He suggests using his table of volatility correlations as a back-of-the-envelope approach to estimate future volatility, which led me to question the stability of the correlations in his table. His table's values are calculated using daily data from 1970-present... but what if you were to calculate correlations using only one year of data, rather than thirty? The chart below shows the results.

The chart shows the rolling one-year (252-day) correlations for the diagonal in Michael's table (e.g. historical and future 2-day volatility, ..., historical and future 252-day volatility). You can see the shorter periods are generally more stable, but are also closer to zero. The rolling one-year correlation between historical and future one-year volatility swings wildly from +/-1 over time.

This isn't to argue that Michael's back-of-the-envelope approach is incorrect, rather it is an attempt to make the approach more robust by weighing long-term market characteristics against recent market behavior.

For those interested, here is the R code I used to replicate Michael's table and create the graph above. An interesting extension of this analysis would be to calculate volatility using TTR's volatility() function instead of standard deviation. I'll leave that exercise to the interested reader.

require(quantmod)

# pull SPX data from Yahoo Finance

getSymbols("^GSPC",from="1970-01-01")

# volatility horizons

GSPC$v2 <- runSD(ROC(Cl(GSPC)),2)

GSPC$v5 <- runSD(ROC(Cl(GSPC)),5)

GSPC$v10 <- runSD(ROC(Cl(GSPC)),10)

GSPC$v21 <- runSD(ROC(Cl(GSPC)),21)

GSPC$v63 <- runSD(ROC(Cl(GSPC)),63)

GSPC$v252 <- runSD(ROC(Cl(GSPC)),252)

# volatility horizon lags

GSPC$l2 <- lag(GSPC$v2,-2)

GSPC$l5 <- lag(GSPC$v5,-5)

GSPC$l10 <- lag(GSPC$v10,-10)

GSPC$l21 <- lag(GSPC$v21,-21)

GSPC$l63 <- lag(GSPC$v63,-63)

GSPC$l252 <- lag(GSPC$v252,-252)

# volatility correlation table

cor(GSPC[,7:18],use="pair")[1:6,7:12]

# remove missing observations

GSPC <- na.omit(GSPC)

# rolling 1-year volatility correlations

GSPC$c2 <- runCor(GSPC$v2,GSPC$l2,252)

GSPC$c5 <- runCor(GSPC$v5,GSPC$l5,252)

GSPC$c10 <- runCor(GSPC$v10,GSPC$l10,252)

GSPC$c21 <- runCor(GSPC$v21,GSPC$l21,252)

GSPC$c63 <- runCor(GSPC$v63,GSPC$l63,252)

GSPC$c252 <- runCor(GSPC$v252,GSPC$l252,252)

# plot rolling 1-year volatility correlations

plot.zoo(GSPC[,grep("c",colnames(GSPC))],n=1,

main="Rolling 252-Day Volitility Correlations")

## 2 comments:

I've noticed odd behaviour like this before in correlations (the swings in the 252 day vols from -1/+1); in this case the effect probably due to the lack of independence of the variables in use. that is, your bottom plot for 1 year historical/future really only has 40 independent points, the rest are (auto)correlated. likewise, when computing the correlation of two rolling series with high degrees of autocorrelation, the correlations get screwy.

sorry, it was late when I commented, and this was not terribly clear. here's the major problem: given series x_i, y_i, the sample correlation is an unbiased estimate of the population correlation when the x_i are independent of each other and the y_i are independent of each other. when they are not, as in the case of rolling 252 period standard deviations, with x_i having 251 period overlap with x_{i+1}, the sample correlation is meaningless, as you have found.

you can test this Monte Carlo style; generate 1000 normal iid variates X, and independently generate the same number of Y; compute the 250 period rolling stdevs. then compute the correlation between the two series of stdevs. repeat this 5000 times. the distribution of correlations will have standard error about zero much wider than suggested by R-Z transform. hth,

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