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EURO/TL KURUNA İLİŞKİN PİYASA RİSKİNİN ÖLÇÜLMESİ: RİSKE MARUZ DEĞER (VaR) YÖNTEMİ İLE BİR UYGULAMA

Year 2017, Volume: 14 Issue: 38, 212 - 239, 28.07.2017

Abstract

Riske Maruz Değer (Value at Risk, VaR) yöntemi,
piyasa riskinin ölçülmesinde yaygın olarak kullanılan bir yöntemdir.
VaR, bir varlığın ya da portföyün değerinde
belli bir dönemde, belli bir güven düzeyinde meydana gelebilecek maksimum değer
kaybını göstermektedir.
Çalışmanın amacı, döviz piyasasında öngörülecek VaR değerleri için uygun
dağılımın ve modelin belirlenmesidir. Çalışmada Ocak 2005-Aralık 2014 dönemlerine
ait EUR/TL günlük getiri serileri kullanılmıştır. VaR değerleri, normal, student-t
ve GED dağılımlarına dayanan simetrik ve asimetrik GARCH modelli
Varyans-Kovaryans yöntemi ile hesaplanmıştır.
Çalışmada %99 güven düzeyinde öngörülen VaR
değerlerinin doğruluğunu ve modellerin performansını test etmek amacıyla Kupiec
(1995) koşulsuz kapsama testi ve Christoffersen (1998) koşullu kapsama testleri
uygulanmıştır. Analiz sonuçları, Euro getiri serileri için student-t dağılımına
dayanan asimetrik modellerin daha doğru VaR öngörülerinde bulunduğunu
göstermiştir. 

