explore the intended and unintended consequences of fed and boj actions

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<title>Intended Or Unintended Consequence</title>

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<h2>Intended Or Unintended Consequence</h2>

<p>A quick glimpse at the US 10y Treasury Bond rate since 2000 seems benign with low volatility and a general downward trend.</p>

<pre><code class="r">require(latticeExtra)
require(quantmod)

US10y &lt;- getSymbols(&quot;^TNX&quot;, from = &quot;2000-01-01&quot;, auto.assign = FALSE)[, 
    4]

asTheEconomist(xyplot(US10y, scales = list(y = list(rot = 1)), 
    main = &quot;US 10y Yield Since 2000&quot;))
</code></pre>

<p>However, if we plot the change in rate over 252 days or 1 year, we might wonder whether the rate is as benign as we initially surmised.  Although this might seem like an acceptable intended consequence of the aggressive monetary easing by the US Fed, I fear that we will face the historically normal unintended consequences.</p>

<pre><code class="r">p1 &lt;- asTheEconomist(xyplot(US10y - lag(US10y, 252), 
    scales = list(y = list(rot = 0)), main = &quot;US 10y Yield - Yearly Change&quot;))
p1
</code></pre>

<p>And if we use some Vanguard US Bond Funds (vbmfx and vustx) as a proxy for the US bond market <a href="https://timelyportfolio.blogspot.com/2013/01/approaching-zero-bound-bonds.html">Approaching the Zero Bound - Bonds</a>, we see that we are now in a market different than the last 4 years.</p>

<pre><code class="r">require(directlabels)
require(reshape2)

getSymbols(&quot;VUSTX&quot;, from = &quot;1990-01-01&quot;)
</code></pre>

<pre><code>## [1] &quot;VUSTX&quot;
</code></pre>

<pre><code class="r">getSymbols(&quot;VBMFX&quot;, from = &quot;1990-01-01&quot;)
</code></pre>

<pre><code>## [1] &quot;VBMFX&quot;
</code></pre>

<pre><code class="r">
bonds.tr &lt;- merge(ROC(VUSTX[, 6], 250), ROC(VBMFX[, 
    6], 250))
colnames(bonds.tr) &lt;- c(&quot;VanguardLongTsy&quot;, &quot;VanguardTotBnd&quot;)
bond.funds.melt &lt;- melt(as.data.frame(cbind(as.Date(index(bonds.tr)), 
    coredata(bonds.tr))), id.vars = 1)
colnames(bond.funds.melt) &lt;- c(&quot;date&quot;, &quot;fund&quot;, &quot;totret250&quot;)
bond.funds.melt$date &lt;- as.Date(bond.funds.melt$date)

p2 &lt;- direct.label(xyplot(bonds.tr, screens = 1, ylim = c(-0.35, 
    0.35), scales = list(y = list(rot = 0)), col = theEconomist.theme()$superpose.line$col, 
    par.settings = theEconomist.theme(box = &quot;transparent&quot;), 
    lattice.options = theEconomist.opts(), xlab = NULL, 
    main = &quot;Vanguard Bond Funds 250 Day Total Return&quot;), 
    list(&quot;last.points&quot;, hjust = 1, cex = 1.2))
p2
</code></pre>

<pre><code class="r">
p3 &lt;- asTheEconomist(horizonplot(totret250 ~ date | 
    fund, origin = 0, horizonscale = 0.05, data = bond.funds.melt, 
    strip = TRUE, strip.left = FALSE, par.strip.text = list(cex = 1.1), 
    layout = c(1, 2), main = &quot;Vanguard Bond Funds 250 Day Total Return&quot;))
p3
</code></pre>

<pre><code class="r">
# print(p2,position=c(0,0.4,1,1),more=TRUE)
# print(update(p3,main=NULL),position=c(0,0,1,0.5))
</code></pre>

<h2>Comparison to Japan</h2>

<p>For some reference, let&#39;s look at a country getting quite a bit of attention lately in the press.  We can add US yields to my favorite chart from <a href="https://timelyportfolio.blogspot.com/2013/05/japan-jgb-yieldsmore-lattice-charts.html">Japan - JGB YieldsMore Lattice Charts</a>.  Most of the difference is in the short end of the curve where the US is still near the minimum since 2012 while Japan is near the max out to 7 years.</p>

<pre><code class="r">url &lt;- &quot;https://www.mof.go.jp/english/jgbs/reference/interest_rate/&quot;
filenames &lt;- paste(&quot;jgbcme&quot;,c(&quot;&quot;,&quot;_2010&quot;,&quot;_2000-2009&quot;,&quot;_1990-1999&quot;,&quot;_1980-1989&quot;,&quot;_1974-1979&quot;),&quot;.csv&quot;,sep=&quot;&quot;)

#load all data and combine into one jgb data.frame
jgb &lt;- read.csv(paste(url,filenames[1],sep=&quot;&quot;),stringsAsFactors=FALSE)
for (i in 2:length(filenames)) {
  jgb &lt;- rbind(jgb,read.csv(paste(url,&quot;/historical/&quot;,filenames[i],sep=&quot;&quot;),stringsAsFactors=FALSE))
}

#now clean up the jgb data.frame to make a jgb xts
jgb.xts &lt;- as.xts(data.matrix(jgb[,2:NCOL(jgb)]),order.by=as.Date(jgb[,1]))
colnames(jgb.xts) &lt;- paste0(gsub(&quot;X&quot;,&quot;JGB&quot;,colnames(jgb.xts)))

