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This work is devoted to the study of modeling geophysical and financial timeseries. A class of volatility models with time-varying parameters is presentedto forecast the volatility of time series in a stationary environment. Themodeling of stationary time series with consistent properties facilitatesprediction with much certainty. Using the GARCH and stochastic volatilitymodel, we forecast one-step-ahead suggested volatility with +/- 2 standardprediction errors, which is enacted via Maximum Likelihood Estimation. Wecompare the stochastic volatility model relying on the filtering technique asused in the conditional volatility with the GARCH model. We conclude that thestochastic volatility is a better forecasting tool than GARCH (1, 1), since itis less conditioned by autoregressive past information.

Volatility Models Applied to Geophysics and High Frequency Financial Market Data