varpy 2.0.1

Value at Risk tools

VaRpy - Value at Risk Models

A Python library for computing Value at Risk (VaR) and Conditional Value at Risk (CVaR) using various statistical distributions and GARCH models.

Download

pip install varpy

Recent Refactoring

The codebase has undergone a major refactoring to improve code organization, maintainability, and consistency. Here are the key changes:

1. Standardized Model Structure

Each VaR model (Normal, Student's t, EVT) follows the same pattern:

• Constructor takes only theta and horizon parameters
• run method accepts returns data as input
• Private helper methods for specific computations
• Consistent method naming and documentation

Example structure:

class SomeVar(BaseVar):
    def __init__(self, theta: float, horizon: int):
        super().__init__(theta, horizon)

    def run(self, ret: NDArray[np.float64]) -> None:
        # 1. Get GARCH forecasts
        # 2. Process innovations
        # 3. Fit distribution
        # 4. Compute VaR/CVaR
        pass

3. Improved Type Hints

• Added comprehensive type hints using numpy.typing
• Consistent use of NDArray[np.float64] for numpy arrays
• Proper return type annotations for all methods

5. Enhanced Documentation

• Comprehensive docstrings for all classes and methods
• Clear parameter and return type documentation
• Consistent documentation style across all models

6. Code Organization

• Moved models to varpy/var/models/ directory
• Consistent file naming (lowercase with underscores)
• Clear separation of concerns between models

Available Models

Normal Distribution

• Assumes returns follow a normal distribution
• Uses GARCH(1,1) with Gaussian innovations
• Suitable for well-behaved financial returns

Student's t Distribution

• Assumes returns follow a Student's t distribution
• Uses GARCH(1,1) with Student's t innovations
• Better suited for heavy-tailed returns

Extreme Value Theory (EVT)

• Uses Generalized Pareto Distribution for tail modeling
• Combines GARCH with EVT for better tail estimation
• Most suitable for extreme risk measurement

Usage Example

import numpy as np
from varpy.var.models import Normal, Student, EVT

# Initialize model
model = Normal(theta=0.05, horizon=1)

# Compute VaR and CVaR
returns = np.array([...])  # Your return data
model.run(returns)

# Get results
var = model.var
cvar = model.cvar

More exemples here

Dependencies

• numpy
• scipy
• arch (for GARCH modeling)

Contributing

When adding new VaR models, please follow the established pattern:

1. Inherit from BaseVar
2. Implement the run method
3. Use private helper methods for specific computations
4. Add comprehensive type hints and documentation
5. Follow the modular design pattern