Sophian Mehalla

We simulate real-world and risk-neutral scenarios that integrate transition climate risk in credit spreads and equities.

With a proper granularity of modeling, shocks prescribed by European regulators related to climate transition risk can be applied to estimate ALM impacts.

We look at an alternative pricing method based on density cosine-series approximation applied to swaptions, which offers a significant gain in computation time.

With better understanding of climate transition risk exposure and stress tests proposed by regulators, internal models can help provide insights.

We present a calibration technique for one complex risk neutral model, relying on neural networks and significantly reducing computational time.

Learn why the best economic scenario generator solution provides the choice of different methods and what challenges real world models pose.