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RMSC6007 Quantitative Risk Management - Group Project

A systematic approach to options risk management with multiple methodologies
Course Project | Spring 2026 | Team [Your Team Name]

📋 Project Overview

This repository implements a comprehensive options risk management framework, covering both standard course requirements and research-grade extensions. Our goal is to demonstrate:

  • Solid understanding of VaR, volatility modeling, and factor-based risk management
  • Production-ready code with proper testing, documentation, and version control
  • Research methodology with reproducible data pipelines

公告（MethodD 数据口径） 我已定义并锁定 MethodD 的 nasdaq-full v1 数据契约，所有人必须使用同一份数据口径复算结果。 入口文件：MethodD/DATA_README_v2.md。

🎯 Implementation Methods

We provide four complementary approaches, each addressing different aspects of options risk:

✅ Method A: Historical VaR

  • Classic Value-at-Risk estimation using historical simulation
  • Suitable for portfolio-level risk assessment
  • Status: ✅ Complete | Owner: [Name]

✅ Method B: GARCH Volatility Modeling

  • Time-series volatility forecasting with GARCH(1,1)
  • Captures volatility clustering in options pricing
  • Status: ✅ Complete | Owner: [Name]

✅ Method C: Factor Exposure Analysis

  • Greeks-based risk decomposition
  • Multi-factor sensitivity analysis
  • Status: ✅ Complete | Owner: [Name]

🧪 Method D: IV Factor Research (Research Extension)

  • Real options chain snapshot collection (t0 → t5 forward capture)
  • Factor validity testing using cross-sectional IV prediction
  • Research-grade pipeline with reproducible data infrastructure
  • Focus: Not P&L optimization, but "Does the factor work? When does it fail?"
  • Status: 🚧 Data collection in progress | Owner: [Name]

💡 Why Method D?
While A/B/C follow standard course requirements, Method D demonstrates:

  • Advanced data engineering (forward data collection to avoid look-ahead bias)
  • Academic rigor (IC analysis, baseline comparison, mechanism validation)
  • Real-world research workflow (scheduled capture, version control, reproducibility)

📂 Repository Structure

RMSC6007_GroupProject/
├── MethodA/                 # Historical VaR implementation
├── MethodB/                 # GARCH volatility models
├── MethodC/                 # Factor exposure analysis
├── MethodD/                 # 🧪 IV factor research (see dedicated README)
│   ├── README.md            # Detailed research protocol
│   ├── tools/               # Snapshot capture & scheduling
│   ├── experiments/         # Factor validation & demo scripts
│   └── outputs/             # Validation reports
├── scripts/                 # Release / automation scripts
└── README.md                # This file

🚀 Quick Start

Prerequisites

python >= 3.9

Option 1: Unified Docker Environment (Recommended)

cd RMSC6007_GroupProject
docker compose build
docker compose run --rm rmsc6007

Inside the container:

cd MethodD
bash run_all_demos.sh

Option 2: Local Python Environment

cd RMSC6007_GroupProject/MethodD
pip install -r requirements.txt
bash run_all_demos.sh

👥 Team Collaboration

Workflow

  1. Create feature branch: git checkout -b feature/method-x-enhancement
  2. Make changes with clear commit messages
  3. Open Pull Request with description and test results
  4. Code review by at least one team member
  5. Merge after approval

Communication

  • Weekly sync: [Day/Time]
  • Issues: Use GitHub Issues for bugs/questions
  • Documentation: Update README when adding features

📊 Deliverables Checklist

  • Project proposal (see GOOGLE_FORM_SUBMISSION.md)
  • Method A implementation + tests
  • Method B implementation + tests
  • Method C implementation + tests
  • Method D validation report (in progress)
  • Final presentation slides
  • Comprehensive project report

📖 Documentation

  • Method D Research Protocol: MethodD/README.md
  • Implementation Summaries: MethodD/IMPLEMENTATION_SUMMARY.md
  • Data Spec: MethodD/DATA_SPECIFICATION.md
  • Covered Call Spec: MethodD/COVERED_CALL_SPECIFICATION.md

🎓 Course Alignment

This project addresses RMSC6007 learning objectives:

Objective Implementation
VaR estimation Method A
Volatility modeling Method B
Factor-based risk Method C
Research methodology Method D
Code quality & testing CI/CD pipeline, unit tests

📝 License & Academic Integrity

This is a course project for RMSC6007. Code is for educational purposes only.
All team members have contributed equally to this work.

📧 Contact

  • Team Lead: [Name] - [Email]
  • Method D Lead: [Name] - [Email]
  • Course: RMSC6007 Quantitative Risk Management
  • Instructor: [Professor Name]