Abstract
In the article the comparative analysis of most common among economists software packages R, EViews and Gretl in financial time series modeling is conducted. Advantages and disadvantages of each software are considered. Volatility is often used as a rough approximation to measuring of financial instruments risk. For the modeling of financial time series volatility Polish stock index WIG was chosen. For describing the volatility of financial time series econometric model of family GARCH is built by means of these packages.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Greene, W.: Econometric Analysis. Prentice-Hall, Upper Saddle River (2008)
Black, F.: Studies of stock price volatility changes. In: Proceedings of the Business and Economic Statistics. American Statistical Association, Washington, DC (1976)
Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis. Wiley, Hoboken (2008)
Martin, V., Hurn, S., Harris, D.: Econometric Modelling with Time Series, Specification Estimation and Testing. Cambridge University Press, Cambridge (2013)
Mills, T.C., Markellos, R.N.: The Econometric Modelling of Financial Time Series. Cambridge University Press, Cambridge (2008)
Chiu, C.-W.J., Harris, R., Stoja, E., Chin, M.: Financial market volatility, macroeconomic fundamentals and investor sentiment. Bank of England. Staff Working Paper No. 608 (2016)
Subbotin, A.V.: Volatility models: from conditional heteroskedasticity to cascades at multiple horizons. Appl. Econom. 15(3), 94–138 (2009)
Leucht, A., Kreiss, J.-P., Neumann, M.H.: A model specification test for GARCH(1,1) processes. Scand. J. Statist. 42(4), 1167–1193 (2015)
Barunik, J., Krehlik, T., Vacha, L.: Modelling and forecasting exchange rate volatility in time-frequency domain. Eur. J. Oper. Res. 251(1), 329–340 (2016). https://doi.org/10.1016/j.ejor.2015.12.010
Panda, P., Deo, M.: Asymmetric and volatility spillover between stock market and foreign exchange market: Indian experience. IUP J. Appl. Financ. 20(4), 69–82 (2014)
Birau, R., Trivedi, J., Antonescu, M.: Modeling S&P bombay stock eschange BAHKEX index volatility patterns using GARCH model. Procedia Econ. Financ. 32, 520–525 (2015). https://doi.org/10.1016/S2212-5671(15)01427-6
Harvey, A., Lange, R.-J.: Modeling the Interactions Between Volatility and Returns. Cambridge Working Papers in Economics. CWPE 1518. https://doi.org/10.17863/cam.5703
Lu, X., Que, D., Cao, G.: Volatility forecast based on hybrid artificial neural network and GARCH-type models. Procedia Comput. Sci. 91, 1044–1049 (2016). https://doi.org/10.1016/j.procs.2016.07.145
Efimova, O., Serletis, A.: Energy markets volatility modelling using GARCH. Energy Econ. 43, 264–273 (2014)
Vejendla, A., Enke, D.: Evaluation of GARCH, RNN and FNN models for forecasting volatility in the financial markets. IUP J. Financ. Risk Manag. 10(1), 41–49 (2013)
Babalos, V., Caporale, G.M., Spagnolo, N.: Equity fund flows and stock market returns in the US before and after the global financial crisis: a VAR-GARCH-in-mean analysis. In: Economics and Finance Working Paper Series, Working Paper No. 16–12. Brunel University London, Department of Economics and Finance, June 2016. https://www.brunel.ac.uk/__data/assets/pdf_file/0009/478314/1612.pdf
Kumari, J., Hiremath, G.S.: Determinants of idiosyncratic volatility: evidence from the Indian stock market. Res. Int. Bus. Financ. 41, 172–184 (2017). https://doi.org/10.1016/j.ribaf.2017.04.022
Walther, T.: Expected shortfall in the presence of asymmetry and long memory. an application to vietnamese stock markets. Pac. Acc. Rev. 29(2), 132–151 (2017). https://doi.org/10.1108/PAR-06-2016-0063
