Writing the article "How much does "Beta" change depending on time?", I learned how to create an animation by using R language. Then, I would like to continue do that in this article.
In this article, I visualize time series of JGB term structure Ministry of Finance Japan publishes because I'm japanese!
You can download these data from here. You can get daily yield curve data, but I visualized these as monthly data for simplicity.
You can easily understand how JGB term structure behave and Japanese yield gradually go down.
The source code to create it is below.
(To run, you need to install xts, animation package, and ImageMagick)
library(xts) #Source of JGB curve source.jgb <- NULL source.jgb[[length(source.jgb) + 1]] <- "http://www.mof.go.jp/jgbs/reference/interest_rate/data/jgbcm_1974-1979.csv" source.jgb[[length(source.jgb) + 1]] <- "http://www.mof.go.jp/jgbs/reference/interest_rate/data/jgbcm_1980-1989.csv" source.jgb[[length(source.jgb) + 1]] <- "http://www.mof.go.jp/jgbs/reference/interest_rate/data/jgbcm_1990-1999.csv" source.jgb[[length(source.jgb) + 1]] <- "http://www.mof.go.jp/jgbs/reference/interest_rate/data/jgbcm_2000-2009.csv" source.jgb[[length(source.jgb) + 1]] <- "http://www.mof.go.jp/jgbs/reference/interest_rate/data/jgbcm_2010.csv" source.jgb[[length(source.jgb) + 1]] <- "http://www.mof.go.jp/jgbs/reference/interest_rate/jgbcm.csv" #From Japanese era To ChristianEra ToChristianEra <- function(x) { era <- substr(x, 1, 1) year <- as.numeric(substr(x, 2, nchar(x))) if(era == "H"){ year <- year + 1988 }else if(era == "S"){ year <- year + 1925 } as.character(year) } #Down load yield curve and convert to xts object GetJGBYield <- function(source.url) { jgb <- read.csv(source.url, stringsAsFactors = FALSE) #Extract date only jgb.day <- strsplit(jgb[, 1], "\\.") #stop warning warn.old <- getOption("warn") options(warn = -1) #From Japanese era To ChristianEra jgb.day <- lapply(jgb.day, function(x)c(ToChristianEra(x[1]), x[2:length(x)])) #From date string to date object jgb[, 1] <- as.Date(sapply(jgb.day, function(x)Reduce(function(y,z)paste(y,z, sep="-"),x))) #Convert data from string to numeric jgb[, -1] <- apply(jgb[, -1], 2, as.numeric) options(warn = warn.old) as.xts(read.zoo(jgb)) } #Down load JBG yield jgb.list <- lapply(source.jgb, GetJGBYield) #convert one xts object jgb.xts <- Reduce(rbind, jgb.list) #Interpolate(nearest value) coredata(jgb.xts) <- na.locf(t(na.locf(t(coredata(jgb.xts))))) #to monthly jgb.xts <- jgb.xts[endpoints(jgb.xts, on="months",k = 1)] #Label for x-axis label.term <- paste(gsub("X", "", colnames(jgb.xts)), "Y", sep="") #The range of y y.max <- c(min(jgb.xts), max(jgb.xts)) #plot one image Snap <- function(val){ term.structure <- coredata(val) index.date <- index(val) par(xaxt="n") plot(t(term.structure),type="l",lwd=3, col = 2, xlab = "Term", ylab = "Rate", ylim = y.max) par(xaxt="s") axis(1, 1:length(label.term), label.term) text(0.5, y.max[2], as.character(index.date), pos = 4) } #save as animation library(animation) saveGIF({ for(i in 1:nrow(jgb.xts)){Snap(jgb.xts[i])} },interval = 0.005)
