Forecasting Insurance Sector Volatility In Amman Stock Exchange Using ARIMA Model | ||
| المجلة العربية للإدارة | ||
| Article 24, Volume 35, Issue 1, June 2015, Pages 467-481 PDF (561.79 K) | ||
| Document Type: بحوث باللغة الإنجلیزیة | ||
| DOI: 10.21608/aja.2015.18039 | ||
| Authors | ||
| M. M. H. Alkhazaleh1; Hussein Ali Al-Zeaud2 | ||
| 1Faculty of Finance , And Business Administration , Department of Finance and Banking , Al-Albyt University , The Hashemite Kingdom of Jordan | ||
| 2Department of Economic and Finance Administration, Faculty of Finance and Business Administration , Al-Albyt University, The Hashemite Kingdom of Jordan | ||
| Abstract | ||
| There are many forecasting techniques that can be used in the financial markets, the importance of forecasting is to able investment community to take their decisions about the future expectations, assets allocation, portfolio management, assets pricing and other benefits. This paper presents the Box-Jenkins model as one of the forecasting techniques, which we can use, in the financial time series. The main aim of this study is to predict volatility of Amman Stock Exchange as one of the emerging markets for the insurance sector. That is adopted to give an investment community a chance to plan about their buying or selling decisions for financial securities in the future. That is achieved by finding the tentative Autoregressive Integrated Moving Average (ARIMA) models that describe the equation of the forecasting sector. The data are accumulated weekly from the web site of Amman Stock Exchange using the historical indices in the period from1/1/2005-1/4/2010. We test the stationary by using unit root test which indicates that there is a stationary at level for insurance 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 model for this study for insurancesector is: From this proposed model we can get the forecasting equation for the insurance sector. Volatility has turned out to be a subject matter of massive significance to almost anyone who is concerned in the financial markets, even as a spectator. In this paper, we present the advantage of ARIMA model in forecasting financial time series data. Amman stock exchange (Jordan) in particular insurance data was selected as a tool to show the ability of ARIMA model in forecasting financial time series, experimentally. Then, the weekly data was used to compute the values of volatilities in this study. Finally, the best ARIMA model was determined. | ||
| References | ||
|
- Al-Shiab, Mohammad. (2006). ''The Predictability of ASE Using the Univariate ARIMA Model'', Economics Administration Science, Vol. 22, No. 2, pp.124-139.
- Al-zoubi Haitham, and Bashir. (2007). ''Market Efficiency, Time Varying Volatility and the Asymmetric Effect in ASE'', Managerial Finance, Vol. 33, No .7, 2007, pp.490-499.
- Bruce L. Bowerman; T. Richard O’Connell and Anne B. Koehler. (2005). Forecasting Time Series and Regression. U.S.A: Thomson Brooks/Cole.
- Chien-Chiang Lee, June-Dee Lee, Chi-Chuan Lee. (2009). “Stock Prices and the Efficient Market Hypothesis: Evidence from a Panel Stationary Test with Structural Breaks”, Japan and the world Economy, Vol. 22. No.1, pp. 49-58.
- Elena Kaloty; Storis K. Staikouras. (2009). “Modeling Stock Market Volatility”, In: Greg N. Gregoriou, Stock Market Volatility. New York: A Chapman & Hall Book.
- Engle and Granger. (1987). ''Co-integration and Error Correction: Representation, Estimation, and Testing'', Econometrica, Vol. 55, No. 2, pp. 251-276.
- Hamao. (1990). "Correlations in Price Changes and Volatility Across International Stock Markets", The Review of Financial Studies, Vol. 3, No. 2, pp. 281-307.
- G. Bekaret and C. R. Harvey. (1997). “Emerging Equity Market Volatility”, Financial Economics, Vol. 43, pp. 29-77.
- Hussein, A. Z. (2009). ''A Symmetric Volatility Phenomenon: An Application to Major European Countries'', International Management Review, Vol. 5, No. 1, pp. 37-49.
- Husein A. Z. (2007). ''Spillover Effect and Dynamic Condition Correlation between Major European USA and Jordan Stock Markets'', Unpublished Thesis, Jordan University.
- John C. B. and A. D. David. (2003). SAS for Forecasting Time Series. 2nd Ed. Cary, NC: Institute Inc, pp. 30-193.
- Juncal Cunado, Javier, Gomez Biscarri, Fernando Perez de Gracia. (2006). ''Changes in the Dynamic Behavior of Emerging Market: Revisiting the Effects of Financial Liberalization'', Emerging Market Review, Vol. l7, pp. 261-278.
- Pankratz A. (1983). Forecasting with Univariate Box-Jenkins Models. New York: John Wiley & Sons.
- Paolo Pasquariello. 92008). ''The Anatomy of Financial Crises: Evidence from the Emerging ADR Market'', International Economics, Vol. 76, pp. 193-207.
- Patrica E. Neor, Rickey C. kirkpatric. (1994). Introduction to Time Series Modeling and Forecasting Business Economics. McGraw-Hill.
- Poon, S. H. and C.W. J. Granger. (2003). ''Forecasting Financial Market Volatility: A Review''. Economic Literature, Vol. 41, No. 2, pp. 478–539.
- Reena A.; I. Carlo and L. Ricardo. (1995). Volatility in Emerging Markets. School of Business, Georgetown University, Washington, Edit Jonathan K, Financial Management Association, pp. 1-31.
- Ritab S. Al-Khouri and M. Ajlouni Moh’d. (2007). ''Narrow Price Limit and Stock Price Volatility: Empirical Evidence from Amman Stock Exchange'', Research, Finance and Economics, Vol. 8, pp. 163-180.
- Yiuman,Tse. (1998). “International Transmission of Information: Evidence from the Euroyen and Eurodollar Futures Markets”, International Money and Finance, Vol. 17, No. 6, pp. 909-929. | ||
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