# view_on_covariance: Views on Covariance Matrix In ffp: Fully Flexible Probabilities for Stress Testing and Portfolio Construction

 view_on_covariance R Documentation

## Views on Covariance Matrix

### Description

Helper to construct views on variance-covariance matrix.

### Usage

```view_on_covariance(x, mean, sigma)

## Default S3 method:
view_on_covariance(x, mean, sigma)

## S3 method for class 'matrix'
view_on_covariance(x, mean, sigma)

## S3 method for class 'xts'
view_on_covariance(x, mean, sigma)

## S3 method for class 'tbl_df'
view_on_covariance(x, mean, sigma)
```

### Arguments

 `x` An univariate or a multivariate distribution. `mean` A `double` for the location parameter of the series in `x`. `sigma` A `matrix` for the target variance-covariance parameter of the series in `x`.

### Value

A `list` of the `view` class.

### Examples

```library(ggplot2)

# Invariant (stationarity)
ret <- diff(log(EuStockMarkets))

# Expectations for location and dispersion parameters
mean <- colMeans(ret) # No active expectations for returns
cor <- matrix(0, ncol = 4, nrow = 4) # diagonal covariance matrix
diag(cor) <- 1                       # diagonal covariance matrix
sds <- apply(ret, 2, sd)             # diagonal covariance matrix
covs <- diag(sds) %*% cor %*% diag(sds) ## diagonal covariance matrix

# prior probabilities (usually equal weight scheme)
prior <- rep(1 / nrow(ret), nrow(ret))

# Views
views <- view_on_covariance(x = ret, mean = mean, sigma = covs)
views

# Optimization
ep <- entropy_pooling(p = prior, Aeq = views\$Aeq, beq = views\$beq, solver = "nlminb")
autoplot(ep)

# original covariance matrix
stats::cov(ret)

# Posterior covariance matrix
ffp_moments(x = ret, p = ep)\$sigma
```

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