FRDS - Financial Research Data Services

frds is a Python library to simplify the complexities often encountered in financial research. It provides a collection of ready-to-use methods for computing a wide array of measures in the literature.

It is developed by Dr. Mingze Gao from the Macquarie University, initially started as as a personal project during his postdoctoral research fellowship at the University of Sydney.

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@mq.edu.au

Supported measures and algorithms

For a complete list of supported built-in measures, please check frds.io/measures/ and frds.io/algorithms.

Supported Measures

• Absorption Ratio
• Contingent Claim Analysis
• Distress Insurance Premium
• Lerner Index (Banks)
• Long-Run Marginal Expected Shortfall (LRMES)
• Marginal Expected Shortfall
• Option Prices
• SRISK
• Systemic Expected Shortfall
• Z-score

Algorithms

• GARCH(1,1)
• GARCH(1,1) - CCC
• GARCH(1,1) - DCC
• GJR-GARCH(1,1)
• GJR-GARCH(1,1) - DCC

Examples

Some simple examples.

Absorption Ratio

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 AbsorptionRatio
>>> 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],
...             ]
...         )
ar = AbsorptionRatio(data)
ar.estimate()
0.7746543307660252

Bivariate GARCH-CCC

Use frds.algorithms.GARCHModel_CCC to estimate a bivariate Constant Conditional Correlation (CCC) GARCH model. The results are as good as those obtained in Stata, marginally better based on log-likelihood.

>>> import pandas as pd
>>> from pprint import pprint
>>> from frds.algorithms import GARCHModel_CCC
>>> data_url = "https://www.stata-press.com/data/r18/stocks.dta"
>>> df = pd.read_stata(data_url, convert_dates=["date"])
>>> nissan = df["nissan"].to_numpy() * 100
>>> toyota = df["toyota"].to_numpy() * 100
>>> model_ccc = GARCHModel_CCC(toyota, nissan)
>>> res = model_ccc.fit()
>>> pprint(res)
Parameters(mu1=0.02745814255283541,
           omega1=0.03401400758840226,
           alpha1=0.06593379740524756,
           beta1=0.9219575443861723,
           mu2=0.009390068254041505,
           omega2=0.058694325049554734,
           alpha2=0.0830561828957614,
           beta2=0.9040961791372522,
           rho=0.6506770477876749,
           loglikelihood=-7281.321453218112)