Real World Algorithmic Exposure: An Innovative New Approach for Nested Simulations

The computation of counterparty credit exposures such as Potential Future Exposure (PFE), as well as Economic Capital calculations by insurance firms, require risk-neutral (RN) pricing at future time horizons along real world (RW) scenarios. The challenge with these calculations is that the nested simulations (often called Brute Force simulations) can be very computationally challenging for large portfolios of deals, requiring billions of simulation paths and taking significant amounts of time. An innovative new approach for nested simulations has been introduced recently. The new approach, called Real World Algorithmic Exposure, combines resampling with the Backward Monte Carlo-based Algorithmic Exposure (also known as American or Least Squares Monte Carlo). The approach avoids the inefficiency of the Brute Force approach and dramatically reduces computation times, by using resampling to bridge the gap between real world economic scenarios and risk-neutral pricing scenarios, and by performing the computation in one step via Backward Monte Carlo. - See more at: http://www.numerix.com/on-demand-webinar/real-world-algorithmic-exposure-innovative-new-approach-nested-simulations#sthash.m0g6VxSR.dpuf