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GARCH yöntemleri kullanarak döviz kuru volatilitelerinin modellenmesi

Yıl 2021, Cilt 7, Sayı 1, 1 - 16, 15.02.2021
https://doi.org/10.30855/gjeb.2021.7.1.001

Öz

Bu çalışmada, genelleştirilmiş otoregresif koşullu değişen varyans (GARCH) modelleri kullanılarak 2005-2019 döneminde ABD Doları (USD) ve Euro (EUR) döviz kurlarının Türk lirası (TRY) karşısında volatilitelerinin modellenmesi amaçlanmaktadır. Kaldıraç etkisi ve oynaklık kümelenmesi gibi döviz kuru getirileri ile ilgili faktörleri ölçmek için hem simetrik hem de asimetrik modeller uygulanmıştır. Simetrik model olan GARCH (1,1) ve asimetrik modeller olan EGARCH (1,1), GJR-GARCH (1,1) ve PGARCH (1,1) modelleri her bir para biriminin TRY karşısında volatilitesini öngörmek için uygulanmıştır. Çalışma sonuçlarına göre, USD/TRY döviz kurlarının oynaklığını tahmin etmek için en uygun yöntem simetrik GARCH (1,1) ve asimetrik GJR-GARCH (1,1) modeller olarak belirlenmiştir. USD/TRY modelinde olduğu gibi EUR/TRY'de PGARCH (1,1) modelinin yanı sıra GARCH (1,1) ve GJR-GARCH (1,1) modelleri en uygun modellerdir. Bununla birlikte EUR/TRY döviz kurlarının oynaklığını tahmin etmek için PGARCH (1,1) modeli de anlamlı bir sonuç sunmaktadır. Tahmin oynaklığı ile ilgili olarak, Kök Ortalama Kare Hata (RMSE), Ortalama Mutlak Hata (MAE) ve Ortalama Mutlak Yüzde Hata (MAPE) testleri kullanılmıştır. Sonuçlara göre, statik GJR-GARCH (1,1) modelinin, hem USD hem EUR için daha yüksek bir volatilite tahmininde bulunabileceği ortaya konulmuştur.

Kaynakça

  • Abdalla, S.Z.S. (2012). Modeling exchange rate volatility using GARCH models: Empirical evidence from Arab countries. International Journal of Economics and Finance, 4(3), 216-229.
  • Adeleye, N. Eviews Time series videos, CrunchEconometrix Youtube channel. Retrieved on 18 December, 2019 from https://www.youtube.com/channel/UCK9hD254JKbCZ4Bf8Iz1s7g.
  • Ané, T. (2006). An analysis of the flexibility of Asymmetric Power GARCH models. Computational Statistics & Data Analysis, 51(2), 1293-1311.
  • Arachchi, K. (2018). Comparison of symmetric and asymmetric GARCH models: Application of exchange rate volatility. American Journal of Mathematics and Statistics, 8(5), 151-159.
  • Black, F. (1976). Studies of stock price volatility changes. Proceedings of the business and economics section of the American statistical association, 177-181.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
  • Bollerslev, T. (1990). Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. Review of Economics and Statistics, 72(3), 498-505.
  • Bosnjak, M., et al.(2016). Modeling exchange rate volatilities in Croatia. Ekonomski Vijesnik/Econviews-Review of Contemporary Business, Entrepreneurship and Economic Issues, 29(1), 81-94.
  • Central Bank of the Republic of Turkey TCMB website. Historical exchange rates in EVDS data central. Retrieved on 8 January, 2020 from https://www.tcmb.gov.tr/.
