EN
TR
VALUE AT RISK IN EMERGING CURRENCY MARKETS:A CASE STUDY OF TURKISH LIRA
Abstract
Daily VaR numbers have been calculated by using EWMA and GARCH models for the seven currencies. The outcome is GARCH provides slightly more accurate analysis than EWMA. The results are satisfactory for forecasting volatility at 95% and 99% confidence level. These two methods enhance the quality of the VaR models. Interestingly, VaR calculations have predicted the April 1994 and February 2001 devaluation in Turkey. It is also observed that the Turkish Lira’s volatility was low during the crawling peg period. However, after February 2001 free floating period caused the volatility to increase. Therefore, volatility forecasts tend to remain high in the post crises period
Keywords
Kaynakça
- Alexander, C. (1996); Risk Management and Analysis, John Wiley & Son Ltd., London.
- Akaike, H. (1973); “Information Theory and the Extension of the Maximum Likelihood Principle” in 2nd International Syposium on Information Theory, B.N. Petrov and F. Csaki, eds., Budapest.
- Avinash, P. (1998); Event Risk Indicator (ERI), J.P. Morgan Technical Document.
- Baillie, R., T. Bollerslev ve H.O. Mikkelsen (1996); “Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity” Journal of Econometrics, Vol.74, No.1, pp. 3-30.
- Barone, A. G. ve G. Kostas (2002); “Nonparametric VaR Techniques: Myths And Realities”, http://www.gloriamundi.org/var/pub/gbkgfhs.pdf, 08.05.2002.
- Best, P. (1999); Implementing Value At Risk, Bidles Ltd., London.
- Bilson, J.F.O. (1999); “Value at Risk for Emerging Market Currencies” Lecture Notes for a course on: Trading, Investment and Risk Management, Summer School in Econometrics, Italy, June, pp.15-20.
- Bollerslev, T. (1986). “Generalised Autoregresive Conditional Heteroscedasticity” Journal of Econometrics 31, pp.307-327.
Ayrıntılar
Birincil Dil
İngilizce
Konular
-
Bölüm
-
Yayımlanma Tarihi
1 Haziran 2005
Gönderilme Tarihi
-
Kabul Tarihi
-
Yayımlandığı Sayı
Yıl 2005 Cilt: 1 Sayı: 1
