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Modeling the Volatility and Forecasting the Stock Price of the German Stock Index (DAX30)

Author

Listed:
  • Tristan Nguyen

    (Fresenius University, Munich, Germany)

  • Thi Thanh Mai Bui

    (Berlin School of Economics and Law, Berlin, Germany)

Abstract

To analyze the factors affecting the price volatility of stocks, microeconomic and macroeco-nomic elements must be considered. This paper selects elements that are appropriate with the daily data of stock prices to build the GARCH family models. External variables such as global oil prices, consumer price index, short interest rates and the exchange rate between the United States Dollar and the Euro are examined. The GARCH models are developed in order to analyze and forecast the stock price of the companies in the DAX 30, which is Germany’s most important stock exchange barometer. The volatility of the residual of the mean function is the important key point in the GARCH approach. This financial application can be extend-ed to analyze other specific shares or stock indexes in any stock market in the world. There-fore, it is necessary to understand the operating procedures of their pricing for risk manage-ment, profitability strategies, cost minimization and, in addition, to construct the optimal port-folio depending on investor’s preferences.

Suggested Citation

  • Tristan Nguyen & Thi Thanh Mai Bui, 2018. "Modeling the Volatility and Forecasting the Stock Price of the German Stock Index (DAX30)," International Journal of Economics and Financial Research, Academic Research Publishing Group, vol. 4(4), pages 72-92, 04-2018.
    • Handle: RePEc:arp:ijefrr:2018:p:72-92
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    References listed on IDEAS

    as
    1. Fátima Irina VILLALBA PADILLA & Miguel FLORES-ORTEGA, 2013. "Forecasting the variance and return of Mexican financial series with symmetric GARCH models," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(580)), pages 61-82, March.
    2. Matei, Marius, 2009. "Assessing Volatility Forecasting Models: Why GARCH Models Take the Lead," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 42-65, December.
    3. repec:bla:jfinan:v:44:y:1989:i:5:p:1115-53 is not listed on IDEAS
    4. Nasseh, Alireza & Strauss, Jack, 2000. "Stock prices and domestic and international macroeconomic activity: a cointegration approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 40(2), pages 229-245.
    5. repec:agr:journl:v:3(580):y:2013:i:3(580):p:61-82 is not listed on IDEAS
    6. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    7. Sanjay Sehgal & Radhika Kapur, 2012. "Relationship between Oil Price Shocks and Stock Market Performance: Evidence for Select Global Equity Markets," Vision, , vol. 16(2), pages 81-92, June.
    8. Engle, Robert F. (ed.), 1995. "ARCH: Selected Readings," OUP Catalogue, Oxford University Press, number 9780198774327.
    9. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    10. Blattberg, Robert C & Gonedes, Nicholas J, 1974. "A Comparison of the Stable and Student Distributions as Statistical Models for Stock Prices," The Journal of Business, University of Chicago Press, vol. 47(2), pages 244-280, April.
    11. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    12. Robert Engle, 2001. "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 157-168, Fall.
    13. Brad Baldauf & G. J. Santoni, 1991. "Stock price volatility: Some evidence from an ARCH model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 11(2), pages 191-200, April.
    14. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    15. Granger, Clive W. J., 1992. "Forecasting stock market prices: Lessons for forecasters," International Journal of Forecasting, Elsevier, vol. 8(1), pages 3-13, June.
    16. Phylaktis, Kate & Ravazzolo, Fabiola, 2005. "Stock prices and exchange rate dynamics," Journal of International Money and Finance, Elsevier, vol. 24(7), pages 1031-1053, November.
    17. Fleming, Jeff & Kirby, Chris & Ostdiek, Barbara, 1998. "Information and volatility linkages in the stock, bond, and money markets," Journal of Financial Economics, Elsevier, vol. 49(1), pages 111-137, July.
    18. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, vol. 61(1), pages 43-76, July.
    19. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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