References

  • Adepoju, A.A.,Yaya, O.S. & Ojo, O.O. (2013). Estimation of Garch Models for Nigerian Exchange Rates under Non-Gaussian Innovations. Journal of Economics and Sustainable Development, 4(3), 88-97.
  • Akan, B., Oktay, A. ve Tüzün, Y. (2003). Parametrik Riske Maruz Değer Yöntemi ve Türkiye Uygulaması. Bankacılar Dergisi, 14(45), 29-40.
  • Akçay, O. C., Alper, C. A. ve Karasulu, M. (1997). Currency Substitution and Exchange Rate Instability: The Turkish Case. European Economic Review, 41, 827-835.
  • Akgüç, Ö. (1998). Finansal Yönetim (7. baskı). İstanbul: Avcıol Basım Yayın.
  • Akhtekhane, S. & Mohammadi, P. (2012). Measuring Exchange Rate Fluctuations Risk Using the Value-at-Risk. Journal of Applied Finance and Banking, 2(3), 65 – 79.
  • Aktaş, M. (2008). Türkiye Piyasalarında Parametrik Riske Maruz Değer Modelinin Taşıdığı Riskler. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10(1), 243-265.
  • Asteriou, D. & Hall, S. (2007). Applied Econometrics. New York: Palgrave Macnillan.
  • Aysoy, C., Balaban, E., Koğar C. I. ve Özcan C. (1996). Daily Volatility in the Turkish Foreign Exchange Market. TCMB Tartışma Tebliğleri, No: 9625, http://www.tcmb.gov.tr/yeni/evds/teblig/96/9625 (Erişim Tarihi: 10.03.2015).
  • Baille, R. T., Bollerslev, T. & Mikkelsen, H. O. (1996). Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 74, 3-30.
  • Baillie R. & Bollerslev T. (1989). The Message in Daily Exchange Rates: A Conditional-Variance Tale. Journal of Business and Economic Statistics, 7(3), 297–305.
  • Baillie R.T. & Bollerslev T. (1991). Intra-day and Inter-Market Volatility in Foreign Exchange Rates. The Review of Economic Studies, 58(3), 565–585.
  • Balaban, E. (2004). Forecasting Exchange Rate Volatility. Working paper, http://ssrn.com/abstract=494482 (Erişim Tarihi: 20.04.2015).
  • Bartram, S. M. (2008). What Lies Beneath: Foreign Exchange Rate Exposure, Hedging and Cash Flows. Journal of Banking & Finance, 32, 1508–1521.
  • Begu, L., Spataru, S. & Marin, E. (2012). Investigating The Evolution of Ron/Eur Exchange Rate: The Choice of Appropriate Model. Journal of Social and Economic Statistics, 2(1), 23-39.
  • Beltratti, A. & Morana, C. (1999). Computing Value at Risk with High Frequency Data. Journal of Empirical Finance, 6, 431-455.
  • Best, P. (1998). Implementing Value at Risk. London: John Wiley & Sons, Inc.
  • Bohdalová, M. (2007). A Comparison of Value–at–Risk Methods for Measurementof the Financial Risk. E-Leader, 1-6.
  • Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307–327.
  • Bollerslev, T. (1987). A Conditional Heteroskedastic Time Series Model for Security Prices and Rates of Return Data. Review of Economics and Statistics, 69(3), 542–547.
  • Bozkuş, S. (2005). Risk Ölçümünde Alternatif Yaklasımlar: Riske Maruz Deger (VaR) ve Beklenen Kayıp (ES) Uygulamaları. DEÜ İİBF Dergisi, 20(2), 27-45.
  • Cera, G., Cera, E. & Lito, G. (2013). A GARCH Model Approach to Calculate The Value at Risk of Albanian Lek Exchange Rate. European Scientific Journal, 9(25), 250-260.
  • Cheng, W. H. & Hung, J. C. (2011). Skewness and Leptokurtosis in GARCH-typed VaR Estimation of Petroleum and Metal Asset Returns. Journal of Empirical Finance, 18, 160–173.
  • Choi, J. J.& Elyasiani, E. (1997). Derivative Exposure and The Interest Rate and Exchange Rate Risks of U.S. Banks. Journal of Financial Services Research, 12(2/3), 267-286.
