BibTex RIS Kaynak Göster

BORSA İSTANBUL-100 ENDEKSİNİN YİYECEK-İÇECEK SEKTÖRÜ ENDEKSİ VE TEKNOLOJİ SEKTÖRÜ ENDEKSİ KARŞISINDAKİ VOLATİLİTESİ

Yıl 2014, Cilt: 9 Sayı: 2, 21 - 30, 01.03.2014

Öz

Bu çalışmada, borsadaki, seçilmiş günlük veriler kullanılarak ARCH-sınıfı modellerin tahminlemesi yapılmıştır. Borsa İstanbul-100 Endeksi kar rakamlarının Yiyecek-İçecek Sektörü Endeksi ve Teknoloji Sektörü Endeksi kar rakamları karşısındaki volatilitesi, 2003-2012 yılları arasındaki 2608 gözlem değeri için volatilite denkleminin tahmin edilmesi suretiyle analiz edilmiştir. Bağımsız değişken olarak Yiyecek-İçecek Sektörü ve Teknoloji Sektörü Endeksleri`nin kullanılmasının sebebi, yiyecek-içecek sektörünün, tüm dünya üzerinde, bazı yönlerden zorunlu mal olması özelliğinden ötürü ekonomideki dalgalanmalardan az etkilenmesi beklenen bir sektör olması; teknoloji sektörünün ise tüm dünyanın yanı sıra Türkiye`de de, küreselleşme ve akıllı cihazlardan ötürü günden güne gelişen bir sektör olmasıdır. Yapılan analizler sonucunda, hem Yiyecek-İçecek Sektörü Endeksi kar volatilitesinin, hem de Teknoloji Sektörü Endeksi kar volatilitesinin, Borsa İstanbul-100 Endeksi kar volatilitesi ile bir ilişkisi olduğu saptanmıştır.

