ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0])
ggres0 = ggmod0.fit(start_params=start_params, method='bfgs', maxiter=2000)
print 'ggres0.params', ggres0.params
g11res = optimize.fmin(
lambda params: -loglike_GARCH11(params, errgjr4 - errgjr4.mean())[0],
[0.93, 0.9, 0.2])
print g11res
llf = loglike_GARCH11(g11res, errgjr4 - errgjr4.mean())
print llf[0]
if 'rpyfit' in examples:
from rpy import r
r.library('fGarch')
f = r.formula('~garch(1, 1)')
fit = r.garchFit(f, data=errgjr4 - errgjr4.mean(), include_mean=False)
if 'rpysim' in examples:
from rpy import r
f = r.formula('~garch(1, 1)')
#fit = r.garchFit(f, data = errgjr4)
x = r.garchSim(n=500)
print 'R acf', tsa.acf(np.power(x, 2))[:15]
arma3 = Arma(np.power(x, 2))
arma3res = arma3.fit(start_params=[-0.2, 0.1, 0.5], maxiter=5000)
print arma3res.params
arma3b = Arma(np.power(x, 2))
arma3bres = arma3b.fit(start_params=[-0.2, 0.1, 0.5],
maxiter=5000,
method='bfgs')
print arma3bres.params
cl = aa['Close']
ret = np.diff(np.log(cl))[-2000:]*1000.
ggmod = Garch(ret - ret.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4)
ggmod.nar = 1
ggmod.nma = 1
ggmod._start_params = np.array([-0.1, 0.1, 0.1, 0.1])
ggres = ggmod.fit(start_params=np.array([-0.1, 0.1, 0.1, 0.0]), maxiter=1000,method='bfgs')
print 'ggres.params', ggres.params
garchplot(ggmod.errorsest, ggmod.h, title='Garch estimated')
from rpy import r
r.library('fGarch')
f = r.formula('~garch(1, 1)')
fit = r.garchFit(f, data = ret - ret.mean(), include_mean=False)
f = r.formula('~arma(1,1) + ~garch(1, 1)')
fit = r.garchFit(f, data = ret)
ggmod0 = Garch0(ret - ret.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4)
ggmod0.nar = 1
ggmod.nma = 1
start_params = np.array([-0.1, 0.1, ret.var()])
ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0])
ggres0 = ggmod0.fit(start_params=start_params, maxiter=2000)
print 'ggres0.params', ggres0.params
g11res = optimize.fmin(lambda params: -loglike_GARCH11(params, ret - ret.mean())[0], [0.01, 0.1, 0.1])
print g11res
llf = loglike_GARCH11(g11res, ret - ret.mean())
ggmod = Garch(ret - ret.mean()) #hgjr4[:nobs])#-hgjr4.mean()) #errgjr4)
ggmod.nar = 1
ggmod.nma = 1
ggmod._start_params = np.array([-0.1, 0.1, 0.1, 0.1])
ggres = ggmod.fit(start_params=np.array([-0.1, 0.1, 0.1, 0.0]),
maxiter=1000,
method='bfgs')
print('ggres.params', ggres.params)
garchplot(ggmod.errorsest, ggmod.h, title='Garch estimated')
use_rpy = False
if use_rpy:
from rpy import r
r.library('fGarch')
f = r.formula('~garch(1, 1)')
fit = r.garchFit(f, data=ret - ret.mean(), include_mean=False)
f = r.formula('~arma(1,1) + ~garch(1, 1)')
fit = r.garchFit(f, data=ret)
ggmod0 = Garch0(ret - ret.mean()) #hgjr4[:nobs])#-hgjr4.mean()) #errgjr4)
ggmod0.nar = 1
ggmod.nma = 1
start_params = np.array([-0.1, 0.1, ret.var()])
ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0])
ggres0 = ggmod0.fit(start_params=start_params, maxiter=2000)
print('ggres0.params', ggres0.params)
g11res = optimize.fmin(
lambda params: -loglike_GARCH11(params, ret - ret.mean())[0],
[0.01, 0.1, 0.1])
print(g11res)
start_params = np.array([-0.6, 0.2, 0.1])
ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0])
ggres0 = ggmod0.fit(start_params=start_params, method='bfgs', maxiter=2000)
print 'ggres0.params', ggres0.params
g11res = optimize.fmin(lambda params: -loglike_GARCH11(params, errgjr4-errgjr4.mean())[0], [0.93, 0.9, 0.2])
print g11res
llf = loglike_GARCH11(g11res, errgjr4-errgjr4.mean())
print llf[0]
if 'rpyfit' in examples:
from rpy import r
r.library('fGarch')
f = r.formula('~garch(1, 1)')
fit = r.garchFit(f, data = errgjr4-errgjr4.mean(), include_mean=False)
if 'rpysim' in examples:
from rpy import r
f = r.formula('~garch(1, 1)')
#fit = r.garchFit(f, data = errgjr4)
x = r.garchSim( n = 500)
print 'R acf', tsa.acf(np.power(x,2))[:15]
arma3 = Arma(np.power(x,2))
arma3res = arma3.fit(start_params=[-0.2,0.1,0.5],maxiter=5000)
print arma3res.params
arma3b = Arma(np.power(x,2))
arma3bres = arma3b.fit(start_params=[-0.2,0.1,0.5],maxiter=5000, method='bfgs')
print arma3bres.params
xr = r.garchSim( n = 100)
