Inference and Asymmetric GARCH-Model with a New Distributed Innovation
Article Sidebar
Page Numbers:
156-179
Digital Object Identifier:
10.58578/mjms.v2i3.3863
Viewed: 265 times
Downloaded: 143 times
LYAS Publisher cordially invites qualified professionals to serve as Editors or Reviewers. Your expertise will make an important contribution to maintaining and strengthening the academic quality of our publications. Interested applicants are kindly requested to complete the application form at the following link: Editors & Reviewers
Please feel free to contact us if you need any further information about the submission process or if you have any additional questions.
Authors:
O. D. Adubisi1*, D. J. Adashu2 
Copyright :
Authors retain copyright and grant the journal right of first publication.
Main Article Content
Abstract
A novel generalized-odd-generalized exponentiated skew-t (GOGEST) innovation density for the generalized autoregressive conditional heteroskedasticity (GARCH) models is proposed. The features of the proposed distribution were derived. The parameter estimates of the proposed distribution through simulation were carried-out with maximum likelihood estimation technique. The performance of the asymmetric GARCH-GOGEST model relative to five other asymmetric GARCH-various existing innovation densities in volatility modeling was investigated using the Bitcoin log-returns. The empirical results showed that the asymmetric GARCH-GOGEST models were superior over the other asymmetric GARCH models. However, the threshold GARCH-GOGEST model outperformed the other models in terms of volatility predictability (out-of-sample).
Citation Metrics:
Downloads
Article Details
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
References
Adubisi, O. D., Abdulkadir, A., & Adashu D. J. (2022a). Improved parameter estimators for the flexible extended skew-t model with extensive simulations, applications and volatility modeling. Scientific African. 19(2023) e01443
Adubisi, O. D., Abdulkadir, A., & Adubisi, C. (2022b). A new hybrid form of the skew-t distribution: estimation methods comparison via Monte Carlo simulation and GARCH model application. Data science in finance and economics, 2(2), 54-79.
Adubisi, O. D., Abdulkadir, A., Abbas, U. F. and Chiroma, H. (2022c). Exponentiated half-logistic skew-t distribution with GARCH-type volatility models. Scientific African, 16(2022), e01253.
Agboola, S., Dikko, H. G. & Asiribo, O. E. (2018). On the exponentiated skewed student-t error distribution on some volatility models: Evidence of standard and poor-500 index return. Asian Journal of Applied Sciences. 11, 38-45.
Agboola, S., Dikko, H. G. & Asiribo, O. E. (2019). Some Volatility Modeling Using New Error Innovation Distribution. International Research Journal of Applied Sciences. 1(1), 57-62.
Alizadeh, M., Ghosh, I., Yousof, h. M., Rasekhi, M. & Hamedani, G.G. (2017). The Generalized Odd Generalized Exponential Family of Distributions: Properties, Characterizations and Application. Journal of Data Science. 16(2017), 443-466.
Altun, E., Tatlidil, H., Ozel, G. & Nadarajah. S. (2018). A new generalized of skew-T distribution with volatility models. Journal of Statistical computation and simulation. 88(7), 1252–1272.
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics. 31, 307-327.
Brooks, C. & Burke, S. P. (2003). Information criteria for GARCH model selection. The European Journal of Finance. 9. (6), 557-580.
Engle, R. F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica. 50, 987-1008.
Feng, L. & Shi, Y. (2017). A simulation study on the distributions of disturbances in the GARCH model. Cogent Economics & Finance. 5, 1-19.
Glosten, L. R., Jagannathan, R. & Runkle, D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. Journal of Finance. 48, 1779-1801.
Gyamerah, S. A. (2019). Modelling the volatility of Bitcoin returns using GARCH models. Quantitative Finance and Economics. 3(4), 739-753.
Harvey, A. & Sucarrat, G. (2013). EGARCH models with fat tails, skewness and leverage. Computational Statistics & Data Analysis. 76, 320-338.
Harvey, A. C. & Chakravarty, T. (2008). Beta-t-(E)GARCH. Cambridge working papers in Economics, University of Cambridge, pp. 137.
Harvey, A. C. (2013). Dynamic Models for volatility and Heavy Tails. Cambridge University Press, New York, pp. 137.
Jones, M. C. & M. J. Faddy (2003). A skew extension of the t-distribution, with applications. J. Roy. Statist. Soc., Ser. B. 65 (2), 159–174.
Jones, M. C. (2001). A skew t distribution. In Probability and Statistical Models with Applications (eds C. A. Charalambides, M. V. Koutras and N. Balakrishnan), London: Chapman and Hall, pp.269–277.
Kosapattarapim, C. (2013). Improving volatility forecasting of GARCH models: applications to daily returns in emerging stock markets. Doctoral dissertation, University of Wollongong, Australia. Assessed through https://ro.uow.edu.au/theses/3999.
Liu, H. & Hung, J. (2010). Forecasting S&P-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH models. Expert Systems with Applications. 37 (7), 4928-4934.
Mandelbrot, E. (1963). The Variation of Certain Speculative Prices. Journal of Business. 35, 394-419.
Maree, A., Card, P., Murphy, A. & Kidman, P. (2017). GARCH models with a new class of heavy-tailed distribution for the silver returns. International Journal of Finance, Insurance and Risk Management. 7 (2), 1351-1355.
Mubarak, M. T., Adubisi, O. D. & Abbas U. F. (2024). A novel hybrid distributed innovation EGARCH model for investigating the volatility of the stock market. RT&A. 19(79), 605-618.
Nagamani, N & Tripathy, M. R. (2018) Estimating common dispersion parameter of several inverse Gaussian populations: A simulation study, Journal of Statistics and Management Systems, 21:7, 1357-1389, DOI: 10.1080/09720510.2018.1503406.
Nakajima, J. & Omori, Y. (2012). Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student-t distribution. Computational Statistics & Data Analysis. 56 (11), 3690-3704.
Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica. 59, 347-370.
Ngunyi, A., Mundia, S. & Omari, C. (2019). Modelling volatility dynamics of cryptocurrencies using GARCH models. Journal of Mathematical Finance. 9, 591-615.
Poon, S. & Granger, C. W. J. (2003). Forecasting volatility in markets: A review. Journal of Economic Literature. XLI, 478-539.
Samson, T. K., Enang, E. T. & Onwukwe, C. E. (2020a). Estimating the Parameters of GARCH Models and Its Extension: Comparison between Gaussian and non-Gaussian Innovation Distributions. Covenant Journal of Physical & Life Sciences (CJPL). 8(1), 46-60.
Samson, T. K., Onwukwe, C. E. & Enang, E. T. (2020b). Modelling Volatility in Nigerian Stock Market: Evidence from Skewed Error Distributions. International Journal of Modern Mathematical Sciences. 18(1), 42-57.
Taboga, M. (2017). Lectures on probability theory and mathematical statistics. 3rd ed., John Wiley & Sons, New York, USA, pp 407-417.
Ural, M. & Demireli, E. (2019). Modeling asymmetric volatility in the Chicago board options exchange volatility index. Centre of Financial and Monetary Research. 22(1), 20-31.
Yelamanchili, R. K. (2020). Modeling Stock Market Monthly Returns Volatility Using GARCH Models Under Different Distributions. International Journal of Accounting & Finance Review. 5(1), 42-50.