The behaviour of some UK equity indices: An application of Hurst and BDS tests 1 A previous version

The characterisation of equity market return series as random in nature has been questioned in recent times by the application of new statistical tools. This study uses recent advances in chaos theory to examine the behaviour of the London Financial Times Stock Exchange (FTSE) All Share, 100, 250 and 350 equity indices. The results reject the hypothesis that the index series examined in this study are random, independent and identically distributed. The results show that the FTSE stock index returns series is not truly random since some cycles or patterns show up more frequently than would be expected in a true random series. A Generalized Autoregressive Conditional Heteroskedasticity (GARCH(1,1)) process appears to explain the behaviour of the index series. The results may have implications for derivative instruments on the indices as well as for weak form market efficiency.

[1]  E. Fama The Behavior of Stock-Market Prices , 1965 .

[2]  M. M. Dryden A Statistical Study of U.K. Share Prices , 1970 .

[3]  Harold T. Davis,et al.  The Analysis of Economic Time Series. , 1942 .

[4]  W. Enders Applied Econometric Time Series , 1994 .

[5]  B. LeBaron,et al.  A test for independence based on the correlation dimension , 1996 .

[6]  Stanley R. Stansell,et al.  NONLINEARITIES IN EMERGING FOREIGN CAPITAL MARKETS , 1993 .

[7]  B. LeBaron,et al.  Nonlinear Dynamics and Stock Returns , 2021, Cycles and Chaos in Economic Equilibrium.

[8]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[9]  H. Akaike A new look at the statistical model identification , 1974 .

[10]  Maurice G. Kendall,et al.  The Analysis of Economic Time‐Series—Part I: Prices , 1953 .

[11]  H. E. Hurst,et al.  Long-Term Storage Capacity of Reservoirs , 1951 .

[12]  A. Lo Long-Term Memory in Stock Market Prices , 1989 .

[13]  E. H. Lloyd,et al.  The expected value of the adjusted rescaled Hurst range of independent normal summands , 1976 .

[14]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[15]  Douglas M. Patterson,et al.  Nonlinear Dynamics, Chaos, And Instability , 1994 .

[16]  Edgar E. Peters Fractal Market Analysis: Applying Chaos Theory to Investment and Economics , 1994 .

[17]  S. W. Cunningham,et al.  The Predictability of British Stock Market Prices , 1973 .

[18]  J. R. Wallis,et al.  Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence , 1969 .

[19]  Stephen L Taylor,et al.  Stock returns and volatility: An empirical study of the UK stock market , 1992 .

[20]  M. T. Greene,et al.  Long-term dependence in common stock returns , 1977 .

[21]  V. Errunza,et al.  Conditional Heteroskedasticity and Global Stock Return Distributions , 1994 .

[22]  David Hsieh Chaos and Nonlinear Dynamics: Application to Financial Markets , 1991 .

[23]  J. R. Wallis,et al.  Small Sample Properties of H and K—Estimators of the Hurst Coefficient h , 1970 .

[24]  T. Willey Testing for nonlinear dependence in daily stock indices , 1992 .