World Academy of Science, Engineering and Technology
Published November 27, 2011 | Version 9775
Journal article Open

Are Asia-Pacific Stock Markets Predictable? Evidence from Wavelet-based Fractional Integration Estimator

Authors/Creators

Description

This paper examines predictability in stock return in developed and emergingmarkets by testing long memory in stock returns using wavelet approach. Wavelet-based maximum likelihood estimator of the fractional integration estimator is superior to the conventional Hurst exponent and Geweke and Porter-Hudak estimator in terms of asymptotic properties and mean squared error. We use 4-year moving windows to estimate the fractional integration parameter. Evidence suggests that stock return may not be predictable indeveloped countries of the Asia-Pacificregion. However, predictability of stock return insome developing countries in this region such as Indonesia, Malaysia and Philippines may not be ruled out. Stock return in the Thailand stock market appears to be not predictable after the political crisis in 2008.

Files

9775.pdf

Files (258.9 kB)

Name Size Download all
md5:a3e9e00d06dcdbbac1dc5e407a5562d2
258.9 kB Preview Download

Additional details

References

  • Antoniou, Antonios, NurayErgul, and Phil Holmes, 1997, Market efficiency, thin trading and non-linear behaviour: Evidence from an emerging market, European Financial Management 3, pp. 175-190.
  • Pesaran, M.H., and A. Timmermann, 1995, Predictability of stock returns: Robustness and economic significance, Journal of Finance 50,pp. 1201-1228.
  • Greene, Myron T., and Bruce D. Fielitz, 1977, Long-term dependence in common stock returns, Journal of Financial Economics 4, pp. 339-349.
  • LO, AW, 1991, Long-term memory in stock market prices, Econometrica 59, 1279-1313.
  • Peters, E.E., 1991. Chaos and order in the capital markets (Wiley).
  • Cajueiro, Daniel O., and Benjamin M. Tabak, 2004, Thehurst exponent over time: Testing the assertion that emerging markets are becoming more efficient, Physica A: Statistical and Theoretical Physics 336, pp. 521-537.
  • Cajueiro, Daniel O., and Benjamin M. Tabak, 2005, Ranking efficiency for emerging equity markets ii, Chaos, Solitons& Fractals 23, pp. 671- 675.
  • DiSario, R., H. Saraoglu, J. McCarthy, and H. Li, 2008, Long memory in the volatility of an emerging equity market: The case of turkey, Journal of International Financial Markets, Institutions and Money 18, pp. 305-312.
  • Kang, S.H., and S.M. Yoon, 2008, Long memory features in the high frequency data of the korean stock market, Physica A: Statistical Mechanics and its Applications 387,pp. 5189-5196. [10] Bond, D., and K.A. Dyson, 2008, Long memory and nonlinearity in stock markets, Applied Financial Economics Letters 4, pp. 45-48. [11] Kasman, A., S. Kasman, and E. Torun, 2009, Dual long memory property in returns and volatility: Evidence from the cee countries' stock markets, Emerging Markets Review 10, pp. 122-139. [12] Aydogan, K., and G.G. Booth, 1988, Are there long cycles in common stock returns?,Southern Economic Journal, pp. 141-149. [13] Lobato, I.N., and N.E. Savin, 1998, Real and spurious long-memory properties of stock-market data, Journal of Business & Economic Statistics, pp. 261-268. [14] Kuswanto, H., and P. Sibbertsen, 2008, A study on spurious long memory, Applied Mathematical Sciences 2, pp. 2713-2734. [15] Hurst, H.E., 1951, {long-term storage capacity of reservoirs}, Trans. Amer. Soc. Civil Eng. 116, pp. 770-808. [16] Granger, C.W.J., and R. Joyeux, 1980, An introduction to long memory time series models and fractional differencing, Journal of time series analysis 1, pp. 15-29. [17] Hosking, J.R.M., 1981, Fractional differencing, Biometrika 68, pp. 165. [18] Jensen, M.J., 2000, An alternative maximum likelihood estimator of long-memory processes using compactly supported wavelets, Journal of Economic Dynamics and Control 24, pp. 361-387. [19] Percival, D.B., and A.T. Walden, 2006. Wavelet methods for time series analysis (Cambridge UnivPr). [20] Gençay, R., F. Selçuk, and B. Whitcher, 2002. An introduction to wavelets and other filtering methods in finance and economics (Academic Pr). [21] Gencay, R., F. Selcuk, and B. Whitcher, 2005, Multiscale systematic risk, Journal of International Money and Finance 24, pp. 55-70. [22] Jensen, M.J., 1999, An approximate wavelet mle of short-and longmemory parameters, Studies in Nonlinear Dynamics & Econometrics 3, pp. 5.