  • Choudhry, M. (2006). An Introductionto Value-At-Risk (Fourth Edition). Great Britain: John Wiley & Sons, Ltd.
  • Christoffersen, P.F. (1998). Evaluating Interval Forecasts. International Economic Review, 39, 841–862.
  • Çağlayan, E. ve Dayıoğlu, T.(2009). Döviz Kuru Getiri Volatitesinin Koşullu Değişen Varyans Modelleri ile Öngörüsü. Ekonometri ve İstatistik Dergisi, 9, 1-16.
  • Çağlayan, E., Ün, T. ve Dayıoğlu, T.(2009). Modelling Exchange Rate Volatility in MIST Countries. International Journal of Business and Social Science, 4(12), 260-269.
  • Çatal, D. ve Albayrak, S. (2013). Riske Maruz Değer Hesabında Karışım Kopula Kullanımı: Dolar-Euro Portföyü. Journal of Yaşar University, 8(31), 5187-5202.
  • Damodaran, A. (2007). Strategic Risk Taking: A Framework for Risk Management. New Jersey: Pearson Prentice Hall.
  • Doukas, J., Patricia H. & Larry P. (2003). Exchange Rate Exposure at the Firm and Industry Level. Financial Markets. Institutions & Instruments, 12(5), 291-346.
  • El-Masry, A., Omneya A. & Amr A. (2007). Exchange Rate Exposure: Do Size and Foreign Operations Matter?. Managerial Finance, 33(9), 741-765.
  • Enders, W. (2009). Applied Econometric Times Series. New Jersey: John Wiley & Sons.
  • Engle R.F. & Gau Y.F. (1997). Conditional Volatility of Exchange Rates Under A Target Zone. University of California, San Diego, Department of Economics Discussion Paper Series 06.
  • Engle, R.F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of Variance of United Kingdom Inflation. Econometrica, 50(4), 987–1007.
  • Fan, Y., Zhang, Y. J., Tsai, H. T. & Wei, Y. M. (2008). Estimating Value at Risk of Crude Oil Price and its Spillover Effect Using The Ged-Garch Approach. Energy Economics, 30, 3156–3171.
  • Glosten, L. R., Jaganathan, R. & Runkle, D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. Journal of Finance, 48(5), 1779-1801.
  • Gozgor, G. & Nokay, P. (2011). Comparing Forecasting Performances Among Volatility Estimation Methods in The Pricing of European Type Currency Options of USD-TL and EURO-TL. Journal of Money, Investment and Banking, 19, 130–142.
  • Güner, B., Mitov, I. & Racheva-Yotova, B. (2013). Fat-Tailed Models for Risk Estimation. (Eds), Fabozzi, Frank J. Encyclopedia of Financial Models II New Jersey: John Wiley & Sons.
  • Gürsakal, S. (2007). Hisse Senedi ve Döviz Piyasası Risklerinin Riske Maruz Değer Yöntemi ile Karşılaştırılması. Uludağ Üniversitesi İİBF Dergisi, 26(2), 61-76.
  • Hill, R. C., Griffiths, W. E. & Lim, G. C. (2010). Principles of Econometrics. USA: John Wiley & Sons.
  • Hsieh, D. A. (1988), The Statistical Properties of Daily Foreign Exchange Rates: 1974-1983. Journal of International Economics, 24, 129-145.
  • Hsieh DA. (1989a). Modeling Heteroscedasticity in Daily Foreign-Exchange Rates. Journal of Business and Economic Statistics, 7(3), 307–317.
  • Hung, J. C., Lee, M. C. & Liu, H. C. (2008). Estimation of Value-at-Risk for Energy Commodities via Fat-Tailed Garch Model. Energy Economics, 30, 1173-1191.
  • Johnston, K. & Scott, E. (2000). GARCH Models and The Stochastic Process Underlying Exchange Rate Price Changes. Journal of Financial and Strategic Decisions, 13(2), 13–24.
  • Jorion, P. (1990). The Exchange-Rate Exposure of U.S. Multinationals. Journal of Business, 63(3), 331-345.
