05 / Independent quantitative engineering · 2026
Financial risk time-series laboratory
Rolling volatility and VaR forecasts built without looking into the future.
The rebuilt project makes the temporal contract executable: every estimate at time t uses only prior observations, horizon variance is explicit, and VaR exceptions are summarized with a finite Kupiec likelihood-ratio statistic.
Evidence register
4/4
tests
Core numerical and data-contract checks
t-1
information set
Strict no-look-ahead rolling estimates
1
backtest
Kupiec unconditional coverage
01 / Problem
Risk estimates can look precise while leaking future data, mishandling horizon variance or failing silently at zero and full exception counts.
02 / Approach
Validated prices become returns, rolling estimators produce volatility and VaR from past-only windows, and exceptions feed an explicitly guarded coverage test and report.
03 / Outcome
The repository provides a synthetic end-to-end demo, data provenance tooling, tested numerical functions, a small API and a reproducible report path.
System trace
How the evidence is produced.
Rebuilt the analysis as a tested Python risk pipeline covering provenance, temporal validation, numerical safeguards, reporting and serving.
- 01Validated prices → timestamped returns
- 02Past-only rolling windows → volatility and VaR
- 03Observed returns → exception sequence
- 04Coverage backtest → report and API
Validation scope
Four tests cover data validity and numerical edge cases. Rolling forecasts enforce past-only information, and Kupiec's statistic stays finite at boundary exception counts.
Known limitation
Coverage frequency alone does not test independence or tail severity. The project has no trading policy, liquidity model, transaction costs, stress programme or live monitoring.
What is inspectable
- The forecasting loop makes the information boundary explicit in code.
- Horizon variance and GJR persistence are calculated with validated parameters.
- Synthetic data makes the public execution path independent of an undocumented dataset.
Next proof to add
- 01Add conditional-coverage and tail-loss backtests with confidence intervals.
- 02Benchmark historical, parametric and simulation-based VaR on governed data.
- 03Add drift monitoring and a clearly defined downstream capital or limit rule.
Main stack