This module allows you to analyze existing cross correlation between Bovespa and NQEGT. You can compare the effects of market volatilities on Bovespa and NQEGT 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 NQEGT. See also your portfolio center. Please also check ongoing floating volatility patterns of Bovespa and NQEGT.
|Horizon||30 Days Login to change|
Predicted Return Density
Bovespa vs. NQEGT
Assuming 30 trading days horizon, Bovespa is expected to generate 0.44 times more return on investment than NQEGT. However, Bovespa is 2.27 times less risky than NQEGT. It trades about 0.06 of its potential returns per unit of risk. NQEGT is currently generating about -0.02 per unit of risk. If you would invest 10,265,460 in Bovespa on September 23, 2019 and sell it today you would earn a total of 488,899 from holding Bovespa or generate 4.76% return on investment over 30 days.
Pair Corralation between Bovespa and NQEGT
|Time Period||3 Months [change]|
Diversification Opportunities for Bovespa and NQEGT
No risk reduction
Overlapping area represents the amount of risk that can be diversified away by holding Bovespa and NQEGT in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on NQEGT 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 NQEGT. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of NQEGT has no effect on the direction of Bovespa i.e. Bovespa and NQEGT go up and down completely randomly.
See also your portfolio center. Please also try Watchlist Optimization module to optimize watchlists to build efficient portfolio or rebalance existing positions based on mean-variance optimization algorithm.