ANALYSIS OF VOLATILITY IN THE CONTEXT OF HETEROGENEOUS MARKET HYPOTHESIS IN EMERGING MARKETS

Yıl 2024, Sayı: 69, 187 - 193, 30.12.2024

M. E. Soykan

https://doi.org/10.18070/erciyesiibd.1499398

Öz

Heterogeneous Market Hypothesis (HMH) was suggested by Müller et al. (1997). HMH claims that financial market is formed by heterogeneous market participants. In this article, Corsi (2009) basic HAR application is discussed in context of HPH for MSCI Emerging Markets Index and results are interpreted. Basic HAR model consists of 3 parts; short-term traders with daily or more frequent trading frequency; medium-term traders with weekly trading frequency and long-term traders with monthly trading frequency or less. As a result, although HAR models mostly measure long-term volatility persistence, since monthly coefficient is insignificant and weekly and daily coefficients are significant in all three equations in analysed series, very long term, ie 1 week or late volatility persistence does not appear in the series in question. Therefore, it can be concluded that main determinants in this market are medium and short-term investors. In other words; while MSCI Emerging Markets Index is only affected by high and medium frequencies, it is not affected by low frequencies (monthly). Considering that mentioned index may be exposed to speculative activities in general, these results support this.

Anahtar Kelimeler

Heterogeneous Market Hypothesis, HAR, Volatility, MSCI Emerging Markets Index, Volatility Persistence

GELİŞMEKTE OLAN PİYASALARDA HETEROJEN PİYASA HİPOTEZİ BAĞLAMINDA VOLATİLİTENİN ANALİZİ

Öz

Heterojen Piyasa Hipotezi (HPH) Müller vd. (1997) tarafınca ileri sürülmüştür. HPH finansal piyasanın heterojen yani homojen olmayan piyasa katılımcıları tarafından oluşturulduğunu iddia etmektedir. Bu makalede de MSCI Gelişmekte Olan Piyasalar Endeksi HPH bağlamında Corsi (2009) temel HAR uygulaması ile ele alınmakta ve sonuçlar yorumlanmaktadır. Temel HAR modeli 3 kısımdan meydana gelmektedir; günlük veya daha sık işlem sıklığına sahip kısa vadeli yatırımcılar; haftalık işlem sıklığına sahip orta vadeli yatırımcılar ve aylık veya daha az işlem sıklığına sahip uzun vadeli yatırımcılar. Sonuç olarak her ne kadar HAR modelleri daha çok uzun vade volatilite kalıcılığını ölçse de, analiz edilen seride üç denklemde de aylık katsayı anlamsız, haftalık ve günlük katsayı ise anlamlı çıktığından, bahsi geçen seride çok uzun vade, yani 1 hafta veya geç volatilite kalıcılığı görünmemektedir. Bu nedenle, bu piyasada esas belirleyicilerin orta ve kısa vadeli yatırımcılar olduğu sonucuna ulaşılabilir. Diğer bir ifadeyle, MSCI Gelişmekte Olan Piyasalar endeksi sadece yüksek ve orta frekanslardan etkilenirken, düşük frekanslardan(aylık) etkilenmemektedir. Bahsi geçen endeksin genelde spekülatif faaliyetlere maruz kalabileceği düşünüldüğünde, bu elde edilen sonuçlar bunu desteklemektedir.

Anahtar Kelimeler

Heterojen Piyasa Hipotezi, HAR, Volatilite, MSCI Gelişmekte Olan Piyasalar, Volatilite Kalıcılığı

