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VOLATİLİTEDEKİ ÇOKLU YAPISAL KIRILMALARIN FİNANSAL RİSK YÖNETİMİ AÇISINDAN ÖNEMİNİN İNCELENMESİ

Year 2021, Volume: 13 Issue: 24, 86 - 110, 31.01.2021

Önder Büberkökü

https://doi.org/10.14784/marufacd.879194
Cited By: 1

Abstract

Bu çalışmada Dolar-TL kurunun finansal riskinin yönetiminde kullanılacak modellerin performansı üzerinde volatilitedeki çoklu yapısal kırılmaların olası etkileri incelenmiştir. Finansal risk yönetim modelleri olarak volatilite öngörü (volatlity forecasting) modelleri ile piyasa riski ölçüm modelleri esas alınmıştır. Volatilitedeki çoklu yapısal kırılmaların tespitinde ICSS algoritması ile Bai ve Perron (1998, 2003) testinden yararlanılmıştır. Zamanla değişen volatilite değerleri ise FIGARCH modeli ile tahmin edilmiştir. Çalışma bulguları, Dolar-TL kurunun volatilitesinin çoklu yapısal kırılmalar içerdiği fakat bu yapısal kırılmaların dikkate alınmasının risk yönetim modellerinin performansını artırmadığı sonucuna işaret etmektedir.

