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arXiv · 1901.09145

Volatility Models Applied to Geophysics and High Frequency Financial Market Data

Maria C Mariani·Md Al Masum Bhuiyan·Osei K Tweneboah·Hector Gonzalez-Huizar·Ionut Florescu
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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.

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Maria C Mariani, Md Al Masum Bhuiyan, Osei K Tweneboah, Hector Gonzalez-Huizar, Ionut Florescu. 2019-01-26. Volatility Models Applied to Geophysics and High Frequency Financial Market Data. https://doi.org/10.1016/j.physa.2018.02.167

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