How Regulation Is Shifting Pricing Quant Roles

# Does pricing quant still have bright future?


# Does pricing quant still have bright future?












With regulators tightening the tether, tradings are shrinking and exotic products are fading out, or as my quant friends told me so.

By the lag of education to the market, more MFE graduates are entering the market but the desk quants and library quants positions are downsizing.

Also the Basel is pushing for standard approaches for CVA and Daniel K Tarullo from FED even suggests replace in-house VaR calculation.

So is there still future for pricing quants? Is it a bad time to consider entering this area?

## Answer by lehalle (score 8, accepted)

https://quant.stackexchange.com/a/12785

Of course banks will continue to structure non linear products, other participants to consume them. Thus pricing and hedging them will be needed.

On the one hand less exotic products and more "standard" ones are needed, giving birth to larger flow desks and business. But on the other hands regulation change the way to valuate them (think about CVA and collateralization in general), modifying the payoffs and demanding to store more details about the products. Regulators and policy makers demand to do all that faster, and to compute risks on more products (clearing more nonlinear products).

As an industry, the financial firms reacts by:

- using software vendors more than in house tools; thus quants doing this kind of tasks will be more in software vendors than banks and funds,

- moving the sophisticated projects from front teams to risk and IT dept (in the latter case in "big data" groups inside IT dept).

if you join a vendor, you will be needed to be very rigorous and to be able to comply with sophisticated functional specs (and invest time in project management knowledge), you will have access to management roles (more people to synchronize), and potentially have to do client facing. If you are in risk dept, you will be ask to have a boarder view that just implementing Euler scheme, and to use robust models rather than fancy ones, but to partnership with academics and vendors to prototype and study nice ones. If you are in IT depts you will have to mix pricing with big data requirement: more data to use, faster, disseminate to more people.

Quants will have to face other challenges outside of pricing: dealing with liquidity at any scale, quant asset management, and big data new challenges (not only use bog data and process them fast, but implement "client relationship management" using facebook like techniques, use open data, etc). But it is another story...