  • Jorion, P. (2000). Value at risk: A New Benchmark for Controlling Risk. New York: Mc Graw Hill Inc.
  • Kupiec, P. (1995). Techniques for Verifying the Accuracy of Risk Management Models. Journal of Derivatives, 3, 73–84.
  • Mazıbaş, M. (2005). İMKB Piyasalarındaki Volatilitenin Modellenmesi ve Öngörülmesi: Asimetrik GARCH Modelleri ile Bir Uygulama. VII. Ekonometri ve İstatistik Sempozyumu (26-27 Mayıs 2005), İstanbul. İstanbul Üniversitesi İktisat Fakültesi Ekonometri Bölümü.,s. 1-29.
  • Nargeleçekenler, M. (2004). Euro Kuru Satış Değerindeki Volatilitenin ARCH ve GARCH Modelleri ile Tahmini. İktisat Fakültesi Mecmuası, 54(2), 153-179.
  • Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometric, 59, 347-370
  • Obi, P. & Sil, S. (2013). VaR and Time-Varying Volatility: A Comparative Study of Three International Portfolios. Managerial Finance, Vol. 39 (7), 625 – 640.
  • Olowe, R. (2009). Modelling Naira/Dollar Exchange Rate Volatility: Application of Garch and Assymetric Models. International Review of Business Research Papers, 5, 377–398.
  • Özden, Ü. (2008). İMKB Bileşik 100 Endeksi Getiri Volatilitesinin Analizi. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 13, 339-350.
  • Özturk, K. (2006). Exchange Rate Volatility: The Case of Turkey. Yayımlanmamış Yüksek Lisans Tezi, Orta Doğu Teknik Üniversitesi, Ankara.
  • Rejeb, A., Salha, O & Rejeb, J. (2012). Value-at-Risk Analysis for the Tunisian Currency Market: A Comparative Study. International Journal of Economics and Financial Issues, 2(2), 110-125.
  • Sadeghi, M. & Shavvalpour, S. (2006). Energy Risk Management and Value at Risk Modeling. Energy Policy, 34, 3367–3373.
  • Sadorsky, P. (2006). Modeling and Forecasting Petroleum Futures Volatility. Energy Economics, 28, 467–488.
  • Simons, K. (1996). Value-at-Risk New Approaches to Risk Management. New England Economic Review, September/October, 1-13.
  • So, M. & Yu, P. (2006). Empirical Analysis of GARCH Models in Value at Risk Estimation. International Financial Markets. Institutions and Money, 16: 180–197.
  • Songül, H. (2010). Otogregresif Değişen Varyans Modelleri: Döviz Kurları Üzerine Uygulama. Uzmanlık Yeterlilik Tezi, Türkiye Cumhuriyeti Merkez Bankası, Ankara.
  • Soytaş, U. ve Ünal, Ö. S. (2010). Türkiye Döviz Piyasalarında Oynaklığın Öngörülmesi ve Risk Yönetimi Kapsamında Değerlendirilmesi. Yönetim ve Ekonomi: Celal Bayar Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 17(1), 121-145.
  • Vee, D., Gonpot, P. ve Sookia, N. (2011). Forecasting Volatility of USD/MUR Exchange Rate Using a GARCH (1, 1) Model with GED and Student’st Errors. University of Mauritius Research Journal, 17 (1), 1-14, 1-14.
  • Vilasuso, J. (2002). Forecasting Exchange Rate Volatility. Economics Letters. 76, 59-64.
  • Wang, J. ve Yang, M. (2009). Asymmetric Volatility in The Foreign Exchange Markets. Journal of International Financial Markets, Institutions and Money, 19, 597–615.
  • Wang, Y. ve Wu, C. (2012). Forecasting Energy Market Volatility Using Garch Models: Can Multivariate Models Beat Univariate Models. Energy Economics, 34(6), 2167–2181.
  • Zakoian, J.M. (1994). Threshold Heteroskedastic Models. Journal of Economic Dynamics and Control, 18, 931-55.
Year 2017, Volume: 14 Issue: 38, 212 - 239, 28.07.2017

