Long-Term Memory in Euronext Stock Indexes Returns: an Econophysics Approach

• Autorzy: Luis M. P. Gomes , Vasco J. S. Soares , Silvio M. A. Gama , Jose A. O. Matos
• Języki publikacji: EN

Abstrakty

EN

The purpose of paper is to assess the long-term memory of stock index returns in the panEuropean platform Euronext (CAC-40, AEX, BEL-20 and PSI-20). We find evidence of time dependency in much of the data, suggesting that the series may best be described as fractional Brownian motion. Modified Rescaled-Range Analysis and Detrended Fluctuation Analysis were used to measure the degree of long memory. The global Hurst exponents evidence persistent long memory in the Dutch, Belgian and Portuguese markets. In the French market, evidence of long memory is inconsistent and weak. Fractal structure suggests non-conformity with the Efficient Market Hypothesis, and may compromise the reliability of asset pricing models. Furthermore, time-dependent Hurst exponents show evidence of weakening persistence in these markets, particularly after the international crises of 2000, 2002 and 2010. A possible explanation for those changes is that the markets may have matured over time, becoming more efficient after these severe events.(original abstract)

Słowa kluczowe

EN

Market effectiveness; Stock market indexes; Capital market

PL

Efektywność rynku; Indeks giełdowy; Rynek kapitałowy

• Czasopismo: Business and Economic Horizons
• Rocznik: 2018
• Numer: vol. 14, iss. 4
• Strony: 862--881