Keywords

Volatilite Çoklu yapısal kırılmalar Risk Yönetimi Döviz Kuru

References

  • ALOUI, Chaker ve HAMIDA, Hela ben. (2014). Modelling and Forecasting Value-at-Risk and Expected Shortfall for GCC Stock Markets: Do Long Memory, Structural Breaks, Asymmetry, and Fat-Tails Matter? The North American Journal of Economics and Finance, 29, 349-380.
  • BAI, Jushan ve PERRON, Pierre. (1998).Estimating and Testing Linear Models with Multiple Structural Changes. Econometrica, 66 (1), 47-78.
  • BAI, Jushan ve PERRON, Pierre. (2003).Computition and Analysis of Multiple Structural Change Models. Journal of Applied Econometrics, 18 (1), 1-22.
  • BAILLIE, Richard T., BOLLERSLEV, Tim ve MIKKELSEN, Hans Ole.(1996). Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 74, 13-30.
  • BELKHOUJA, Mustapha ve BOUTAHARY, Mohamed. (2011). Modeling Volatility with Time-Varying FIGARCH Models. Economic Modelling, 28 (3), 1106-1116.
  • BENTES, Sonia R. (2015). Forecasting Volatility in Gold Returns under the GARCH, IGARCH and FIGARCH Frameworks: New Evidence. Physica A, 438: 355–364.
  • BOLLERSLEV, Tim ve MIKKELSEN, Hans Ole. (1996). Modeling and Pricing Long Memory in Stock Market Volatility. Journal of Econometrics, 73 (1), 151-184.
  • BOLLERSLEV, Tim ve WOOLDRIDGE, Jeffrey M. (1992). Quasi Maximum Likelihood Estimation and Inference in Dynamic Models with Time Varying Covariances. Econometric Reviews 11 (2), 143-172.
  • BÜBERKÖKÜ, Onder ve KIZILDERE, Celal. (2017). BİST100 Endeksinin Volatilite Dinamiklerinin İncelenmesi. V Anadolu International Conference in Economics.11-13 Mayıs, Eskişehir,Türkiye. https://www. researchgate.net/publication /337007633_BIST100 _Endeksinin_Volatilite_ Ozelliklerinin_ Incelenmesi
  • CHARFEDDINE, Lanouar ve GUÉGAN, Dominique. (2012). Breaks or Long Memory Behavior: An Empirical Investigation. Physica A: Statistical Mechanics and its Applications, 391 (22), 5712-5726.
  • CHARFEDDINE, Lanouar. (2014). True or Spurious Long Memory in Volatility: Further Evidence on the Energy Futures Markets. Energy Policy,71, 76–93.
  • CUARESMA, Jesu´s Crespo, HLOUSKOVA, Jaroslava, KOSSMEIER Stephan ve OBERSTEINER, Michael. (2004). Forecasting Electricity Spot-Prices Using Linear Univariate Time Series. Aplied Energy, 77 (1), 87-106.
  • ÇEVİK, Emrah İsmail ve TOPALOĞLU, Gültekin. (2014). Volatilitede Uzun Hafıza ve Yapısal Kırılma: Borsa İstanbul Örneği. Balkan Sosyal Bilimler Dergisi, 3(6), 40-55.
  • DICKEY, David. A. ve FULLER, Wayne A. (1979). Distribution of the Estimators for Autoregressive Time Series with Unit Root. Journal of the American Statistical Association, 74, 427–431.
  • DIEBOLD, Francis X. ve MARIANO, Robert S. (1995). Comparing Predictive Accuracy. Journal of Business and Economic Statistics, 13, 253–63.
  • ENGLE, Robert F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50, 987-1007.
  • ENGLE, Robert F. ve MANGANELLI, Simone (2004). CAViaR: Conditional Autoregressive Value-at-Risk By Regression Quantiles. Journal of Business & Economic Statistics, 22 (4), 367–381.
  • EWING, Bradley T. ve MALIK, Farooq. (2013). Volatility Transmission Between Gold and Oil Futures Under Structural Breaks. International Review of Economics & Finance, 25,113-121.
  • GEWEKE, John ve PORTER-HUDAK, Susan. (1983). The Estimation and Application of Long Memory Time Series Models. Journal of Time Series Analysis 4, 4, 221–238, (1983).
  • HENDRICKS, Darryll. (1996). Evaluation of Value-at-Risk Modeling Using Historical Data. Economics Policy Review, 2(1), 39-70.
  • HWANG, Soosung, SATCHELL, Steve E. ve PEREIRA, Pedro L. Valls. (2007). How Persistent is Stock Return Volatility ? An Answer with Markov Regime Switching Stochastic Volatility Models. Journal of Business Finance & Accounting, 34 (5-6), 1002-1024.
  • INCLÁN, Carla ve TIAO, George C. (1994). Use of Cumulative Sums of Squares for Retrospective Detection Of Changes of Variance. Journal of The American Statistical Association, 89 (427) , 913–923.
