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Studying The Volatility Of The Romanian Investment Funds With The Arch And Garch Models Using The "R" Software

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  • Antoniade Ciprian ALEXANDRU

    (Ecological University of Bucharest)

Abstract

In recent years more and more complex software packages and more specialized are used to model and to explain economic process. In this paper we present a study on Romanian’s investment funds volatility in ARCH and GARCH models using programming environment “R”. Representative elements of capital market developments we consider the largest investment funds with a large portfolio in energy field. Given the size of the fund it could be consider as an index for energy field in Romania. Also, with this study we want to highlight the advantages of using the package "rugarch" that can implement a set of ARCH.

Suggested Citation

  • Antoniade Ciprian ALEXANDRU, 2013. "Studying The Volatility Of The Romanian Investment Funds With The Arch And Garch Models Using The "R" Software," Working papers 03, Ecological University of Bucharest, Department of Economics.
  • Handle: RePEc:eub:wpaper:2013-03
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    References listed on IDEAS

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    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
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    3. Nelson, Daniel B & Cao, Charles Q, 1992. "Inequality Constraints in the Univariate GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 229-235, April.
    4. Cristiana Tudor, 2008. "Modelarea volatilitatii seriilor de timp prin modele GARCH simetrice," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 11(30), pages 183-208, (4).
    5. Caragea, Nicoleta & Alexandru, Ciprian Antoniade & Dobre, Ana Maria, 2012. "Bringing New Opportunities to Develop Statistical Software and Data Analysis Tools in Romania," MPRA Paper 48772, University Library of Munich, Germany.
    6. Taylor, Stephen J., 1987. "Forecasting the volatility of currency exchange rates," International Journal of Forecasting, Elsevier, vol. 3(1), pages 159-170.
    7. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
    8. 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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    Cited by:

    1. repec:ntu:ntugeo:vol2-iss1-14-028 is not listed on IDEAS
    2. Ciprian Alexandru & Nicoleta Caragea & Ana - Maria Dobre, 2014. "R-evolution in Time Series Analysis Software Applied on R-omanian Capital Market," Computational Methods in Social Sciences (CMSS), "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 2(1), pages 28-34, June.
    3. Simion Luciana & Antonia Mihai, 2022. "The Effects of the Political Turbulences on the Stock Exchange Indices," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 16(1), pages 1376-1389, August.

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    More about this item

    Keywords

    R packages; programming language; capital market; data analysis; regression models;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance

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