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

Physica A: Statistical Mechanics and its Applications, 2018, vol. 503, issue C, 304-321

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.

Keywords: ADF test; KPSS test; Financial time series; Geophysical time series; GARCH model; Maximum likelihood estimation; Stochastic volatility model; Seismogram (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:503:y:2018:i:c:p:304-321

DOI: 10.1016/j.physa.2018.02.167

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