2018 Volatility Recap

2018 brought more volatility to the markets, which so far has spilled into 2019. Let’s take a look at the long term volatility history picture using the Dow Jones Industrial Average:

Indeed, 2018 was the most volatile year since 2011. Relatively speaking however, the volatility is on the low end for a bear market, which I believe started in late December.

The above chart was produced using the following R code:

library(quantmod)
library(ggplot2)
library(ggthemes)
library(grid)

dji.annual.volatility = function(dji, year) {
   dates = paste("/", as.character(year), sep="")
   dji = na.exclude(dji[dates])
   djiVol = aggregate(dji, as.numeric(format(index(dji), "%Y")),
               function(ss) coredata(tail(TTR:::volatility(
               ss,
               n=NROW(ss),
               calc="close"), 1)))
   
   xx = ecdf(as.vector(djiVol))(as.numeric(tail(djiVol,1)))
   print(xx)
   absRets = na.exclude(abs(ROC(dji[dates], type="discrete")))
   yy = as.numeric(format(index(absRets), "%Y"))
   zz = aggregate(absRets, yy, function(ss) tail(cumprod(1+ss),1))
   print(as.vector(tail(zz,1)))
   
   df = cbind(as.data.frame(index(djiVol)), coredata(djiVol))
   colnames(df) = c("Year", "Volatility")
   
   gg = qplot(x=Year, y=Volatility, data=df, geom="line", colour=Volatility, xlab="Year", ylab="Volatility")
   gg = gg + theme_solarized(base_size=16, base_family="verdana", light=TRUE)

   return(list(plot=gg, dji=dji, dji.vol=djiVol, crystal.ball=zz, df=df))
}

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