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The determinants of exchange rates and the movements of EUR/RON exchange rate via non-linear stochastic processes

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Listed:
  • Petrică Andreea-Cristina

    (The Bucharest University of Economic Studies, Bucharest, Romania)

  • Stancu Stelian

    (The Bucharest University of Economic Studies, Bucharest, Romania)

Abstract

Modeling exchange rate volatility became an important topic for research debate starting with 1973, when many countries switched to floating exchange rate system. In this paper, we focus on the EUR/RON exchange rate both as an economic measure and present the implied economic links, and also as a financial investment and analyze its movements and fluctuations through two volatility stochastic processes: the Standard Generalized Autoregressive Conditionally Heteroscedastic Model (GARCH) and the Exponential Generalized Autoregressive Conditionally Heteroscedastic Model (EGARCH). The objective of the conditional variance processes is to capture dependency in the return series of the EUR/RON exchange rate. On this account, analyzing exchange rates could be seen as the input for economic decisions regarding Romanian macroeconomics - the exchange rates being influenced by many factors such as: interest rates, inflation, trading relationships with other countries (imports and exports), or investments - portfolio optimization, risk management, asset pricing. Therefore, we talk about political stability and economic performance of a country that represents a link between the two types of inputs mentioned above and influences both the macroeconomics and the investments. Based on time-varying volatility, we examine implied volatility of daily returns of EUR/RON exchange rate using the standard GARCH model and the asymmetric EGARCH model, whose parameters are estimated through the maximum likelihood method and the error terms follow two distributions (Normal and Student’s t). The empirical results show EGARCH(2,1) with Asymmetric order 2 and Student’s t error terms distribution performs better than all the estimated standard GARCH models (GARCH(1,1), GARCH(1,2), GARCH(2,1) and GARCH(2,2)). This conclusion is supported by the major advantage of the EGARCH model compared to the GARCH model which consists in allowing good and bad news having different impact on the volatility. The EGARCH model is able to model volatility clustering, persistence, as well as the leverage effect.

Suggested Citation

  • 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.
    • Handle: RePEc:vrs:poicbe:v:11:y:2017:i:1:p:937-948:n:99
    DOI: 10.1515/picbe-2017-0099
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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.
    2. 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.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. 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.
    5. Christophe Chorro & Dominique Guegan & Florian Ielpo & Hanjarivo Lalaharison, 2014. "Testing for Leverage Effect in Financial Returns," Documents de travail du Centre d'Economie de la Sorbonne 14022, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
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