代写代考 - PowCoder代写

1 Importing packages

[1]: #importing packages
import statsmodels.api as sm
from statsmodels.tsa.stattools import adfuller
import pandas as pd
import numpy as np
import statsmodels.formula.api as smf
from sklearn import linear_model
import matplotlib.pyplot as plt
from scipy import stats
import datetime

2 Reading Excel file saved in hard drive

[2]: #reading the file
df = pd.read_excel(“C:\\Users\\rluck\\OneDrive\\shares.xlsx”)

[2]: Date Price
0 1998-01-02 975.039978
1 1998-01-05 977.070007
2 1998-01-06 966.580017
3 1998-01-07 964.000000
4 1998-01-08 956.049988

3 Calculating annual return

[3]: #computing the annual return from S&P500
df[‘R’] = 100*np.log(df[‘Price’]/df[‘Price’].shift(1))

[3]: Date Price R
0 1998-01-02 975.039978 NaN
1 1998-01-05 977.070007 0.207983
2 1998-01-06 966.580017 -1.079422
3 1998-01-07 964.000000 -0.267279
4 1998-01-08 956.049988 -0.828109

[4]: df.tail(10)

[4]: Date Price R
984 2001-12-03 1129.900024 -0.841649
985 2001-12-04 1144.800049 1.310084
986 2001-12-05 1170.349976 2.207284
987 2001-12-06 1167.099976 -0.278081
988 2001-12-07 1158.310059 -0.755992
989 2001-12-10 1139.930054 -1.599519
990 2001-12-11 1136.760010 -0.278478
991 2001-12-12 1137.069946 0.027261
992 2001-12-13 1119.380005 -1.567977
993 2001-12-14 1123.089966 0.330882

4 Remove the first row Nan
[5]: #Selecting the sample from

dta =df.iloc[1:900]
dta.head()

[5]: Date Price R
1 1998-01-05 977.070007 0.207983
2 1998-01-06 966.580017 -1.079422
3 1998-01-07 964.000000 -0.267279
4 1998-01-08 956.049988 -0.828109
5 1998-01-09 927.690002 -3.011257

[6]: dta.tail()

[6]: Date Price R
895 2001-07-23 1191.030029 -1.650407
896 2001-07-24 1171.650024 -1.640547
897 2001-07-25 1190.489990 1.595195

898 2001-07-26 1202.930054 1.039531
899 2001-07-27 1205.819946 0.239950

5 Plotting the time series: Stock Returns (R)

[7]: #plotting the series
plt.plot(dta[“R”])

[7]: []

[8]: import matplotlib.pyplot as plt
_ = plt.hist(dta[‘R’],bins=100)
_ = plt.xlabel(‘OBS’)
_ = plt.ylabel(‘R’)
plt.show()

6 Q4(a) CDF & 1% quantile

[9]: import numpy as np
import scipy
import matplotlib.pyplot as plt
import seaborn as sns
dta=dta[‘R’]
# generate samples from normal distribution (discrete data)
norm_cdf = scipy.stats.norm.cdf(dta) # calculate the cdf – also discrete

# plot the cdf
sns.lineplot(x=dta, y=norm_cdf)
plt.show()

[10]: #lower 1% quantile
np.percentile(dta,1)

[10]: -3.0849950142334084

7 Q4b-c:GARCH(1,1), GJR and EGARCH

[11]: from arch import arch_model

8 GARCH(1,1)

[12]: #GARCH(1,1)
model = arch_model(dta, mean=’constant’, vol=’GARCH’, p=1, q=1)
res_1 =model.fit()
res_1.summary

Iteration: 1, Func. Count: 6, Neg. LLF: 1478.37238353009
Iteration: 2, Func. Count: 15, Neg. LLF: 1477.9764253323951
Iteration: 3, Func. Count: 26, Neg. LLF: 1477.970683730851
Iteration: 4, Func. Count: 34, Neg. LLF: 1476.571267302416
Iteration: 5, Func. Count: 43, Neg. LLF: 1476.10266273572
Iteration: 6, Func. Count: 49, Neg. LLF: 1475.8308574534146
Iteration: 7, Func. Count: 56, Neg. LLF: 1475.682907378089

Iteration: 8, Func. Count: 62, Neg. LLF: 1475.6514266378354
Iteration: 9, Func. Count: 68, Neg. LLF: 1475.6494614480196
Iteration: 10, Func. Count: 74, Neg. LLF: 1475.6487752742607
Iteration: 11, Func. Count: 80, Neg. LLF: 1475.6484969528722
Iteration: 12, Func. Count: 86, Neg. LLF: 1475.6484950954127
Optimization terminated successfully. (Exit mode 0)

