  We present a computational method for measuring financial risk by estimating
the Value at Risk and Expected Shortfall from financial series. We have made
two assumptions: First, that the predictive distributions of the values of an
asset are conditioned by information on the way in which the variable evolves
from similar conditions, and secondly, that the underlying random processes can
be described using piecewise Gaussian processes. The performance of the method
was evaluated by using it to estimate VaR and ES for a daily data series taken
from the S&P500 index and applying a backtesting procedure recommended by the
Basel Committee on Banking Supervision. The results indicated a satisfactory
performance.
