Chapter 2. Univariate Volatility Modeling
R and Julia
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Listing 2.1/2.2
ARCH and GARCH estimation in R
library(rugarch)
p = read.csv('index.csv')
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)
Listing 2.1/2.2
ARCH and GARCH estimation in Julia
using CSV, Statistics, DataFrames;
p = CSV.read("index.csv",DataFrame)
y = diff(log.(p[:,1]))*100
y = y .- mean(y)
using ARCHModels;
arch1 = fit(ARCH{1}, y; meanspec = NoIntercept);
println("ARCH(1) model:", "\n", arch1)
arch4 = fit(ARCH{4}, y; meanspec = NoIntercept);
println("ARCH(4) model:", "\n", arch4)
garch4_1 = fit(GARCH{1,4}, y; meanspec = NoIntercept);
println("GARCH(4,1) model:", "\n", garch4_1)
garch1_1 = fit(GARCH{1,1}, y; meanspec = NoIntercept);
println("GARCH(1,1) model:", "\n", garch1_1)
tgarch1_1 = fit(GARCH{1,1}, y; meanspec = NoIntercept, dist = StdT);
println("tGARCH(1,1) model:", "\n", tgarch1_1)
Listing 2.3/2.4
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)
Listing 2.3/2.4
Advanced ARCH and GARCH estimation in Julia
leverage_garch1_1 = fit(TGARCH{1, 1,1}, y; meanspec = NoIntercept);
println("GARCH(1,1) with leverage effects model:", "\n", leverage_garch1_1)
Financial Risk Forecasting
Market risk forecasting with R, Julia, Python and Matlab. Code, lecture slides, implementation notes, seminar assignments and questions.© All rights reserved, Jon Danielsson,