The increase of available data is a challenge for classical mathematical models. Biology, physics, finance, economics and other social sciences are each affected in their own way. Lusenn promotes the formulation and the use of probabilities which have , over time, shown their effectiveness in all these areas.
In finance, stochastic models or local volatility models, have been developed since the mid-1990s. They help improve market analysis, capturing the effects of implied volatility, but they come up against the multi-scale properties of the data they process. Time series, for example, experience scale variations, and these variations make it more difficult to develop a model capable of reproducing multiple scale phenomena. One of the goals of Lusenn is to address these challenges by creating models that capture such complicated processes and make them applicable to pricing and hedging derivatives.
Lusenn is a subsidiary of Sivienn.