Kaynakça

  • Bollerslev, T., (1986). “Generalized Autoregressive Conditional Heteroscdasticity.” Journal of Econometrics 31, 307-327, NorthHolland.
  • Bollerslev, T. and Ghsyels, E., (1996). “PeriodicAutoregressive Conditional Heteroscedasticity.” Journal of Business and EconomicStatistic 14, 139–151.
  • Brailsford, T.J. and Faff, R.W., (1993). “Modelling Australian stock market volatility”, Australian Journal of Management 18,pp. 109-132.
  • Coulson, N. and Robins, R., (1985). “Aggregate Economic Activity and the Variance of Inflation: Another Look.” Economics Letters 17, 71–75.
  • Cragg, J., (1982). “Estimation and Testing in Testing in Time Series Regression Models with Heteroscedastic Disturbances.” Journal of Econometrics 20, 135–157.
  • Cromwell, J.B, Labys, W.C., and Terraza, M., (1994). “Univariate Tests for Time Series Models.” Sage University Paper series on Quantitative Applications in the Social Sciences, 07-099, Thousand Oaks, CA: Sage.
  • Davidson, R. and Mackinnon, J.G., (1993). “Estimation and Inference in Econometrics.” New York: Oxford University Press. Diamond, Peter A., (1967). "The Role of a Stock Market in a General Equilibrium Model with Technological Uncertainty". American Economic Review57, 759–776.
  • Ding, Z., Granger, C.W.J., and Engle, R.F., (1993). “A Long Memory Property of Stock Market Returns and A New Model.” Journal of Empirical Finance1, 83–106.
  • Domowitz, I. and Hakkio, C., (1985). “Conditional Variance and the Risk Premium in the Foreign Exchange Market.” Journal of International Economics 19, 47–66.
  • Engle, R., Hendry, D., and Trumble D., (1985). “Small Sample Properties of ARCH Estimators and Tests.” Canadian Journal of Economics18, 66–93.
  • Engle, R., Lilen, D., and Robins, R., (1987). “Estimating Time Varying Risk Premia in the Term Structure: The ARCH-M Model.” Econometrica 55, 391–407.
  • Engle, Robert F., (1982). “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation.” Econometrica 50, 987-1007.
  • Engle, R.F., (1983). “Estimates of the Variances of U.S. Inflation Based on the ARCH Model.” Journal of Money, Credit and Banking 15, 286-301.
  • Fleming, J., Kirby, C., and Ostdiek, B., (2006). “Stochastic Volatility, Trading Volume, and The Daily Flow of Information.” Journal of Business 79, 1551–1590.
  • Gilson, Ronald J. and Black, Bernard S., (1998). "Venture Capital and the Structure of Capital Markets: Banks Versus Stock Markets". Journal of Financial Economics47, 243–277.DOI:10.2139, SSRN: 46909.
  • Glosten, L.R., Jagannathan, R., and Runkle, D.E., (1993). “On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks.” Journal of Finance48, 1779–1801.
  • Greene, W.H., (2003). “Econometric Analysis.” Fifth Edition, Prentice Hall, New Jersey.
  • Gökçe, A., (2001). “İstanbul Menkul Kıymetler Borsası Getirilerindeki Volatilitenin ARCH Teknikleri ile Ölçülmesi.” G.Ü. İ.İ.B.F. Dergisi, 35-58.
  • Harvey, A.C., (1991). “The Econometric Analysis of Time Series.” Second Edition, The MIT Press, Cambridge.
  • Jane, T. and Ding, C.G., (2009). “On the multivariate EGARCH model.” Applied Economics Letters 16(17), 1757-1761.
  • Lee, J. and Brorsen, B.W., (1997). “A Non-Nested Test of GARCH vs. EGARCH Models.” Applied Economics Letters 4(12), 765-768.
  • Mckenzie, M.D., Mitchell, H., Brooks, R.D., and Faff, R.W., (2001). “Power ARCH Modelling of Commodity Futures Data on the London Metal Exchange.” The European Journal of Finance 7(1), 22Mcmillan, D.G. and Speight, A.E.H., (2003). “Asymmetric Volatility Dynamics in High Frequency FTSE-100 Stock Index Futures.” Applied Financial Economics 13(8), 599-607.ISSN: 09603107 (Print), 1466-4305(Online).
  • Mitchell, H. and Mckenzie, M.D., (2008). “A comparison of alternative techniques for selecting an optimum ARCH model.” Journal of Statistical Computation and Simulation 78(1), 51-67. Nelson, D.B., (1991). “Conditional Heteroscedasticity in Asset Returns: A New Approach.” Econometrica59, 347–70.
  • Park, B., (2002). “Asymmetric Volatility of Exchange Rate Returns under the EMS: Some Evidence from Quantile Regression Approach for TGarch Models.” International Economic Journal 16(1), 105-125.
  • Schwert, G.W., (1990). “Stock market volatility.” Financial Analysts Journal 46(3),pp: 23-34.
  • Silber, Kenneth, (2009). "The Earliest Securities Markets". Research Magazine32 (2), 44–47.
  • Su, Y.C., Huang, H.C., and Lin, Y.J., (2011). “GJR-GARCH Model in Value-at-Risk of Financial Holdings.” Applied Financial Economics 21(24), 1819-1829.
  • Wang, R., and Chen, J.J., (2012). “ARCH effects, trading volume and the information flow interpretation: empirical evidence from the Chinese stock markets.” Journal of Chinese Economic and Business Studies 10(2), 169-191.
  • Zakoian, J M., (1994). “Threshold Heteroscedastic Models.” Journal of Economic Dynamics and Control18, 931–995.

VOLATILITY OF BORSA İSTANBUL-100 INDEX AROUND THE FOOD-BEVERAGE SECTOR INDEX AND THE TECHNOLOGY SECTOR INDEX

Yıl 2014, Cilt: 9 Sayı: 2, 21 - 30, 01.03.2014

Öz

In this study, ARCH-class models are estimated by using chosen daily data on stock exchange market. Volatility of stock market returns of the Borsa İstanbul-100 (BIST-100) Index around The Food-Beverage Sector Index and The Technology Sector Index is analyzed by estimating the volatility equation between the years of 2003 and 2012 and for 2608 observations. The reason of using the Food-Beverage Sector and Technology Sector Indexes as independent variables is that the food and beverage sector all around the world is the one which is expected less affected by fluctuations in economy because of the characteristics of being obligatory good in some aspects and that technology sector has been developing day by day via globalization and smart devices besides all of the world in Turkey as well. As a result of the analyses made, it is ascertained that there is a relationship between the BIST-100 Index volatility and both The Food-Beverage Sector Index return volatility, and also The Technology Sector Index return volatility.