Current function value: 1475.6484950951449
Iterations: 12
Function evaluations: 86
Gradient evaluations: 12

[12]: |t| 95.0% Conf. Int.
—————————————————————————
mu 0.0503 4.034e-02 1.247 0.212 [-2.876e-02, 0.129]

Volatility Model
===========================================================================

coef std err t P>|t| 95.0% Conf. Int.
—————————————————————————
omega 0.0685 3.987e-02 1.718 8.574e-02 [-9.636e-03, 0.147]
alpha[1] 0.0875 3.351e-02 2.610 9.052e-03 [2.179e-02, 0.153]
beta[1] 0.8739 4.556e-02 19.183 5.140e-82 [ 0.785, 0.963]
===========================================================================

Covariance estimator: robust
ARCHModelResult, id: 0x26ba2d35988>

[13]: from arch.univariate import EGARCH

resi = arch_model(dta, mean =’constant’,vol=’GARCH’, p=1,o=1, q=1)
resi = resi.fit(update_freq=5, disp=’off’)

[13]: Constant Mean – GJR-GARCH Model Results
==============================================================================
Dep. Variable: R R-squared: -0.000
Mean Model: Constant Mean Adj. R-squared: -0.000
Vol Model: GJR-GARCH Log-Likelihood: -1447.88
Distribution: Normal AIC: 2905.76
Method: Maximum Likelihood BIC: 2929.76

No. Observations: 899
Date: Thu, Aug 06 2020 Df Residuals: 894
Time: 20:19:12 Df Model: 5

Mean Model
==============================================================================

coef std err t P>|t| 95.0% Conf. Int.
——————————————————————————
mu -6.0831e-04 4.394e-02 -1.384e-02 0.989 [-8.673e-02,8.551e-02]

Volatility Model
===========================================================================

coef std err t P>|t| 95.0% Conf. Int.
—————————————————————————
omega 0.0809 5.934e-02 1.363 0.173 [-3.540e-02, 0.197]
alpha[1] 0.0000 7.091e-02 0.000 1.000 [ -0.139, 0.139]
gamma[1] 0.2094 7.451e-02 2.810 4.957e-03 [6.332e-02, 0.355]
beta[1] 0.8520 0.115 7.439 1.011e-13 [ 0.628, 1.076]
===========================================================================

Covariance estimator: robust
ARCHModelResult, id: 0x26ba50f9888

[14]: from arch.univariate import EGARCH

model = arch_model(dta, mean =’constant’,vol=’EGARCH’, p=1,o=1, q=1)
res =model.fit(update_freq=5)

Iteration: 5, Func. Count: 50, Neg. LLF: 1447.6823394342935
Iteration: 10, Func. Count: 87, Neg. LLF: 1444.6700658480654
Optimization terminated successfully. (Exit mode 0)

Current function value: 1444.6671831233068
Iterations: 14
Function evaluations: 115
Gradient evaluations: 14

[14]: Constant Mean – EGARCH Model Results
==============================================================================
Dep. Variable: R R-squared: -0.001

Mean Model: Constant Mean Adj. R-squared: -0.001
Vol Model: EGARCH Log-Likelihood: -1444.67
Distribution: Normal AIC: 2899.33
Method: Maximum Likelihood BIC: 2923.34

No. Observations: 899
Date: Thu, Aug 06 2020 Df Residuals: 894
Time: 20:19:12 Df Model: 5

Mean Model
==============================================================================

coef std err t P>|t| 95.0% Conf. Int.
——————————————————————————
mu -6.9738e-03 3.763e-02 -0.185 0.853 [-8.073e-02,6.678e-02]

Volatility Model
=============================================================================

coef std err t P>|t| 95.0% Conf. Int.
—————————————————————————–
omega 0.0243 1.557e-02 1.558 0.119 [-6.265e-03,5.477e-02]
alpha[1] 0.0862 3.307e-02 2.608 9.114e-03 [2.142e-02, 0.151]
gamma[1] -0.1707 4.196e-02 -4.068 4.738e-05 [ -0.253,-8.846e-02]
beta[1] 0.9448 2.856e-02 33.082 5.375e-240 [ 0.889, 1.001]
=============================================================================

Covariance estimator: robust
ARCHModelResult, id: 0x26ba50b94c8

11 4d Plotting residuals and conditional volatility

[15]: #Standardised residual plots
fig =res.plot(annualize=’D’)

