Hisse Senetleri Piyasalarında Heterojenlik Analizi: Borsa İstanbul Örneği

Bu çalışmada amaç, Heterojen Piyasa Hipotezi (HPH) çerçevesinde oynaklık ve bu oynaklığın farklı zaman dilimlerinde farklı olmasına bağlı olarak, Borsa İstanbul Hisse Senetleri Piyasası'nın heterojenlik yapısının analizini yapmaktadır. Hisse senetleri piyasasında işlem yapan karar birimlerinin çeşitliliği, farklı zaman aralıklarında fiyat hareketlerinin farklılaşmasına neden olmaktadır. Bu farklılaşmanın temelinde ise, söz konusu karar birimlerinin piyasa, risk ve buna yönelik algılama biçimleri konusunda çeşitlilik göstermesidir. Davranışsal finans açısından heterojenlik olarak tanımlanan bu çeşitlilik, finans literatüründe genel olarak Heterojen Piyasa Hipotezi (HPH) çerçevesinde ele alınmaktadır. Bu hipotezin kaynağında özellikle hisse senetleri piyasasındaki kendi adına işlem yapanlar ile belirli bir kurumla ilişkili olarak işlem yapanların davranışları arasında farklılaşma olmasıdır. Bundan dolayı söz konusu süreç standart oynaklık modelleri aracılığıyla açıklanamadığından, literatürde heterojen piyasa hipotezini esas alan yeni teknikler geliştirilmiştir. Bu çalışmada kullanılan teknik, hipotezin geçerliliğini ortaya koyan bir bulgu sunmaktadır. Bununla birlikte, piyasadaki fiyat hareketlerinden hesaplanan oynaklığa dayalı bir yaklaşım sunan teknik yoluyla, piyasanın heterojenliği ile karar birimlerinin heterojenliği arasındaki ilişki de ortaya konmaktadır. Böylece Borsa Istanbul Hisse Senetleri Piyasası'nın heterojen bir piyasa özelliğine sahip olup olmadığını analiz etmenin yanında; bu çalışmada piyasa katılımcılarının söz konusu oluşum üzerindeki etkisi de incelenmektedir. Buna göre elde edilen ampirik bulgular çerçevesinde politika önerileri sunularak literatüre katkı sunulması hedeflenmektedir.

Anahtar Kelimeler:

