Gray Smoke

When researching for the Indian market, I have been trying to find out how many urban household are there and the income level. On 13th September a very interesting article by Indicus Analytics was published in the Mint newspaper. Using data from that, we can see that there are 7 Million households above the income of Rs. 5L rupees out of which 2.1 Million households are there in the 5 Metros alone. Population Cities Notable city Households (M) Total Population (M) Households with > Rs 5L Income Metro >5 M  5 Delhi, Mumbai, Kolkata, Chennai, Bangalore 12.1 49.2 17.9% Mini-Metro > 3 M 5 Hyderabad, Ahmadabad, Pune, Coimbatore, Chandigarh 3.8 15.9 18.6% Tier I > 1 Million 33 Allahabad, Jodhpur, Vishakapatam 10.5 48.2 13.8% Tier II Between 0.6-1M 28 Bhuvaneshwar, Thiruvanantapuram, Ranc...

An amazing article by Joe Nocera that demystify how VaR mislead everyone to take more risk. RISK Mismanagement - What Led to the Financial Meltdown - NYTimes.com I am a great fan of Nicholas Taleb. I am not an expert on Statistics or Modeling but I find it appalling how data can be misrepresented for vested interests. One of the most interesting aspects was that VaR gave a 99% risk prediction for the next day. 99% may sound reassuring but the remaining 1% percent failure means 3.5 days in a year. I first encountered 99% folly when I saw my Internet SLA had 99% up time. It means that 3.5 days in a year, I could not do business. The cost of calculating the confidence level or delivering the service is an exponential curve. 95% may cost $1 Million, 99% will cost $10 Million, 99.9% will cost $100 Million. As 95% is not reassuring, it makes sense to get 99% assurance by spending the marginal cost but not worth spending money to get 99.9% assurance. In 99% we do not build any backups and ...

Finally an amazing article by Joe Nocera that demystify how VaR mislead everyone to take more risk. RISK Mismanagement - What Led to the Financial Meltdown - NYTimes.com I am a great fan of Nicholas Taleb. I am not an expert on Statistics or Modeling but I find it appalling how data can be misrepresented for vested interests. One of the most interesting aspects was that VaR gave a 99% risk prediction for the next day. 99% may sound reassuring but the remaining 1% percent failure means 3.5 days in a year. I first encountered 99% folly when I saw my Internet SLA had 99% uptime. It means that 3.5 days in a year, I could not do business. The cost of calculating the confidence level or delivering the service is an exponential curve. 95% may cost $1 Million, 99% will cost $10 Million, 99.9% will cost $100 Million. As 95% is not reassuring, it makes sense to get 99% assurance by spending the marginal cost but not worth spending money to get 99.9% assurance. In 99% we do not build any back...