Persistence characteristics of Latin American financial markets

Persistence characteristics of the Chinese stock markets

“…Scalograms are plots of colorized squared resonance (correlation) coefficients of the proper time-frequency correlation of the time series with the properly time-frequency scaled (Morlet-6) wavelets. They show how well the time series Table 1 for each series and the scale a = 22 days (≈1 month of trading days), using the Chi-squared statistic introduced in Kyaw et al (2006), as shown in Appendix A. Notice that (a) all three indices are persistent, but that (b) both the Shanghai Stock Index and the Shenzen (A Share) Stock Index are about equally less persistent than the Shenzen B Share Index, which is most persistent and therefore has most unpredictable market risk.…”

“…The estimation of the degree of long-term dependence in this paper follows the improved methodology discussed in Kyaw et al (2006). They present a strong argument for the robustness of measuring long-term dependence, i.e., the degree of market persistence, by identifying the monofractal (or global) Hurst exponent of the price series: The measurement model is the encompassing Fractal Brownian Motion (FBM), in which the rate of return is an affine process with fractionally differenced white noise innovations (Elliott & van der Hoek, 2003;Granger & Joyeux, 1980;Los, 2003;Mandelbrot and Van Ness, 1968):…”

“…Recent empirical financial market research has demonstrated that the monofractal Hurst exponent, a measure of such long-term persistence, tends to provide a good characterization of the distributional scaling characteristics of financial price diffusion processes (Kyaw, Los, & Zong, 2006;Los, 2003;Mills, 1999). We try to find out if this holds also true for the Chinese stock markets.…”

“…Some research attempts to shed light on causal relationship between firm characteristics and stock prices (Chen & Xiong, 2001;Wang et al, 2004). However, those traditional time-series models are criticized for not being able to model long-term dependence too well and for requiring (and often presuming) Gaussian distribution characteristics for the residuals (Kyaw et al, 2006).…”

“…In addition, they demonstrate that the visualization of the wavelet resonance coefficients and their power spectra, in the form of localized scalograms and averaged scalegrams, forcefully assists with the detection and measurement of several nonlinear types of market price diffusion. Kyaw et al (2006) improve this methodology by analyzing the degree of long-term dependence of Latin American financial markets, measuring monofractal Hurst exponents from wavelet MRA of Latin American stock and currency markets. They find that the financial rates of return from are nonnormal, non-stationary, non-ergodic and also long-term dependent.…”