Heterogeneous Market Hypothesis in Major European Stock Exchanges

Year 2024, Volume: 11 Issue: 1, 134 - 152, 31.03.2024

Aykut Karakaya , Melih Kutlu

https://doi.org/10.30798/makuiibf.1220275

Abstract

The aim of this study is to investigate heterogeneous market efficiency in European stock exchanges using Augmented HAR-RV model. According to the heterogeneous market efficiency hypothesis, investors create portfolios according to different time horizons and different market situations may arise in the reflection of information on price. We find evidence of the validity of the heterogeneous market efficiency model in European stock exchanges. Investors interpret information differently at different time horizons. Medium- and long-term investment decisions are a major influence. These results help explain the volatility that may occur in different time horizons. Portfolio diversification should also be made according to different investments in different horizons. Short-term global volatility shock has been effective on European stock markets.

Keywords

Heterogeneous Market Hypothesis, European Stock Exchanges, Realized Volatility, HAR Model

References

• Alexeev, V. & Tapon, F. (2011). Testing weak form efficiency on the Toronto Stock Exchange. Journal of Empirical Finance, 18 (4), pp. 661–691. https://doi.org/10.1016/j.jempfin.2011.05.002
• Allen, L. & Rai A. (1996). Operational efficiency in banking: An international comparison, Journal of Banking & Finance, 20, 655-672. https://doi.org/10.1016/0378-4266(95)00026-7
• Beaver, W. (1981). Market Efficiency. The Accounting Review, 56(1), pp. 23-37. https://www.jstor.org/stable/246460
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, pp. 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
• Buncic, D. & Gisler, K. I. M. (2016). Global equity market volatility spillovers: A broader role for the United States. International Journal of Forecasting, 32, pp. 1317–1339. https://doi.org/10.1016/j.ijforecast.2016.05.001
• Chan, K.C., Gup, B.E. & Pan, M. S. (1997). International Stock Market Effıciency & Integration: A Study of Eighteen Nations. Journal of Business Finance & Accounting, 24(6), pp. 803-813. https://doi.org/10.1111/1468-5957.00134
• Cheong, C. W. (2013). The computational of stock market volatility from the perspective of heterogeneous market hypothesis. Journal of Economic Computation & Economic Cybernetics Studies & Research. 47 (2), pp. 247-260.
• Cheong, C. W., Cheng, L. M., & Yap, G.L.C. (2016). Heterogeneous Market Hypothesis Evaluations Using Various Jump-Robust Realized Volatility. Romanian Journal of Economic Forecasting, 19(4), pp. 50-64.
• Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), pp. 174–196.
• Cowles, A. (1933). Can Stock Market Forecasters Forecasts. Econometrica 1(3), pp. 309-324. https://doi.org/10.2307/1907042
• Cowles, A. (1944). Stock Market Forecasting. Econometrica, pp. 12(3/4), 206-214.
• Cowles, A. (1960). A Revision of Previous Conclusions Regarding Stock Price Behavior. Econometrica, 28 (4), pp. 909-915. https://doi.org/10.2307/1907573
• Dacorogna M.M, Müller U.A., Jost , C., Pictet, O.V., Olsen, R.B. & Ward, J.R. (1995). Heterogeneous real-time trading strategies in the foreign exchange market. The European Journal of Finance, 1, pp. 383-405. https://doi.org/10.1080/13518479500000026
• Dacorogna, M. M, Müller U., Olsen, R. & Pictet, O. (2001). Defining efficiency in heterogeneous markets. Quantitative Finance, 1 (2), pp. 198-201. https://doi.org/10.1080/713665666