Abstract

References

  • Adepoju, A.A.,Yaya, O.S. & Ojo, O.O. (2013). Estimation of Garch Models for Nigerian Exchange Rates under Non-Gaussian Innovations. Journal of Economics and Sustainable Development, 4(3), 88-97.
  • Akan, B., Oktay, A. ve Tüzün, Y. (2003). Parametrik Riske Maruz Değer Yöntemi ve Türkiye Uygulaması. Bankacılar Dergisi, 14(45), 29-40.
  • Akçay, O. C., Alper, C. A. ve Karasulu, M. (1997). Currency Substitution and Exchange Rate Instability: The Turkish Case. European Economic Review, 41, 827-835.
  • Akgüç, Ö. (1998). Finansal Yönetim (7. baskı). İstanbul: Avcıol Basım Yayın.
  • Akhtekhane, S. & Mohammadi, P. (2012). Measuring Exchange Rate Fluctuations Risk Using the Value-at-Risk. Journal of Applied Finance and Banking, 2(3), 65 – 79.
  • Aktaş, M. (2008). Türkiye Piyasalarında Parametrik Riske Maruz Değer Modelinin Taşıdığı Riskler. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10(1), 243-265.
  • Asteriou, D. & Hall, S. (2007). Applied Econometrics. New York: Palgrave Macnillan.
  • Aysoy, C., Balaban, E., Koğar C. I. ve Özcan C. (1996). Daily Volatility in the Turkish Foreign Exchange Market. TCMB Tartışma Tebliğleri, No: 9625, http://www.tcmb.gov.tr/yeni/evds/teblig/96/9625 (Erişim Tarihi: 10.03.2015).
  • Baille, R. T., Bollerslev, T. & Mikkelsen, H. O. (1996). Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 74, 3-30.
  • Baillie R. & Bollerslev T. (1989). The Message in Daily Exchange Rates: A Conditional-Variance Tale. Journal of Business and Economic Statistics, 7(3), 297–305.
  • Baillie R.T. & Bollerslev T. (1991). Intra-day and Inter-Market Volatility in Foreign Exchange Rates. The Review of Economic Studies, 58(3), 565–585.
  • Balaban, E. (2004). Forecasting Exchange Rate Volatility. Working paper, http://ssrn.com/abstract=494482 (Erişim Tarihi: 20.04.2015).
  • Bartram, S. M. (2008). What Lies Beneath: Foreign Exchange Rate Exposure, Hedging and Cash Flows. Journal of Banking & Finance, 32, 1508–1521.
  • Begu, L., Spataru, S. & Marin, E. (2012). Investigating The Evolution of Ron/Eur Exchange Rate: The Choice of Appropriate Model. Journal of Social and Economic Statistics, 2(1), 23-39.
  • Beltratti, A. & Morana, C. (1999). Computing Value at Risk with High Frequency Data. Journal of Empirical Finance, 6, 431-455.
  • Best, P. (1998). Implementing Value at Risk. London: John Wiley & Sons, Inc.
  • Bohdalová, M. (2007). A Comparison of Value–at–Risk Methods for Measurementof the Financial Risk. E-Leader, 1-6.
  • Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307–327.
  • Bollerslev, T. (1987). A Conditional Heteroskedastic Time Series Model for Security Prices and Rates of Return Data. Review of Economics and Statistics, 69(3), 542–547.
  • Bozkuş, S. (2005). Risk Ölçümünde Alternatif Yaklasımlar: Riske Maruz Deger (VaR) ve Beklenen Kayıp (ES) Uygulamaları. DEÜ İİBF Dergisi, 20(2), 27-45.
  • Cera, G., Cera, E. & Lito, G. (2013). A GARCH Model Approach to Calculate The Value at Risk of Albanian Lek Exchange Rate. European Scientific Journal, 9(25), 250-260.
  • Cheng, W. H. & Hung, J. C. (2011). Skewness and Leptokurtosis in GARCH-typed VaR Estimation of Petroleum and Metal Asset Returns. Journal of Empirical Finance, 18, 160–173.
  • Choi, J. J.& Elyasiani, E. (1997). Derivative Exposure and The Interest Rate and Exchange Rate Risks of U.S. Banks. Journal of Financial Services Research, 12(2/3), 267-286.
  • Choudhry, M. (2006). An Introductionto Value-At-Risk (Fourth Edition). Great Britain: John Wiley & Sons, Ltd.
  • Christoffersen, P.F. (1998). Evaluating Interval Forecasts. International Economic Review, 39, 841–862.
  • Çağlayan, E. ve Dayıoğlu, T.(2009). Döviz Kuru Getiri Volatitesinin Koşullu Değişen Varyans Modelleri ile Öngörüsü. Ekonometri ve İstatistik Dergisi, 9, 1-16.
  • Çağlayan, E., Ün, T. ve Dayıoğlu, T.(2009). Modelling Exchange Rate Volatility in MIST Countries. International Journal of Business and Social Science, 4(12), 260-269.
  • Çatal, D. ve Albayrak, S. (2013). Riske Maruz Değer Hesabında Karışım Kopula Kullanımı: Dolar-Euro Portföyü. Journal of Yaşar University, 8(31), 5187-5202.
  • Damodaran, A. (2007). Strategic Risk Taking: A Framework for Risk Management. New Jersey: Pearson Prentice Hall.
  • Doukas, J., Patricia H. & Larry P. (2003). Exchange Rate Exposure at the Firm and Industry Level. Financial Markets. Institutions & Instruments, 12(5), 291-346.
  • El-Masry, A., Omneya A. & Amr A. (2007). Exchange Rate Exposure: Do Size and Foreign Operations Matter?. Managerial Finance, 33(9), 741-765.
  • Enders, W. (2009). Applied Econometric Times Series. New Jersey: John Wiley & Sons.
  • Engle R.F. & Gau Y.F. (1997). Conditional Volatility of Exchange Rates Under A Target Zone. University of California, San Diego, Department of Economics Discussion Paper Series 06.
  • Engle, R.F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of Variance of United Kingdom Inflation. Econometrica, 50(4), 987–1007.
