For as long as can be remembered, financial markets have thrived under the paradigm that if we do not know future prices, then they must be stochastically determined, therefore Gaussian mathematics is the tool to use. In this webinar, we ask: how sure can we be? Are there no other ways?
For people who use directly financial models, or who build or manage them, or for those for whom ideas of stochastic modeling in financial markets percolate into their own work, they probably are accustomed to the good old paradigm. Some even have forgotten to question if anything unknown anything but stochastic.
This will apply to future market prices, operational risk events, and most other unknowns in most fields of human knowledge. Once this question is solved, the universe will be mankind's oyster. We can try some more modest approaches.
Concomitant with, or maybe due to, advances in stochastic calculus, stochastic approaches, sometimes with heavy-RAM-based computer tools, we can now declare that we can put a value on most financial derivatives. We only forget to add, assuming we have some ideas about what causes the underlying. The assumption of multiple causes that can be melted into some aggregation for empirical quantification, is too comfortable to pass by.
However, the crisis of 2007-08 is but one example that took us outside our comfort zone. It painfully proved that the assumptions needed a revisit, but also that a few chunks of modeling have to be added to take into account black swans, arbitrary behaviors and market dynamics.
We then establish a typology of market behaviors, based upon the understanding we can have on the dynamics of the market at a given time, together with an assessment of the value of forecasts, and tell when forecasting efforts are doomed to fail.