Co-Integration of Stock Markets Using Wavelet Theory and Data Mining
The most powerful argument for cross-border investing is the risk reduction due to low correlation of world's stock markets. Risk diversification has become more important as financial markets globalize. The financial markets are becoming increasingly related due to advanced information technology which lowers the transaction costs. Diversifying internationally in markets with low correlation with domestic markets reduces the systematic risk and investors must be willing to take advantage of these correlations to reduce volatility in their portfolios. The paper tries to analyze the long-run relationship among seven prominent stock indices using Wavelet theory. The emanating co-integration results are further substantiated using the Apriori association rule mining. The findings suggest that there is strong to moderate co-integration among many stock markets.