Using R as visualizing Volatility Index VIX

On September 22, 2003, the CBOE began disseminating price level information using revised methodology for the CBOE Volatility Index, VIX. A spreadsheet with more than 13 years of price history data using this new methodology is now available.

VIX?

In 1993, the Chicago Board Options Exchange® (CBOE®) introduced the CBOE Volatility Index®, VIX®, and it quickly became the benchmark for stock market volatility. It is widely followed and has been cited in hundreds of news articles in the Wall Street Journal, Barron’s and other leading financial publications. Since volatility often signifies financial turmoil, VIX is often referred to as the “investor fear gauge”.
Source: http://www.cboe.com/micro/vix/faq.aspx#1

So, I found this example from there. I change it little bit. In this exampele it use whole time series you will found there. Now let’s look at closer this amazing approach…

#required library
require(quantmod)
require(ggplot2)
require(reshape2)
require(plyr)
require(scales)

# download data source
input <- read.table("http://www.cboe.com/publish/ScheduledTask/MktData/datahouse/vixcurrent.csv", header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE)

# creating dataframe
dat<-data.frame(date=index(VIX),VIX)
#look what we get
head(dat)

#date conversation
input$date <- as.Date(input$Date, "%m/%d/%Y")

## below some example do to that
## read in date/time info in format 'm/d/y h:m:s'
## dates <- c("02/27/92", "02/27/92", "01/14/92", "02/28/92", "02/01/92")
## times <- c("23:03:20", "22:29:56", "01:03:30", "18:21:03", "16:56:26")
## x <- paste(dates)
## y <- strptime(x, "%m/%d/%y")

# extract year
input$year<-as.numeric(as.POSIXlt(input$date)$year+1900)

# and  the month too OK
input$month<-as.numeric(as.POSIXlt(input$date)$mon+1)

# monts into right order and giving factor name
input$monthf<-factor(input$month,levels=as.character(1:12),labels=c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"),ordered=TRUE)

# week days
input$weekday = as.POSIXlt(input$date)$wday

# week day ordering and gibing factor name (weekdays)
input$weekdayf<-factor(input$weekday,levels=rev(1:7),labels=rev(c("Mon","Tue","Wed","Thu","Fri","Sat","Sun")),ordered=TRUE)

# yearmonth
input$yearmonth<-as.yearmon(input$date)
input$yearmonthf<-factor(input$yearmonth)

# week of year by rows
input$week <- as.numeric(format(input$date,"%W"))

# making week to start at number 1
input<-ddply(input,.(yearmonthf),transform,monthweek=1+week-min(week))

# Ok, Now we are ready to plot

P<- ggplot(input, aes(monthweek, weekdayf, fill = VIX.Close)) +
geom_tile(colour = "white") + facet_grid(year~monthf) + scale_fill_gradient(low="yellow", high="red") +
opts(title = "Heatmap - view of future expected stock market volatility (risk)") + xlab("Week of Month") + ylab("")

#show the plot
P

# save the picture
# example - output graph to jpeg file
jpeg("heatmaps.jpg")
P
dev.off()

...and output will look like this:

That’s it,

Cheers, Marko