, built on his previous presentation introducing Real World Algorithmic Exposure and took a deep dive into the innovative new resampling approach, outlining the theory behind it along with showcasing examples of resampling in action and a comparison of the Algorithmic Exposure and Brute Force approaches.

DR. SUN ADDRESSED THE FOLLOWING:

  • Real World Models
    • Real World vs. Risk Neutral Models
    • Examples of Real World Models
  • Real World Algorithmic Exposures for Advanced Risk Measures
    • Theory of Resampling
    • Model Independent Decoupling Approach
    • Resampling Example
  • Algorithmic Exposure vs. Nested Monte Carlo Approach
    • Resampling vs. Monte Carlo on Monte Carlo
    • Comparison and Contrast with an Example

 

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