  • JENSEN, Mark J. ve MAHEU, John M.(2014). Estimating A Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture. Journal of Econometrics, 178, 523–538.
  • JUNG, Robert C. ve MADERITSCH, R. (2014). Structural Breaks in Volatility Spillovers Between International Financial Markets : Contagion or Mere Interdependence ?. Journal of Banking and Finance, 47, 331-342.
  • KAECK, Andreas ve ALEXANDER, Carol.(2012).Volatility Dynamics for the S&P 500: Further Evidence from Non-Affine, Multi-Factor Jump Diffusions. Journal of Banking & Finance, 36, 3110–3121.
  • KANG, Sang Hoon, CHO, Hwan-Gue ve YOON, Seong-Min. (2009). Modeling Sudden Volatility Changes: Evidence from Japanese And Korean Stock Markets. Physica A: Statistical Mechanics and its Applications, 388 (17), 3543–3550
  • KANG, Sang Hoon, KANG, Sang-Mok ve YOON, Seong-Min. (2009). Forecasting Volatility of Crude Oil Markets. Energy Economics, 31, 119–125.
  • LARSSON, Karl ve NOSSMAN, Marcus. (2011). Jumps And Stochastic Volatility in Oil Prices: Time Series Evidence. Energy Economics, 33(3), 504-514.
  • LO, Andrew, W. (1991). Long Term Memory in Stock Market Prices. Econometrica, 59, 1279–1313.
  • LOUZIS, Dimitrios P., XANTHOPOULOS-SISINIS, Spyros ve REFENES, Apostolos P. (2014). Realized Volatility Models and Alternative Value-at-Risk Prediction Strategies. Economic Modelling, 40, 101–116.
  • LUX, Thomas, SEGNON, Mawuli ve GUPTA, Rangan. (2016). Forecasting Crude Oil Price Volatility and Value-at-Risk: Evidence From Historical and Recent Data. Energy Economics, 56: 117–133.
  • MENSI, Walid, HAMMOUDEH, Shawkat ve KANG, Sang Hoon. (2015). Precious Metals, Cereal, Oil and Stock Market Linkages and Portfolio Risk Management: Evidence from Saudi Arabia. Economic Modelling, 51, 340-358.
  • MENSI, Walid, HAMMUDEH, Shawkat ve YOON, Seong-Min (2014). Structural Breaks and Long Memory in Modeling and Forecasting Volatility of Foreign Exchange Markets of Oil Exporters: The Importance of Scheduled and Unscheduled News Annoucements. International Review of Economics and Finance, 30, 101-119.
  • MINCER, Jacob ve ZARNOWITZ, Victor. (1969). “The Evaluation of Economic Forecasts,” in J. Mincer, ed., Economic Forecasts and Expectations (New York: National Bureau of Economic Research).
  • ÖZDEMİR, Arife, VERGİLİ, Gizem ve ÇELİK, İsmail. (2018). Döviz Piyasalarının Etkinliği Üzerinde Uzun Hafızanın Rolü: Türk Döviz Piyasasında Ampirik Bir Araştırma. BDDK Bankacılık ve Finansal Piyasalar, 12 (1), 87-107.
  • PHILLIPS, Peter C.B. ve PERRON, Pierre. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75 (2), 335–346.
  • POOTER, Michiel ve DIJK, Dick.(2004).Testing for Changes in Volatility in Heteroskedastic Time Series- A Further Examination. Econometric Institute Report EI 2004-38, 1-39. file:///C:/Users/asus/Downloads/ei200438.pdf.
  • SANSÓ, Andreu, ARAGÓ, Vicent ve CARRION-I SILVESTRE, Josep Lluís. (2004). Testing for Change in the Unconditional Variance of Financial Time Series. Revista de Economía Financiera, 4, 32–53.
  • SHIROTA, Shinichiro, HIZU, Takayuki ve OMORI, Yasuhiro. (2014). Realized Stochastic Volatility with Leverage and Long Memory. Computational Statistics and Data Analysis, 76, 618–641.
  • TSE, Yiu Kuen. (2002). Residual-based Diagnostics for Conditional Heteroscedasticity Models. The Econometrics Journal, 5 (2), 358–374.
  • URAL, Mert ve KÜÇÜKÖZMEN, C. Coşkun. (2011). Analyzing the Dual Long Memory in Stock Market Returns. Ege Academic Review, 11, 19-28.
  • WANG, Ping. (2011). Pricing Currency Options with Support Vector Regression and Stochastic Volatility Model with Jumps. Expert Systems with Applications, 38(1), 1-7.
  • WANG, Ping. (2011). Pricing Currency Options with Support Vector Regression and StochasticVolatility Model with Jumps. Expert Systems with Applications, 38, 1–7.
  • YOUSSEF, Manel, BELKACEM, Lotfi ve MOKNI, Khaled. (2015). Value-at-Risk Estimation of Energy Commodities: A Long-Memory GARCH–EVT Approach. Energy Economics, 51, 99–110.

