Araştırma Makalesi
BibTex RIS Kaynak Göster

The Modelling of Exchange Rate Volatility Using Arch-Garch Models: The Case of Turkey

Yıl 2020, Cilt: 9 Sayı: 2, 834 - 843, 24.04.2020
https://doi.org/10.33206/mjss.541309

Öz

This study investigates the most appropriate method for modelling the volatility for nominal exchange rate by using the ARCH type models. The research covers the period of 2002-2017 of nominal exchange rate using daily data. It is observed that the volatility of nominal exchange rate has the ARCH effect and the most appropriate model for forecasting the volatility of nominal exchange rate is GARCH(1,2) because it has the lowest Akaike Information Criterion. Furthermore, during the crises and uncertain periods, the volatility of nominal exchange rate series increases and volatility clustering is observed, meaning high volatility tends to follow high volatility and it is true for vice versa. 

Kaynakça

  • Akgiray, V., (1989). Conditional heteroscedasticity in time series of stock returns: evidence and forecasts. The Journal of Business, 62, pp. 55-80.
  • Alberg, D., Shalit, H., and Yosef, R., (2008). Estimating stock market volatilityusing asymmetric GARCH models. Applied Financial Economics, 18 (15), pp. 1201-1208.
  • Baillie, R.T. and De Gennaro, R.P., (1990). Stock returns and volatility. Journal of Financial and Quantitative Analysis, 25. No.2.
  • Bollerslev, T., (1986). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, 31, pp. 307-327.
  • Cao, C.Q. and Tsay, R.S., (1992). Nonlinear time –series analysis of stock volatilities. Journal of Applied Econometrics, 7, pp. 165-185.
  • Dralle, B., (2011). Modelling volatility in financial time series. Master’s Thesis, Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg.
  • Engle, R.F., (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom Inflation. Econometrica, 50. 987-1007.
  • Engle, R.F., (1982). Statistical models for financial volatility. Financial Analysts Journal, 49, pp. 72-78.
  • Fama, E.F., (1965). The behavior of stock market prices. Journal of Business, 38(1), pp. 34-105.
  • Huang, D., Wang, H., and Yao, Q., 2008. Estimating GARCH models : when to use what? The Econometrics Journal, 11, pp. 27-38.
  • Karmakar, M., (2006). Stock market volatility in the long run, 1961-2005. Economic and Political Weekly, 41, No. 18, pp. 1796-1802.
  • Mandelbrot, B., (1963). The variation of certain speculative prices. Journal of Business. 26, pp. 394-419.
  • Oskooee, M.B. and Hegerty, S.W., 2007. Exchange rate volatility and trade flows: a review article. Journal of Economic Studies, 34.
  • Talke, I.S., (2003). Modelling volatility in financial time series data, Master’s Thesis, Mathematics, Statistics, and Information Technology, University of Kwazulu-Natal, Pietermaritzburg.

Arch-Garch Modelleri Kullanılarak Döviz Kurundaki Dalgalanmanın Modellenmesi: Türkiye Örneği

Yıl 2020, Cilt: 9 Sayı: 2, 834 - 843, 24.04.2020
https://doi.org/10.33206/mjss.541309

Öz

Bu çalışma, ARCH tipi modelleri kullanarak nominal döviz kurundaki oynaklığı modelleyen en uygun metodu bulmaya çalışmaktadır. Araştırma verisi 2002-2017 yılları için günlük verileri kapsamaktadır. Döviz kurundaki dalgalanmanın ARCH etkisine sahip olduğu ve nominal döviz kurunu tahminde en uygun modelin en düşük Akaike bilgi kriterine sahip olmasından dolayı GARCH(1,2) olduğu bulunmuştur. Ayrıca, kriz ve belirsizlik dönemlerinde nominal döviz kuru serisinde artışlar olduğu ve yüksek dalgalanmayı yüksek dalgalanmanın takip ettiği kümelenmenin görüldüğü gözlemlenmiştir.

