If Japan starts to spiral out of control, then what do they do? A spiral would be a sudden move higher in JGB rates with a simultaneous crash in the Japanese Yen. Their response would be to try to slow the positive feedback loop through external interv...

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See the last post for all the details. I just could not help creating a couple more. Variations on Favorite Plot - Time Series Line of JGB Yields by Maturity

p2 <- xyplot(value ~ date | indexname, data = jgb.melt, <br>    type = "l", layout = c(length(unique(jgb.melt$indexname)), <br>        1), panel = function(x, y, ...) {<br>        panel.abline(h = c(min(y), max(y)))<br>        panel.xyplot(x = x, y = y, ...)<br>        panel.text(x = x[length(x)/2], y = max(y), <br>            labels = levels(jgb.melt$indexname)[panel.number()], <br>            cex = 0.7, pos = 3)<br>    }, scales = list(x = list(tck = c(1, 0), alternating = 1), <br>        y = list(tck = c(1, 0), lwd = c(0, 1))), strip = FALSE, <br>    par.settings = list(axis.line = list(col = 0)), <br>    xlab = NULL, ylab = "Yield", main = "JGB Yields by Maturity Since Jan 2012")<br>p2 <- p2 + layer(panel.abline(h = pretty(jgb.melt$value), <br>    lty = 3))<br>p2<br>

From TimelyPortfolio

<br>jgb.xts.diff <- jgb.xts["2012::", ] - matrix(rep(jgb.xts["2012::", <br>    ][1, ], NROW(jgb.xts["2012::", ])), ncol = NCOL(jgb.xts), <br>    byrow = TRUE)<br>jgb.diff.melt <- xtsMelt(jgb.xts.diff)<br>jgb.diff.melt$date <- as.Date(jgb.diff.melt$date)<br>jgb.diff.melt$value <- as.numeric(jgb.diff.melt$value)<br>jgb.diff.melt$indexname <- factor(jgb.diff.melt$indexname, <br>    levels = colnames(jgb.xts))<br><br>p4 <- xyplot(value ~ date | indexname, data = jgb.diff.melt, <br>    type = "h")<br><br>update(p2, ylim = c(min(jgb.diff.melt$value), max(jgb.melt$value) + <br>    0.5)) + p4<br>

From TimelyPortfolio

<br>update(p2, ylim = c(min(jgb.diff.melt$value), max(jgb.melt$value) + <br>    0.5), par.settings = list(axis.line = list(col = "gray70"))) + <br>    update(p4, panel = function(x, y, col, ...) {<br>        # do color scale from red(negative) to<br>        # blue(positive)<br>        cc.palette <- colorRampPalette(c(brewer.pal("Reds", <br>            n = 9)[7], "white", brewer.pal("Blues", <br>            n = 9)[7]))<br>        cc.levpalette <- cc.palette(20)<br>        cc.levels <- level.colors(y, at = do.breaks(c(-0.3, <br>            0.3), 20), col.regions = cc.levpalette)<br>        panel.xyplot(x = x, y = y, col = cc.levels, <br>            ...)<br>    })<br>

From TimelyPortfolio

<br><br>p5 <- horizonplot(value ~ date | indexname, data = jgb.diff.melt, <br>    layout = c(1, length(unique(jgb.diff.melt$indexname))), <br>    scales = list(x = list(tck = c(1, 0))), xlab = NULL, <br>    ylab = NULL)<br><br>p5<br>

From TimelyPortfolio

<br>update(p2, ylim = c(0, max(jgb.melt$value) + 0.5), <br>    panel = panel.xyplot) + p5 + update(p2, ylim = c(0, <br>    max(jgb.melt$value)))<br>

From TimelyPortfolio Variations on Yield Curve Evolution ...

This blog is littered with posts about Japan. In one sentence, I think Japan presents opportunity and is a very interesting real-time test of much of my macro thinking. Proper visualization is absolutely essential for me to understand all of the dynami...

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