Commenced in January 2007
Paper Count: 30835
Forecasting the Volatility of Geophysical Time Series with Stochastic Volatility Models
Abstract:This work is devoted to the study of modeling geophysical time series. A stochastic technique with time-varying parameters is used to forecast the volatility of data arising in geophysics. In this study, the volatility is defined as a logarithmic first-order autoregressive process. We observe that the inclusion of log-volatility into the time-varying parameter estimation significantly improves forecasting which is facilitated via maximum likelihood estimation. This allows us to conclude that the estimation algorithm for the corresponding one-step-ahead suggested volatility (with ±2 standard prediction errors) is very feasible since it possesses good convergence properties.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132226Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 509
 S. J. Fong and Z. Nannan (2011), Towards an Adaptive Forecasting of Earthquake Time Series from Decomposable and Salient Characteristics, The Third International Conferences on Pervasive Patterns and Applications - ISBN: 978-1-61208-158-8, 53-60.
 R. F. Engle (1982), Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica, 50(4), 987-1007.
 T. Bollerslev (1986), Generalized Autoregressive Conditional Heteroskedasticity, J. Econometrics, 31, 307-327.
 M. C. Mariani and O. K. Tweneboah (2016), Stochastic differential equations applied to the study of geophysical and financial time series, Physica A, 443, 170-178.
 Y. Hamiel, R. Amit, Z. B. Begin, S. Marco, O. Katz, A. Salamon, E. Zilberman, and N. Porat (2009), The seismicity along the Dead Sea fault during the last 60,000 years. Bulletin of Seismological Society of America, 99(3), 2020-2026.
 P. Brockman and M. Chowdhury (1997), Deterministic versus stochastic volatility: implications for option pricing models, Applied Financial Economics, 7, 499-505.
 F. J. Rubio and A. M. Johansen (2013), A simple approach to maximum intractable likelihood estimation, Electronic Journal of Statistics, 7, 1632-1654.
 A. Janssen and H. Drees (2016), A stochastic volatility model with flexible extremal dependence structure, Bernoulli, 22(3), 1448-1490.
 S. J. Taylor (1982), Financial returns modeled by the product of two stochastic processes, A study of daily sugar prices, 1961-79. Time Series Analysis: Theory and Practice, ZDB-ID 7214716, 1, 203-226.
 J. F. Commandeur and S. J. Koopman (2007), An Introduction to State Space Time Series Analysis, Oxford University press, 107-121.
 S. R. Eliason (1993), Maximum Likelihood Estimation-Logic and Practice, Quantitative applications in the social sciences, 96, 1-10.
 N. K. Gupta and R. K. Mehra (1974), Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations, IEEE Transactions on Automatic Control, 19(6), 774-783.
 R. H. Jones (1980), Maximum likelihood fitting of ARMA models to time series with missing observations, Technometrics, 22(3), 389-395.
 T. Cipra and R. Romera (1991), Robust Kalman Filter and Its Application in Time Series Analysis, Kybernetika, 27(6), 481-494.
 M. P. Beccar-Varela, H. Gonzalez-Huizar, M. C. Mariani, and O. K. Tweneboah (2016), Use of wavelets techniques to discriminate between explosions and natural earthquakes, Physica A: Statistical Mechanics and its Applications, 457, 42-51.
 S. E. Said and D. A. Dickey (1984), Testing for Unit Roots in Autoregressive Moving-Average Models with Unknown Order, Biometrika, 71, 599-607.