frds 1.3.0

Financial Research Data Services

FRDS - Financial Research Data Services

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.

Installation

pip install frds

Note

This library is still under development and breaking changes may be expected.

If there's any issue (likely), please contact me at [e-mail]

Supported measures

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

Examples

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)
0.7746543307660252

Another 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