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Volatility-based deviation constraint

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

A volatility-based deviation constraint bounds position deviations according to asset volatility, allowing larger deviations for lower-volatility assets.

A volatility-based deviation constraint limits each optimized position relative to its target while incorporating asset volatility. It permits larger deviations for lower-volatility assets, making the rebalancing constraint less uniform than a fixed-tolerance bound. section 13.3.2

Use it to express differentiated rebalancing tolerances:

  • Compare each optimized position with its target position. section 13.3.2
  • Allow lower-volatility assets to move farther from target than higher-volatility assets. section 13.3.2
  • Combine the constraint with long-only and full-investment requirements in the optimization. section 13.3.2

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

How deep it goes
Defines the constraint and its trade-off but stops short of giving its implementation.
Examples
Contrasts volatility-based bounds with fixed-tolerance bounds such as a 5% deviation around a $4,000 AGG target.
What it assumes
Assumes target positions, investment amounts, and basic rebalancing optimization constraints.
What you can do after
Evaluate how asset volatility changes allowable deviations in a rebalancing optimization.
How it connects
Connects portfolio rebalancing with portfolio optimization, target weights, asset volatility, and deviation constraints.

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.