  • Dritsaki, C. (2019). Modeling the volatility of exchange rate currency using GARCH model, Economia Internazionale / International Economics, 72(2), 209-230.
  • Engle, R. and Patton, A. (2001). What good is a volatility model?. Quantitative Finance, 1(2), 237-245.
  • Engle, R.F., and Ng, V.K. (1993). Measuring and testing the impact of news on volatility. Journal of Finance, 48(5), 1749–1778.
  • Epaphra, M. (2017). Modeling exchange rate volatility: Application of the GARCH and EGARCH models. Journal of Mathematical Finance, 7(1), 121-143.
  • Friedman, D. and Stoddard, V. (1982). Short-run fluctuations in foreign exchange rates: evidence from the data 1973-1979. Journal of international Economics, 13(1-2), 171-186.
  • Ganbold, B., et al. (2017). Exchange rate volatility: A forecasting approach of using the ARCH family along with ARIMA SARIMA and semi-structural-SVAR in Turkey, Published in: Uluslararası Ekonomi, Finans ve Ekonometri Öğrenci Sempozyumu (EFEOS),(1), No. ISBN: 978-605-82381-1-4 (17 May 2017), 144-182.
  • Glosten, L. et al. (1993), Relationship between the expected value and the volatility of the nominal excess return on stocks, The Journal of Finance, 48 (5), 1779-1801.
  • Göksu, A. and Ergun, U. (2013). Applied econometrics with Eviews applications. First edition, International Burch University, Sarajevo.
  • Hsieh, D.A. (1989). Modeling heteroscedasticity in daily foreign-exchange rates. Journal of Business and Economic Statistics, 7(3), 307-317.
  • International Monetary Fund IMF website, The end of the Bretton Woods system (1972–81), Retrieved on 22 December, 2019 from https://www.imf.org/external/about/histend.htm.
  • Karuthedath, Samsudheen K. and Shanmugasundaram, G. (2012). Foreign exchange rate volatility of Indian Rupee/US Dollar. XI Capital Markets Conference, Indian Institute of Capital Markets (UTIICM), 21-22.
  • Nguyen T.K.D. (2018). Modelling exchange rate volatility using GARCH model: An empirical analysis for Vietnam. Econometrics for Financial Applications. Studies in Computational Intelligence, 760, 941-952.
  • Omari, C. et al. (2017). Modeling USD/KES exchange rate volatility using GARCH models. Journal of Economics and Finance (IOSR-JEF), 8(1), 15-26.
  • Stokes, H. et al (2004). General autoregressive conditional heteroscedastic (GARCH) modeling using SCAB34S-GARCH and SCA WorkBench, Scientific Computing Associates Corp. University of Illinois at Chicago.
  • Taylor, S. J. (1987). Forecasting the volatility of currency exchange rates. International Journal of Forecasting, 3(1), 159 – 70.
  • Vandeput, N., (2019). Forecast KPIs: RMSE, MAE, MAPE & Bias. Towards Data Science Website, Retrieved on 29 January, 2021 from https://towardsdatascience.com.
  • Wang, P. (2008). Financial econometrics. Financial econometrics: Second edition, Routledge Advanced Texts in Economics and Finance, London.

