frds is an open-sourced Python package for computing a collection of major academic measures used in the finance literature in a simple and straightforward way.
pip install frdsThis library is still under development and breaking changes may be expected.
If there's any issue (likely), please contact me at mingze.gao@sydney.edu.au
More to be added. For a complete list of supported built-in measures, please check frds.io/measures/.
- Absorption Ratio
- Contingent Claim Analysis
- Distress Insurance Premium
- Long-Run MES
- Marginal Expected Shortfall (MES)
- SRISK
- Systemic Expected Shortfall (SES)
- Z-score
The primary purpose of frds is to offer ready-to-use functions.
For example, Kritzman, Li, Page, and Rigobon (2010) propose an Absorption Ratio that measures the fraction of the total variance of a set of asset returns explained or absorbed by a fixed number of eigenvectors. It captures the extent to which markets are unified or tightly coupled.
>>> import numpy as np
>>> from frds.measures import absorption_ratio
>>> data = np.array( # Hypothetical 6 daily returns of 3 assets.
... [
... [0.015, 0.031, 0.007, 0.034, 0.014, 0.011],
... [0.012, 0.063, 0.027, 0.023, 0.073, 0.055],
... [0.072, 0.043, 0.097, 0.078, 0.036, 0.083],
... ]
... )
>>> absorption_ratio.estimate(data, fraction_eigenvectors=0.2)
0.7746543307660252Another example, Distress Insurance Premium (DIP) proposed by Huang, Zhou, and Zhu (2009) as a systemic risk measure of a hypothetical insurance premium against a systemic financial distress, defined as total losses that exceed a given threshold, say 15%, of total bank liabilities.
>>> from frds.measures import distress_insurance_premium
>>> # hypothetical implied default probabilities of 6 banks
>>> default_probabilities = np.array([0.02, 0.10, 0.03, 0.20, 0.50, 0.15]
>>> correlations = np.array(
... [
... [ 1.000, -0.126, -0.637, 0.174, 0.469, 0.283],
... [-0.126, 1.000, 0.294, 0.674, 0.150, 0.053],
... [-0.637, 0.294, 1.000, 0.073, -0.658, -0.085],
... [ 0.174, 0.674, 0.073, 1.000, 0.248, 0.508],
... [ 0.469, 0.150, -0.658, 0.248, 1.000, -0.370],
... [ 0.283, 0.053, -0.085, 0.508, -0.370, 1.000],
... ]
... )
>>> distress_insurance_premium.estimate(default_probabilities, correlations)
0.28661995758