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1. I bet not many people in another industry know about the bar of another department well.
2. Yes and no.
Q = risk-neutral measure, P = physical measure.
Q quant also deal with data, but the key focus is on the relationship between a derivatives and the underlying, which is described through a stochastic model under the risk neutral measure.
Q quants are more common on the sell-side, as the seller need a good model for pricing, hedging, risk management and etc...but surly it is not limited to sell side.
while data scientist are also not only limited to buy side. just a simple example of a commercial bank employing a data scientist to work on the credit risk of the banking book.
3. No matter CS, DS, Applied Math , stat, none of them are field specify (like FE). The focus of these master are on the methodology itself, which can be applied in many fields. (and DS, ML are now the hottest field now, and thus CS > applied maths).
The key issue here is, neither a CS, ML, Maths, Stat master will taught about the subject matter knowledge of quantitative finance modelling practice. Some may be good enough to link that up themselves, but some not. A MFE is a bridge between them. Some maybe able to swim across the river themselves, while some may need to buy the ticket for the bridge.
By the way, I will hesitate in saying that DS is a subset of CS. I will rather say it is like 60-80% CS + 20-40% stat. Your bioinformatic background helps too here. (therefore quite many DS master is offer by the CS and stat department together / a standalone DS institute) |
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楼主
发表于 2019-7-26 04:10:55
