zaneguqi / Extreme-Risk

Using Extreme Value Theory (EVT) to Estimate Value-at-Risk (VaR) and Expected shortfall (ES)

Extreme-Risk

Using Extreme Value Theory (EVT) to Estimate Value-at-Risk (VaR) and Expected shortfall (ES) The Peaks-Over-Threshold (POT) model of extreme value theory, and GPD distribution give more accurate description on tail distribution of financial losses.

Simple Usage

import pandas as pd
import numpy as np
import pot


df = pd.read_csv("data/msci1.csv")
df.index = pd.to_datetime(df["Date"], format='%m/%d/%Y')
df = df.drop(['Date'], axis=1)

for col in df.columns.values:
    df[col] = np.log(df[col]) - np.log(df[col].shift(1))
df = df.dropna()

data = -df["Italy"]
#US	UK	Switzerland	Sweden	Spain	Singapore	Norway	Netherlands	Japan	Italy

print(data)


fitted_gpd = pot.gpd_pot(data, tu=0.95, fit="mle")
print(fitted_gpd.Beta, fitted_gpd.Xi)

fitted_gpd = pot.gpd_pot(data, tu=0.95, fit="mom")
print(fitted_gpd.Beta, fitted_gpd.Xi)

fitted_gpd = pot.gpd_pot(data, tu=0.95, fit="pwm")
print(fitted_gpd.Beta, fitted_gpd.Xi)

print(fitted_gpd.quantile(q=0.99))

pot.mean_exc(data)

fitted_gpd.qq_plot()
fitted_gpd.pp_plot()