This module allows you to analyze existing cross correlation between Bovespa and NQTH. You can compare the effects of market volatilities on Bovespa and NQTH 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 NQTH. See also your portfolio center. Please also check ongoing floating volatility patterns of Bovespa and NQTH.
|Horizon||30 Days Login to change|
Predicted Return Density
Bovespa vs. NQTH
Assuming 30 trading days horizon, Bovespa is expected to generate 1.83 times more return on investment than NQTH. However, Bovespa is 1.83 times more volatile than NQTH. It trades about 0.07 of its potential returns per unit of risk. NQTH is currently generating about 0.09 per unit of risk. If you would invest 9,457,800 in Bovespa on May 18, 2019 and sell it today you would earn a total of 272,842 from holding Bovespa or generate 2.88% return on investment over 30 days.
Pair Corralation between Bovespa and NQTH
|Time Period||2 Months [change]|
Diversification Opportunities for Bovespa and NQTH
Very weak diversification
Overlapping area represents the amount of risk that can be diversified away by holding Bovespa and NQTH in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on NQTH 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 NQTH. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of NQTH has no effect on the direction of Bovespa i.e. Bovespa and NQTH go up and down completely randomly.
See also your portfolio center. Please also try Risk-Return Analysis module to view associations between returns expected from investment and the risk you assume.