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Full Information by Kernel-Damping

Source: R/full_information.R
kernel_normal.Rd

Historical realizations receive a weight proportional to their distance from a target mean.

Usage

kernel_normal(x, mean, sigma)

# S3 method for default
kernel_normal(x, mean, sigma)

# S3 method for numeric
kernel_normal(x, mean, sigma)

# S3 method for matrix
kernel_normal(x, mean, sigma)

# S3 method for ts
kernel_normal(x, mean, sigma)

# S3 method for xts
kernel_normal(x, mean, sigma)

# S3 method for tbl_df
kernel_normal(x, mean, sigma)

# S3 method for data.frame
kernel_normal(x, mean, sigma)

Arguments

x

An univariate or a multivariate distribution.

mean

A numeric vector in which the kernel should be centered.

sigma

The uncertainty (volatility) around the mean.

Value

A numerical vector of class ffp with the new probabilities distribution.

See also

crisp exp_decay

Examples

library(ggplot2)

ret <- diff(log(EuStockMarkets[ , 1]))
mean <- -0.01 # scenarios around -1%
sigma <- var(diff(ret))

kn <- kernel_normal(ret, mean, sigma)
kn
#> <ffp[1859]>
#> 0.0007806016 0.0007261808 0.0003335773 0.0006663388 0.0007309458 ... 7.07516e-05

autoplot(kn) +
  scale_color_viridis_c()


# A larger sigma spreads out the distribution
sigma <- var(diff(ret)) / 0.05
kn <- kernel_normal(ret, mean, sigma)

autoplot(kn) +
  scale_color_viridis_c()