Correlation VXX vs XIV, Year 2011

Chart 1 Correlation %XIV vs %VXX, Year 2011

showing good, negative, linear correlation (R squared = 0.994)

Click charts to enlarge

%VXX %XIV

Sum 20.69 -32.38

Days 252 252

Average 0.08 -0.13

StDevP 4.66 4.63

Chart 2 Correlation VXX vs XIV, Year 2011

showing some interesting patterns and voids

and pretty good, negative, exponential correlation (R squared = 0.914)

but not as good as Chart 1

Chart 3 showing VXX vs XIV, Year 2011 by month

Chart showing VXX and XIV by month, Year 2011

Chart 4 showing VXX vs XIV, Year 2011 by month

Chart 5 showing VXX vs XIV, Year 2011

VXX XIV ratio

Sum 8418 2976 964

Days 252 252 252

Average 33.40 11.81 3.83

StDevP 8.90 3.95 2.67

Chart 6 Google Finance Chart XIV vs VXX, Year 2011

showing percentage changes from Jan 3, 2011

Google Finance Chart

Charts 4, 5 and 6 show that both VXX and XIV are not good long-term hedges against volatility, especially XIV unless it's shorted, which was down 46% last year:

VXX XIV

Dec 30, 2011 35.53 6.51

Dec 31, 2010 37.61 11.95

% Decrease 6% 46%

Click here to read an article that also concludes its better to short VXX than go long XIV, as Charts 4,5 and 6 suggest too.

It may seem odd that VXX and XIV cross each other near zero % twice, then diverge with XIV down 46% and VXX down only 6% at year end. The primary reason for this divergence can be seen in Charts 4 and 5 — in the last 5 months of 2011, increased VIX volatility caused VXX price to increase significantly from its low, and XIV price to decrease significantly from its high. Contango contributed to decline prices.

The exponential regression equation

For exponential trend lines a transformation to a linear model takes place. The optimal curve fitting is related to the linear model and the results are interpreted accordingly.

The exponential regression follows the equation y=b*exp(a*x) or y=b*m^x, which is transformed to ln(y)=ln(b)+a*x or ln(y)=ln(b)+ln(m)*x respectively.

a = SLOPE(LN(Data_Y);Data_X)

The variables for the second variation are calculated as follows:

m = EXP(SLOPE(LN(Data_Y);Data_X))

b = EXP(INTERCEPT(LN(Data_Y);Data_X))

Calculate the coefficient of determination by

r² = RSQ(LN(Data_Y);Data_X)

Besides m, b and r² the array function LOGEST provides additional statistics for a regression analysis.

Source

DATA

Click to enlarge

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