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Risk-free return

As explained in Build a Robo-Advisor with Python (From Scratch)

The risk-free rate is the return associated with an investment assumed to have no default risk over the relevant time horizon.

A risk-free return is the known return on an investment whose maturity matches the investment horizon and whose cash flows contain no intermediate coupons. Under these conditions, the asset has zero volatility. A one-month Treasury bill fits a one-month horizon; a ten-year government bond sold after one month does not, because its price may rise or fall. section 2.4

Adding a risk-free asset changes portfolio construction: combinations of it and a tangent portfolio form a capital allocation line above the efficient frontier. The investor controls risk exposure through the allocation between the two assets. section 2.4

The risk-free return enters the optimization as follows:

  • Represent the risk-free asset's return as R_f and its portfolio weight as w_0. section 2.8
  • Constrain total expected return with μᵀw + R_f w_0 = μ_p and total weights with 1ᵀw + w_0 = 1. section 2.8
  • Eliminate w_0 to obtain excess-return terms, μ − R_f1, in the minimum-variance problem. section 2.8
  • Solve the resulting equations for optimal risky-asset weights using the covariance matrix and the Lagrange multiplier. section 2.8

Blending the tangent portfolio and the risk-free asset

A portfolio with 70% in the tangent portfolio and 30% in the risk-free asset lies 70% of the way toward the tangent portfolio. A more risk-averse allocation reverses those proportions. section 2.4

Figure 2.4 Risk–reward plot with a capital allocation line
figure

Use the risk–reward plot to see how the risk-free point connects to the tangent portfolio through the capital allocation line. section 2.4

How Build a Robo-Advisor with Python (From Scratch) covers it

How deep it goes
Derives constrained optimal weights and related formulas, while separating deeper mathematical derivations into an optional appendix.
Examples
Uses one-month Treasury bills, ten-year bonds sold early, portfolio allocations, and a risk–reward plot.
What it assumes
Assumes portfolio weights, expected returns, covariance matrices, and efficient-frontier concepts.
What you can do after
Apply it via portfolio optimization equations and Python plotting to analyze risk-free and risky-asset combinations.
How it connects
Links risk-free assets to efficient frontiers, tangent portfolios, capital allocation lines, and capital market lines.

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This explainer was generated by Manning's knowledge graph from the text of Build a Robo-Advisor with Python (From Scratch). The section links point to the parts of the book it draws on.