Often validators have to validate derivative pricing models. Banks generally have two systems: one for risk management and other front office. It is advisable that both the systems should be exactly similar but practically this is practice is not common. Due to the impact front office system play in trading, front office systems are more sophisticated. This makes them expensive. So for that reasons often banks use cheaper or home made systems for their risk management requirements.
Front office engine’s pricing model’s calculations tend to be more accurate due to their impact in businesses’s PnL directly. Pricing models in risk systems are simplified because in risk calculation exsercises like VaR/PFE calculations a derivative has to be priced multiple times, so there is a time constraints.
Difference in observed in derivative pricing models in front and risk management system from input data perspective and model perspective.
Input data perspective:
- Front office systems uses sophisticated techniques in interpolation of yield curve where as risk management systems get away by simple linear interpolation.
- Options based products use volatility smile (simple equity options), volatility surface (FX options, caps/floors), volatility cubes (swaptions).
- Data in front office is generally has more data points in smiles, surfaces or cubes than in comparison with to risk management systems.
- Interpolation follow the same rule as discussed above.
- Risk models require historical data to calculate VaR/PFE. Often for illiquid currencies as well as for exotic derivatives historical data is not adequate, in these scenarios alternate data is used. For example if volatility of a particular currency is not available then volatility of an alternative currency is used as a proxy.
The challenge multiplies when there are multiple currencies in the portfolio. The issue of data triangulation multiplies because often in risk management, same simplifications are applied to each currency which may not be valid for every currency. Often risk managers prioritize their data based on their portfolio concentration.
For exotic derivatives front office pricing models use sophisticated techniques like Monte Carlo simulations where as risk models use approximate techniques. These approximate techniques are often analytical approximations of derivative pricers where analytical solution is not tractable. For example: To price an American put option front office uses Monte Carlo simulations but it is generally observed that the risk models use approximated analytical formulas.
Due to such reasons there are often differences in pricing of front office and risk systems. Validators often use one system to benchmark another and hence use them as a leverage in validation exercise. Even though the pricing may not match but sensitivities should match.