Maria C Mariani, Md Al Masum Bhuiyan, Osei K Tweneboah, Hector Gonzalez-Huizar, Ionut Florescu
arXiv 26 Jan 2019 · Statistics — Applications · publishedPhysica A Statistical Mechanics and its Applications (2018) · 7 citations (OpenAlex)
arXiv:1901.09145 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Engle, R.F (1982) Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation | 0.644 | 2 | 2 | 100% |
| 2 | Bollerslev, T (1986) Generalized Autoregressive Conditional Heteroskedasticity | 0.644 | 2 | 2 | 100% |
| 3 | Brockman, P., and Chowdhury, M (1997) Deterministic versus stochastic volatility: implications for option pricing models | 0.405 | 1 | 1 | 100% |
| 4 | Brys, G., Hubert, M., and Struyf, A (2004) A Robustification of the Jarque-bera test of normality | 0.405 | 1 | 1 | 100% |
| 5 | Cipra, T. and Romera, T (1991) Robust Kalman Filter and Its Application in Time Series Analysis | 0.405 | 1 | 1 | 100% |
| 6 | Commandeur, J.F., and Koopman, S.J (2007) An Introduction to State Space Time Series Analysis | 0.405 | 1 | 1 | 100% |
| 7 | Eliason, S.R (1993) Maximum Likelihood Estimation-Logic and Practice | 0.405 | 1 | 1 | 100% |
| 8 | Fong, S.J., and Nannan, Z (2011) Towards an Adaptive Forecasting of Earthquake Time Series from Decomposable and Salient Characteristics | 0.405 | 1 | 1 | 100% |
| 9 | N. K. Gupta and R. K. Mehra (1974) Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations | 0.405 | 1 | 1 | 100% |
| 10 | Hamiel, Y., Amit, R., Begin, Z.B., Marco, S., Katz, O., Salamon, A.,… (2009) The seismicity along the Dead Sea fault during the last 60,000 years | 0.405 | 1 | 1 | 100% |
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