Kaynakça

  • Akgiray, V., (1989). Conditional heteroscedasticity in time series of stock returns: evidence and forecasts. The Journal of Business, 62, pp. 55-80.
  • Alberg, D., Shalit, H., and Yosef, R., (2008). Estimating stock market volatilityusing asymmetric GARCH models. Applied Financial Economics, 18 (15), pp. 1201-1208.
  • Baillie, R.T. and De Gennaro, R.P., (1990). Stock returns and volatility. Journal of Financial and Quantitative Analysis, 25. No.2.
  • Bollerslev, T., (1986). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, 31, pp. 307-327.
  • Cao, C.Q. and Tsay, R.S., (1992). Nonlinear time –series analysis of stock volatilities. Journal of Applied Econometrics, 7, pp. 165-185.
  • Dralle, B., (2011). Modelling volatility in financial time series. Master’s Thesis, Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg.
  • Engle, R.F., (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom Inflation. Econometrica, 50. 987-1007.
  • Engle, R.F., (1982). Statistical models for financial volatility. Financial Analysts Journal, 49, pp. 72-78.
  • Fama, E.F., (1965). The behavior of stock market prices. Journal of Business, 38(1), pp. 34-105.
  • Huang, D., Wang, H., and Yao, Q., 2008. Estimating GARCH models : when to use what? The Econometrics Journal, 11, pp. 27-38.
  • Karmakar, M., (2006). Stock market volatility in the long run, 1961-2005. Economic and Political Weekly, 41, No. 18, pp. 1796-1802.
  • Mandelbrot, B., (1963). The variation of certain speculative prices. Journal of Business. 26, pp. 394-419.
  • Oskooee, M.B. and Hegerty, S.W., 2007. Exchange rate volatility and trade flows: a review article. Journal of Economic Studies, 34.
  • Talke, I.S., (2003). Modelling volatility in financial time series data, Master’s Thesis, Mathematics, Statistics, and Information Technology, University of Kwazulu-Natal, Pietermaritzburg.
Toplam 14 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makalesi
Yazarlar

Fuat Sekmen 0000-0002-8854-8737

Galip Afşin Ravanoğlu 0000-0001-5485-4384

Yayımlanma Tarihi 24 Nisan 2020
Gönderilme Tarihi 18 Mart 2019
Yayımlandığı Sayı Yıl 2020 Cilt: 9 Sayı: 2

Kaynak Göster

APA Sekmen, F., & Ravanoğlu, G. A. (2020). The Modelling of Exchange Rate Volatility Using Arch-Garch Models: The Case of Turkey. MANAS Sosyal Araştırmalar Dergisi, 9(2), 834-843. https://doi.org/10.33206/mjss.541309
AMA Sekmen F, Ravanoğlu GA. The Modelling of Exchange Rate Volatility Using Arch-Garch Models: The Case of Turkey. MJSS. Nisan 2020;9(2):834-843. doi:10.33206/mjss.541309
Chicago Sekmen, Fuat, ve Galip Afşin Ravanoğlu. “The Modelling of Exchange Rate Volatility Using Arch-Garch Models: The Case of Turkey”. MANAS Sosyal Araştırmalar Dergisi 9, sy. 2 (Nisan 2020): 834-43. https://doi.org/10.33206/mjss.541309.
EndNote Sekmen F, Ravanoğlu GA (01 Nisan 2020) The Modelling of Exchange Rate Volatility Using Arch-Garch Models: The Case of Turkey. MANAS Sosyal Araştırmalar Dergisi 9 2 834–843.
IEEE F. Sekmen ve G. A. Ravanoğlu, “The Modelling of Exchange Rate Volatility Using Arch-Garch Models: The Case of Turkey”, MJSS, c. 9, sy. 2, ss. 834–843, 2020, doi: 10.33206/mjss.541309.
ISNAD Sekmen, Fuat - Ravanoğlu, Galip Afşin. “The Modelling of Exchange Rate Volatility Using Arch-Garch Models: The Case of Turkey”. MANAS Sosyal Araştırmalar Dergisi 9/2 (Nisan 2020), 834-843. https://doi.org/10.33206/mjss.541309.
JAMA Sekmen F, Ravanoğlu GA. The Modelling of Exchange Rate Volatility Using Arch-Garch Models: The Case of Turkey. MJSS. 2020;9:834–843.
MLA Sekmen, Fuat ve Galip Afşin Ravanoğlu. “The Modelling of Exchange Rate Volatility Using Arch-Garch Models: The Case of Turkey”. MANAS Sosyal Araştırmalar Dergisi, c. 9, sy. 2, 2020, ss. 834-43, doi:10.33206/mjss.541309.
Vancouver Sekmen F, Ravanoğlu GA. The Modelling of Exchange Rate Volatility Using Arch-Garch Models: The Case of Turkey. MJSS. 2020;9(2):834-43.

MANAS Journal of Social Studies (MANAS Sosyal Araştırmalar Dergisi)     


16155