Year 2021, Volume: 13 Issue: 24, 86 - 110, 31.01.2021

Önder Büberkökü

https://doi.org/10.14784/marufacd.879194
Cited By: 1

Abstract

References

  • ALOUI, Chaker ve HAMIDA, Hela ben. (2014). Modelling and Forecasting Value-at-Risk and Expected Shortfall for GCC Stock Markets: Do Long Memory, Structural Breaks, Asymmetry, and Fat-Tails Matter? The North American Journal of Economics and Finance, 29, 349-380.
  • BAI, Jushan ve PERRON, Pierre. (1998).Estimating and Testing Linear Models with Multiple Structural Changes. Econometrica, 66 (1), 47-78.
  • BAI, Jushan ve PERRON, Pierre. (2003).Computition and Analysis of Multiple Structural Change Models. Journal of Applied Econometrics, 18 (1), 1-22.
  • BAILLIE, Richard T., BOLLERSLEV, Tim ve MIKKELSEN, Hans Ole.(1996). Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 74, 13-30.
  • BELKHOUJA, Mustapha ve BOUTAHARY, Mohamed. (2011). Modeling Volatility with Time-Varying FIGARCH Models. Economic Modelling, 28 (3), 1106-1116.
  • BENTES, Sonia R. (2015). Forecasting Volatility in Gold Returns under the GARCH, IGARCH and FIGARCH Frameworks: New Evidence. Physica A, 438: 355–364.
  • BOLLERSLEV, Tim ve MIKKELSEN, Hans Ole. (1996). Modeling and Pricing Long Memory in Stock Market Volatility. Journal of Econometrics, 73 (1), 151-184.
  • BOLLERSLEV, Tim ve WOOLDRIDGE, Jeffrey M. (1992). Quasi Maximum Likelihood Estimation and Inference in Dynamic Models with Time Varying Covariances. Econometric Reviews 11 (2), 143-172.
  • BÜBERKÖKÜ, Onder ve KIZILDERE, Celal. (2017). BİST100 Endeksinin Volatilite Dinamiklerinin İncelenmesi. V Anadolu International Conference in Economics.11-13 Mayıs, Eskişehir,Türkiye. https://www. researchgate.net/publication /337007633_BIST100 _Endeksinin_Volatilite_ Ozelliklerinin_ Incelenmesi
  • CHARFEDDINE, Lanouar ve GUÉGAN, Dominique. (2012). Breaks or Long Memory Behavior: An Empirical Investigation. Physica A: Statistical Mechanics and its Applications, 391 (22), 5712-5726.
  • CHARFEDDINE, Lanouar. (2014). True or Spurious Long Memory in Volatility: Further Evidence on the Energy Futures Markets. Energy Policy,71, 76–93.
  • CUARESMA, Jesu´s Crespo, HLOUSKOVA, Jaroslava, KOSSMEIER Stephan ve OBERSTEINER, Michael. (2004). Forecasting Electricity Spot-Prices Using Linear Univariate Time Series. Aplied Energy, 77 (1), 87-106.
  • ÇEVİK, Emrah İsmail ve TOPALOĞLU, Gültekin. (2014). Volatilitede Uzun Hafıza ve Yapısal Kırılma: Borsa İstanbul Örneği. Balkan Sosyal Bilimler Dergisi, 3(6), 40-55.
  • DICKEY, David. A. ve FULLER, Wayne A. (1979). Distribution of the Estimators for Autoregressive Time Series with Unit Root. Journal of the American Statistical Association, 74, 427–431.
  • DIEBOLD, Francis X. ve MARIANO, Robert S. (1995). Comparing Predictive Accuracy. Journal of Business and Economic Statistics, 13, 253–63.
  • ENGLE, Robert F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50, 987-1007.
  • ENGLE, Robert F. ve MANGANELLI, Simone (2004). CAViaR: Conditional Autoregressive Value-at-Risk By Regression Quantiles. Journal of Business & Economic Statistics, 22 (4), 367–381.
  • EWING, Bradley T. ve MALIK, Farooq. (2013). Volatility Transmission Between Gold and Oil Futures Under Structural Breaks. International Review of Economics & Finance, 25,113-121.
  • GEWEKE, John ve PORTER-HUDAK, Susan. (1983). The Estimation and Application of Long Memory Time Series Models. Journal of Time Series Analysis 4, 4, 221–238, (1983).
  • HENDRICKS, Darryll. (1996). Evaluation of Value-at-Risk Modeling Using Historical Data. Economics Policy Review, 2(1), 39-70.
  • HWANG, Soosung, SATCHELL, Steve E. ve PEREIRA, Pedro L. Valls. (2007). How Persistent is Stock Return Volatility ? An Answer with Markov Regime Switching Stochastic Volatility Models. Journal of Business Finance & Accounting, 34 (5-6), 1002-1024.
  • INCLÁN, Carla ve TIAO, George C. (1994). Use of Cumulative Sums of Squares for Retrospective Detection Of Changes of Variance. Journal of The American Statistical Association, 89 (427) , 913–923.
  • JENSEN, Mark J. ve MAHEU, John M.(2014). Estimating A Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture. Journal of Econometrics, 178, 523–538.
  • JUNG, Robert C. ve MADERITSCH, R. (2014). Structural Breaks in Volatility Spillovers Between International Financial Markets : Contagion or Mere Interdependence ?. Journal of Banking and Finance, 47, 331-342.
