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stochvol (version 1.1.3)

volplot: Plotting Quantiles of the Latent Volatilities

Description

Displays quantiles of the posterior distribution of the volatilities over time as well as predictive distributions of future volatilities.

Usage

volplot(x, forecast = 0, dates = NULL, show0 = FALSE, col = NULL,
       	forecastlty = NULL, tcl = -0.4, mar = c(1.9, 1.9, 1.9, 0.5),
       	mgp = c(2, 0.6, 0), simobj = NULL, ...)

Arguments

x
svdraws object.
forecast
nonnegative integer or object of class svpredict, as returned by predict.svdraws. If an integer greater than 0 is provided, predict.svdraws
dates
vector of length ncol(x$latent), providing optional dates for labeling the x-axis. The default value is NULL; in this case, the axis will be labeled with numbers.
show0
logical value, indicating whether the initial volatility exp(h_0/2) should be displayed. The default value is FALSE.
col
vector of color values (see par) used for plotting the quantiles. The default value NULL results in gray lines for all quantiles expect the median, which is displayed in black.
forecastlty
vector of line type values (see par) used for plotting quantiles of predictive distributions. The default value NULL results in dashed lines.
tcl
The length of tick marks as a fraction of the height of a line of text. See par for details. The default value is -0.4, which results in slightly shorter tick marks than usual.
mar
numerical vector of length 4, indicating the plot margins. See par for details. The default value is c(1.9, 1.9, 1.9, 0.5), which is slightly smaller than the R-defaults.
mgp
numerical vector of length 3, indicating the axis and label positions. See par for details. The default value is c(2, 0.6, 0), which is slightly smaller than the R-defaults.
simobj
object of class svsim as returned by the SV simulation function svsim. If provided, ``true'' data generating values will be added to the plot(s).
...
further arguments are passed on to the invoked ts.plot function.

Value

  • Called for its side effects. Returns argument x invisibly.

See Also

updatesummary, paratraceplot, paradensplot, plot.svdraws.

Examples

Run this code
## Simulate a short and highly persistent SV process 
sim <- svsim(100, mu = -10, phi = 0.99, sigma = 0.2)

## Obtain 5000 draws from the sampler (that's not a lot)
draws <- svsample(sim$y, draws = 5000, burnin = 100,
		  priormu = c(-10, 1), priorphi = c(20, 1.5),
		  priorsigma = 0.2)

## Plot the latent volatilities and some forecasts
volplot(draws, forecast = 10)

## Re-plot with different quantiles
newquants <- c(0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99)
draws <- updatesummary(draws, quantiles=newquants)

volplot(draws, forecast = 10)

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