Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
The missing link between beta and volatility is correlation.
Previously
“4 and a half myths about beta in finance” attempted to dislodge several myths about beta, including that beta is about volatility.
“Low (and high) volatility strategy effects” showed a plot of beta versus volatility for stocks in the S&P 500 for estimates from 2006. This post looks at the same thing at the other end of the time frame used in that post.
Data
Betas and volatilities of almost all the stocks in the S&P 500 are estimated on the 250 trading days from 2011 March 01 to 2012 February 24. This should be thought of as a random — not data-snooped — window. Shorthand for this time period is the “final” window.
Pictures
Figure 1 shows beta versus volatility for the stocks. Like the similar graph in “Low (and high) volatility strategy effects” there is an outlier with high volatility but moderate beta. But it is a different stock.
Figure 1: The volatility versus beta estimates from the “final” window.
Figure 2: Returns for the S&P 500 and NFLX in the “final” window.
Figure 3: Density of correlation of stocks to the S&P 500 during the “final” period — the vertical blue line is NFLX correlation.
Figure 4 is an indication of the dynamics of the beta-volatility relationship.
Figure 4: Beta versus volatility from 2006 to the “final” period (blue points).
Figure 5: The beta-volatility dynamics of NFLX using a 250 trading day window.
Summary
The correlation of a stock to the index is a determinant of the stock’s beta along with its volatility.
Beta is not constant.
Appendix R
segment plot
The code to produce Figure 4 is:
plot(sp5.volfin, sp5.betafin, xlab="Volatility", ylab="Beta", col="steelblue", xlim=range(sp5.vol06, sp5.volfin), ylim=range(sp5.beta06, sp5.betafin)) segments(sp5.volfin, sp5.betafin, sp5.vol06, sp5.beta06, col="gold") points(sp5.volfin, sp5.betafin, col="steelblue")
beta-volatility dynamics
Two functions were written to create Figure 5 — one to do the computing, and one to do the actual plotting. They are pp.rollbetavol
and pp.timeline
. The second of these fails to say it in the function definition, but they are both in the public domain — you can do whatever you like with them.
Subscribe to the Portfolio Probe blog by Email
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.