Python for Finance

leftTail=int(n*(1-confidence_level))
print(leftTail)
#
VaR2=position*ret2[leftTail]
print("Holding=",position, "VaR=", round(VaR2,4), "tomorrow")
('Holding=', 26503.070499999998, 'VaR=', -816.7344, 'tomorrow')
The result shows that the 1-day VaR is $817. Recall that the VaR based on the
normality is $648. If the second method is more accurate, the first method
underestimates our potential loss by 20%. This is a huge number in terms of risk
evaluation! The following codes are for an n-day period based on sorting:
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Chapter 11
ret = x.aclose[1:]/x.aclose[:-1]-1
position=n_shares*x.close[0]
#
# Method 1: based on normality
mean=np.mean(ret)
std=np.std(ret)
meanNdays=(1+mean)**nDays-1
stdNdays=std*np.sqrt(nDays)
z=norm.ppf(confidence_level)
VaR1=position*z*stdNdays
print("Holding=",position, "VaR1=", round(VaR1,0), "in ", nDays,
"Days")
#
# method 2: calculate 10 day returns
ddate=[]
d0=x.date
for i in range(0,np.size(logret)):
ddate.append(int(i/nDays))
y=pd.DataFrame(logret,index=ddate,columns=['retNdays'])
logRet=y.groupby(y.index).sum()
retNdays=np.exp(logRet)-1
#
VaR2=position*z*np.std(retNdays)
print("Holding=",position, "VaR2=", round(VaR2,0), "in ", nDays,
"Days")
#
# Method III
ret2=np.sort(retNdays)
n=np.size(ret2)
leftTail=int(n*(1-confidence_level))
print(leftTail)
#
VaR3=position*ret2[leftTail]
print("Holding=",position, "VaR=", round(VaR3,0), "in ",nDays, "Days")
('Holding=', 24853.456000000002, 'VaR1=', 2788.0, 'in ', 10, 'Days')