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Empirical Results of Modeling EUR/RON Exchange Rate using ARCH, GARCH, EGARCH, TARCH and PARCH models

Author

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  • Andreea – Cristina PETRICA

    (Bucharest University of Economic Studies)

  • Stelian STANCU

    (Bucharest University of Economic Studies)

Abstract

The aim of this study consists in examining the changes in the volatility of daily returns of EUR/RON exchange rate using on the one hand symmetric GARCH models (ARCH and GARCH) and on the other hand the asymmetric GARCH models (EGARCH, TARCH and PARCH), since the conditional variance is time-varying. The analysis takes into account daily quotations of EUR/RON exchange rate over the period of 04th January 1999 to 13th June 2016. Thus, we are modeling heteroscedasticity by applying different specifications of GARCH models followed by looking for significant parameters and low information criteria (minimum Akaike Information Criterion). All models are estimated using the maximum likelihood method under the assumption of several distributions of the innovation terms such as: Normal (Gaussian) distribution, Student’s t distribution, Generalized Error distribution (GED), Student’s with fixed df. Distribution, and GED with fixed parameter distribution. The predominant models turned out to be EGARCH and PARCH models, and the empirical results point out that the best model for estimating daily returns of EUR/RON exchange rate is EGARCH(2,1) with Asymmetric order 2 under the assumption of Student’s t distributed innovation terms. This can be explained by the fact that in case of EGARCH model, the restriction regarding the positivity of the conditional variance is automatically satisfied.

Suggested Citation

  • Andreea – Cristina PETRICA & Stelian STANCU, 2017. "Empirical Results of Modeling EUR/RON Exchange Rate using ARCH, GARCH, EGARCH, TARCH and PARCH models," Romanian Statistical Review, Romanian Statistical Review, vol. 65(1), pages 57-72, March.
    • Handle: RePEc:rsr:journl:v:65:y:2017:i:1:p:57-72
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    References listed on IDEAS

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    1. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    2. Muhammad Mansoor Baig & Waheed Aslam & Qaiser Malik & Muhammad Bilal, 2015. "Volatility of Stock Markets (an Analysis of South Asian and G8 Countries)," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 11(6), pages 58-70, December.
    3. Daniel Traian PELE, 2012. "Estimating the probability of stock market crashes for Bucharest Stock Exchange using stable distributions," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(7(572)), pages 5-12, July.
    4. Fat Codruta Maria & Dezsi Eva, 2011. "Exchange-Rates Forecasting: Exponential Smoothing Techniques And Arima Models," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 499-508, July.
    5. Dima Alberg & Haim Shalit & Rami Yosef, 2008. "Estimating stock market volatility using asymmetric GARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 18(15), pages 1201-1208.
    6. 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).
    7. Christoffersen, Peter, 2011. "Elements of Financial Risk Management," Elsevier Monographs, Elsevier, edition 2, number 9780123744487.
    8. Ghulam Ali, 2013. "EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH Models for Pathogens at Marine Recreational Sites," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 2(3), pages 1-6.
    9. 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. Ngo Thai Hung, 2021. "Volatility Behaviour of the Foreign Exchange Rate and Transmission Among Central and Eastern European Countries: Evidence from the EGARCH Model," Global Business Review, International Management Institute, vol. 22(1), pages 36-56, February.
    2. Roberta Muramatsu & Pedro Raffy Vartanian & Gabriel de Andrade Moraes, 2023. "A Behavioral Interpretation of Volatility Patterns in Brazilian Stock Market: Analysis of Pre and Post-COVID-19 Periods from 2019 to 2021," International Journal of Business and Management, Canadian Center of Science and Education, vol. 18(4), pages 1-24, August.
    3. Petrică Andreea-Cristina & Stancu Stelian, 2017. "The determinants of exchange rates and the movements of EUR/RON exchange rate via non-linear stochastic processes," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 11(1), pages 937-948, July.

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    More about this item

    Keywords

    Exchange Rate Volatility; Heteroscedasticity; Symmetric GARCH Models; Asymmetric GARCH Models; Fat-tails; Volatility Clustering; Leverage Effect;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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