Quant-Risk-Portfolio

Quantitative risk portfolio, covering Monte Carlo simulation, portfolio optimization, and a comprehensive quant finance reference spanning 20+ concepts — from Basel III market risk to factor models and backtesting methodology.

Built as a self-study and portfolio project in preparation for Quantitative Risk Analyst and AI/ML in Finance roles.

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Contents

Folder	Topic	Key Methods
MonteCarloVAR	Market Risk – VaR & CVaR	Monte Carlo, Cholesky, Kupiec Backtest
PortfolioOptimization	Portfolio Construction	Markowitz, Risk Parity, Efficient Frontier
QuantFinanceLearningNBs	Quant Finance Reference	30+ concepts, theory + code

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Project Highlights

01 · Monte Carlo Value at Risk & Expected Shortfall

• 10,000-scenario Monte Carlo engine with correlated asset returns via Cholesky decomposition
• VaR at 95% and 99% confidence levels (1-day holding period)
• Expected Shortfall / CVaR at 97.5% — the Basel III / FRTB regulatory standard
• Component VaR for risk attribution by asset
• Kupiec Proportion-of-Failures backtest with χ² test statistic
• Multi-day scaling via the square-root-of-time rule (Basel capital horizons)

02 · Portfolio Optimization — Efficient Frontier & Risk Parity

• Efficient Frontier traced via 200-point constrained optimization (SLSQP, long-only)
• Minimum Variance Portfolio, Maximum Sharpe Ratio (Tangency) Portfolio
• Risk Parity — equal risk contribution allocation (Bridgewater All Weather approach)
• Capital Market Line and Tobin separation theorem
• Static backtest with cumulative return, drawdown, Sharpe, and Calmar ratio comparison
• Risk contribution decomposition across all strategies

03 · Quant Finance Learning Notebooks

Three structured reference notebooks covering:

• Module 1: Returns, volatility, correlation, drawdown, leverage, beta/alpha, Fama-French
• Module 2: Sharpe, Sortino, Information Ratio, CAPM, VaR/CVaR theory, MPT & Black-Litterman
• Module 3: Mean reversion, momentum, pairs trading (cointegration), backtesting biases, transaction costs, fixed income, corporate actions

Each notebook includes formal mathematical derivations, Python implementations, visualizations, and academic references.

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Regulatory & Theoretical Context

Standard	Relevance in this Repository
Basel III / FRTB	ES at 97.5% as primary risk metric; Kupiec backtesting
Basel II	VaR at 99%, 10-day horizon via √T rule
Markowitz (1952)	Mean-variance optimization; Efficient Frontier
Fama-French (1993, 2015)	3- and 5-factor models; risk attribution
Carhart (1997)	4-factor model including momentum
Engle-Granger (1987)	Cointegration test for pairs trading
Black-Litterman (1992)	Robust portfolio optimization

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Getting Started

Requirements

git clone https://github.com/<your-username>/quant-risk-python.git
cd quant-risk-python
pip install -r requirements.txt

Run a notebook

jupyter notebook MonteCarloVAR/var_monte_carlo.ipynb

All notebooks use synthetic market data generated from realistic parameters — no external data sources required. To use real market data, replace the data generation section with yfinance:

import yfinance as yf
data = yf.download(['SAP.DE', 'ALV.DE', 'SIE.DE'], start='2019-01-01', end='2024-01-01')
returns = data['Adj Close'].pct_change().dropna()

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Repository Structure

Quant-Risk-Portfolio/
│
├── README.md
├── LICENSE
├── requirements.txt
├── .gitignore
│
├── MonteCarloVAR/
│   ├── var_monte_carlo.ipynb
│   └── README.md
│
├── PortfolioOptimization/
│   ├── portfolio_optimization.ipynb
│   └── README.md
│
└── QuantFinanceLearningNBs/
    ├── quant_finance_01_market_fundamentals.ipynb
    ├── quant_finance_02_risk_measures_mpt.ipynbb
    ├── quant_finance_03_strategies_backtesting.ipynb
    └── README.md

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References

• Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77–91.
• Sharpe, W.F. (1964). Capital Asset Prices: A Theory of Market Equilibrium. Journal of Finance, 19(3).
• Fama, E. & French, K. (1993). Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, 33(1).
• Carhart, M. (1997). On Persistence in Mutual Fund Performance. Journal of Finance, 52(1).
• Engle, R. & Granger, C. (1987). Co-Integration and Error Correction. Econometrica, 55(2).
• Artzner, P. et al. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3).
• Black, F. & Litterman, R. (1992). Global Portfolio Optimization. Financial Analysts Journal.
• Basel Committee on Banking Supervision (2019). Minimum Capital Requirements for Market Risk (FRTB).
• Hull, J.C. (2018). Options, Futures, and Other Derivatives (10th ed.). Pearson.
• de Prado, M.L. (2018). Advances in Financial Machine Learning. Wiley.
• Grinold, R. & Kahn, R. (1999). Active Portfolio Management. McGraw-Hill.

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Author

Florian Ebner
M.Sc. Bioinformatics · Johann Wolfgang Goethe-Universität Frankfurt
LinkedIn · florianebner96@googlemail.com

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License

This project is licensed under the MIT License — see LICENSE for details.