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T18 · Volatility Management

Conditional Volatility Targeting

Dion Bongaerts et al. (3 authors), 2020 · Financial Analysts Journal

1%
pass@1 across 16 systems
1/16
systems with ≥1 successful attempt
1/5
1/5 attempts passed for the best system
80 attempts · 16 systems × 5
Claude Code + Opus 5
0%
Claude Code + Opus 4.8
0%
Claude Code + Opus 4.7
0%
Codex + GPT-5.6-sol
0%
Codex + GPT-5.5
0%
OpenCode + Kimi-K3
0%
OpenCode + Opus 4.7
0%
OpenCode + GPT-5.5
0%
OpenCode + Sonnet 4.6
0%
OpenCode + Qwen3.7-Max
20%
OpenCode + GLM-5.2
0%
OpenCode + DeepSeek V4-Pro
0%
OpenCode + GLM-5.1
0%
OpenCode + Qwen 3.6 Plus
0%
OpenCode + MiniMax-M3
0%
OpenCode + MiniMax M2.7
0%
all checkers pass Type-A clean, Type-B fail fail checking timeout no result / incomplete
Strategy

What the paper asks for.

Per-asset long-only volatility targeting that scales next-month positions by min(sigma_target / sigma_hat, L_max) ONLY in the top OR bottom expanding quintile of past-month realized vols (state I_t = 1); otherwise hold the asset unscaled. Reduces drawdown vs unconditional vol-targeting while preserving (or improving) Sharpe.

Type
technical
Implementation
pandas
Rebalance
monthly
Long-short
no
Declared task setup
Data
Bundled public task data
Operational scope
The paper-defined methodological core is retained; the fixed instruction instantiates only the data mappings, interfaces, and evaluation conventions needed for execution.
Six-stage quantitative strategy pipeline
  1. 01 Data loading
  2. 02 Feature engineering
  3. 03 Model fitting
  4. 04 Signal generation
  5. 05 Portfolio construction
  6. 06 Execution
Checker suite

18 properties · 80 attempts.

Each row is one requirement the strategy must satisfy. The bar reports the share of observed checker verdicts that pass; missing checker output is not converted into a property verdict.

TYPE A · DOMAIN-LEVEL (4)
  • A2
    no global normalize
    70/76
  • A3
    no future data access
    67/76
  • A4
    causal consistency
    76/76
  • A10
    e2e metric consistency
    1/76
TYPE B · PAPER-SPECIFIC (14)
  • B1
    realized vol uses calendar month
    §§3 “realized volatility in the previous month, t-1 (as the equally weighted standard deviation of daily returns)”
    provenance · paper-explicit
    73/76
  • B2
    exclude last trading day of month
    §§3 “We excluded the last trading day of the month to ensure that sigma_hat_{t-1} would be known one day ahead of rebalancing”
    provenance · paper-explicit
    73/76
  • B3
    sigma target expanding window
    §§3 (Eq. 1 surroundings) “where sigma_target is the long-term realized volatility calculated using all daily returns up to and including month t-1”
    provenance · paper-explicit
    70/76
  • B4
    sigma target minimum warmup 10y
    §§3 “We required at least 10 years of returns for calculating the long-term volatility. Therefore, our volatility-targeting analysis starts 10 years after the starting dates of the two datasets”
    provenance · paper-explicit
    52/76
  • B5
    state classification expanding quintile
    §§3 “we sorted realized volatility of all months up to t-1 into quintiles. If the volatility of month t-1 was in the highest (lowest) quintile, we predicted a high-volatility (low-volatility) state for month t; otherwise, we predicted a medium-volatility state for month t”
    provenance · paper-explicit
    74/76
  • B6
    indicator it extreme iff top or bottom quintile
    §§3 (Eq. 3 surroundings) “indicator variable I_t equals 1 if month t is predicted to be an extreme volatility state and equals 0 otherwise”
    provenance · paper-explicit
    74/76
  • B7
    scaling formula eq3
    §§3 Eq. 3 “r_t^scaled = I_t * r_t * min(sigma_target/sigma_hat_{t-1}, L_max) + (1 - I_t) * r_t”
    provenance · paper-explicit
    73/76
  • B8
    l max value is 2
    §§Risk management “Cap leverage at 200% in low-volatility states”
    provenance · paper-explicit
    73/76
  • B9
    monthly rebalance
    §§3 “Rebalancing occurs monthly using realized volatility estimates”
    provenance · paper-explicit
    70/76
  • B10
    scaling applied to full following month
    §§3 Eq. 3 “r_t scaled ... applied to month t”
    provenance · paper-inferred
    75/76
  • B11
    no short or directional signal
    §§3 “we constructed the conditional strategy to reduce exposure and risk in high-volatility states, increase exposure and risks by taking on leverage in low-volatility states, and maintain an unscaled exposure in medium-volatility states”
    provenance · paper-explicit
    74/76
  • B12
    vol estimator equally weighted
    §§3 “as the equally weighted standard deviation of daily returns”
    provenance · paper-explicit
    73/76
  • B13
    transaction costs modeled
    §§Transaction Costs “transaction cost of 3 basis points (bps) of notional value traded for equity futures for the last 20 years of the sample period and 5 bps for the earlier period”
    provenance · paper-explicit
    76/76
  • B14
    vol target per asset
    §§3 “we set the volatility target in month t as, based on all return data available prior to month t, the long-term volatility of that asset”
    provenance · paper-explicit
    76/76
What this task reveals

The lowest-passing observed requirement is e2e metric consistency: 1 of 76 observed checker verdicts pass. The full property list above shows whether errors concentrate in domain-level validity or the paper-specific strategy logic.

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