Kaynakça

  • Bollerslev, T., (1986). “Generalized Autoregressive Conditional Heteroscdasticity.” Journal of Econometrics 31, 307-327, NorthHolland.
  • Bollerslev, T. and Ghsyels, E., (1996). “PeriodicAutoregressive Conditional Heteroscedasticity.” Journal of Business and EconomicStatistic 14, 139–151.
  • Brailsford, T.J. and Faff, R.W., (1993). “Modelling Australian stock market volatility”, Australian Journal of Management 18,pp. 109-132.
  • Coulson, N. and Robins, R., (1985). “Aggregate Economic Activity and the Variance of Inflation: Another Look.” Economics Letters 17, 71–75.
  • Cragg, J., (1982). “Estimation and Testing in Testing in Time Series Regression Models with Heteroscedastic Disturbances.” Journal of Econometrics 20, 135–157.
  • Cromwell, J.B, Labys, W.C., and Terraza, M., (1994). “Univariate Tests for Time Series Models.” Sage University Paper series on Quantitative Applications in the Social Sciences, 07-099, Thousand Oaks, CA: Sage.
  • Davidson, R. and Mackinnon, J.G., (1993). “Estimation and Inference in Econometrics.” New York: Oxford University Press. Diamond, Peter A., (1967). "The Role of a Stock Market in a General Equilibrium Model with Technological Uncertainty". American Economic Review57, 759–776.
  • Ding, Z., Granger, C.W.J., and Engle, R.F., (1993). “A Long Memory Property of Stock Market Returns and A New Model.” Journal of Empirical Finance1, 83–106.
  • Domowitz, I. and Hakkio, C., (1985). “Conditional Variance and the Risk Premium in the Foreign Exchange Market.” Journal of International Economics 19, 47–66.
  • Engle, R., Hendry, D., and Trumble D., (1985). “Small Sample Properties of ARCH Estimators and Tests.” Canadian Journal of Economics18, 66–93.
  • Engle, R., Lilen, D., and Robins, R., (1987). “Estimating Time Varying Risk Premia in the Term Structure: The ARCH-M Model.” Econometrica 55, 391–407.
  • Engle, Robert F., (1982). “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation.” Econometrica 50, 987-1007.
  • Engle, R.F., (1983). “Estimates of the Variances of U.S. Inflation Based on the ARCH Model.” Journal of Money, Credit and Banking 15, 286-301.
  • Fleming, J., Kirby, C., and Ostdiek, B., (2006). “Stochastic Volatility, Trading Volume, and The Daily Flow of Information.” Journal of Business 79, 1551–1590.
  • Gilson, Ronald J. and Black, Bernard S., (1998). "Venture Capital and the Structure of Capital Markets: Banks Versus Stock Markets". Journal of Financial Economics47, 243–277.DOI:10.2139, SSRN: 46909.
  • Glosten, L.R., Jagannathan, R., and Runkle, D.E., (1993). “On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks.” Journal of Finance48, 1779–1801.
  • Greene, W.H., (2003). “Econometric Analysis.” Fifth Edition, Prentice Hall, New Jersey.
  • Gökçe, A., (2001). “İstanbul Menkul Kıymetler Borsası Getirilerindeki Volatilitenin ARCH Teknikleri ile Ölçülmesi.” G.Ü. İ.İ.B.F. Dergisi, 35-58.
  • Harvey, A.C., (1991). “The Econometric Analysis of Time Series.” Second Edition, The MIT Press, Cambridge.
  • Jane, T. and Ding, C.G., (2009). “On the multivariate EGARCH model.” Applied Economics Letters 16(17), 1757-1761.
  • Lee, J. and Brorsen, B.W., (1997). “A Non-Nested Test of GARCH vs. EGARCH Models.” Applied Economics Letters 4(12), 765-768.
  • Mckenzie, M.D., Mitchell, H., Brooks, R.D., and Faff, R.W., (2001). “Power ARCH Modelling of Commodity Futures Data on the London Metal Exchange.” The European Journal of Finance 7(1), 22Mcmillan, D.G. and Speight, A.E.H., (2003). “Asymmetric Volatility Dynamics in High Frequency FTSE-100 Stock Index Futures.” Applied Financial Economics 13(8), 599-607.ISSN: 09603107 (Print), 1466-4305(Online).
  • Mitchell, H. and Mckenzie, M.D., (2008). “A comparison of alternative techniques for selecting an optimum ARCH model.” Journal of Statistical Computation and Simulation 78(1), 51-67. Nelson, D.B., (1991). “Conditional Heteroscedasticity in Asset Returns: A New Approach.” Econometrica59, 347–70.
  • Park, B., (2002). “Asymmetric Volatility of Exchange Rate Returns under the EMS: Some Evidence from Quantile Regression Approach for TGarch Models.” International Economic Journal 16(1), 105-125.
  • Schwert, G.W., (1990). “Stock market volatility.” Financial Analysts Journal 46(3),pp: 23-34.
  • Silber, Kenneth, (2009). "The Earliest Securities Markets". Research Magazine32 (2), 44–47.
  • Su, Y.C., Huang, H.C., and Lin, Y.J., (2011). “GJR-GARCH Model in Value-at-Risk of Financial Holdings.” Applied Financial Economics 21(24), 1819-1829.
  • Wang, R., and Chen, J.J., (2012). “ARCH effects, trading volume and the information flow interpretation: empirical evidence from the Chinese stock markets.” Journal of Chinese Economic and Business Studies 10(2), 169-191.
  • Zakoian, J M., (1994). “Threshold Heteroscedastic Models.” Journal of Economic Dynamics and Control18, 931–995.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Maliye
Yazarlar

