Morgan Stanley uses 3 billion data points each day to measure its equity risk. Here’s how it’s using AWS to do it.

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When the US stock market closes at 4:00 p.m. each afternoon in New York, it typically marks an end to the day for the equity traders working across Wall Street’s banks. Once they mark and sign off on their positions, they’re out the door. 

For others who sit near trading desks, however, their work is just beginning. One such example is the risk employees — and the data models they run — that calculate a bank’s exposure to the markets after the close of each trading day. 

As is the case with many other banking functions, speed and scale are key. That’s why more firms have looked to lean on the public cloud to be more efficient.

It was a trend highlighted by Vikas Chawla, an executive director on Morgan Stanley’s enterprise technology and services team, at a symposium hosted by Amazon Web Services on Tuesday. 

Morgan Stanley’s embrace of cloud technology has enabled the bank to more flexibly model the exposure it faces from “a few million positions” on its equities trading desk each day, Chawla said. The tech means the bank can scale up — or down — the amount of computing power it needs on a daily basis. 

“Sometimes on a good day, we could have six hours to calculate risk, but in times of high volatility, it’s possible that we might have half the time, in three hours. Now this is where the cloud is very attractive,” Chawla said. 

A bank’s risk model might consider billions of data points each day

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Value-at-risk, or VaR, is the high-level metric most often associated with risk management. It’s a model that takes into account three factors: a time-frame (typically a day, a month, or a 250-day trading year); the positions a bank has in equities, currencies, or any other securities; and hypothetical risk scenarios …read more

Source:: Businessinsider

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