exp_decay: Full Information by Exponential Decay

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

Full Information by Exponential Decay

Description

Exponential smoothing twists probabilities by giving relatively more weight to recent observations at an exponential rate.

Usage

exp_decay(x, lambda)

## Default S3 method:
exp_decay(x, lambda)

## S3 method for class 'numeric'
exp_decay(x, lambda)

## S3 method for class 'matrix'
exp_decay(x, lambda)

## S3 method for class 'ts'
exp_decay(x, lambda)

## S3 method for class 'xts'
exp_decay(x, lambda)

## S3 method for class 'data.frame'
exp_decay(x, lambda)

## S3 method for class 'tbl'
exp_decay(x, lambda)

Arguments

x	An univariate or a multivariate distribution.
lambda	A double for the decay parameter.

Details

The half-life is linked with the lambda parameter as follows:

• HL = log(2) / lambda.

For example: log(2) / 0.0166 is approximately 42. So, a parameter lambda of 0.0166 can be associated with a half-life of two-months (21 * 2).

Value

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

See Also

crisp kernel_normal half_life

Examples

library(ggplot2)

# long half_life
long_hl <- exp_decay(EuStockMarkets, 0.001)
long_hl
autoplot(long_hl) +
  scale_color_viridis_c()

# short half_life
short_hl <- exp_decay(EuStockMarkets, 0.015)
short_hl
autoplot(short_hl) +
  scale_color_viridis_c()

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