Do improved estimates lead to improved performance?
Let us hypothesize that using a robust estimate of the variance-covariance matrix will outperform the sample variance covariance matrix. In theory, better estimates should lead to better results. We will use the moments_robust()
function that was defined in chapter 3 and the portfolio specification from the last exercise.
This exercise is part of the course
Intermediate Portfolio Analysis in R
Exercise instructions
- Run the optimization using the
moments_robust()
function to estimate moments. The optimization backtest will use the same parameters used previously, quarterly rebalancing with training period and rolling window to use 5 years of data. Assign the results to a variable namedopt_rebal_rb_robust
. - Chart the weights.
- Chart the component percentage contribution to risk.
- Compute the portfolio returns using
Return.portfolio()
. Assign the returns to a variable namedreturns_rb_robust
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Run the optimization
opt_rebal_rb_robust <- optimize.portfolio.rebalancing(R = ___,
momentFUN = ___,
portfolio = ___,
optimize_method = "random", rp = rp,
trace = TRUE,
rebalance_on = ___,
training_period = ___,
rolling_window = ___)
# Chart the weights
# Chart the percentage contribution to risk
chart.RiskBudget(___, match.col = "StdDev", risk.type = ___)
# Compute the portfolio returns
returns_rb_robust <- Return.portfolio(R = ___, weights = ___)
colnames(returns_rb_robust) <- "rb_robust"