Time-Frequency Wavelet Analysis of Stock-Market Co-Movement Between and Within Geographic Trading Blocs

Bilel Kaffel and Fathi Abid

Chapter 36 in Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning:(In 4 Volumes), 2020, pp 1399-1437 from World Scientific Publishing Co. Pte. Ltd.

Abstract: In the context of globalization, through a growing process of market liberalization, an advanced technology and an economic trading bloc, national stock markets have become more interdependent, which limits the international portfolio diversification opportunities. This chapter investigates the degree of stock market co-movement between and within 13 developed European Union markets, six developing Latin American markets, two developed North American markets, 10 developing Asian markets, Norway, Switzerland, Australia and Japan markets. The research methodology employed includes wavelet correlation, wavelet multiple cross-correlation and wavelet coherence. Results show a positive correlation across intra and inter trading blocs in all investment horizons and over time, and they show that the linkage between stock returns increases with the time scale, implying that the international diversification benefits have largely disappeared in globalized world markets. Moreover, we found a high degree of co-movement at low frequencies in crisis and no crisis periods, which indicates a fundamental relationship between stock market returns. Finally, multiple cross-correlation analysis reveals that stock markets are positively correlated at all wavelet scales and at all lags, and it reveals that France’s stock market is the potential leader or follower of the other European and other major world stock markets at low and high frequencies.

Keywords: Financial Econometrics; Financial Mathematics; Financial Statistics; Financial Technology; Machine Learning; Covariance Regression; Cluster Effect; Option Bound; Dynamic Capital Budgeting; Big Data (search for similar items in EconPapers)
JEL-codes: C01 C1 G32 (search for similar items in EconPapers)
Date: 2020
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