I am trying to implement CVaR portfolio optimisation in R. Basically trying to replicate the Matlab approach used in this paper:
To do this I need to perform nonlinear optimisation with nonlinear constraints.
I have tried to use the nloptr package, but found the derivative calculation for the gradient of matrices beyond me.
Instead I have opted for the NlcOptim package which formulates the constraints in the same way as the Matlab function used in the paper.
library(NlcOptim)
# ====================================================================
# Just generate arbitrary returns data and bootstrap -----------------
asset_returns <- rbind(c(0.1, 0.05, 0.05, 0.01, 0.06),
c(0.05, 0.05, 0.06, -0.01, 0.09),
c(0.025, 0.05, 0.07, 0.02, -0.1),
c(0.01, 0.05, 0.08, -0.02, -0.01),
c(0.01, 0.05, 0.08, 0.00, 0.2),
c(0.005, 0.05, 0.09, 0.005, -0.15),
c(0.01, 0.05, 0.08, 0.01, -0.01),
c(0.012, 0.05, 0.00, -0.01, -0.01),
c(0.015, 0.05, 0.00, 0.03, 0.05),
c(0.02, 0.05, -0.01, 0.04, 0.03))
# Returns for 5 assets over 10 trading periods
nAssets <- ncol(asset_returns)
nReturns <- nrow(asset_returns)
nPeriods <- 4
nSims <- 10
# BOOTSTRAP ---------------------------------------------------------
sim_period_returns <- matrix(nrow = nSims, ncol = nAssets)
for (k in 1:nSims) {# run nSims simulations
sim_returns <- matrix(nrow = nPeriods, ncol = nAssets)
sample_order <- sample(nReturns, nPeriods)
for (i in 1:nPeriods) {
sim_returns[i,] <- asset_returns[sample_order[i],]
}
sim_prices <- rbind(rep(1, nAssets), 1 + sim_returns)
for (j in 1:nAssets) {
sim_period_returns[k, j] <- prod(sim_prices[, j]) - 1
}
}
# ------------------------------------------------------------------------
# ========================================================================
# The important stuff ====================================================
returns <- sim_period_returns
alpha <- 0.95
CVaR_limit <- 0.025
UB <- 0.75
LB <- 0.05
# Inequality constraints
A <- rbind(c(rep(0, nAssets), 1, 1/((1-alpha)*nSims) * rep(1, nSims)),
cbind(- returns, -1, diag(nSims)))
b <- as.matrix(c(-CVaR_limit, rep(0, nSims)), nrow = nSims, ncol = 1)
# Equality constraints
Aeq <- c(rep(1, nAssets), 0, rep(0, nSims))
beq <- 1
# Upper and lower bounds
UB <- c(rep(UB, nAssets), Inf, rep(Inf, nSims))
LB <- c(rep(LB, nAssets), 0, rep(0, nSims))
# Initial portfolio weights
w0 <- rep(1/nAssets, nAssets)
VaR0 <- quantile(returns %*% w0, alpha, names = F)
w0 <- c(w0, VaR0, rep(0, nSims))
objective_function <- function(x) {
# objective function to minimise
return (-colMeans(returns) %*% x[1:nAssets])
}
# **********************************************
# The solnl function giving the error based on the above inputs
solnl(X = w0,
objfun = objective_function,
A = A,
B = b,
Aeq = Aeq,
Beq = beq,
lb = LB,
ub = UB)
# **********************************************
# ===================================================================
I am receiving the following error:
Error in if (eq > 0 & ineq > 0) { : argument is of length zero
I have read the package source code and tried to figure out what is causing this error, but am still at a loss.