Analyzing the leverage effect in stock indexes with autoregressive generalized variance models
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Apr 12, 2017
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Abstract
In developing and developed economies understanding the movement of stock market is extremely important to understand the riskiness of the investment, general behavior of the economy and taking the right position against the forthcoming financial events. In this study, the volatility of Turkish, Brazilian, German, London and New York Stock Indices are analyzed with ARCH type of modeling and the leverage effect is researched for the period between 04.01.2011 to 26.05.2015. There are two interesting results of this study. Firstly; it was seen that in all of those stock markets there is a leverage effect which means the negative movement in volatility is stronger than the positive one. Secondly the general structure of the ARCH type of modeling which explains the leverage effect shows similarity between those developed and developing markets.
Keywords: GARCH, TGARCH, leverage, stock exchange.
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References
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Karahanoglu, I., & Ercan, H. (2015). BNK10 Endeksindeki Kaldırac Etkisinin Genelleştirilmiş Otoregresif Koşullu Varyans Modeli İle Analiz Edilmesi. Uluslararası Alanya Isletme Fakultesi Dergisi, 7(3).
Karolyi, G. A. (1995). A multivariate GARCH model of international transmissions of stock returns and volatility: The case of the United States and Canada. Journal of Business & Economic Statistics, 13(1), 11-25.
Kutlar, A., & Torun, P. (2013). IMKB 100 Endeksi Icin Uygun GenelestirilmisVaryans Modeli Secimi, Erciyes Universitesi IIBF Dergisi, 42, 1-24
Literman, B. (2003). Modern Investment Management: An Equilibrium Approach, Wiley & Sons, USA
Mandelbrot, B.B. (1963). The Variation of Certain Speculative Prices, Journal of Bussines, 36(4), 394-419
Nelson, D.B., & Cao, C.Q., (1992). Inequality Constraints in the Univariate GARCH Model, Journal of Business and Economic Statistics, 10, 229-235
Ozden, U.H. (2008). İMKB Bileşik100 Endeksi Getiri Volatilitesinin Analizi, İstanbul Ticaret Universitesi Sosyal Bilimler Dergisi, 7(13), 339-350
Posedel, P. (2005). Properties and estimation of GARCH (1, 1) model. Metodoloski zvezki, 2(2), 243.
Almeida, D. D., & Hotta, L. K. (2014). THE LEVERAGE EFFECT AND THE ASYMMETRY OF THE ERROR DISTRIBUTION IN GARCH-BASED MODELS: THE CASE OF BRAZILIAN MARKET RELATED SERIES. Pesquisa Operacional, 34(2), 237-250.
Angelidis, T., Benos, A., & Degiannakis, S. (2004). The use of GARCH models in VaR estimation. Statistical methodology, 1(1), 105-128.
Atakan, T. (2009). Istanbul Menkul Kiymetler Borsasi’nda Degiskenligin (Volatilitenin) ARCH-GARCH Yontemleri İle Modellenmesi. Yonetim Dergisi, 62, 48-61.
Bolleslav, T. (1986). Generalized Autoregressive Conditional Heterscesdaticity, Journal of Econometrics, 31, 307-327
Demir, I., & Cene, E. (2012). IMKB 100 endeksindeki kaldirac etkisinin ARCH modelleriyle iki alt donemde incelenmesi/Investigating leverage effect on Turkish stock market with ARCH models within two sub-groups. Istanbul Universitesi Isletme Fakultesi Dergisi, 41(2), 214.
Du, X., Cindy, L. Y., & Hayes, D. J. (2011). Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis. Energy Economics, 33(3), 497-503.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
Frances, H.P., & Dijk, D.V. (1998). Estimating Stock Market Volatility using GARCH Models, Journal of Forecasting, 15, 229-235
Gokce, A. (2001). Istanbul menkul kıymetler borsası getirilerindeki volatilitenin ARCH teknikleri ile olculmesi. İktisadi ve İdari Bilimler Fakultesi Dergisi, 3(1), 1-23.
Gujarati, D.N. (2010).Temel Ekonometri, Litaratur Yayinları, 33, Istanbul.
Hepsag, A. (2013). Cok degiskenli Stokastik Oynaklik Modelleri: Petrol Piyasasi Finansal Piyasalarda Islem Goren Sanayi Endeksi Arasindaki Oynaklik Etkilesimi Uzerine Bir Uygulama (Doktora Tezi), Istanbul.
Jorion, P. & Zhang, G. (2008). Credit Contagion from Counterparty Risk. FDIC Center for Financial Research Working Paper
Karabacak, M., Mecik, O., & Genc, E. (2014). Kosullu Degisen Varyans Modelleri ile BIST 100 Endeks Getirisi ve AltIn Getiri Serisi Volatilitesinin Tahmini, UluslararasI Alanya Isletme Dergisi, 1, 79-90
Karahanoglu, I., & Ercan, H. (2015). BNK10 Endeksindeki Kaldırac Etkisinin Genelleştirilmiş Otoregresif Koşullu Varyans Modeli İle Analiz Edilmesi. Uluslararası Alanya Isletme Fakultesi Dergisi, 7(3).
Karolyi, G. A. (1995). A multivariate GARCH model of international transmissions of stock returns and volatility: The case of the United States and Canada. Journal of Business & Economic Statistics, 13(1), 11-25.
Kutlar, A., & Torun, P. (2013). IMKB 100 Endeksi Icin Uygun GenelestirilmisVaryans Modeli Secimi, Erciyes Universitesi IIBF Dergisi, 42, 1-24
Literman, B. (2003). Modern Investment Management: An Equilibrium Approach, Wiley & Sons, USA
Mandelbrot, B.B. (1963). The Variation of Certain Speculative Prices, Journal of Bussines, 36(4), 394-419
Nelson, D.B., & Cao, C.Q., (1992). Inequality Constraints in the Univariate GARCH Model, Journal of Business and Economic Statistics, 10, 229-235
Ozden, U.H. (2008). İMKB Bileşik100 Endeksi Getiri Volatilitesinin Analizi, İstanbul Ticaret Universitesi Sosyal Bilimler Dergisi, 7(13), 339-350
Posedel, P. (2005). Properties and estimation of GARCH (1, 1) model. Metodoloski zvezki, 2(2), 243.