Financial Research Data Services
Project description
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 mingze.gao@sydney.edu.au
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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.