Heterogeneous Market Hypothesis, HAR Model

Heterogeneity Analysis of the Stock Markets: The Case of Borsa Istanbul

PDF

___

Arneodo, A., J. Muzy, and D. Sornette (1998). Casual cascade in stock market from the”infrared” to the“ultraviolet”. European Physical Journal B 2, 277.
Barndorff-Nielsen E. and N. Shephard (2002). Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of Royal Statistical Society, Series B, 64:253–280.
Buccheri and F. Corsi (2017). HARK the SHARK: Realized volatility modelling with measurement errors and nonlinear dependencies. SSRN Electronic Journal, 2017Clements Adam and Preve Daniel P. A. (2019). A Practical Guide to Harnessing the HAR Volatility Model. National Centre for Econometrics Research, Australia. NCER Working Paper: 120, http://www.ncer.edu.au/papers/documents/WP120.pdf , (Access date: 15.06.2019)
Cipollini, G.M. Gallo, and E. Otranto (2017). On heteroscedasticity and regimes in volatility forecasting. SSRN Electronic Journal, 2017.
Choi, H. and Varian, H. (2012). Predicting the present with google trends. Economic Record 88(s1), 2–9.
Corsi F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2):174–196, 2009.
Corsi F. and Reno R. (2009). HAR volatility modelling with heterogeneous leverage and jumps. https://web.stanford.edu/group/SITE/archive/SITE_2009/segment_1/s1_papers/corsi.pdf, (Access date: 15.06.2019)
Dacorogna, M., Muller, U., Dav, R., Olsen, R. and Pictet, O. (1998). Modelling short-term volatility with garch and harch models. Nonlinear Modelling of High Frequency Financial Time Series, pp. 161–76. Edited by C. Dunis and B. Zhou, John Wiley, Chichester.
De Lima, P.J.F., (1998). Nonlinearities and Nonstationarities in Stock Returns. Journal of Business & Economics Statistics 16, pp. 227 – 236.
Diebold and R.S. Mariano (1995). Comparing predictive accuracy. Journal of Business and Economics Statistics, 13:253–263.
Dickey, D. A. and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74(366), 427–431.
Hansen, A. Lunde, and J. Nason (2003). Choosing the best volatility models: the model conﬁdence set approach. Oxford Bulletin of Economics and Statistics, 65:839–861, 2003.
Hansen B. and J. Racine (2018). Bootstrap Model Averaging Unit Root Inference. McMaster University - Department of Economics Working Paper No. 2018-09 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3156028 (Access date: 15.06.2019)
Fama, E. (1965). Random walks in stock market prices. Financial Analysts Journal 21(5), 55–59.
Fama, E. (1970). Eﬃcient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance 25, 383–417.
Fama, E. (1991). Eﬃcient Capital Markets: II. Journal of Finance 46(5), 1575–1617.
Fama E. F. (1998). Market eﬃciency, long-term returns, and behavioral ﬁnance. Journal of Financial Economics 49 (1998) 283—306, http://www.e-m-h.org/Fama98.pdf , (Access date: 05.06.2019)
Khan Muhammad Yousaf (2015). Advances In Applied Nonlinear Time Series Modeling, Dissertation an der Fakultät für Mathematik, Informatik und Statistik der Ludwig–Maximilians–Universität München, https://core.ac.uk/download/pdf/79055317.pdf , (Access date: 05.06.2019)
Kwiatkowski, D., P.C.B. Phillips, P. Schmidt and Y. Shin (1992). Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root. Journal of Econometrics, 54, 159-178.
LeRoy, S. (1976). Eﬃcient capital markets: Comment. Journal of Finance 31, 139–141.
Linton, Oliver (2019). Comparison between Chinese, US, and European Stock Markets. Cambridge University, July 2019, https://obl20.com/wp-content/uploads/2019/07/Chinesestock2.pdf , (Access date: 05.06.2019)
Lo, Andrew W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management 30: 15–29.
Lo, Andrew W. (2005). Reconciling Efficient Markets with Behavioral Finance: The Adaptive Markets Hypothesis. Journal of Investment Consulting 7: 21–44.
Lynch, P. E. (2000). Adaptive models for speculative turbulence, PhD thesis, Department of Electrical and Electronic Engineering, University of Manchester Institute of Science and Technology. Manchester, M60. UK.
Lynch, P. and G. Zumbach (2003). Market heterogeneities and the causal structure of volatility. QuantitativeFinance 3(4), 320–331.
Lux, T. and M. Marchesi, (1999). Scaling and Criticality in a Stochastic Multi-Agent Model of a Financial Market. Nature, 397, pp. 498 - 500.
Mandelbrot, Benoit B. (2005). The inescapable need for fractal tools in finance. Annals of Finance, Springer, vol. 1(2), pages 193-195, October.
Malkiel, B. (2003). The eﬃcient market hypothesis and its critics. Journal of Economic Perspectives 17(1), 5982.
Morris, S., (1994). Trade with heterogeneous prior beliefs and asymmetric information. Econometrica. 62 (6), 1327-1347.
Muller, U. , M. Dacorogna, R. Dave, R. Olsen, O. Pictet, and J. Weizsacker (1997). Volatilities of different time resolutions – analyzing the dynamics of market components. Journal of Empirical Finance, 4:213–239, 1997.
Osier, C.L., (1995). Exchange rate dynamics and speculator horizons. Journal of International Money and Finance 14 (5), 695-719.
Patton, A. J. (2011). Volatility forecast comparison using imperfect volatility proxies. Journal of Econometrics 160(1), 246–256.
Patton A. J. and K. Sheppard (2015). Good volatility, bad volatility: Signed jumps and the persistence of volatility. Review of Economics and Statistics, 97:683–697.
Peters, E. (1991). Chaos and Order in the Capital Markets - A New View of Cycles, Prices, and Market Volatility. John Wiley & Sons, Inc.
Peters, E. (1994). Fractal Market Analysis – Applying Chaos Theory to Investment and Analysis. New York: John Wiley & Sons, Inc.
Romano and M. Wolf, (2017). Resurrecting weighted least squares. Journal of Econometrics, 197:1–19, 2017.
Shiller, R.J. (2003). From Efficient Market Theory to Behavioral Finance. Journal of Economic Perspectives—Volume 17, Number 1—Winter 2003—Pages 83–104. https://pubs.aeaweb.org/doi/pdfplus/10.1257/089533003321164967 , (Access date: 25.06.2019)
Vozlyublennaia, N. (2014). Investor attention, index performance, and return predictability. Journal of Banking & Finance 41, 17–35.
Taylor (2017). Realised variance forecasting under Box-Cox transformations. International Journal of Forecasting, 33:770–785, 2017.
Westerlund J. and P.K. Narayan (2012). Does the choice of estimator matter when forecasting returns?. Journal of Banking and Finance, 36:2632–2640, 2012.