Volatility-Control Index

Install and import#

npm install fintech-algorithms

import { calculate } from "fintech-algorithms/index-and-benchmark-engineering/strategy-indices/volatility-control-index";

Signature#

calculate(data)

Scales exposure to hold realised volatility near a target, cutting it when markets get rough. Exposure is set from *trailing* volatility, so the mechanism always acts after the fact — it dampens rather than avoids.

Parameters#

Returns#

{ exposures, strategyReturns, levels, endingLevel }

The exposure actually taken each period alongside the returns — the exposure path is what explains the strategy's behaviour. No turnover, trade, or cost figure is produced.

Errors#

• When lookback < 2, or returns has no more than lookback observations — throws
• When targetVolatility, maxExposure, or annualization is not positive — throws
• When a strategy return drives the index level to zero or below — throws

Complexity: time O(n × lookback), space O(n).

Worked example#

verified This is the worked example published in the article, replayed by the test suite on every run. The output cannot drift.

Input#

{
  "returns": [0.004, -0.003, 0.005, 0.002, -0.012, 0.015],
  "lookback": 4,
  "annualization": 252,
  "targetVolatility": 0.1,
  "maxExposure": 1.5,
  "cashReturn": 0.0001,
  "baseLevel": 1000
}

Call#

calculate(data)

Returns#

object with 4 fields: exposures, strategyReturns, levels, endingLevel

{
  "exposures": [1, 1, 1, 1, 1.5, 0.84685],
  "strategyReturns": [0.004, -0.003, 0.005, 0.002, -0.01805, 0.012718],
  "levels": [1000, 1004, 1000.988, 1005.99294, 1008.004926, 989.810437],
  "endingLevel": 1006.077264
}

Diagrams#

Calculation flow#

flowchart LR
    A["Point-in-time inputs"] --> B["Validate units and timing"]
    B --> C{"Contract feasible?"}
    C -->|No| D["Reject with reason"]
    C -->|Yes| E["Calculate Volatility-Control Index"]
    E --> F["Recompute invariants"]
    F --> G{"Checks pass?"}
    G -->|No| D
    G -->|Yes| H["Publish audited output"]

stateDiagram-v2
    [*] --> FrozenInputs
    FrozenInputs --> Validated: contract passes
    FrozenInputs --> Rejected: missing or infeasible
    Validated --> Calculated: apply named rule
    Calculated --> Audited: invariants pass
    Calculated --> Rejected: invariant fails
    Audited --> Published: version and timestamp recorded
    Published --> Revised: approved correction
    Revised --> FrozenInputs: rebuild from retained source state

How it works#

This page states the contract — how to call it correctly. The article explains the concept: why it works, and where it breaks.

Read the article →

References#

• S&P Dow Jones Indices Index Mathematics Methodology — S&P Dow Jones Indices
• S&P DJI Equity Indices Policies & Practices — S&P Dow Jones Indices
• FTSE Russell Capping Methodology — FTSE Russell, LSEG
• FTSE Russell Index Policy and Methodology Library — FTSE Russell, LSEG
• MSCI Global Investable Market Indexes Methodology Library — MSCI
• MSCI Minimum Volatility Indexes Methodology — MSCI
• S&P Risk Control 2.0 Indices Methodology — S&P Dow Jones Indices
• Principles for Financial Benchmarks — International Organization of Securities Commissions
• Regulation (EU) 2016/1011 — European Union
• Portfolio Selection — Harry Markowitz
• On the Properties of Equally-Weighted Risk Contributions Portfolios — Sébastien Maillard, Thierry Roncalli, and Jérôme Teïletche
• Fundamental Indexation — Robert Arnott, Jason Hsu, and Philip Moore
• FTSE Currency Hedging Methodology Overview — FTSE Russell, LSEG

The rest of the Strategy Indices family#

• Leveraged Daily-Reset IndexApplies a leverage multiple that resets every day.
• Inverse Daily-Reset IndexAn inverse multiple reset daily.
• Fixed-Decrement IndexDeducts a fixed number of index points per year.
• Percentage-Decrement IndexThe same idea as a fixed decrement, but proportional to the level.
• Index-of-IndicesCombines several index return streams into one under fixed weights.