  • Fan, Y., Zhang, Y. J., Tsai, H. T. & Wei, Y. M. (2008). Estimating Value at Risk of Crude Oil Price and its Spillover Effect Using The Ged-Garch Approach. Energy Economics, 30, 3156–3171.
  • Glosten, L. R., Jaganathan, R. & Runkle, D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. Journal of Finance, 48(5), 1779-1801.
  • Gozgor, G. & Nokay, P. (2011). Comparing Forecasting Performances Among Volatility Estimation Methods in The Pricing of European Type Currency Options of USD-TL and EURO-TL. Journal of Money, Investment and Banking, 19, 130–142.
  • Güner, B., Mitov, I. & Racheva-Yotova, B. (2013). Fat-Tailed Models for Risk Estimation. (Eds), Fabozzi, Frank J. Encyclopedia of Financial Models II New Jersey: John Wiley & Sons.
  • Gürsakal, S. (2007). Hisse Senedi ve Döviz Piyasası Risklerinin Riske Maruz Değer Yöntemi ile Karşılaştırılması. Uludağ Üniversitesi İİBF Dergisi, 26(2), 61-76.
  • Hill, R. C., Griffiths, W. E. & Lim, G. C. (2010). Principles of Econometrics. USA: John Wiley & Sons.
  • Hsieh, D. A. (1988), The Statistical Properties of Daily Foreign Exchange Rates: 1974-1983. Journal of International Economics, 24, 129-145.
  • Hsieh DA. (1989a). Modeling Heteroscedasticity in Daily Foreign-Exchange Rates. Journal of Business and Economic Statistics, 7(3), 307–317.
  • Hung, J. C., Lee, M. C. & Liu, H. C. (2008). Estimation of Value-at-Risk for Energy Commodities via Fat-Tailed Garch Model. Energy Economics, 30, 1173-1191.
  • Johnston, K. & Scott, E. (2000). GARCH Models and The Stochastic Process Underlying Exchange Rate Price Changes. Journal of Financial and Strategic Decisions, 13(2), 13–24.
  • Jorion, P. (1990). The Exchange-Rate Exposure of U.S. Multinationals. Journal of Business, 63(3), 331-345.
  • Jorion, P. (2000). Value at risk: A New Benchmark for Controlling Risk. New York: Mc Graw Hill Inc.
  • Kupiec, P. (1995). Techniques for Verifying the Accuracy of Risk Management Models. Journal of Derivatives, 3, 73–84.
  • Mazıbaş, M. (2005). İMKB Piyasalarındaki Volatilitenin Modellenmesi ve Öngörülmesi: Asimetrik GARCH Modelleri ile Bir Uygulama. VII. Ekonometri ve İstatistik Sempozyumu (26-27 Mayıs 2005), İstanbul. İstanbul Üniversitesi İktisat Fakültesi Ekonometri Bölümü.,s. 1-29.
  • Nargeleçekenler, M. (2004). Euro Kuru Satış Değerindeki Volatilitenin ARCH ve GARCH Modelleri ile Tahmini. İktisat Fakültesi Mecmuası, 54(2), 153-179.
  • Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometric, 59, 347-370
  • Obi, P. & Sil, S. (2013). VaR and Time-Varying Volatility: A Comparative Study of Three International Portfolios. Managerial Finance, Vol. 39 (7), 625 – 640.
  • Olowe, R. (2009). Modelling Naira/Dollar Exchange Rate Volatility: Application of Garch and Assymetric Models. International Review of Business Research Papers, 5, 377–398.
  • Özden, Ü. (2008). İMKB Bileşik 100 Endeksi Getiri Volatilitesinin Analizi. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 13, 339-350.
  • Özturk, K. (2006). Exchange Rate Volatility: The Case of Turkey. Yayımlanmamış Yüksek Lisans Tezi, Orta Doğu Teknik Üniversitesi, Ankara.
  • Rejeb, A., Salha, O & Rejeb, J. (2012). Value-at-Risk Analysis for the Tunisian Currency Market: A Comparative Study. International Journal of Economics and Financial Issues, 2(2), 110-125.
  • Sadeghi, M. & Shavvalpour, S. (2006). Energy Risk Management and Value at Risk Modeling. Energy Policy, 34, 3367–3373.
  • Sadorsky, P. (2006). Modeling and Forecasting Petroleum Futures Volatility. Energy Economics, 28, 467–488.
  • Simons, K. (1996). Value-at-Risk New Approaches to Risk Management. New England Economic Review, September/October, 1-13.
  • So, M. & Yu, P. (2006). Empirical Analysis of GARCH Models in Value at Risk Estimation. International Financial Markets. Institutions and Money, 16: 180–197.
  • Songül, H. (2010). Otogregresif Değişen Varyans Modelleri: Döviz Kurları Üzerine Uygulama. Uzmanlık Yeterlilik Tezi, Türkiye Cumhuriyeti Merkez Bankası, Ankara.
  • Soytaş, U. ve Ünal, Ö. S. (2010). Türkiye Döviz Piyasalarında Oynaklığın Öngörülmesi ve Risk Yönetimi Kapsamında Değerlendirilmesi. Yönetim ve Ekonomi: Celal Bayar Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 17(1), 121-145.
  • Vee, D., Gonpot, P. ve Sookia, N. (2011). Forecasting Volatility of USD/MUR Exchange Rate Using a GARCH (1, 1) Model with GED and Student’st Errors. University of Mauritius Research Journal, 17 (1), 1-14, 1-14.
  • Vilasuso, J. (2002). Forecasting Exchange Rate Volatility. Economics Letters. 76, 59-64.
  • Wang, J. ve Yang, M. (2009). Asymmetric Volatility in The Foreign Exchange Markets. Journal of International Financial Markets, Institutions and Money, 19, 597–615.
  • Wang, Y. ve Wu, C. (2012). Forecasting Energy Market Volatility Using Garch Models: Can Multivariate Models Beat Univariate Models. Energy Economics, 34(6), 2167–2181.
  • Zakoian, J.M. (1994). Threshold Heteroskedastic Models. Journal of Economic Dynamics and Control, 18, 931-55.
There are 66 citations in total.

