# Volatility Models Applied to Geophysics and High Frequency Financial Market Data

## TL;DR

This paper compares GARCH and stochastic volatility models for forecasting volatility in geophysical and financial time series, finding stochastic volatility generally provides more accurate predictions.

## Contribution

It introduces a class of volatility models with time-varying parameters and compares their effectiveness in forecasting volatility in stationary environments.

## Key findings

- Stochastic volatility models outperform GARCH in forecasting accuracy.
- Both models predict volatility within +/- 2 standard errors.
- Stochastic volatility is less influenced by autoregressive past information.

## Abstract

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.