CQF case studies
CQF · Put your knowledge to work in context.
The Certificate in Quantitative Finance (CQF) is described by its provider as "The world's leading master's-level professional qualification in quant finance." It is awarded by the CQF Institute, part of Fitch Learning, and is delivered by Fitch Learning. The programme is taught online and part-time over six months, with content spanning quantitative finance and machine learning, taught by industry practitioners rather than purely academic staff. The CQF case studies are 5 cases and about 3,772 words.
1Leakage in an Intraday Tree Model: Random CV Versus Walk-Forward
You are reviewing a US equities intraday alpha model built by a small systematic desk. The model is a gradient-boosted tree predicting the sign of the next 30-minute mid-price return for 40 liquid names, trained on three years of 1-minute bars.
737 words, about 4 min2Neural Surrogate for a Rough-Volatility Pricer: Pricing Versus Risk Approval
Your bank's front-office quant team has replaced a 200,000-path Monte Carlo pricer with a neural-network surrogate. The underlying model is a rough-volatility stochastic-volatility model with six parameters.
796 words, about 4 min3Plug-In Mean-Variance With Weak ML Forecasts: Signal or Optimizer?
You manage a 250-name US equity long-only portfolio with a 6% tracking-error budget. A machine-learning team supplies monthly cross-sectional return forecasts; the historical cross-sectional information coefficient is 0.03 and forecast dispersion is modest.
757 words, about 4 min4Twelve Configurations of a Trend Strategy: Deflating the Best Sharpe
A commodity trading advisor has backtested a volatility-targeted trend-following strategy on 15 liquid futures over 10 years of daily data. The research process tried 12 configurations: three lookback windows (20, 60, and 120 days), two volatility targets (10% and 15%), and two execution assumptions (next-open and VWAP).
774 words, about 4 min5PD Model Drift: Cut-Off, Calibration, and the 8:1 Cost Ratio
A mid-sized bank has a logistic-regression probability-of-default model for 80,000 SME term loans. The portfolio default rate is 3.2% and the model's out-of-time AUC is 0.78, with a Brier score of 0.028.
708 words, about 4 min