MoCaX boosts the performance of several complex numerical calculations

It enhances the capability of any measurement or process based on repeated calls to a pricing function

Counterparty risk, funding risk and IMM capital calculations need to price each derivative typically one million times in each run. This is a clear case where MoCaX will increase most substantially the performance of the calculation.

The computational gain that MoCaX delivers is measured in orders of magnitude, ranging from 100 for vanilla products to 10,000+ of books of exotics.

Calculations that are simply imposible without MoCaX, are now posible in a short time-frame.


Running Monte Carlo simulations within Monte Carlo simulations are known to be imposible in a reasonable time frame… MoCaX Intelligence enables them; we can do now what was imposible before.

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Market Risk engines have to simulate changes in prices of derivatives from several hundred to many thousands of times in each run, depending on the methodology.

These engines also benefit substantially from MoCaX when they need to do a full revaluation of the book of derivatives in the simulation.

Sometimes, Lattice methods with linear interpolation are used in this context. MoCaX is many orders of magnitude better in two ways:

  1. It delivers an accurate price and it is an error-free method
  2. On top, it is as fast as other ultrafast methods like standard interpolation

This is most important not only for accurate XVA pricing with its sensitivities, but also for regulatory capital. The reason is that regulators do not tend to approve pricing models if they are not demonstrated to be accurate or “conservative. MoCaX solves this problem.

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Stress testing and scenario analysis require the generation of multiple prices. MoCaX is ideal for this purpose, as it accelerates the process substantially, hence providing a wider range of computational capabilities for the risk manager.

This capability is enhanced by the fact that MoCaX pricers can be saved and reused in a risk engine, or in several separated risk engines. This enables the optimal use of an institution’s pricing functionality across different departments.



As soon as we incorporate special features to an OTC derivative, its pricing becomes too complex. Often, we need to run numerical simulations like Monte Carlo, Trees or Finite Differences in order to calculate its risk-neutral price, as we cannot solve the standard Black-Scholes equation that gives us its arbitrage-free value.

MoCaX accelerates those calculations by several orders of magnitude in many contexts, hence enabling a pricing system to deliver timely and accurate results.


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