# 2018 Volatility Recap

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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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