This module allows you to analyze existing cross correlation between Bovespa and Taiwan Wtd. You can compare the effects of market volatilities on Bovespa and Taiwan Wtd and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in Bovespa with a short position of Taiwan Wtd. See also your portfolio center. Please also check ongoing floating volatility patterns of Bovespa and Taiwan Wtd.
|Horizon||30 Days Login to change|
Predicted Return Density
Bovespa vs. Taiwan Wtd
Assuming 30 trading days horizon, Bovespa is expected to generate 1.42 times more return on investment than Taiwan Wtd. However, Bovespa is 1.42 times more volatile than Taiwan Wtd. It trades about 0.11 of its potential returns per unit of risk. Taiwan Wtd is currently generating about -0.15 per unit of risk. If you would invest 9,328,500 in Bovespa on May 17, 2019 and sell it today you would earn a total of 475,500 from holding Bovespa or generate 5.1% return on investment over 30 days.
Pair Corralation between Bovespa and Taiwan Wtd
|Time Period||2 Months [change]|
Diversification Opportunities for Bovespa and Taiwan Wtd
Very good diversification
Overlapping area represents the amount of risk that can be diversified away by holding Bovespa and Taiwan Wtd in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on Taiwan Wtd and Bovespa is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on Bovespa are associated (or correlated) with Taiwan Wtd. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Taiwan Wtd has no effect on the direction of Bovespa i.e. Bovespa and Taiwan Wtd go up and down completely randomly.
See also your portfolio center. Please also try Pattern Recognition module to use different pattern recognition models to time the market across multiple global exchanges.