Details

Journal Section Araştırma Makaleleri
Authors

Samet Evci

Serkan Yılmaz Kandır

Publication Date July 28, 2017
Published in Issue Year 2017 Volume: 14 Issue: 38

Cite

APA Evci, S., & Kandır, S. Y. (2017). EURO/TL KURUNA İLİŞKİN PİYASA RİSKİNİN ÖLÇÜLMESİ: RİSKE MARUZ DEĞER (VaR) YÖNTEMİ İLE BİR UYGULAMA. Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 14(38), 212-239.
AMA Evci S, Kandır SY. EURO/TL KURUNA İLİŞKİN PİYASA RİSKİNİN ÖLÇÜLMESİ: RİSKE MARUZ DEĞER (VaR) YÖNTEMİ İLE BİR UYGULAMA. Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. July 2017;14(38):212-239.
Chicago Evci, Samet, and Serkan Yılmaz Kandır. “EURO/TL KURUNA İLİŞKİN PİYASA RİSKİNİN ÖLÇÜLMESİ: RİSKE MARUZ DEĞER (VaR) YÖNTEMİ İLE BİR UYGULAMA”. Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 14, no. 38 (July 2017): 212-39.
EndNote Evci S, Kandır SY (July 1, 2017) EURO/TL KURUNA İLİŞKİN PİYASA RİSKİNİN ÖLÇÜLMESİ: RİSKE MARUZ DEĞER (VaR) YÖNTEMİ İLE BİR UYGULAMA. Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 14 38 212–239.
IEEE S. Evci and S. Y. Kandır, “EURO/TL KURUNA İLİŞKİN PİYASA RİSKİNİN ÖLÇÜLMESİ: RİSKE MARUZ DEĞER (VaR) YÖNTEMİ İLE BİR UYGULAMA”, Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, vol. 14, no. 38, pp. 212–239, 2017.
ISNAD Evci, Samet - Kandır, Serkan Yılmaz. “EURO/TL KURUNA İLİŞKİN PİYASA RİSKİNİN ÖLÇÜLMESİ: RİSKE MARUZ DEĞER (VaR) YÖNTEMİ İLE BİR UYGULAMA”. Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 14/38 (July 2017), 212-239.
JAMA Evci S, Kandır SY. EURO/TL KURUNA İLİŞKİN PİYASA RİSKİNİN ÖLÇÜLMESİ: RİSKE MARUZ DEĞER (VaR) YÖNTEMİ İLE BİR UYGULAMA. Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. 2017;14:212–239.
MLA Evci, Samet and Serkan Yılmaz Kandır. “EURO/TL KURUNA İLİŞKİN PİYASA RİSKİNİN ÖLÇÜLMESİ: RİSKE MARUZ DEĞER (VaR) YÖNTEMİ İLE BİR UYGULAMA”. Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, vol. 14, no. 38, 2017, pp. 212-39.
Vancouver Evci S, Kandır SY. EURO/TL KURUNA İLİŞKİN PİYASA RİSKİNİN ÖLÇÜLMESİ: RİSKE MARUZ DEĞER (VaR) YÖNTEMİ İLE BİR UYGULAMA. Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. 2017;14(38):212-39.

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