Description:

frds 2.4.1

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 University of Sydney, as a personal project during his postdoctoral research fellowship.
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 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)

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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