• Davies, R.B. & Studnicka, Z. (2018). The heterogeneous impact of Brexit: Early indications from the FTSE. European Economic Review, 110, pp. 1–17. https://doi.org/10.1016/j.euroecorev.2018.08.003
• De Bondt, W. & Thaler, R. (1985). Does the stock market overreact? Journal of Finance, 40(3), pp. 793-805. https://doi.org/10.2307/2327804
• Dhankar, R.S. (2019). Risk-Return Relationship & Portfolio Management. Springer.
• Dickey, D.A. & Fuller, W.A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74 (366), pp. 427- 431. https://doi.org/10.2307/2286348
• Dickey, D. A. & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49, pp. 1057–72.
• Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, pp. 987–1007.
• Grossman, S., & Stiglitz, J. (1980). On the Impossibility of Informationally Efficient Markets. The American Economic Review, 70(3), pp. 393-408.
• Fabozzi, F.J., Modigliani, F. & Jones, F. J. (2014). Foundations of Financial Markets & Institutions, 4th Edition, Pearson Education Limited.
• Fama, E. (1965a). The Behavior of Stock-Market Prices. The Journal of Business, 38 (1), pp. 34‑105.
• Fama, E. (1965b). Random Walks in Stock Market Prices. Financial Analysts Journal, 21(5), pp. 55-59.
• Fama E. (1970). Efficient Capital Markets: A Review of Theory & Empirical Work. Journal of Finance, 25(2), pp. 383-417.
• Fama E. (1991). Efficient Capital Markets: II. Journal of Finance. 46(5), pp. 1575-1617. https://doi.org/10.2307/2328565
• Harvey, A.C., (2013). Dynamic Models for Volatility & Heavy Tails, with Application to Financial & Economic Time Series. Cambridge University Press.
• Kendall, M. (1953). The analysis of Economic Time-Series-Part I: Prices. Journal of the Royal Society, 116 (1) pp. 11 34. https://doi.org/10.2307/2980947
• Kwiatkowski, D., Phillips P.C.B., Schmidt P. & Shin Y., (1992). Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root: How Sure Are We that Economic Time Series Have A Unit Root? Journal of Econometrics, 54, pp. 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y
• Kwon, Y. K. & Park H. Y. (1986). Heterogeneous Information, Market Efficiency & the Volatility of Equilibrium Stock Prices. Bureau of Economic & Business Research University of Illinois, Urbana-Champaign Working Paper, 1220, pp. 1-16.
• LeBaron, B. (2001). Evolution & Time Horizons in an Agent-Based Stock Market. Macroeconomic Dynamics, 5, pp. 225-254.
• Lee, C.C., Tsong, C.C. & Lee, C.F. (2014). Testing for the Effıcient Market Hypothesis in Stock Prices. International Evidence from Nonlinear Heterogeneous Panels. Macroeconomic Dynamics, 18, pp. 943–958.
• Liu, X., Song, H. & Romilly, P. (1997). Are Chinese stock markets efficient? A cointegration & causality analysis. Applied Economic Letters, 4(8), pp. 511-515. https://doi.org/10.1080/758536636
• Ljung, G. M. & Box, G. E. P. (1978). On a Measure of a Lack of Fit in Time Series Models. Biometrika, 65, pp. 297-303. https://doi.org/10.2307/2335207
• Lo, A. W. & MacKinlay, A.C. (1988). Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test. Review of Financial Studies, 1, pp. 41– 66.
• Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30, pp. 15– 29.
• Lo, A. W. (2005). Reconciling Efficient Markets with Behavioral Finance: The Adaptive Markets Hypothesis. Journal of Investment Consulting, 7, pp. 21– 44.
• Lo, A. W. (2017). The Adaptive Markets: Financial Evolution at the Speed of Thought, Princeton University Press.
• Lux, T. (2008). The markov-switching multifractal model of asset returns. Journal of Business & Economic Statistics, 26(2), pp. 194–210. https://doi.org/10.1198/073500107000000403
