Views on Volatility — view_on

Helper to construct views on volatility.

Usage

view_on_volatility(x, vol)

# S3 method for default
view_on_volatility(x, vol)

# S3 method for matrix
view_on_volatility(x, vol)

# S3 method for xts
view_on_volatility(x, vol)

# S3 method for tbl_df
view_on_volatility(x, vol)

Arguments

x: An univariate or a multivariate distribution.
vol: A double for the target volatility structure of the series in x.

Value

A list of the view class.

Examples

library(ggplot2)

# Invariant
ret <- diff(log(EuStockMarkets))
n   <- nrow(ret)

# Expected a volatility 30% higher than historical average
vol <- apply(ret, 2, stats::sd) * 1.3

# Prior Probabilities
prior <- rep(1 / n, n)

# Views
views <- view_on_volatility(x = ret, vol = vol)
views
#> # ffp view
#> Type:  View On Volatility
#> Aeq :  Dim 4 x 1859 
#> beq :  Dim 4 x 1 

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


# Desired volatility
vol
#>        DAX        SMI        CAC       FTSE 
#> 0.01339109 0.01202505 0.01434014 0.01034505 

# Posterior volatility matches very closely with the desired volatility
sqrt(diag(ffp_moments(x = ret, p = ep)$sigma))
#>        DAX        SMI        CAC       FTSE 
#> 0.01341064 0.01204973 0.01434835 0.01033864