MONTE CARLO APPROXIMATIONS: AS EXOTIC AS THE NAME GOES???

I get easily carried away by all things European; the architecture, the art, the culture, the accents, the visage (of women strictly). No surprise that out of assortments of quantification and approximation techniques, the Monte Carlo method catches my attention more. It's soooo Italian.

Monte Carlo in general is the name of the administrative area under the Principality of Monaco. Of course I know that because of Wikipedia and because I saw Selena Gomez strutting around it in a sunshine dress and certainly because Monaco rings of one of the finest racing circuits and records some of the most celebrated Formula 1 races ever.

However, the Monte Carlo Simulation seemed like too much unnecessary work for estimating Option prices. Most of you would remember it from your Financial Economics Exam (MFE) of SOA and I suppose its applications in Finance are just as many as the only two pages dedicated for it in the ASM Manual.

Tinkering with RSLN-2

I have decided to dedicate a fraction of my time to “Stochastic Modeling:  Theory and Reality from an Actuarial Perspective” book. Even after six years into the profession my shallow perspective tells me that this should be our primary skill set.

As a first step I jumped straight to the section on “Regime Switching Models” to study the RSLN (Regime Switching Log-normal) model. The Actuarial Literature elsewhere tends to be inundated with this model whenever it comes to Stock models. Actuaries seem to be rebels against normal statisticians who would prefer regressive models over probability models.