• Lynch, P. E. & Zumbach G. O. (2003). Market heterogeneities & the causal structure of volatility. Quantitative Finance, 3(4), pp. 320-331. https://doi.org/10.1088/1469-7688/3/4/308
• McMillan, D. G. & Speight A.E. (2006). Volatility Dynamics & Heterogeneous Markets, Int. J. Fin. Econ., 11, pp. 115–121. https://doi.org/10.1002/ijfe.281
• Merton, R. (1980). On estimating the expected return on the market: an exploratory investigation. Journal of Financial Economics, 8, pp. 323–361. https://doi.org/10.1016/0304-405X(80)90007-0
• Munir, Q. & Mansur, K. (2009): Is Malaysian stock market efficient? Evidence from threshold unit root tests. Economics Bulletin, 29 (2), pp. 1359-1370.
• Müller U.A., Dacorogna M.M., Dave, R.D., Pictet, O.V., Olsen, R.B. & Ward, J.R. (1993). Fractals & intrinsic time: A challenge to econometricians, Invited presentation at the XXXIXth lnternational AEA Conference on Real Time Econometrics, Research Report UAM.1993-08-16, Olsen & Associates, Zurich.
• Müller U.A., Dacorogna M.M., Dave, R.D., Olsen, R.B., Pictet , O.V. & Weizsacker, J. E. (1997). Volatilities of different time resolutions-Analyzing the dynamics of market components. Journal of Empirical Finance, 4, pp. 213-239. https://doi.org/10.1016/S0927-5398(97)00007-8
• Narayan, P. K. & Smyth, R. (2004). Is South Korea's stock market efficient? Applied Economics Letters, 11 (11), pp. 707-710. https://doi.org/10.1080/1350485042000236566
• Peters, E.E., (1994). Fractal market analysis: Applying Chaos Theory to Investment & Economics. John Wiley & Sons, Inc.
• Phillips, P.C.B. & Perron P. (1988). Testing for a Unit Root in Time Series Regression, Biometrika, 75, pp. 335-346. https://doi.org/10.2307/2336182
• Realized Volatility (2020). Oxford-Man Institute's Quantitative Finance Realized Library; Version: 0.3. Retrieved from https://realized.oxford-man.ox.ac.uk/data.
• Rubinstein, M. (1975). Securities Market Efficiency in an Arrow-Debreu Economy. American Economic Review, 65 (5), pp. 812-824.
• Samuelson, P. (1965). Proof that Properly Anticipated Prices Fluctuate. Industrial Management Review, 6(2), pp. 41-49.
• Saunders, A., Cornett, M.M. (2015). Financial Markets & Institutions, 6th Edition McGraw-Hill Education: New York.
• Tao, Q., Wei, Y., Liu, J. & Zhang, T. (2018). Modeling & forecasting multifractal volatility established upon the heterogeneous market hypothesis. International Review of Economics & Finance, 54, pp. 153-153.
• Taylor, S.J. (1994). Modeling stochastic volatility: a review & comparative study. Math. Finance, 4, pp. 183–204. https://doi.org/10.1111/j.1467-9965.1994.tb00057.x
• Volatility Index (2020). Federal Reserve Bank of St. Louis; Chicago Board Options Exchange Volatility Index. Retrieved from https://alfred.stlouisfed.org.
• Wei, Y., & Wang, P. (2008). Forecasting volatility of SSEC in Chinese stock market using multifractal analysis. Physica A Statistical Mechanics & Its Applications, 387(7), pp. 1585–1592. https://doi.org/10.1016/j.physa.2007.11.015
• Worthington, A.C. & Higgs, H. (2004). Random walks & market efficiency in European equity markets. Global Journal of Finance & Economics, 1(1), pp. 59-78.
• Zivot, E.& Andrews, K. (1992). Further Evidence on the Great Crash, the Oil Price Shock, and The Unit Root Hypothesis. Journal of Business and Eco¬nomic Statistics, 10(3), pp. 251–270. https://doi.org/10.2307/1391541
• Zuckerman, E.W. (2012). Market Efficiency: A sociological perspective. Handbook of the sociology of finance. Alex Preda & Karin Knorr-Cetina(Eds.), Oxford University Press.