摘要
In this paper, we introduce a novel algorithm, based on the wavelet transform, to measure stock market development. This algorithm is applied to the return series of fourteen worldwide market indices from 1996 to 2005. We find that a comparison of the return series in terms of the quantity of fractional Gaussian noise (fGn), for different values of Hurst exponent (H), facilitates the classification of stock markets according to their degree of development. We also observe that the simple classification of stock markets into “emerging” or “developing” and “mature” or “developed” is no longer sufficient. However, stock markets can be grouped into three categories that we named emerging, intermediate and mature.
In this paper, we introduce a novel algorithm, based on the wavelet transform, to measure stock market development. This algorithm is applied to the return series of fourteen worldwide market indices from 1996 to 2005. We find that a comparison of the return series in terms of the quantity of fractional Gaussian noise (fGn), for different values of Hurst exponent (H), facilitates the classification of stock markets according to their degree of development. We also observe that the simple classification of stock markets into “emerging” or “developing” and “mature” or “developed” is no longer sufficient. However, stock markets can be grouped into three categories that we named emerging, intermediate and mature.
