  This work is devoted to the study of modeling geophysical and financial time
series. A class of volatility models with time-varying parameters is presented
to forecast the volatility of time series in a stationary environment. The
modeling of stationary time series with consistent properties facilitates
prediction with much certainty. Using the GARCH and stochastic volatility
model, we forecast one-step-ahead suggested volatility with +/- 2 standard
prediction errors, which is enacted via Maximum Likelihood Estimation. We
compare the stochastic volatility model relying on the filtering technique as
used in the conditional volatility with the GARCH model. We conclude that the
stochastic volatility is a better forecasting tool than GARCH (1, 1), since it
is less conditioned by autoregressive past information.
