Description: Filtered Historical Simulation - An Overview of the scenario generating technique for speculative assets prices created and developed by Giovanni Barone-Adesi & Kostas Giannopoulos
overview (224) filtered historical simulation (2) giovanni barone-adesi (2) kostas giannopoulos (2)
FHS is a scenario generating technique for speculative assets prices (risk factors). In contrast to some other techniques where the scenarios are generated ad hoc, FHS uses a combination of nonlinear econometric models and past returns to build the probability distribution of possible values that the asset (risk factor) could take in the days ahead. Risk estimates are directly derived from the tails of the distribution.
The FHS is a kind of historical simulation since uses past returns as innovations in modeling the randomness of the asset prices. It has however one major improvement; the row returns are first scaled by the volatility that prevailed that day and then are multiplied by the current forecast of volatility. The first pass, the scaling, is necessary in order to make the past returns stationary and to render them suitable innovations for a simulation process. The second step, the re-scaling, endows the historica
From the statistical perspective the FHS is seen as a semi-parametric model. The price series is not forced to conform to any kind of probability distribution, but rather the data are allowed to talk themselves. Risk estimation is highly dependent on good prediction of uncommon events, or catastrophic risk. Simulation models that draw innovations from theoretical distributions, such as the parametric Monte Carlo, indeed smooth the empirical distribution of the data and consequently they may underestimate "c