Asset Pricing — Illustrated Knowledge Base

A collection of Jupyter notebooks covering foundational and intermediate asset pricing topics: yield curves, fixed income instruments, derivatives, Monte Carlo simulations, and quantitative financial theory.

These notebooks are illustrative and educational. They demonstrate concepts with working code, not production systems.

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Structure

notebooks/
├── 📁 01_yield_curves/
│   ├── 01_nss_ecb.ipynb            # Nelson-Siegel-Svensson curve fitting (ECB data)
│   └── 02_bootstrapping_ois.ipynb  # OIS curve bootstrapping
│
├── 📁 02_instruments/
│   ├── 01_bonds.ipynb              # Bond pricing and duration
│   ├── 02_swaps.ipynb              # Interest rate swaps
│   ├── 03_fx_forwards.ipynb        # FX forwards and covered interest parity
│   ├── 04_options_vol.ipynb        # Options and volatility surfaces
│   ├── 05_cds.ipynb                # Credit default swaps
│   ├── 06_carry_trade.ipynb        # FX carry trade mechanics
│   ├── 07_futures.ipynb            # Futures pricing
│   ├── 08_fi_strategy_rv.ipynb     # Fixed income relative value strategies
│   ├── 09_callable_bond.ipynb      # Callable bond pricing
│   └── 10_exotic_equity_derivatives.ipynb  # Exotic equity derivatives
│
├── 📁 03_simulations/
│   ├── 01_mc_rate_paths.ipynb      # Monte Carlo interest rate paths
│   ├── 02_mc_bond_swap_pricing.ipynb
│   ├── 03_mc_callable_bonds.ipynb
│   ├── 04_mc_equity_paths.ipynb    # GBM equity path simulation
│   ├── 05_vasicek_model.ipynb      # Vasicek short-rate model
│   ├── 06_hw_model.ipynb           # Hull-White model
│   ├── 07_cir_model.ipynb          # Cox-Ingersoll-Ross model
│   ├── 08_cva.ipynb                # Credit Valuation Adjustment
│   ├── 09_fva.ipynb                # Funding Valuation Adjustment
│   ├── 10_mva.ipynb                # Margin Valuation Adjustment
│   └── 11_xva_aggregation.ipynb    # XVA aggregation
│
├── 📁 04_financial_theory/
│   ├── basic single period .ipynb  # Single-period model, state prices, arbitrage
│   ├── Vries_derivatives-pricing.ipynb  # Derivatives pricing fundamentals
│   └── 📁 timeseries/
│       ├── ts_finance.ipynb            # Financial time series analysis
│       ├── ts_garch_risk_management.ipynb  # GARCH models for risk
│       └── ts_var_xgboost.ipynb        # VaR with XGBoost

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Dependencies

External packages

All standard packages (numpy, pandas, scipy, matplotlib, statsmodels, QuantLib, etc.) are declared in pyproject.toml.

nb_utils — local utilities

nb_utils/ provides shared notebook setup (base(), asset_pricing()) and the date-keyed data cache. Installed automatically with pip install ..

quant_risk — internal engine

All notebooks import quant_risk, which comes from the quant-risk-engine repository. See the Runbook for how to install it.

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Data

Notebooks load OIS and NSS curves from a local date-keyed cache (data/processed/). Data files are gitignored — populate the cache before running notebooks:

python scripts/build_dataset.py                        # today's data
python scripts/build_dataset.py --date 2025-12-31 --pin  # historical + pin
python scripts/build_dataset.py --list                 # show cached dates

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Quickstart

See RUNBOOK.md for full setup instructions.

python3 -m venv .venv
source .venv/bin/activate
pip install .
# To use the local quant-risk-engine instead of GitHub:
# pip install . --no-deps && pip install -e ../quant-risk-engine
python scripts/build_dataset.py
jupyter lab

pytest tests/