#get Yen from the Fed
#getSymbols(&quot;DEXJPUS&quot;,src=&quot;FRED&quot;)

xtsMelt &lt;- function(data) {
  require(reshape2)

#translate xts to time series to json with date and data
  #for this behavior will be more generic than the original
  #data will not be transformed, so template.rmd will be changed to reflect

#convert to data frame
  data.df &lt;- data.frame(cbind(format(index(data),&quot;%Y-%m-%d&quot;),coredata(data)))
  colnames(data.df)[1] = &quot;date&quot;
  data.melt &lt;- melt(data.df,id.vars=1,stringsAsFactors=FALSE)
  colnames(data.melt) &lt;- c(&quot;date&quot;,&quot;indexname&quot;,&quot;value&quot;)
  #remove periods from indexnames to prevent javascript confusion
  #these . usually come from spaces in the colnames when melted
  data.melt[,&quot;indexname&quot;] &lt;- apply(matrix(data.melt[,&quot;indexname&quot;]),2,gsub,pattern=&quot;[.]&quot;,replacement=&quot;&quot;)
  return(data.melt)
  #return(df2json(na.omit(data.melt)))

}

jgb.melt &lt;- xtsMelt(jgb.xts[&quot;2012::&quot;,])
jgb.melt$date &lt;- as.Date(jgb.melt$date)
jgb.melt$value &lt;- as.numeric(jgb.melt$value)
jgb.melt$indexname &lt;- factor(
  jgb.melt$indexname,
  levels = colnames(jgb.xts)
)
jgb.melt$maturity &lt;- as.numeric(
  substr(
    jgb.melt$indexname,
    4,
    length( jgb.melt$indexname ) - 4
  )
)
jgb.melt$country &lt;- rep( &quot;Japan&quot;, nrow( jgb.melt ))

#now get the US bonds from FRED
USbondssymbols &lt;- paste0(&quot;DGS&quot;,c(1,2,3,5,7,10,20,30))

ust.xts &lt;- xts()
for (i in 1:length( USbondssymbols ) ) {
  ust.xts &lt;- merge( 
    ust.xts,
    getSymbols( 
     USbondssymbols[i],
     auto.assign = FALSE,
     src = &quot;FRED&quot;
    )
  )
}

ust.melt &lt;- na.omit( xtsMelt( ust.xts[&quot;2012::&quot;,] ) )

ust.melt$date &lt;- as.Date(ust.melt$date)
ust.melt$value &lt;- as.numeric(ust.melt$value)
ust.melt$indexname &lt;- factor(
  ust.melt$indexname,
  levels = colnames(ust.xts)
)
ust.melt$maturity &lt;- as.numeric(
  substr(
    ust.melt$indexname,
    4,
    length( ust.melt$indexname ) - 4
  )
)
ust.melt$country &lt;- rep( &quot;US&quot;, nrow( ust.melt ))

bonds.melt &lt;- rbind( jgb.melt, ust.melt )

p4 &lt;- xyplot(
  value ~ date | maturity,
  groups = country,
  data = bonds.melt,
  type = &quot;l&quot;,
  col = c(brewer.pal(9,&quot;Blues&quot;)[5],brewer.pal(9,&quot;Blues&quot;)[7]),
  layout = c( length( unique( bonds.melt$maturity ) ), 1 ),
  panel = function(x, y, subscripts, col, ...) {
    panel.abline(
      h = c( 
        sapply( by( y, bonds.melt[subscripts,&quot;country&quot;], summary ), min ),
        sapply( by( y, bonds.melt[subscripts,&quot;country&quot;], summary ), max )
      ),
      #if(!col){
      col = col[1:length(unique(bonds.melt[subscripts,&quot;country&quot;]))]
      #} else col = trellis.par.get()$superpose.line$col[1:length(unique(bonds.melt[subscripts,&quot;country&quot;]))]
    )
    panel.xyplot( x = x, y = y, subscripts = subscripts, col = col, ... )
    panel.text(
      x = x[ length(unique(x)) / 2],
      y = mean(
        c(
          sapply( by( y, bonds.melt[subscripts,&quot;country&quot;], summary ), max )[1],
          sapply( by( y, bonds.melt[subscripts,&quot;country&quot;], summary ), min )[2]
        )
      ),
      labels = paste0(unique(bonds.melt$maturity)[panel.number()],&quot;y&quot;),
      cex = 0.8,
      #font = 2,
      col = &quot;black&quot;,
      adj = c(0.5,0.5)
    )
  },
  scales = list( 
    x = list(
      draw = FALSE
      #tck = c(1,0),
      #alternating = 1,
      #at = min(bonds.melt$date),
      #labels = format(min(bonds.melt$date),&quot;%Y&quot;)
    ),
    y = list( tck = c(1,0), lwd = c(0,1) )
  ),
  strip = FALSE,
  par.settings = list(axis.line = list(col = 0)),
  xlab = NULL,
  ylab = &quot;Yield&quot;,
  main = &quot;JGB and US Yields by Maturity Since Jan 2012&quot;
)
p4 &lt;- p4 + layer(
  panel.abline(
    h = pretty(bonds.melt$value,4),
    lty = 3
  )
)
p4
</code></pre>

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