Package to build risk model for factor pricing model. For further details, please refer to the documentation
Install this via pip (or your favourite package manager):
pip install factor-pricing-model-risk-model
The library contains the pipelines to build the risk model. You can run the pipelines interactively in Jupyter Notebook.
import fpm_risk_model
The project provides frameworks to create multi-factor risk model on an "enterprise-like" level.
The target audiences are researchers, developers and fund management looking for flexibility in creating risk models.
Basically, there are three major features provided in the library
- Factor risk model creation
- Covariance estimator
- Tracking risk model accuracy
The factor risk model is created by fitting instrument returns (which could be weekly, daily, or even higher granularity) and other related parameters into the model, and its products are factor exposures, factor returns, factor covariance, and residual returns (idiosyncratic returns).
For example, to create a simple statistical PCA risk model,
from fpm_risk_model.statistics import PCA
risk_model = PCA(n_components=5)
risk_model.fit(X=returns)
# Get fitted factor exposures
risk_model.factor_exposures
Then, the risk model can be transformed by the returns of a larger homogeneous universe.
risk_model.transform(y=model_returns)
For further details, please refer to the section in the documentation.
Currently, covariance estimation is supported in factor risk model, and the estimation depends on the fitted results.
For example, a risk model transformed by model universe returns can derive the pairwise covariance and correlation for the model universe.
risk_model.transform(y=model_returns)
cov = risk_model.cov()
corr = risk_model.corr()
The following features will be supported in the near future
- Covariance shrinkage
- Covariance estimation from returns
For further details, please refer to the section in the documentation.
The library also focuses on the predictability interpretation of the risk model, and provides a few benchmarks to examine the following metrics
For example, to examine the bias statistics of a risk model regarding
an equally weighted portfolio (of which its weights are denoted as weights
),
pass the instrument observed returns (denoted as returns
), and either
a rolling risk model (to compute the volatility forecast) or a time series
of volatility forecasts.
from fpm_risk_model.accuracy import compute_bias_statistics
compute_bias_statistics(
X=returns,
weights=weights,
rolling_timeframe=36,
...
)
The following major features will be enhanced
- Factor exposures computation from factor returns (Q1 2023)
- Shrinking covariance (Q1 2023)
- Exponential decay weighted least squares regression (Q1-Q2 2023)
- Multiple types of running engine, e.g. Tensorflow (Q1-Q2 2023)
- Multi-asset class factor model (Q2 2023)
- Fundamental type risk model (Q3 2023)
All levels of contributions are welcomed. Please refer to the contributing section for development and release guidelines.