Volatility is latent, so every estimate embeds a window, grid, annualization factor, or model.

Base R; zoo, xts, highfrequency, and TTR are optional.

annualized volatility = sqrt(k) × sd(returns); realized variance = sum(r_j^2)

1. Step 1Define every variable and unit.
2. Step 2Check assumptions and information timing.
3. Step 3Calculate, verify, and interpret in context.

Variable

390 seeded minute returns with a 1.2% daily target.

Model choice

Variance scales by k under the conventional independent-increment approximation, so standard deviation scales by sqrt(k).

Calculation

The book run's one-minute estimate was 1.129% against a 1.2% simulation target; coarser grids differ through finite-sample cross-products.

Interpretation

Without microstructure noise, the exercise isolates sampling variation rather than real tick-data bias.

Reasonableness check

Real high-frequency data add market microstructure, gaps, and timestamp issues; this simulation does not.

Without microstructure noise, the exercise isolates sampling variation rather than real tick-data bias. Limitation: Real high-frequency data add market microstructure, gaps, and timestamp issues; this simulation does not.

Realized-volatility estimates vary across aggregation intervals.

Run the self-contained starter code in the protected STATLAB R runner. The code prints an auditable result and avoids network or unrestricted file access.

Compare your result with the definition, units, and model assumptions—not only with a target number.

Complete these without looking at the selected solutions. More than one defensible program may exist.

These are compact solution routes. Confirm dimensions, units, and any changed modelling choices.

Compare rolling, annualized, realized, and range-based measures with explicit units and sampling choices.

Name the assumption, evidence you would seek, and how the recommendation might change.

Reach 70% to complete the chapter. Explanations appear after submission.