R and Python Chapter 2. Univariate Volatility Modeling

# Chapter 2. Univariate Volatility Modeling

### R and Python

Copyright 2011 - 2023 Jon Danielsson. This code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This code is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. The GNU General Public License is available at: www.gnu.org/licenses.

##### ARCH and GARCH estimation in R
library(rugarch)
y=diff(log(p\$Index))*100
y=y-mean(y)
spec1 = ugarchspec(variance.model = list( garchOrder = c(1, 1)),
mean.model = list( armaOrder = c(0,0),include.mean = FALSE))
res1 = ugarchfit(spec = spec1, data = y)
spec2 = ugarchspec(variance.model = list( garchOrder = c(1, 0)),
mean.model = list( armaOrder = c(0,0),include.mean = FALSE))
res2 = ugarchfit(spec = spec2, data = y)
spec3 = ugarchspec(variance.model = list( garchOrder = c(1, 1)),
mean.model = list( armaOrder = c(0,0),include.mean = FALSE),
distribution.model = "std")
res3 = ugarchfit(spec = spec3, data = y)

##### START
import numpy as np
p = np.loadtxt('index.csv', delimiter = ',', skiprows = 1)
y = np.diff(np.log(p), n=1, axis=0)*100
y = y-np.mean(y)
from arch import arch_model
am = arch_model(y, mean = 'Zero', vol='Garch', p=1, o=0, q=0, dist='Normal')
arch1 = am.fit(update_freq=5, disp = "off")
am = arch_model(y, mean = 'Zero', vol='Garch', p=4, o=0, q=0, dist='Normal')
arch4 = am.fit(update_freq=5, disp = "off")
am = arch_model(y, mean = 'Zero', vol='Garch', p=4, o=0, q=1, dist='Normal')
garch4_1 = am.fit(update_freq=5, disp = "off")
am = arch_model(y, mean = 'Zero', vol='Garch', p=1, o=0, q=1, dist='Normal')
garch1_1 = am.fit(update_freq=5, disp = "off")
am = arch_model(y, mean = 'Zero', vol='Garch', p=1, o=0, q=1, dist='StudentsT')
tgarch1_1 = am.fit(update_freq=5, disp = "off")
print("ARCH(1) model:", "\n", arch1.summary(), "\n")
print("ARCH(4) model:", "\n", arch4.summary(), "\n")
print("GARCH(4,1) model:", "\n", garch4_1.summary(), "\n")
print("GARCH(1,1) model:", "\n", garch1_1.summary(), "\n")
print("tGARCH(1,1) model:", "\n", tgarch1_1.summary(), "\n")


##### Advanced ARCH and GARCH estimation in R
spec4 = ugarchspec(variance.model = list(model="apARCH", garchOrder = c(1, 1)),
mean.model = list( armaOrder = c(0,0),include.mean = FALSE))
res4 = ugarchfit(spec = spec4, data = y)
spec5 = ugarchspec(variance.model = list(model="apARCH", garchOrder = c(1, 1)),
mean.model = list( armaOrder = c(0,0),include.mean = FALSE), fixed.pars=list(delta=2))
res5 = ugarchfit(spec = spec5, data = y)
show(res5)

##### Advanced ARCH and GARCH estimation in Python
am = arch_model(y, mean = 'Zero', vol='Garch', p=1, o=1, q=1, dist='Normal')
leverage_garch1_1 = am.fit(update_freq=5, disp = "off")
am = arch_model(y, mean = 'Zero', vol='Garch', p=1, o=0, q=1, dist='Normal', power = 1.0)
power_garch1_1 = am.fit(update_freq=5, disp = "off")
print("Leverage effects:", "\n", leverage_garch1_1.summary(), "\n")
print("Power model:", "\n", power_garch1_1.summary(), "\n")


##### Financial Risk Forecasting
Market risk forecasting with R, Julia, Python and Matlab. Code, lecture slides, implementation notes, seminar assignments and questions.