  • KAECK, Andreas ve ALEXANDER, Carol.(2012).Volatility Dynamics for the S&P 500: Further Evidence from Non-Affine, Multi-Factor Jump Diffusions. Journal of Banking & Finance, 36, 3110–3121.
  • KANG, Sang Hoon, CHO, Hwan-Gue ve YOON, Seong-Min. (2009). Modeling Sudden Volatility Changes: Evidence from Japanese And Korean Stock Markets. Physica A: Statistical Mechanics and its Applications, 388 (17), 3543–3550
  • KANG, Sang Hoon, KANG, Sang-Mok ve YOON, Seong-Min. (2009). Forecasting Volatility of Crude Oil Markets. Energy Economics, 31, 119–125.
  • LARSSON, Karl ve NOSSMAN, Marcus. (2011). Jumps And Stochastic Volatility in Oil Prices: Time Series Evidence. Energy Economics, 33(3), 504-514.
  • LO, Andrew, W. (1991). Long Term Memory in Stock Market Prices. Econometrica, 59, 1279–1313.
  • LOUZIS, Dimitrios P., XANTHOPOULOS-SISINIS, Spyros ve REFENES, Apostolos P. (2014). Realized Volatility Models and Alternative Value-at-Risk Prediction Strategies. Economic Modelling, 40, 101–116.
  • LUX, Thomas, SEGNON, Mawuli ve GUPTA, Rangan. (2016). Forecasting Crude Oil Price Volatility and Value-at-Risk: Evidence From Historical and Recent Data. Energy Economics, 56: 117–133.
  • MENSI, Walid, HAMMOUDEH, Shawkat ve KANG, Sang Hoon. (2015). Precious Metals, Cereal, Oil and Stock Market Linkages and Portfolio Risk Management: Evidence from Saudi Arabia. Economic Modelling, 51, 340-358.
  • MENSI, Walid, HAMMUDEH, Shawkat ve YOON, Seong-Min (2014). Structural Breaks and Long Memory in Modeling and Forecasting Volatility of Foreign Exchange Markets of Oil Exporters: The Importance of Scheduled and Unscheduled News Annoucements. International Review of Economics and Finance, 30, 101-119.
  • MINCER, Jacob ve ZARNOWITZ, Victor. (1969). “The Evaluation of Economic Forecasts,” in J. Mincer, ed., Economic Forecasts and Expectations (New York: National Bureau of Economic Research).
  • ÖZDEMİR, Arife, VERGİLİ, Gizem ve ÇELİK, İsmail. (2018). Döviz Piyasalarının Etkinliği Üzerinde Uzun Hafızanın Rolü: Türk Döviz Piyasasında Ampirik Bir Araştırma. BDDK Bankacılık ve Finansal Piyasalar, 12 (1), 87-107.
  • PHILLIPS, Peter C.B. ve PERRON, Pierre. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75 (2), 335–346.
  • POOTER, Michiel ve DIJK, Dick.(2004).Testing for Changes in Volatility in Heteroskedastic Time Series- A Further Examination. Econometric Institute Report EI 2004-38, 1-39. file:///C:/Users/asus/Downloads/ei200438.pdf.
  • SANSÓ, Andreu, ARAGÓ, Vicent ve CARRION-I SILVESTRE, Josep Lluís. (2004). Testing for Change in the Unconditional Variance of Financial Time Series. Revista de Economía Financiera, 4, 32–53.
  • SHIROTA, Shinichiro, HIZU, Takayuki ve OMORI, Yasuhiro. (2014). Realized Stochastic Volatility with Leverage and Long Memory. Computational Statistics and Data Analysis, 76, 618–641.
  • TSE, Yiu Kuen. (2002). Residual-based Diagnostics for Conditional Heteroscedasticity Models. The Econometrics Journal, 5 (2), 358–374.
  • URAL, Mert ve KÜÇÜKÖZMEN, C. Coşkun. (2011). Analyzing the Dual Long Memory in Stock Market Returns. Ege Academic Review, 11, 19-28.
  • WANG, Ping. (2011). Pricing Currency Options with Support Vector Regression and Stochastic Volatility Model with Jumps. Expert Systems with Applications, 38(1), 1-7.
  • WANG, Ping. (2011). Pricing Currency Options with Support Vector Regression and StochasticVolatility Model with Jumps. Expert Systems with Applications, 38, 1–7.
  • YOUSSEF, Manel, BELKACEM, Lotfi ve MOKNI, Khaled. (2015). Value-at-Risk Estimation of Energy Commodities: A Long-Memory GARCH–EVT Approach. Energy Economics, 51, 99–110.
There are 44 citations in total.

Details

Primary Language Turkish
Journal Section Makaleler
Authors

Önder Büberkökü

Publication Date January 31, 2021
Submission Date March 26, 2020
Published in Issue Year 2021 Volume: 13 Issue: 24

Cite

APA Büberkökü, Ö. (2021). VOLATİLİTEDEKİ ÇOKLU YAPISAL KIRILMALARIN FİNANSAL RİSK YÖNETİMİ AÇISINDAN ÖNEMİNİN İNCELENMESİ. Finansal Araştırmalar Ve Çalışmalar Dergisi, 13(24), 86-110. https://doi.org/10.14784/marufacd.879194

Cited By

YÜKSEK FREKANSLI TÜRK LİRASI GETİRİLERİNDE YAPISAL KIRILMALARIN BELİRLENMESİ
Journal of Research in Business
https://doi.org/10.54452/jrb.1458441