Forecasting the Volatility of Geophysical Time Series with Stochastic Volatility Models

This work is devoted to the study of modeling
geophysical time series. A stochastic technique with time-varying
parameters is used to forecast the volatility of data arising in
geophysics. In this study, the volatility is defined as a logarithmic
first-order autoregressive process. We observe that the inclusion of
log-volatility into the time-varying parameter estimation significantly
improves forecasting which is facilitated via maximum likelihood
estimation. This allows us to conclude that the estimation algorithm
for the corresponding one-step-ahead suggested volatility (with ±2
standard prediction errors) is very feasible since it possesses good
convergence properties.

Authors:

• Maria C. Mariani
• Md Al Masum Bhuiyan
• Osei K. Tweneboah
• Hector G. Huizar

Keywords:

• Augmented Dickey Fuller Test
• geophysical time series
• maximum likelihood estimation
• stochastic volatility model.

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