Twórcy

autor

Luis M. P. Gomes

• Polytechnic Institute of Porto, Portugal

autor

Vasco J. S. Soares

• Portucalense University Infante D. Henrique (UPT), Portugal

autor

Silvio M. A. Gama

• University of Porto, Portugal

autor

Jose A. O. Matos

• University of Porto, Portugal

Bibliografia

• Andrews, D. W. K. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica, 59, 817-858.
• Assaf, A., Cavalcante, J. (2004). Long-range dependence in the returns and volatility of the Brazilian stock market. European Review of Economics and Finance 3, 5-22.
• Beran, J. A. (1994). Statistics for long-memory processes. Monographs on Statistics and Applied Probability 61, Chapman & Hall, New York, ISBN 978-0412049019
• Braun, R., Jenkinson, T., Stoff, I. (2017). How persistent is private equity performance? Evidence from deal-level data. Journal of Financial Economics, 123(2), 273-291.
• Chen, C. W. S., Yu, T.H.K. (2005). Long-term dependence with asymmetric conditional heteroscedasticity in stock returns. Physica A: Statistical Mechanics and its Applications, 353(1), 413-424.
• Cheung, Y-W. (1990). Long memory in foreign exchange rates and sampling properties of some statistical procedures related to long memory models. Ph.D. Dissertation, University of Pennsylvania, Philadelphia.
• Cheung, Y-W., Lai, K. S. (1995). A search for long memory in international stock market returns. Journal of International Money and Finance 14(4), 597-615.
• Chow, K. V., Pan, M-S., Sakano, R. (1996). On the long-term or short-term dependence in stock prices: evidence from international stock markets. Review of Quantitative Finance and Accounting, 6, 181-194.
• Christodoulou-Volos, C., Siokis, F. M. (2006). Long range dependence in stock market returns. Applied Financial Economics, 16(18), 1331-1338.
• Costa, R. L., Vasconcelos, G. L. (2003). Long-range correlations and nonstationarity in the Brazilian stock market. Physica A: Statistical Mechanics and its Applications, 329(1-2), 231-248
• Di Matteo, T. (2007). Multi-scaling in Finance. Quantitative Finance, 7(1), 21-36.
• Eitelman, P., Vitanza, J. (2008). A non-random walk revisited: short- and long-term memory in asset prices (International Finance Discussion Papers, N. 956). Board of Governors of the Federal Reserve System (Available at SSRN: http://ssrn.com/abstract=1311889) (accessed 25.01.18).
• Embrechts, P., Maejima, M. (2002). Selfsimilar processes. Princeton University Press, ISBN 978-0691096278
• Fama, E. E. (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25, 383-417.
• Fama, E. E., French, K. R. (1988). Permanent and temporary components of stock prices. Journal of Political Economy, 96, 246-273.
• Ferreira, P. (2018). Efficiency or speculation? A time-varying analysis of European sovereign debt. Physica A: Statistical Mechanics and its Applications, 490, 1295-1308.
• Geweke, J., Porter-Hudak, S. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis, 4(4), 221-238.
• Granger, C.W.J. (1980). Long memory relationships and the aggregation of dynamic models. Journal of Econometrics, 14, 227-238.
• Grech, D., Mazur, Z. (2005). Statistical properties of old and new techniques in detrended analysis of time series. Acta Physica Polonica B 36(8), 2403-2413.
• Horta, P., Lagoa, S., Martins, L. (2014). The impact of the 2008 and 2010 financial crises on the Hurst exponentes of international stock markets: Implications for efficiency and contagion. International Review of Financial Analysis, 35, 140-153.
• Hurst, H.E. (1951). Long term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116, 770-799.
• Jacobsen, B. (1996). Long term dependence in stock returns. Journal of Empirical Finance, 3, 393-417.
• Kristoufek, L. (2009). Distinguishing between short and long range dependence: finite sample properties of rescaled range and modified rescaled range. Munich Personal RePEc Archive, MPRA Paper N. 16424 (Available at http://mpra.ub.uni-muenchen.de/16424/) (accessed 20.01.18).
• Kristoufek, L. (2010). Rescaled range analysis and detrended fluctuation analysis: finite sample properties and confidence intervals. AUCO Czech Economic Review, 4(3), 315-329
• Kristoufek, L. (2012). Fractal markets hypothesis and the global financial crisis: Scaling, investment horizons and liquidity. Advances in Complex Systems, 15(6), 1250065-13
• Kyaw, A.N., Los, A.C., Zong, S. (2006). Persistence characteristics of Latin American financial markets. Journal of Multinational Financial Management, 16, 269-290
• Lillo, F., Farmer, J. (2004). The long memory of the efficient market. Studies in Nonlinear Dynamics & Econometrics, 8(3). https://doi.org/10.2202/1558-3708.1226.
• Lipka, J. M., Los, C. A. (2002). Persistence characteristics of European stock indexes (Working Paper). Kent, Kent State University.
• Lipka, J. M., Los, C. A. (2003). Long-term dependence characteristics of European stock indices. Kent State University Department of Finance Working Paper (Available at SSRN: http://ssrn.com/abstract=388020 or doi:10.2139/ssrn.388020) (accessed 23.01.18).
• Lo, A. W. (1991). Long-term memory in stock market prices. Econometrica, 59(5), 1279-1313.
• 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, 41-66
• Los, C. A., Yu, B. (2008). Persistence characteristics of the Chinese stock markets. International Review of Financial Analysis, 17, 64-82
• Lux, T. (1996). Long-term stochastic dependence in financial prices: evidence from the German stock market. Applied Economics Letters, 3, 701-706
• Mandelbrot, B. B. (1971). When can price be arbitraged efficiently? A limit to the validity of the random walk and martingale models. Review of Economics and Statistics, 53, 225-236.
• Mandelbrot, B. B. (1972). A statistical methodology for non-periodic cycles: from the covariance to R/S analysis. Annals of Economic and Social Measurement, 1(3), 259-290.
• Mandelbrot, B. B. (1975). Limit theorems on the self-normalized range for weakly and strongly dependent processes. Zeitschrift fur Wahrscheinlichkeitstheorie und Verwandte Gebiete, 31(4), 271-285.
• Mandelbrot, B. B., Taqqu, M. (1979). Robust R/S analysis of long-run serial correlation. Bulletin of International Statistical Institute, 48(Book 2), 59-104.
• Mandelbrot, B. B., Wallis, J. R. (1969). Robustness of the rescaled range R/S in the measurement of noncyclic long-run statistical dependence. Water Resources Research, 5, 967-988.
• Mandelbrot, B. B., Wallis, J. R. (1969). Some long-run properties of geophysical records. Water Resources Research, 5, 321-340.
• Matos, J. A. O., Gama, S. M. A., Ruskin, H. J., Duarte, J.A.M.S. (2004). An econophysics approach to the Portuguese Stock Index PSI-20. Physica A: Statistical Mechanics and its Applications, 342, 665-676.
• Matos, J. A. O., Gama, S. M. A., Ruskin, H. J., Sharkasi, A. A., Crane, M. (2008). Time and scale Hurst exponent analysis for financial markets. Physica A: Statistical Mechanics and its Applications, 387, 3910-3915.
• Newey, W. K., West, K. D. (1987). A simple positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703-708.
• Núñez, H. F. S., Martínez, F. V., Villareal, C. C. (2017). Is there long memory in stock markets, or does it depend on the model, period or frequency? Ensayos, 36(1), 1-24.
• Oh, G., Kim, S., Um, C-J. (2006). Statistical properties of the returns of stock prices of international markets. (Available at http://arxiv.org/pdf/physics/0601126v1.pdf) (accessed 25.01.18).
• Peng, C-K., Buldyrev, S., Havlin, S., Simons, M., Stanley, H., Golderberger, A. (1994). Mosaic organization of DNA sequences. Physical Review E, 49(2), 1685-1689.
• Robinson, P. M. (1994). Time series with strong dependence. In C.A. Sims (Ed.) Proceedings of the Sixth World Congress of Advances in Econometrics (pp. 47-95), Vol. 1. Cambridge University Press, Cambridge, UK.
• Robinson, P. M. (1995). Gaussian semiparametric estimation of long-range dependence. Annals of Statistics, 23, 1630-1661.
• Sadique, S., Silvapulle, P. (2001). Long-term memory in stock market returns: international evidence. International Journal of Finance and Economics, 6, 59-67.
• Weron, R. (2002). Estimating long-range dependence: finite sample properties and confidence intervals. Physica A: Statistical Mechanics and its Applications, 312(1-2), 285-299.

Identyfikatory

DOI

10.15208/beh.2018.59