Sharma, P., Vipul: Forecasting stock index volatility with GARCH models: international evidence. Stud. Econ. Financ. 32(4), 445–463 (2015). https://doi.org/10.1108/sef-11-2014-0212
Vasudevan, R.D., Vetrivel, S.C.: Forecasting stock market volatility using GARCH models: evidence from the Indian stock market. Asian J. Res. Soc. Sci. Humanit. 6(8), 1565–1574 (2016). https://doi.org/10.5958/2249-7315.2016.00694.8
AL-Najjar, D.: Modelling and estimation of volatility using ARCH/GARCH models in Jordan’s stock market. Asian J. Financ. Acc. 8(1), 152–167 (2016). https://doi.org/10.5296/ajfa.v8i1.9129
Innocent, N., Mung’atu, J.K.: Modeling time-varying variance-covariance for exchange rate using multivariate GARCH model. Int. J. Thesis Proj. Diss. 4(2), 49–65 (2016)
Spulbar, C., Nitoi, M.: The impact of political and economic news on the EURO/RON exchange rate: a GARCH approach. Annals of the ‘Constantin Brâncuşi’ University of Târgu Jiu, Economy Series 4, 52–58 (2012)
Renfro, C.G.: The Practice of Econometric Theory: An Examination of the Characteristics of Econometric Computation. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-75571-5
Renfro, C.G.: A compendium of existing econometric software packages. J. Econ. Soc. Meas. 29, 359–409 (2009)
Ooms, M., Doornik, J.A.: Econometric software development: past, present and future. Stat. Neerl. 60(2), 206–224 (2006). https://doi.org/10.1111/j.1467-9574.2006.00317.x
Liboschik, T., Fokianos, K., Fried, R.: tscount: an R package for analysis of count time series following generalized linear models. Vignette of R package tscount version 1.3.0 (2015)
Ranganatham, M., Madhumati, R.: Investment Analysis and Portfolio Management. Pearson Education (Singapore) Pte. Ltd., Singapore (2005)
Engle, R.: Autoregressive conditional heteroscedasticity with estamates of the variance of United Kingdom inflation. Econometrica 50(4), 987–1008 (1982)
Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. J. Econom. 31, 307–327 (1986)
Minelli, M., Chambers, M., Dhiraj, A.: Big Data Technology, in Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. Wiley, Hoboken (2013)
Google Trends. https://www.google.com/trends/explore?date=all&q=%2Fm%2F0212jm,EViews,gretl&hl=en-US
Gnu Regression Econometrics and Time-Series Library. http://gretl.sourceforge.net/
EViews 9.5 Feature List. http://www.EViews.com/EViews9/ev9features.html
Le, T.S.: Statistical&Programming Features of R. https://www.linkedin.com/pulse/statistical-programming-features-r-thiensi-le
Google Finance. https://www.google.com/finance/historical?q=WSE:WIG&ei=98ZNU6CMMMPJsQfdOQ
Matei, M.: Assessing volatility forecasting models: why GARCH models take the lead. Rom. J. Econ. Forecast. 4, 42–65 (2009)
Ghalanos, A.: Introduction to the rugarch package (2015). https://cran.r-project.org/web/packages/rugarch/vignettes/Introduction_to_the_rugarch_package.pdf
Fisher, T.J., Gallagher, C.M.: New weighted portmanteau statistics for time series goodness of fit testing. J. Am. Stat. Assoc. 107(498), 777–787 (2012). https://doi.org/10.1080/01621459.2012.688465
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Liashenko, O., Kravets, T., Krytsun, K. (2018). Software Packages for Econometrics: Financial Time Series Modeling. In: Bassiliades, N., et al. Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2017. Communications in Computer and Information Science, vol 826. Springer, Cham. https://doi.org/10.1007/978-3-319-76168-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-76168-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76167-1
Online ISBN: 978-3-319-76168-8
eBook Packages: Computer ScienceComputer Science (R0)