https://oldena.lpnu.ua/handle/ntb/52461| Title: | The Methods Bayesian Analysis of the Threshold Stochastic Volatility Model |
| Authors: | Bidyuk, Peter Gozhyj, Aleksandr Kalinina, Iryna Szymanski, Zdislaw Beglytsia, Volodymyr |
| Affiliation: | National Technical University of Ukraine "Ighor Sikorsky Kyiv Polytechnic Institute" Petro Mohyla Black Sea National University Splolecna Akademia Nauk |
| Bibliographic description (Ukraine): | The Methods Bayesian Analysis of the Threshold Stochastic Volatility Model / Peter Bidyuk, Aleksandr Gozhyj, Iryna Kalinina, Zdislaw Szymanski, Volodymyr Beglytsia // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 70–74. — (Big Data & Data Science Using Intelligent Approaches). |
| Bibliographic description (International): | The Methods Bayesian Analysis of the Threshold Stochastic Volatility Model / Peter Bidyuk, Aleksandr Gozhyj, Iryna Kalinina, Zdislaw Szymanski, Volodymyr Beglytsia // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 70–74. — (Big Data & Data Science Using Intelligent Approaches). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | Bayesian analysis stochastic volatility Threshold stohastic volatility model |
| Number of pages: | 5 |
| Page range: | 70-74 |
| Start page: | 70 |
| End page: | 74 |
| Abstract: | The paper considers the Bayesian analysis of the threshold stochastic volatility models. Studies of methods for analyzing stochastic volatility and improving models of stochastic volatility significantly improve the quality of forecast models and their estimates. Bayesian inference is performed by tailoring Markov chain Monte Carlo (MCMC) or sequential Monte Carlo (SMC) schemes that take into account the specific characteristics of models. The results of applying the method demonstrated in models heteroscedastic non-stationary processes. |
| URI: | https://ena.lpnu.ua/handle/ntb/52461 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| References (Ukraine): | [1] R. Engle, “ Autoregressive conditional heteroscedasticity with estimates of variance of united kingdom inflation,” Econometrica. vol. 50. pp. 987–1008. 1982. [2] R. Engle, and T. Bollerslev, “Modelling the persistence of conditional variances,” Econometric Reviews. vol. 5, no. 1, pp. 1–50. 1986. [3] M. Asai, M. McAleer, and Jun. Yu, “Multivariate stochastic volatility: areview”. Econometric Reviews, vol. 25, pp.145-75, 2006. [4] M. Asai, and M. McAleer, “The structure of dynamic correlations in multi variate stochastic volatility models,” Journal of Econometrics, vol.150, pp.182-192, 2009. [5] T. Bollerslev, “Generalized autoregressive conditional heteroskedasticity”. Journal of Econometrics. vol. 31, no. 3. pp. 307–327, 1986. [6] T. Bollerslev, “A conditionally heteroskedastic time series model forspeculative prices and rates of return ,“ The Review of Economics and Statistics. vol. 69, no. 3. pp. 542–547, 1987. [7] E. Jacquier, N. G. Polson, and P. E. Rossi. “Bayesian analysis of stochastic volatility models,” Journal of Business and Economic Statistics, 20, pp.69-87, 1994. [8] E. Jacquier, N. G. Polson, and P. E. Rossi. “Bayesian analysis of stochastic volatility models with fat-tails and correlated errors,” Journal of Econometrics, vol.122, pp.185-212, 2004. [9] N. Shephard, Stochastic Volatility: Selected Readings. Oxford: University Press. 2005. [10] N. Shephard, and T. A. Andersen, Stochastic Volatility: Origins and Overview. Handbook of Financial Time Series. New York: Springer. 