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Volatility models applied to geophysics and high frequency financial market data

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

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.

Suggested Citation

  • Mariani, Maria C. & Bhuiyan, Md Al Masum & Tweneboah, Osei K. & Gonzalez-Huizar, Hector & Florescu, Ionut, 2018. "Volatility models applied to geophysics and high frequency financial market data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 304-321.
    • Handle: RePEc:eee:phsmap:v:503:y:2018:i:c:p:304-321
    DOI: 10.1016/j.physa.2018.02.167
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    References listed on IDEAS

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    1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    2. Commandeur, Jacques J.F. & Koopman, Siem Jan, 2007. "An Introduction to State Space Time Series Analysis," OUP Catalogue, Oxford University Press, number 9780199228874.
    3. Mariani, Maria C. & Tweneboah, Osei K., 2016. "Stochastic differential equations applied to the study of geophysical and financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 170-178.
    4. Paul Brockman & Mustafa Chowdhury, 1997. "Deterministic versus stochastic volatility: implications for option pricing models," Applied Financial Economics, Taylor & Francis Journals, vol. 7(5), pages 499-505.
    5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Maria C. Mariani & Peter K. Asante & Md Al Masum Bhuiyan & Maria P. Beccar-Varela & Sebastian Jaroszewicz & Osei K. Tweneboah, 2020. "Long-Range Correlations and Characterization of Financial and Volcanic Time Series," Mathematics, MDPI, vol. 8(3), pages 1-18, March.
    2. Mariani, Maria C. & Bhuiyan, Md Al Masum & Tweneboah, Osei K. & Beccar-Varela, Maria P. & Florescu, Ionut, 2020. "Analysis of stock market data by using Dynamic Fourier and Wavelets techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    3. Chunliang Deng & Xingfa Zhang & Yuan Li & Qiang Xiong, 2020. "Garch Model Test Using High-Frequency Data," Mathematics, MDPI, vol. 8(11), pages 1-17, November.

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