Modelling exchange rate volatility using GARCH models

Yıl 2021, Cilt 7, Sayı 1, 1 - 16, 15.02.2021
https://doi.org/10.30855/gjeb.2021.7.1.001

Öz

This paper aims to model the volatility of USD and EUR exchange rates against TRY for the period from January 2005 to December 2019 using the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models. Both symmetric and asymmetric models have been applied to measure factors that are related to the exchange rate returns such as leverage effect and volatility clustering. The symmetric GARCH (1,1) model and the asymmetric EGARCH (1,1), GJR-GARCH (1,1), and PGARCH (1,1) have been applied to each currency against TRY. The results of this paper conclude that the most adequate model for estimating volatility of the USD/TRY exchange rates are the symmetric GARCH (1,1) and asymmetric GJR-GARCH (1,1) models. Moreover in USD/TRY returns, GARCH (1,1) and GJR-GARCH (1,1) models are the most appropriate models along with PGARCH (1,1) in EUR/TRY as well. Regarding forecasting volatility, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) tests have been used. Based on the results, the static forecast of GJR-GARCH (1,1) is the best model in predicting the future pattern for both USD and EUR.

Kaynakça

  • Abdalla, S.Z.S. (2012). Modeling exchange rate volatility using GARCH models: Empirical evidence from Arab countries. International Journal of Economics and Finance, 4(3), 216-229.
  • Adeleye, N. Eviews Time series videos, CrunchEconometrix Youtube channel. Retrieved on 18 December, 2019 from https://www.youtube.com/channel/UCK9hD254JKbCZ4Bf8Iz1s7g.
  • Ané, T. (2006). An analysis of the flexibility of Asymmetric Power GARCH models. Computational Statistics & Data Analysis, 51(2), 1293-1311.
  • Arachchi, K. (2018). Comparison of symmetric and asymmetric GARCH models: Application of exchange rate volatility. American Journal of Mathematics and Statistics, 8(5), 151-159.
  • Black, F. (1976). Studies of stock price volatility changes. Proceedings of the business and economics section of the American statistical association, 177-181.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
  • Bollerslev, T. (1990). Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. Review of Economics and Statistics, 72(3), 498-505.
  • Bosnjak, M., et al.(2016). Modeling exchange rate volatilities in Croatia. Ekonomski Vijesnik/Econviews-Review of Contemporary Business, Entrepreneurship and Economic Issues, 29(1), 81-94.
  • Central Bank of the Republic of Turkey TCMB website. Historical exchange rates in EVDS data central. Retrieved on 8 January, 2020 from https://www.tcmb.gov.tr/.
  • Dritsaki, C. (2019). Modeling the volatility of exchange rate currency using GARCH model, Economia Internazionale / International Economics, 72(2), 209-230.
  • Engle, R. and Patton, A. (2001). What good is a volatility model?. Quantitative Finance, 1(2), 237-245.
  • Engle, R.F., and Ng, V.K. (1993). Measuring and testing the impact of news on volatility. Journal of Finance, 48(5), 1749–1778.
  • Epaphra, M. (2017). Modeling exchange rate volatility: Application of the GARCH and EGARCH models. Journal of Mathematical Finance, 7(1), 121-143.
  • Friedman, D. and Stoddard, V. (1982). Short-run fluctuations in foreign exchange rates: evidence from the data 1973-1979. Journal of international Economics, 13(1-2), 171-186.
  • Ganbold, B., et al. (2017). Exchange rate volatility: A forecasting approach of using the ARCH family along with ARIMA SARIMA and semi-structural-SVAR in Turkey, Published in: Uluslararası Ekonomi, Finans ve Ekonometri Öğrenci Sempozyumu (EFEOS),(1), No. ISBN: 978-605-82381-1-4 (17 May 2017), 144-182.
  • Glosten, L. et al. (1993), Relationship between the expected value and the volatility of the nominal excess return on stocks, The Journal of Finance, 48 (5), 1779-1801.
  • Göksu, A. and Ergun, U. (2013). Applied econometrics with Eviews applications. First edition, International Burch University, Sarajevo.
  • Hsieh, D.A. (1989). Modeling heteroscedasticity in daily foreign-exchange rates. Journal of Business and Economic Statistics, 7(3), 307-317.
  • International Monetary Fund IMF website, The end of the Bretton Woods system (1972–81), Retrieved on 22 December, 2019 from https://www.imf.org/external/about/histend.htm.
  • Karuthedath, Samsudheen K. and Shanmugasundaram, G. (2012). Foreign exchange rate volatility of Indian Rupee/US Dollar. XI Capital Markets Conference, Indian Institute of Capital Markets (UTIICM), 21-22.
  • Nguyen T.K.D. (2018). Modelling exchange rate volatility using GARCH model: An empirical analysis for Vietnam. Econometrics for Financial Applications. Studies in Computational Intelligence, 760, 941-952.
  • Omari, C. et al. (2017). Modeling USD/KES exchange rate volatility using GARCH models. Journal of Economics and Finance (IOSR-JEF), 8(1), 15-26.
  • Stokes, H. et al (2004). General autoregressive conditional heteroscedastic (GARCH) modeling using SCAB34S-GARCH and SCA WorkBench, Scientific Computing Associates Corp. University of Illinois at Chicago.
  • Taylor, S. J. (1987). Forecasting the volatility of currency exchange rates. International Journal of Forecasting, 3(1), 159 – 70.
  • Vandeput, N., (2019). Forecast KPIs: RMSE, MAE, MAPE & Bias. Towards Data Science Website, Retrieved on 29 January, 2021 from https://towardsdatascience.com.
  • Wang, P. (2008). Financial econometrics. Financial econometrics: Second edition, Routledge Advanced Texts in Economics and Finance, London.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme Finans
Bölüm Makaleler
Yazarlar

Basma ALMISSHAL (Sorumlu Yazar)
Karadeniz Technical University
0000-0001-7885-1217
Türkiye


Mustafa EMİR
KARADENİZ TEKNİK ÜNİVERSİTESİ
Türkiye

Destekleyen Kurum International Symposium on Business & Economics, Ankara-Turkey
Yayımlanma Tarihi 15 Şubat 2021
Yayınlandığı Sayı Yıl 2021, Cilt 7, Sayı 1

Kaynak Göster

APA Almısshal, B. & Emir, M. (2021). Modelling exchange rate volatility using GARCH models . Gazi İktisat ve İşletme Dergisi , 7 (1) , 1-16 . DOI: 10.30855/gjeb.2021.7.1.001
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Gazi İktisat ve İşletme Dergisi Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı ile lisanslanmıştır.