A Wavelet-Based Method to Measure Stock Market Development

• 作者: Adel Al Sharkasi Heather J. Ruskin José Matos Martin Crane Sílvio M. A. Gama
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: WAVELET TRANSFORM Hurst EXPONENT (H) STOCK Market CLASSIFICATIONS
• 年，卷（期）: 2014,（1）
• 页码范围: 89-96
• 页数: 8页
• 分类号: R73

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.