Computing lower and upper bounds for the (smallest or largest) VaR
VaRbound()
computes lower and upper bounds for the lower or upper
Value-at-Risk bound.
VaRbound(alpha, N, qmargins, bound = c("upper", "lower"), verbose = FALSE)
alpha |
confidence level in (0,1). |
N |
tail discretization parameter; see Embrechts et al. (2013). |
qmargins |
|
bound |
|
verbose |
|
Due to the nature of the rearrangement algorithm, note that this purely R based implementation can be slow.
numeric
vector of length two, containing the lower and
upper bound for the (chosen) Value-at-Risk estimate.
Marius Hofert.
Embrechts, P., Puccetti, G., and Rüschendorf, L. (2013), Model uncertainty and VaR aggregation, Journal of Banking and Finance 37(8), 2750–2764.
qPar <- function(p, theta) (1-p)^(-1/theta)-1 qmar <- lapply(1:3, function(j) function(p) qPar(p, theta=2.5)) ## bounds for the largest VaR VaRbound(0.99, N=50, qmargins=qmar) ## bounds for the smallest VaR VaRbound(0.99, N=50, qmargins=qmar, bound="lower")
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