empirical_stats {ffp}

Summary Statistics for Empirical Distributions

Description

Computes the mean, standard deviation, skewness, kurtosis, Value-at-Risk (VaR) and Conditional Value-at-Risk CVaR) under flexible probabilities.

Usage

empirical_stats(x, p, level = 0.01)

## Default S3 method:
empirical_stats(x, p, level = 0.01)

## S3 method for class 'numeric'
empirical_stats(x, p, level = 0.01)

## S3 method for class 'matrix'
empirical_stats(x, p, level = 0.01)

## S3 method for class 'xts'
empirical_stats(x, p, level = 0.01)

## S3 method for class 'ts'
empirical_stats(x, p, level = 0.01)

## S3 method for class 'data.frame'
empirical_stats(x, p, level = 0.01)

## S3 method for class 'tbl_df'
empirical_stats(x, p, level = 0.01)

Arguments

x

A time series defining the scenario-probability distribution.

p

An object of the ffp class.

level

A number with the desired probability level. The default is level = 0.01.

Details

The data in x and p are expected to have the same number of rows (size).

Value

A tidy tibble with 3 columns:

• stat: a column with Mu, Std, Skew, Kurt, VaR and CVaR.

• name: the asset names.

• value: the computed value for each statistic.

Examples

library(dplyr, warn.conflicts = FALSE)
library(ggplot2)

ret <- diff(log(EuStockMarkets))

# with equal weights (standard scenario)
ew  <- rep(1 / nrow(ret), nrow(ret))
empirical_stats(x = ret, p = as_ffp(ew)) %>%
  ggplot(aes(x = name, y = value)) +
  geom_col() +
  facet_wrap(~stat, scales = "free") +
  labs(x = NULL, y = NULL)

# with ffp
exp_smooth <- exp_decay(ret, 0.015)
empirical_stats(ret, exp_smooth) %>%
  ggplot(aes(x = name, y = value)) +
  geom_col() +
  facet_wrap(~stat, scales = "free") +
  labs(x = NULL, y = NULL)