2009. [11] T. Bollerslev, R. Chou, K. Kroner, “Arch modeling in finance : A review of the theory and empirical evidence,” Journal of Econometrics. vol. 52, no. 1-2, pp. 5–59, 1992. [12] T. Andersen, “Stochastic autoregressive volatility: a framework for volatility modeling, “Mathematical Finance. vol. 4, pp. 75– 102, 1994. [13] S. Taylor, “Modeling stochastic volatility: A review and comparative study,” Mathematical Finance.Vol. 4, no. 2, pp. 183–204, 1994. [14] Capobianco, E. State-spaces to chastic volatility models: areview of estimation algorithms. Applied Stochastic Models and Data Analysis. vol. 12, pp. 265–279,1996. [15] T. Andersen, T. Bollerslev, S. Lange, “Forecasting financial market volatility: Sample frequency visa-vis forecast horizon,” Journal of Empirical Finance. vol. 6, no. 5, pp. 457–477,1999. [16] S. Babichev, V. Lytvynenko, M. Korobchynskyi, and M.A. Taiff, “Objective clustering inductive technology of gene expression sequences features,” Communications in Computer and Information Science, 716, pp. 359-372, 2017. |
| References (International): | [1] R. Engle, " Autoregressive conditional heteroscedasticity with estimates of variance of united kingdom inflation," Econometrica. vol. 50. pp. 987–1008. 1982. [2] R. Engle, and T. Bollerslev, "Modelling the persistence of conditional variances," Econometric Reviews. vol. 5, no. 1, pp. 1–50. 1986. [3] M. Asai, M. McAleer, and Jun. Yu, "Multivariate stochastic volatility: areview". Econometric Reviews, vol. 25, pp.145-75, 2006. [4] M. Asai, and M. McAleer, "The structure of dynamic correlations in multi variate stochastic volatility models," Journal of Econometrics, vol.150, pp.182-192, 2009. [5] T. Bollerslev, "Generalized autoregressive conditional heteroskedasticity". Journal of Econometrics. vol. 31, no. 3. pp. 307–327, 1986. [6] T. Bollerslev, "A conditionally heteroskedastic time series model forspeculative prices and rates of return ," The Review of Economics and Statistics. vol. 69, no. 3. pp. 542–547, 1987. [7] E. Jacquier, N. G. Polson, and P. E. Rossi. "Bayesian analysis of stochastic volatility models," Journal of Business and Economic Statistics, 20, pp.69-87, 1994. [8] E. Jacquier, N. G. Polson, and P. E. Rossi. "Bayesian analysis of stochastic volatility models with fat-tails and correlated errors," Journal of Econometrics, vol.122, pp.185-212, 2004. [9] N. Shephard, Stochastic Volatility: Selected Readings. Oxford: University Press. 2005. [10] N. Shephard, and T. A. Andersen, Stochastic Volatility: Origins and Overview. Handbook of Financial Time Series. New York: Springer. 2009. [11] T. Bollerslev, R. Chou, K. Kroner, "Arch modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics. vol. 52, no. 1-2, pp. 5–59, 1992. [12] T. Andersen, "Stochastic autoregressive volatility: a framework for volatility modeling, "Mathematical Finance. vol. 4, pp. 75– 102, 1994. [13] S. Taylor, "Modeling stochastic volatility: A review and comparative study," Mathematical Finance.Vol. 4, no. 2, pp. 183–204, 1994. [14] Capobianco, E. State-spaces to chastic volatility models: areview of estimation algorithms. Applied Stochastic Models and Data Analysis. vol. 12, pp. 265–279,1996. [15] T. Andersen, T. Bollerslev, S. Lange, "Forecasting financial market volatility: Sample frequency visa-vis forecast horizon," Journal of Empirical Finance. vol. 6, no. 5, pp. 457–477,1999. [16] S. Babichev, V. Lytvynenko, M. Korobchynskyi, and M.A. Taiff, "Objective clustering inductive technology of gene expression sequences features," Communications in Computer and Information Science, 716, pp. 359-372, 2017. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Bidyuk_P-The_Methods_Bayesian_Analysis_70-74.pdf | 310.44 kB | Adobe PDF | View/Open | |
| 2018_Bidyuk_P-The_Methods_Bayesian_Analysis_70-74__COVER.png | 543.92 kB | image/png | View/Open |
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