12 ACF and PACF of Standardised Residuals (dt) and Standard-
ised Residuals Squared (dts)

[16]: dt=res.resid/res.conditional_volatility

13 Standardised Residuals
[17]: sm.graphics.tsa.plot_acf(dt.values.squeeze(),lags=16)

sm.graphics.tsa.plot_pacf(dt.values.squeeze(),lags=16)

[18]: r,q,p=sm.tsa.acf(dt.values.squeeze(),qstat=True)
data = np.c_[range(1,41),r[1:],q,p]
table =pd.DataFrame(data,columns =[‘lag’,”AC”,”Q”,”Prob(>Q)”])
print(table.set_index(‘lag’))

AC Q Prob(>Q)
1.0 0.023511 0.498594 0.480118
2.0 0.009502 0.580118 0.748219
3.0 -0.048597 2.715134 0.437661
4.0 -0.023518 3.215702 0.522399
5.0 -0.045766 5.113457 0.402191
6.0 -0.000914 5.114214 0.529250
7.0 -0.039940 6.562797 0.475773
8.0 -0.005681 6.592140 0.581207
9.0 -0.018728 6.911354 0.646348
10.0 0.045250 8.776947 0.553395
11.0 -0.046517 10.750744 0.464375
12.0 0.086353 17.560259 0.129709
13.0 0.055651 20.391598 0.085867
14.0 0.003819 20.404947 0.117883
15.0 0.023698 20.919521 0.139420
16.0 -0.046797 22.928449 0.115661
17.0 0.027888 23.642689 0.129473
18.0 -0.049931 25.934858 0.101257

19.0 0.036480 27.159779 0.100986
20.0 -0.026914 27.827276 0.113552
21.0 -0.059934 31.141168 0.071340
22.0 -0.034555 32.244006 0.073330
23.0 0.011467 32.365596 0.092776
24.0 0.034136 33.444312 0.095040
25.0 0.013432 33.611528 0.116476
26.0 0.018026 33.913014 0.137228
27.0 0.067144 38.100821 0.076282
28.0 -0.017172 38.375053 0.091511
29.0 0.056514 41.348610 0.064183
30.0 -0.005811 41.380084 0.080782
31.0 -0.013936 41.561314 0.097461
32.0 -0.050735 43.966145 0.077387
33.0 -0.016447 44.219145 0.091800
34.0 -0.091372 52.037197 0.024621
35.0 -0.028167 52.781009 0.027331
36.0 0.010745 52.889367 0.034398
37.0 -0.001613 52.891812 0.043694
38.0 -0.011688 53.020320 0.053535
39.0 0.069750 57.602506 0.027760
40.0 -0.088893 65.053707 0.007387

C:\Users\rluck\anaconda3\lib\site-packages\statsmodels\tsa\stattools.py:572:
FutureWarning: fft=True will become the default in a future version of
statsmodels. To suppress this warning, explicitly set fft=False.

FutureWarning

14 Standardised Residuals Squared

[19]: sm.graphics.tsa.plot_pacf(dts.values.squeeze(),lags=16)
sm.graphics.tsa.plot_acf(dts.values.squeeze(),lags=16)

[20]: r,q,p=sm.tsa.acf(dts.values.squeeze(),qstat=True)
data = np.c_[range(1,41),r[1:],q,p]
table =pd.DataFrame(data,columns =[‘lag’,”AC”,”Q”,”Prob(>Q)”])
print(table.set_index(‘lag’))

AC Q Prob(>Q)
1.0 -0.038583 1.342757 0.246548
2.0 0.031452 2.236045 0.326926
3.0 -0.012011 2.366469 0.499907
4.0 0.017259 2.636040 0.620452
5.0 0.022684 3.102243 0.684226
6.0 -0.035242 4.228776 0.645748
7.0 0.024418 4.770187 0.687985
8.0 0.020524 5.153137 0.741090
9.0 -0.034055 6.208615 0.718871
10.0 0.023009 6.690968 0.754262
11.0 0.008363 6.754767 0.818581
12.0 -0.010135 6.848560 0.867457
13.0 -0.005896 6.880340 0.908212
14.0 -0.004423 6.898247 0.938563
15.0 -0.026918 7.562152 0.940136
16.0 0.039820 9.016709 0.912724
17.0 0.034510 10.110429 0.898916
18.0 0.026269 10.744899 0.904867

