Refine constraints and objectives

Here we hypothesize that refining constraints and/or objectives will improve performance. Let us add a risk budget objective to set a minimum and maximum percentage contribution to risk for each asset. We will be building on the portfolio specification we created. This is a more complex optimization problem and will require a global solver so we will use random portfolios as the optimization method.

This exercise is part of the course

Intermediate Portfolio Analysis in R

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Exercise instructions

Add a risk budget objective risk_budget to port_spec where risk is defined as standard deviation. Set the minimum percentage risk to 5% and the maximum percentage risk to 10%.
Run the optimization with quarterly rebalancing. Set the training period and rolling window to use 5 years of data. Assign the results to a variable named opt_rebal_rb.
Chart the weights.
Chart the component percentage contribution to risk.
Compute the portfolio returns using Return.portfolio(). Assign the returns to a variable named returns_rb.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.


# Add a risk budge objective
port_spec <- add.objective(portfolio = ___, 
                           type = ___, 
                           name = ___, 
                           min_prisk = ___, 
                           max_prisk = ___)

# Run the optimization
opt_rebal_rb <- optimize.portfolio.rebalancing(R = ___, 
                                               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 <- Return.portfolio(R = ___, weights = ___)
colnames(returns_rb) <- "risk_budget"

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