Kaynakça

• Bollerslev, T., Patton, A.J., ve Quaedvlieg, R. (2016). Exploiting the errors: A simple approach for improved volatility forecasting. Journal of Econometrics, 192(1), 1-18. https://www.sciencedirect.com/
• Bozkuş, S., ve Kahyaoğlu, H. (2020). Heterogeneity Analysis of the Stock Markets: The Case of Borsa Istanbul. Journal of Yasar University, 15, 170-179. https://journal.yasar.edu.tr
• Buccheri, G., ve Corsi, F. (2019). HARK the SHARK: Realized Volatility Modeling with Measurement Errors and Nonlinear Dependencies. SSRN Electronic Journal, 1-41. www.ssrn.com/index.cfm/en/
• Chen, Y., Hardle, W.K., ve Pigorsch, U. (2010). Localized realized volatility modeling, Journal of the American Statistical Association, 105(492), 1376-1393. www.tandfonline.com
• Cipollini, F., Gallo, G.M., ve Otranto, E. (2017). On Heteroskedasticity and Regimes in Volatility Forecasting. SSRN Electronic Journal, 1-22. https://dx.doi.org/10.2139/ssrn.3037550
• Clements, A., ve Preve, D.P.A. (2019). A Practical Guide to Harnessing the HAR Volatility Model. Journal of Banking and Finance, 1-12. https://www.sciencedirect.com/
• Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 1-26. academic.oup.com/jfec
• Corsi, F., Mittnik, S., Pigorsch, C., ve Pigorsch, U. (2008). The volatility of realized volatility. Econometric Reviews, 27(1-3), 46-78. www.tandfonline.com
• Dacorogna, M.M., Müller, U.A., Pictet, O.V., Olsen, R.B.Dav, R., Olsen,R., ve Pictet,O. (1997). Modelling short-term volatility with garch and harch models. SSRN Electronic Journal, 1-15. https://papers.ssrn.com/
• Eroğlu, B.A., İkizlerli, D., ve Yener, H. (2021). Reexamination of the BIST 100 Stock Price Volatility with Heterogeneous Autoregressive Realized Volatility Models. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 25(2), 457-476. https://dergipark.org.tr/ tr/pub/ataunisosbil/aim-and-scope
• Freitas, G.M. (2020). Forecasting FTSE-100 volatility using HAR-type models (Yayımlanmamış Yüksek Lisans Tezi), Universidade do Minho, Portekiz
• Huang, C., Gong, X., Chen, X., ve Wen, F. (2013). Measuring and Forecasting Volatility in Chinese Stock Market Using HAR-CJ-M Model. Hindawi Publishing Corporation, 1-14. https://www.hindawi.com
• Khan, M.Y. (2015). Advances In Applied Nonlinear Time Series Modelling (Yayımlanmamış yüksek lisans tezi) Ludwig-Maximilians-Universitat München, Almanya, core.ac.uk/download/pdf/79055317.pdf
• Liang, C., Li, Y., Ma, F., ve Wei,Y. (2021). Global equity market volatilities forecasting: A comparison of leverage effects, jumps, and overnight information. International Review of Financial Analysis, 75. https://www.sciencedirect.com/
• Liang, C., Li, Y., Ma, F., ve Zhang,Y. (2022). Forecasting international equity market volatility: A new approach. Journal of Forecasting, https://doi.org/10.1002/for.2869
• Liu, G., Wei, Y., Chen, Y., Yu, J., ve Hu, Y. (2018). Forecasting the value-at-risk of Chinese stock market using the HARQ model and extreme value theory. Physica A: Statistical Mechanics and its Applications, 499, 288-297. https://www.sciencedirect.com/
• Liu, M., Choo, W.C., Lee, C.C., ve Lee, C.C. (2022). Trading volume and realized volatility forecasting: Evidence from the China stock market. Journal of Forecasting, https://doi.org/10.1002/for.2897
• Luo, J., Chen, Z., ve Wang, S. (2023). Realized volatility forecast of financial futures using time-varying HAR latent factor models. Journal of Management Science and Engineering, 8, 214-243. https://www.sciencedirect.com/
• McAleer, M., ve Medeiros, M.C. (2008). A multiple regime smooth transition heterogeneous autoregressive model for long memory and asymmetries. Journal of Econometrics, 147(1), 104-119. https://www.sciencedirect.com/
• Müller, U., Dacorogna,M., Dav,R., Pictet,O., Olsen,R., ve Ward,J. (1993). Fractals and intrinsic time-a challenge to econometricians. XXXIXth International AEA Conference on Real Time Econometrics, Luxembourg, 1-24. https://www.researchgate.net
• Müller, U., Dacorogna, M., Dav, R., Olsen, R. , Pictet, O., ve Von Weizsacker, J. (1997). Volatilities of different time resolutions-analysing the Dynamics of market components. Journal of Empirical Finance, 4, 213-239. https://www.sciencedirect.com/
• Türensal, M.M. (2021). Heterojen Otoregresif Modeller Yardımı ile Gerçekleşen Oynaklık Tahmini: BIST 100 Örneği (Yayımlanmamış yüksek lisans tezi). Trakya Üniversitesi Sosyal Bilimler Enstitüsü, Edirne
• Wen, F., Gong, X., ve Cai, S. (2016). Forecasting the volatility of crude oil futures using HAR-type models with structural breaks. Energy Economics, 59, 400-413. https://www.sciencedirect.com/