Using ARCH and GARCH mathematical models to predict the general budget deficit for the period from (2008) to (2023)
DOI:
https://doi.org/10.31185/wjps.283Abstract
: In this research, (ARCH) and (GARCH) were used, which are mathematical models for time series, as the study of time series is considered one of the topics of great importance in analyzing the behavior of various phenomena and interpreting them by analyzing their change according to time, adopting different time periods. Among the most prominent time series are series Finance, which is volatile, that is, characterized by the characteristic of instability (volatility), which makes the use of (ARMA) models inaccurate in its predictive results, which work on the assumption that the variance of the random error, which is distributed normally, and which is constant over time. As for time series, this condition is This is not true and it shows variation and fluctuations at different periods of the series.
This research aims to choose the best model for the conditional variance of the remainder of the time series model in the process of studying the surplus and deficit in the general financial budget of Iraq on a monthly basis for the period from January 2008 to September 2023. Accordingly, we will build a statistical model using the (ARCH) family and also know the risk element or Uncertainty) by using preference criteria to build time series models and then using the estimated model to predict the variance fluctuations of that model, The study showed that the best forecasting model is GARCH(2,0).
References
Aielli, G. P. and Caporin, M. (2015). "Dynamic Principal Components: a New Class of Multivariate GARCH Models", Faculty of Economics and Business. Working Paper .194
Bauwens, L. , Laurent, S. , and Rombouts, J.V., (2006), "Multivariate GARCH Models : A Survey", Journal of applied Econometrics, Vol. 21,No. 1, PP. 79 - 109 .
Dhaene, G. and Wu, J. (2016). "Mixed–Frequency Multivariate GARCH", Faculty of Economics and Business Discussion Paper Series DPS 16.12
Doan , T. A. (2014), "RATS Handbook for ARCH/GARCH and Volatility Models", Draft version. Estima, Evanston, IL.
Engle, R. (2002), "Dynamic Conditional Correlation : Asimple Class of multivariate Generalized Autoregressive Conditional Heteroskedasticity Models", Journal of Business and Economic Statistics , Vol. 20 , NO.3 PP.339-350.
Fengler, M. R., and Herwartz, H. (2015), "Measuring Spot Variance Spillover when (Co)variances are time – varying – the case of Multivariate GARCH Model " University of St Gallen , School of Economics and Political Science Department of Economics , Discussion paper no 2015-17.
Francq, C. , Horvath, L. and Zakoian, J. M. (2014), "Variance Targeting Estimation of Multivariate GARCH Modes", Journal of Financial Econometrics , MPERA paper No 57794 16:28 UTC vol. 14. 353-382.
Francq, C. and zakoian, J. M. (2014). "Estimating Multivariate GARCH and Stochastic Correlation models equation by equation", MPRA Paper No. 54250. posted 9. March 23:47 UTC
Kubilay, M. M. (2012 ), "The Volatility Spillover Among Acountrys Foreign exchange Bound and Stock Markets Amultivariate GARCH analysis", Master Thesis, The Graduate School of Applied Mathematics of Middle East Technical University.
Ngailo, E. , Luvanda, E. and Massawe, E. S. (2014),"Time Series Modelling with Application toTanzania Inflation", Journal of Data Analysis and Information Processing,Vol. 2, No. 02, PP. 49.
Nortey, E. N., Ngoh, D. D., Doku-Amponsah, K., and Ofori-Boateng, K. (2015), "Modeling inflation rates and exchange rates in Ghana : application of multivariate GARCH Models", Springer plus, Vol. 4, No, 1. PP.1-10.
Pedelil, X. , Fokianos, K. , and Pourahmadi, M.(2015)," TwoCholesky log-GARCH models for multivariate volatilites", Journal of Statistical Modelling,Vol. 15, no. 3. pp 233-255
Pelinescu, E. (2014), "Volatility analysis of the Romanian exchange rate", Procedia Economics and Finance,Vol. 8, 543-549
Raddant, M. and Wagner, F. (2016), "Multivariate GARCH for a large number of stocks ",arXiv preprint arXiv:1609.07051..
Sakthivel, P. , Bodkhe, N. and Kamaiah, B. ( 2012), "Correlation and Volatility Transmission across International Stock Markets: ABivariate GARCH Analysis", International Journal of Economics and Finance, Vol. 4, NO. 3. PP.253.
Sedlak, J. , Hartman, J. (2013), "Forecasting Conditional Correlation for Exchange Rate using Multivariate GARCH Models with Historical Value- AT-Risk application ", Bachelor Thesis ,Uppsala University .
Silvennoinen, A . and Terasvirta, T. (2008) " Multivariate GARCH models"Working Paper Series in Economics and Finance No 669
Yang, S. Y. and Doong, S. C. (2004), "Price and Volatility Spillovers between Stock Prices and Exchange Rates : Empirical Evidence from The G-7 Countries ", International Journal of Business and Economics, Vol. 3, No.2, PP, 139 – 153 .
Jameel, S. O., Salih, A. M., Jaleel, R. A., & Zahra, M. M. (2022). On The Neutrosophic Formula of Some Matrix Equations Derived from Data Mining Theory and Control Systems. International Journal of Neutrosophic Science (IJNS), 19(1).
Yonis, M. (2011), "Stock Market Co- Movement and Volatility Spillover between USA and South Africa", Master Thesis, UMEA University .
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