kernel_entropy: Partial Information Kernel-Damping

In ffp: Fully Flexible Probabilities for Stress Testing and Portfolio Construction

Partial Information Kernel-Damping

Description

Find the probability distribution that can constrain the first two moments while imposing the minimal structure in the data.

Usage

kernel_entropy(x, mean, sigma = NULL)

## Default S3 method:
kernel_entropy(x, mean, sigma = NULL)

## S3 method for class 'numeric'
kernel_entropy(x, mean, sigma = NULL)

## S3 method for class 'matrix'
kernel_entropy(x, mean, sigma = NULL)

## S3 method for class 'ts'
kernel_entropy(x, mean, sigma = NULL)

## S3 method for class 'xts'
kernel_entropy(x, mean, sigma = NULL)

## S3 method for class 'tbl_df'
kernel_entropy(x, mean, sigma = NULL)

## S3 method for class 'data.frame'
kernel_entropy(x, mean, sigma = NULL)

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. When NULL, only the mean is constrained.

Value

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

See Also

double_decay

Examples

library(ggplot2)

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

ke <- kernel_entropy(ret, mean, sigma)
ke

autoplot(ke) +
  scale_color_viridis_c()

ffp documentation built on Sept. 29, 2022, 5:10 p.m.