Python과 데이터 분석을 활용해 진행한 금융시장, 자산가격, 포트폴리오, 파생상품 가격결정 관련 프로젝트를 정리하는 GitHub 프로필입니다.
주요 프로젝트는 한국 주식시장과 ETF 시장을 기반으로 하며, factor strategy, index tracking, beta estimation, risk measurement, empirical asset pricing paper replication, OTC derivatives pricing 등을 다룹니다. 각 저장소에는 가능한 범위에서 분석 workflow, code, 주요 결과, 발표 자료를 함께 정리했습니다.
| 프로젝트 | 설명 |
|---|---|
| Enhanced Index Tracking on KOSPI200 | KOSPI200을 대상으로 deep learning 기반 index tracking 및 enhanced index tracking 전략을 실험한 프로젝트 |
| MAX Anomaly Strategy | residual MAX anomaly를 활용한 한국 주식시장 전략 실험 프로젝트 |
| Korean Equity Strategy Lab | 한국 주식시장에서 여러 factor strategy를 탐색하고 backtest한 노트북 모음 |
| Bayesian Conditional Beta for KOSPI200 | KOSPI200 ETF의 conditional beta와 시장 국면 변화를 Bayesian 방식으로 분석한 프로젝트 |
| COVID-19 Tail Risk Transmission | COVID-19 전후 ETF 자산군 간 tail risk 전이를 VaR, CVaR, GARCH, CoVaR 관점에서 분석한 프로젝트 |
| OTC Derivatives Pricing | 장외 파생상품 pricing 스터디 노트북과 보조 C++ 코드를 정리한 저장소로, Monte Carlo, FDM, volatility surface, IR derivatives, credit derivatives를 다룸 |
| Fama-French 1992 Korean Market Replication | Fama and French (1992)의 주식수익률 횡단면 분석을 한국 시장 데이터로 재현한 프로젝트 |
| Convenience Store Database System | 편의점 유통 및 판매 도메인을 기반으로 한 ERD, MySQL schema, C query application 프로젝트 |
| System Programming Projects | 시스템프로그래밍 과제 코드 정리 저장소 |
| Operating Systems Projects | 운영체제 Pintos 과제 코드 정리 저장소 |
- 한국 주식시장 분석
- ETF 및 포트폴리오 분석
- Factor strategy와 backtesting
- Beta estimation과 market regime analysis
- VaR, CVaR, GARCH, CoVaR 기반 risk analysis
- OTC derivatives pricing과 computational finance
- Empirical asset pricing paper replication
- Database design 및 systems programming
Python (pandas, NumPy, SciPy, statsmodels, PyTorch, scikit-learn, matplotlib), Jupyter Notebook, MySQL, C, Git
FnGuide DataGuide/Quantiwise, WRDS CRSP/Compustat
This GitHub profile organizes my finance, quantitative research, derivatives pricing, database, and systems programming projects using Python, data analysis, SQL, and C.
Most research projects are based on the Korean equity and ETF markets, covering topics such as factor strategies, index tracking, beta estimation, risk measurement, empirical asset pricing paper replication, and OTC derivatives pricing. Each repository includes the analysis workflow, code, main results, and presentation materials where possible.
| Project | Description |
|---|---|
| Enhanced Index Tracking on KOSPI200 | Deep learning-based index tracking and enhanced index tracking strategies for the KOSPI200 |
| Korean Equity Strategy Lab | A collection of notebooks exploring and backtesting factor strategies in the Korean equity market |
| Bayesian Conditional Beta for KOSPI200 | Bayesian analysis of conditional beta and market regime changes for a KOSPI200 ETF |
| COVID-19 Tail Risk Transmission | Tail-risk transmission analysis across ETF asset classes around COVID-19 using VaR, CVaR, GARCH, and CoVaR |
| OTC Derivatives Pricing | Study notes and notebook collection for OTC derivatives pricing, covering Monte Carlo, finite difference methods, volatility surface, interest-rate derivatives, and credit derivatives |
| Fama-French 1992 Korean Market Replication | Replication of Fama and French (1992) cross-sectional stock return analysis using Korean market data |
| MAX Anomaly Strategy | Strategy experiment on the residual MAX anomaly in the Korean equity market |
| Convenience Store Database System | ERD, MySQL schema, and C query application project for a convenience store distribution domain |
| System Programming Projects | Organized archive of system programming assignment code |
| Operating Systems Projects | Organized archive of operating systems Pintos assignment code |
- Korean equity market analysis
- ETF and portfolio analysis
- Factor strategies and backtesting
- Beta estimation and market regime analysis
- Risk analysis using VaR, CVaR, GARCH, and CoVaR
- OTC derivatives pricing and computational finance
- Empirical asset pricing paper replication
- Database design and systems programming
Python, pandas, NumPy, SciPy, statsmodels, PyTorch, scikit-learn, matplotlib, Jupyter Notebook, MySQL, C, Git
FnGuide DataGuide/Quantiwise, WRDS CRSP/Compustat
Some projects do not include raw data due to file size or data licensing restrictions. Instead, the repositories are organized around README files, code, selected results, and presentation materials so that the analysis workflow can still be reviewed.