Contents of the volume

2019, Volume 72 - Issue 2

ISSN: 2499-8265
RSS feed citation: At RePEc
Publication date: 02 May 2019

THE FELDSTEIN-HORIOKA PUZZLE AND THE GLOBAL FINANCIAL CRISIS: EVIDENCE FROM SOUTH AFRICA USING ASYMMETRIC COINTEGRATION ANALYSIS

Andrew Phiri

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THE CAUSAL IMPACT OF STOCK MARKET DEVELOPMENT ON ECONOMIC DEVELOPMENT IN THE UAE: AN ASYMMETRIC APPROACH

Abdulnasser Hatemi-J

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THE IMPACT OF THE DIVIDEND TAX IN SOUTH AFRICA: A DYNAMIC CGE MODEL APPROACH

Lumengo Bonga-Bonga, Jean Luc Erero

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MODELING THE VOLATILITY OF EXCHANGE RATE CURRENCY USING GARCH MODEL

Chaido Dritsaki

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AN EMPIRICAL ANALYSIS FOR THE US OF THE EFFECTS OF GOVERNMENT BUDGET DEFICITS ON THE EX ANTE REAL INTEREST RATE YIELDS ON THIRTY-YEAR AND TWENTY-YEAR TREASURY BONDS

Richard J. Cebula, Maggie Foley

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ACKNOWLEDGEMENT TO REFEREES

Amedeo Amato

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Genoa Chamber of Commerce
Economia Internazionale / International Economics

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Corresponding author

Chaido DRITSAKI, Department of Accounting and Finance, Western Macedonia University of Applied Sciences, Kila, Kozani, Greece

Modeling the Volatility of Exchange Rate Currency using GARCH Model

Pages

209-230

Abstract

In this paper, we study GARCH models with their modifications in order to study the volatility of Euro/US dollar exchange rate. Given that there are ARCH effects on exchange rate returns Euro/US dollar, we estimated ARCH(p), GARCH(p,q) and EGARCH(p,q) including these effects on mean equation. These models were estimated with maximum likelihood method using the following distributions: normal, t-student and generalized error distribution. The log likelihood function was maximized using Marquardt’s algorithm (1963) in order to search for optimal parameter of all models. The results showed that ARIMA(0,0,1)-EGARCH(1,1) model with generalized error distribution is the best in order to describe exchange rate returns and also captures the leverage effect. Finally, for the forecasting of ARIMA(0,0,1)-EGARCH(1,1) model both the dynamic and static procedure is used. The static procedure provides better results on the forecasting rather than the dynamic.

JEL classification

C22, C32, C53

Keywords

Exchange Rate, Volatility, ARIMA-GARCH Models, Forecasting

Index

  1. Introduction
  2. Literature review
  3. Theoretical background
  4. Data
  5. Empirical results
  6. Forecasting
  7. Discussion and conclusion

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