print(model.summary())
#5.
cny = web.DataReader('CNY=X', 'yahoo', dt.datetime(2015, 1, 1),
dt.datetime(2015, 12, 31))
ret = (cny.Close - cny.Close.shift(1)) / cny.Close.shift(1)
ret = ret.dropna()
cny.Close.plot()
ret.plot()
plot_acf(ret, lags=20)
plot_pacf(ret, lags=20)
LjungBox = stattools.q_stat(stattools.acf(ret)[1:13], len(ret))
LjungBox[1][-1]
(ret**2).plot()
plot_acf(ret**2, lags=20)
plot_pacf(ret**2, lags=20)
LjungBox = stattools.q_stat(stattools.acf(ret**2)[1:13], len(ret))
LjungBox[1][-1]
from arch.univariate import ARX, GARCH
model = ARX(ret, lags=1)
model.volatility = GARCH()
res = model.fit()
print(res.summary())
eqCurves['Buy and Hold'].plot()
plt.legend()
plt.show()
# # From Arch website
# In[273]:
from arch.univariate import ARX
ar = ARX(Y, lags=30)
print(ar.fit().summary())
# In[270]:
from arch.univariate import ARCH, GARCH
ar.volatility = GARCH(p=3, o=0, q=3)
res = ar.fit(update_freq=0, disp='off')
p(res.summary())
# In[265]:
from arch.univariate import StudentsT
ar.distribution = StudentsT()
res = ar.fit(update_freq=0, disp='off')
p(res.summary())
# In[266]:
arf = ar.forecast(horizon=forecast_steps,
start=Y.index[-1],
params=res.params,
from statsmodels.tsa.arima_model import ARMA
import pandas
import numpy
import statsmodels.api as sm
prices = pandas.read_csv("prices.csv", parse_dates=['Date'], index_col=0)
tickers = prices.columns[:-2]
prices = prices.resample('W').agg(lambda x: x[-1])
prices.dropna(axis=0, how='any', inplace=True)
rf = prices['^TNX'].values[:-1]
rf /= (52 * 100)
returns = prices.iloc[:, :-1].pct_change()[1:]
rm = returns['^GSPC'].values
ri = returns.iloc[:, :-1].values
Ri = ri - rf[:, numpy.newaxis]
Rm = rm - rf
model = sm.OLS(Ri, sm.add_constant(Rm))
results = model.fit()
alpha, beta = results.params
epsilon = numpy.sqrt(Ri.var(axis=0) - beta**2 * Rm.var(axis=0))
output = pandas.DataFrame(columns=['alpha', 'beta', 'epsilon'],
index=tickers,
data=numpy.array([alpha, beta, epsilon]).T)
output.to_csv("coefficients.csv")
from arch.univariate import ARX, GARCH
arx = ARX(rm, lags=1)
arx.volatility = GARCH()
res = arx.fit(disp='off')
pandas.DataFrame(res.params).to_csv("parameters.csv")
# ARCH effect
ar_res = ar_select_order(rates, 5).model.fit()
# Test of no serial correlation and homoskedasticity
print(ar_res.diagnostic_summary())
print(ar_res.summary())
plt.figure()
plt.plot(ar_res.resid)
# a = ar_res.resid
# a_res = ar_select_order(a, 5).model.fit()
# print(a_res.diagnostic_summary())
# Fit with GARCH(p, q)
ar = ARX(rates, lags=[1, 2]) # Mean model
ar.volatility = GARCH(p=1, q=1) # Volatility model
res = ar.fit()
res.plot()
print(res.summary())
# Forecast
drop = len(data) - len(rates)
start = 3254 - 2 - drop
end = 3262 - 2 - drop
var = res.forecast(start=start, horizon=5,
method='simulation').variance[start:1 + end]
var.plot()
entry = [
'2012:06:20',
'2012:06:21',
# In[58]: # AR from arch.univariate import ARX ar = ARX(ts_data, lags = [1, 3, 12]) # print(ar.fit().summary()) # In[60]: # Volatility Processes from arch.univariate import ARCH, GARCH ar.volatility = ARCH(p=5) res = ar.fit(update_freq=0, disp='off') # print(res.summary()) fig = res.plot() # Distribution from arch.univariate import StudentsT ar.distribution = StudentsT() res = ar.fit(update_freq=0, disp='off') # print(res.summary()) # In[61]: # price to return