Mehmet Serhan Özkan Bu kişi benim

Yayımlanma Tarihi 1 Mart 2014
Yayımlandığı Sayı Yıl 2014 Cilt: 9 Sayı: 2

Kaynak Göster

APA Özkan, M. S. (2014). VOLATILITY OF BORSA İSTANBUL-100 INDEX AROUND THE FOOD-BEVERAGE SECTOR INDEX AND THE TECHNOLOGY SECTOR INDEX. Social Sciences, 9(2), 21-30. https://doi.org/10.12739/NWSA.2014.9.2.3C0119
AMA Özkan MS. VOLATILITY OF BORSA İSTANBUL-100 INDEX AROUND THE FOOD-BEVERAGE SECTOR INDEX AND THE TECHNOLOGY SECTOR INDEX. Social Sciences. Mart 2014;9(2):21-30. doi:10.12739/NWSA.2014.9.2.3C0119
Chicago Özkan, Mehmet Serhan. “VOLATILITY OF BORSA İSTANBUL-100 INDEX AROUND THE FOOD-BEVERAGE SECTOR INDEX AND THE TECHNOLOGY SECTOR INDEX”. Social Sciences 9, sy. 2 (Mart 2014): 21-30. https://doi.org/10.12739/NWSA.2014.9.2.3C0119.
EndNote Özkan MS (01 Mart 2014) VOLATILITY OF BORSA İSTANBUL-100 INDEX AROUND THE FOOD-BEVERAGE SECTOR INDEX AND THE TECHNOLOGY SECTOR INDEX. Social Sciences 9 2 21–30.
IEEE M. S. Özkan, “VOLATILITY OF BORSA İSTANBUL-100 INDEX AROUND THE FOOD-BEVERAGE SECTOR INDEX AND THE TECHNOLOGY SECTOR INDEX”, Social Sciences, c. 9, sy. 2, ss. 21–30, 2014, doi: 10.12739/NWSA.2014.9.2.3C0119.
ISNAD Özkan, Mehmet Serhan. “VOLATILITY OF BORSA İSTANBUL-100 INDEX AROUND THE FOOD-BEVERAGE SECTOR INDEX AND THE TECHNOLOGY SECTOR INDEX”. Social Sciences 9/2 (Mart 2014), 21-30. https://doi.org/10.12739/NWSA.2014.9.2.3C0119.
JAMA Özkan MS. VOLATILITY OF BORSA İSTANBUL-100 INDEX AROUND THE FOOD-BEVERAGE SECTOR INDEX AND THE TECHNOLOGY SECTOR INDEX. Social Sciences. 2014;9:21–30.
MLA Özkan, Mehmet Serhan. “VOLATILITY OF BORSA İSTANBUL-100 INDEX AROUND THE FOOD-BEVERAGE SECTOR INDEX AND THE TECHNOLOGY SECTOR INDEX”. Social Sciences, c. 9, sy. 2, 2014, ss. 21-30, doi:10.12739/NWSA.2014.9.2.3C0119.
Vancouver Özkan MS. VOLATILITY OF BORSA İSTANBUL-100 INDEX AROUND THE FOOD-BEVERAGE SECTOR INDEX AND THE TECHNOLOGY SECTOR INDEX. Social Sciences. 2014;9(2):21-30.