Volatility Models Applied to Geophysics and High Frequency Financial Market Data

Maria C Mariani, Md Al Masum Bhuiyan, Osei K Tweneboah, Hector Gonzalez-Huizar and 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.

Date: 2019-01
New Economics Papers: this item is included in nep-ets and nep-for
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Published in Physica A: Statistical Mechanics and its Applications, Volume 503, 1 August 2018, Pages 304-321

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