A comparison of developed and emerging equity market return volatility at different time scales

Purpose - The purpose of this paper is to examine the volatility of daily returns in a sample of developed and emerging equity markets at different time scales through wavelet decomposition. Such information is vital for international investors who have different time horizons for their investment decisions and trading strategies. Design/methodology/approach - The wavelet technique used here allows the return series to be viewed at different frequency by decomposing the series into different time horizons known as time scales. The decomposed return series enable investigation of return variability at different return intervals. Findings - In an analysis at different time scales, there is no evidence to suggest that the return dynamics of developed and emerging markets are different. In both types of markets, return variance is time scale dependent, satisfying a pure power law process, and the variability in returns is more likely to be due to the dynamics at the lower time scales. While emerging markets generally exhibit a higher level of volatility, the relative contribution from each time scale is quite similar to that of the developed markets. Originality/value - The difference in the return dynamics between emerging and developed markets is observed at the lowest time scale. This is an indication that differences in the return dynamics between the two types of markets may be more likely in the short term (high frequency) rather than in the long term. A plausible reason for this is speculative trading. Such information is vital for international investors who have different time horizons for their investment decisions and trading strategies.

[1]  Viviana Fernandez,et al.  The CAPM and value at risk at different time-scales , 2006 .

[2]  Time Diversification, Safety-First and Risk , 1999 .

[3]  D. Galagedera,et al.  Wavelet timescales and conditional relationship between higher-order systematic co-moments and portfolio returns , 2008 .

[4]  Francis Haeuck In,et al.  The relationship between stock returns and inflation: new evidence from wavelet analysis , 2005 .

[5]  A. M. Masih,et al.  Systematic Risk and Time Scales: New Evidence from an Application of Wavelet Approach to the Emerging Gulf Stock Markets , 2010 .

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

[7]  A Wavelet Analysis of MENA Stock Markets , 2005 .

[8]  R. Gencay,et al.  Scaling properties of foreign exchange volatility , 2001 .

[9]  H. Levy,et al.  Portfolio Composition and the Investment Horizon , 1994 .

[10]  P. Samuelson Lifetime Portfolio Selection by Dynamic Stochastic Programming , 1969 .

[11]  D. Galagedera,et al.  Wavelet Timescales and Conditional Relationship between Higher-Order Systematic Co-Moments and Portfolio Returns: Evidence in Australian Data , 2004 .

[12]  A. Walden,et al.  Wavelet Methods for Time Series Analysis , 2000 .

[13]  Brandon Whitcher,et al.  Systematic risk and timescales , 2003 .

[14]  Donald P. Percival,et al.  On estimation of the wavelet variance , 1995 .

[15]  P. Robinson Semiparametric Analysis of Long-Memory Time Series , 1994 .

[16]  Francesca Carrieri,et al.  Characterizing World Market Integration Through Time , 2003 .

[17]  Mohammed Alzahrani,et al.  Systematic Risk and Time Scales: New Evidence from an Application of Wavelet Approach to the Emerging Gulf Stock Markets , 2010 .