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CttQuantLab

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  1. Deep-Reinforcement-Learning-for-Portfolio-Optimisation Deep-Reinforcement-Learning-for-Portfolio-Optimisation Public

    Deep reinforcement learning for portfolio optimisation with risk controls (DDPG/PPO). HKUST IEDA4000F research codebase.

    Python 3 1

  2. Quantifying-VIX-Tail-Risk Quantifying-VIX-Tail-Risk Public

    GARCH and Compound Poisson models for VIX tail-risk quantification (HKUST IEDA4000E)

    Jupyter Notebook

  3. Mean-Variance-Optimal-Delta-Hedging-of-Short-Strangles-on-Bitcoin-Futures-Options Mean-Variance-Optimal-Delta-Hedging-of-Short-Strangles-on-Bitcoin-Futures-Options Public

    Mean-variance optimal delta-hedging of short strangles on Bitcoin futures options (HKUST IEDA3330)

    Python

  4. Empirical-Asset-Pricing-via-Machine-Learning Empirical-Asset-Pricing-via-Machine-Learning Public

    Replication of Gu, Kelly & Xiu (2020) ML asset pricing: GBRT, Elastic Net, OLS-3, and Fama-French benchmarks on the GKX characteristic panel.

    Jupyter Notebook

  5. Comparative-Analysis-of-Machine-Learning-Models-for-Fraud-Detection Comparative-Analysis-of-Machine-Learning-Models-for-Fraud-Detection Public

    Supervised ML pipeline comparing classifiers for imbalanced online payment fraud detection (IEDA3560, HKUST).

    Python

  6. Transparent-Credit-Scoring-with-Explainable-Machine-Learning- Transparent-Credit-Scoring-with-Explainable-Machine-Learning- Public

    Interpretable multiclass credit scoring with SHAP explainability (LogReg, RF, XGBoost). HKUST IEDA4500 research codebase.

    Python

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