Python for Finance

Chapter 11
#
sum=0.0
for i in np.arange(m):
sum+=ret2[i]
ret3=sum/m
ES=position*ret3
print("Holding=",position, "Expected Shortfall=", round(ES,4),
"tomorrow")
('m=', 12)
('Holding=', 26503.070499999998, 'Expected Shortfall=', -1105.1574,
'tomorrow')
Since there are 11 returns are less the 12th returns, the expected shortfall will be the
average of those 12 returns times our portfolio market value on the evaluation day:
Appendix A – data case 7 – VaR estimation for
individual stocks and a portfolio
There are three objectives of this dataset:
• Understand the concepts and methodology related to a VaR
• Estimate a VaR for individual stocks
• Estimate a VaR for a portfolio
The question is: What are your VaRs for each stock and for an equal-weighted
portfolio over 10 days for a 99% confidence interval? Assume that the data period is
from February 7, 2012 to February 7, 2017 and you have a $1m investment (position
in Equation 1):
i Company name Ticker Industry
1 Microsoft Corporation MSFT Application software
2 Apple Inc. AAPL Personal computer
3 Home Depot, Inc. HD Home improvement services
4 Citigroup Inc. C Money Center Banks
5 Wal-Mart Stores, Inc. WMT Discount, variety stores
6 General Electric Corporation GE Technology
The concrete steps are given here:
1. Retrieve the daily data from Yahoo! Finance.
2. Estimate the daily returns.
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