MODELING DAILY AMMAN STOCK EXCHANGE VOLATILITY FOR SERVICES SECTOR

Year 2016, Volume: 5 Issue: 3, 339 - 349, 30.09.2016

Salih Turan Katircioglu

https://doi.org/10.17261/Pressacademia.2016321976

Abstract

There are many  forecasting techniques that can be used to
help the investment community in building their 
policies in the future, which lead to an appropriate choices of the assets
involved in the portfolios, managing it , and pricing these assets accurately.
In this paper we are trying to afford one of these methods recognized as ARIMA
model, which is used in analyzing 
financial time series data. The 
target of this paper is 
forecasting  services sector
volatility in Amman Stock Exchange . As a result investment community can rely
on this type of analysis to make the future prospects of selling and
buying  financial securities.  Using 
historical indices data 
accumulated daily from the web site of Amman Stock Exchange for period
3/1/2010-10/5/2015. Stationarity achieved at level for  services sector , and then use a minimum mean
square error, t-statistics value and p-statistics value to choose the best
ARIMA models at 95% confidence interval. The resulted models for this study for
services sector is ARIMA(0,0,1), lastly, the best ARIMA model was formed and
tabulated in the entire paper.  

Keywords

Volatility, ARIMA Model, Financial Time Series, Forecasting

References

• Al-Shiab M. 2006, ''The Predictability of ASE Using the Univariate ARIMA Model'', Economics Administration Science, vol. 22, no. 2, pp.124- 139.
• Andersen, T. , Bollerslev, T., Diebold, F. & Labys, P. 2003, “Modeling and forecasting realized volatility”, Econometrica vol. 71, no 2, pp. 579-625.
• Bruce, L. , Richard T. O’Connell and Anne B. 2005, “Koehler,Forecasting time series and regression”,Thomson Brooks/Cole, USA.
• Danielsson, J., H. S. Shin, and J.-P. Zigrand .2012. “Procyclical leverage and endogenous risk”.Mimeo, LSE, .http://www.riskresearch.org
• Danielsson, J.et al, 2016. “ Learning from History: Volatility and Financial Crises “. http://www.riskresearch.org.
• David ,A. and John, C.2003. “SAS for forecasting Time Series, Second Edition”. Cary, NC: Institute Inc.
• Elena K. & Storis K. 2009,“Modeling Stock Market Volatility”,Greg N. Gregoriou,stock market volatility,AChapman&Hallbook,New York.
• Enders, Walter 2004, Applied Econometric Time Series (Second ed.). New York:John Wiley. pp. 170–175.
• Engle and Granger, 1987,''Co-integration and Error Correction: Representation, Estimation,and Testing'', Econometrica,vol55,no.2,pp.251- 276
• Hamao et al, 1990,"Correlations in Price Changes and Volatility Across International Stock Markets", The Review of Financial Studies, vol. 3, no. 2,pp. 281-307.
• Geert B and Campbell H,1997,”Emerging equity market volatility”, Financial Economics, vol43, pp. 29-77.
• Hussein, A, Z,2009, ''Asymmetric Volatility Phenomenon: An Application to Major European Countries'',International Management Review, vol. 5, no. 1, pp. 37-49.
• Hussein.A. Z,2011, “Modeling & Forecasting Volatility using ARIMA model”, European Journal of Economics, Finance and Administrative Science,vol. 35, pp. 109-125.
• John C. and David A.,2003” SAS for Forecasting Time Series, Second Edition. Cary, NC: Institute Inc, pp. 30-193.
• Juncal C, Javier, Gomez B,Fernando Perez de Gracia, 2006''Changes in the Dynamic Behavior of Emerging Market:Revisiting the effects of financial liberalization'',Emerging Market Review,Voll7,pp. 261-278.
• Mustafa M. Ali A, Shalini B. 2015,”Modeling and forecasting by using time series ARIMA models”. International Journal of Engineering Research & Technology (IJERT), vol. 4 ,no. 3.pp. 914-918.
• Pankratz A, 1983,”Forecasting with Univariate Box-Jenkins Models”,Jhon Wiley &Sons,New York.
• Patricia E. Gayner and Rickey C.kirkpatrick,1994,”Introduction To Time- Series Modeling And Forecasting In Business And Economics” Mc Graw-Hill .U.S.
• Poon, S.-H., Granger, C.W.J,2003, ''Forecasting financial market volatility: A review''. Economic Literature,vol. 41, no. 2,pp. 478–539.
• Ritab S. Al-Khouri and Moh’d M. Ajlouni, 2007,''Narrow Price Limit and Stock Price Volatility: Empirical Evidence from Amman Stock Exchange'' Research, Finance and Economics, vol8, pp. 163-180.
• Stephen.S and John.K,2007,“Forecasting volatility in the financial markets”,3rd edition, Elsevier, pp47. The box-jenkins method-NCSS Statistical Software NCSS.com,LLC https://en.wikipedia.org/wiki/Unit_root_test https://www.otexts.org/fpp/8/1