19.0 0.038315 12.096183 0.881473
20.0 0.002313 12.101114 0.912552
21.0 0.107708 22.803556 0.354541
22.0 -0.015359 23.021423 0.400521
23.0 -0.043127 24.741213 0.363765
24.0 0.018051 25.042830 0.403436
25.0 0.063808 28.816092 0.271677
26.0 0.011376 28.936166 0.313993
27.0 0.083034 35.340649 0.130445
28.0 -0.003951 35.355164 0.159763
29.0 0.032513 36.339372 0.163886
30.0 -0.045949 38.307351 0.142017
31.0 0.031797 39.250832 0.146821
32.0 0.074017 44.369182 0.071658
33.0 0.034348 45.472696 0.072746
34.0 -0.029492 46.287188 0.077851
35.0 0.001479 46.289238 0.096038
36.0 -0.031647 47.229249 0.099705
37.0 -0.006140 47.264678 0.120226
38.0 -0.020120 47.645511 0.135735
39.0 -0.009904 47.737896 0.159112
40.0 -0.024120 48.286462 0.172926

FutureWarning

15 Standardised Residuals Statistics
[21]: std_resid = res.resid / res.conditional_volatility

resid = res.resid
df = pd.concat([std_resid, resid], 1)
df.columns = [‘Std Resids’, ‘Resids’]
subplot = df.plot(kind=’kde’, xlim=(-6, 6))

16 Standardised Residuals Statistics
[22]: stats.describe(dt)

[22]: DescribeResult(nobs=899, minmax=(-4.4341245633175355, 3.298921120454863),
mean=0.010834927485779064, variance=0.9995416472236635,
skewness=-0.23125990208915737, kurtosis=0.7657947338486237)

[23]: skewness =-0.23125990208915737
kurtosis =0.7657947338486237
JB =(skewness**2+0.25*(kurtosis**2))*nobs/6

[23]: 29.980381797460023

[24]: dt.describe()

[24]: count 899.000000
mean 0.010835
std 0.999771
min -4.434125
25% -0.611477
50% 0.024652
75% 0.675022

max 3.298921
dtype: float64

17 Residuals Statistics
[25]: stats.describe(resid)

[25]: DescribeResult(nobs=899, minmax=(-7.036783685554021, 4.971570575558916),
mean=0.03060410465469678, variance=1.693610695043089,
skewness=-0.12413029499760052, kurtosis=2.041118218908278)

[26]: skewness =-0.12413029499760052
kurtosis =2.041118218908278
JB =(skewness**2+0.25*(kurtosis**2))*nobs/6

[26]: 158.36622569956484

[27]: resid.describe()

[27]: count 899.000000
mean 0.030604
std 1.301388
min -7.036784
25% -0.711097
50% 0.034797
75% 0.815742
max 4.971571
Name: resid, dtype: float64

18 Forecasts
[28]: forecasts =res.forecast()

s=forecasts.variance.tail(1)

899 1.632889

[29]: sd= forecasts.residual_variance.iloc[-1:]

899 1.632889

[30]: sm =forecasts.mean.tail(1)

899 -0.006974

19 Value-at-Risk (VaR)

[31]: q= dt.quantile(0.01)

[31]: -2.4238806396103247

[32]: res = model.fit(last_obs=(2001,28,7), update_freq=5)
forecasts = res.forecast(horizon=1)
print(forecasts.variance.dropna().head())

Iteration: 5, Func. Count: 50, Neg. LLF: 1447.6823394342935
Iteration: 10, Func. Count: 87, Neg. LLF: 1444.6700658480654
Optimization terminated successfully. (Exit mode 0)

Current function value: 1444.6671831233068
Iterations: 14
Function evaluations: 115
Gradient evaluations: 14

899 1.632889

[33]: cond_mean=forecasts.mean
cond_mean.tail(1)

899 -0.006974

[34]: cond_var=forecasts.variance
cond_var.tail(1)

899 1.632889

[35]: P= 10000000
VaR = (cond_mean – np.sqrt(cond_var)* q)*P/100
VaR.tail(1)

899 309037.145464

Importing packages
Reading Excel file saved in hard drive
Calculating annual return
Remove the first row the time series: Stock Returns (R)
Q4(a) CDF & 1% quantile
Q4b-c:GARCH(1,1), GJR and EGARCH
GARCH(1,1)
4d Plotting residuals and conditional volatility
ACF and PACF of Standardised Residuals (dt) and Standardised Residuals Squared (dts)
Standardised Residuals
Standardised Residuals Squared
Standardised Residuals Statistics
Standardised Residuals Statistics
Residuals Statistics
Value-at-Risk (VaR)

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