Are Asia-Pacific Stock Markets Predictable? Evidence from Wavelet-based Fractional Integration Estimator
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.
[1] 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.
[2] Pesaran, M.H., and A. Timmermann, 1995, Predictability of stock
returns: Robustness and economic significance, Journal of Finance
50,pp. 1201-1228.
[3] Greene, Myron T., and Bruce D. Fielitz, 1977, Long-term dependence in
common stock returns, Journal of Financial Economics 4, pp. 339-349.
[4] LO, AW, 1991, Long-term memory in stock market prices,
Econometrica 59, 1279-1313.
[5] Peters, E.E., 1991. Chaos and order in the capital markets (Wiley).
[6] 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.
[7] Cajueiro, Daniel O., and Benjamin M. Tabak, 2005, Ranking efficiency
for emerging equity markets ii, Chaos, Solitons& Fractals 23, pp. 671-
675.
[8] 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.
[9] 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.
[1] 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.
[2] Pesaran, M.H., and A. Timmermann, 1995, Predictability of stock
returns: Robustness and economic significance, Journal of Finance
50,pp. 1201-1228.
[3] Greene, Myron T., and Bruce D. Fielitz, 1977, Long-term dependence in
common stock returns, Journal of Financial Economics 4, pp. 339-349.
[4] LO, AW, 1991, Long-term memory in stock market prices,
Econometrica 59, 1279-1313.
[5] Peters, E.E., 1991. Chaos and order in the capital markets (Wiley).
[6] 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.
[7] Cajueiro, Daniel O., and Benjamin M. Tabak, 2005, Ranking efficiency
for emerging equity markets ii, Chaos, Solitons& Fractals 23, pp. 671-
675.
[8] 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.
[9] 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.
@article{"International Journal of Business, Human and Social Sciences:58842", author = "Pei. P. Tan and Don. U.A. Galagedera and Elizabeth A.Maharaj", title = "Are Asia-Pacific Stock Markets Predictable? Evidence from Wavelet-based Fractional Integration Estimator", abstract = "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.", keywords = "Asia-Pacific stock market,long-memory, return predictability, wavelet", volume = "5", number = "11", pages = "1589-5", }
