Pair Correlation Between SP 500 and IPC

This module allows you to analyze existing cross correlation between S&P 500 and IPC. You can compare the effects of market volatilities on SP 500 and IPC 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 SP 500 with a short position of IPC. See also your portfolio center. Please also check ongoing floating volatility patterns of SP 500 and IPC.
Investment Horizon     30 Days    Login   to change
Symbolsvs
 S&P 500  vs   IPC
 Performance (%) 
      Timeline 

Pair Volatility

Assuming 30 trading days horizon, S&P 500 is expected to generate 0.68 times more return on investment than IPC. However, S&P 500 is 1.46 times less risky than IPC. It trades about 0.2 of its potential returns per unit of risk. IPC is currently generating about -0.14 per unit of risk. If you would invest  255,715  in S&P 500 on October 25, 2017 and sell it today you would earn a total of  3,993  from holding S&P 500 or generate 1.56% return on investment over 30 days.

Correlation Coefficient

Pair Corralation between SP 500 and IPC
-0.37

Parameters

Time Period1 Month [change]
DirectionNegative 
StrengthInsignificant
Accuracy95.45%
ValuesDaily Returns

Diversification

Very good diversification

Overlapping area represents the amount of risk that can be diversified away by holding S&P 500 and IPC in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on IPC and SP 500 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 S&P 500 are associated (or correlated) with IPC. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of IPC has no effect on the direction of SP 500 i.e. SP 500 and IPC go up and down completely randomly.
    Optimize

Comparative Volatility

 Predicted Return Density 
      Returns