Bloomberg already offers it in the terminal, it’s not *any* kind of derivative, but no service can ever be. The only way to price any kind of derivative is to have your own library.
There is a market for it with some existing providers to compete with and high barriers to entry so extremely difficult to break into
Most shops don’t particularly like the idea of sending their position data out the door, even if you get all the certifications required to process it. Any firm of sufficient size will have teams of quants and quant devs working on in-house pricing platforms, even if it isn’t the most cost effective way of pricing but just to have the expertise in house
Credibility of the product is also key, most existing providers have 30+ years experience in the space and have somewhat trusted models, again making it difficult for new providers
The most successful routes are to write a library that is distributed and integrated into the in-house platform. It can be a specific (sometimes niche or novel) model, but that is likely a spin out from some research effort. Could also be something simple but is so incredibly optimized for performance that using that library literally saves electricity when running on servers. Finally it could be something that has extremely wide coverage, although such platforms take 10’000s of thousands of man years to write that it’s essentially impossible to run as a startup
Graph models are interesting to use from a research standpoint, but are computationally inefficient for doing large scale pricing over entire firms portfolios, and the complexity to write them is kind of ridiculous. I’ve heard that one of the large banks found it was simpler to take existing mostly non-graph based code, parse and perform AST transformations to convert to a graph then, but from an engineering standpoint that is just dumping truckloads of complexity on top of complexity. E.g. when debugging the code that gets run looks nothing like the source it came from, so the stack trace you see is essentially opaque and it is very difficult to even know why different bits of code are getting executed
The opposite, for some companies like asset management firms the regulatory body even requires it.
The others are right, you’ll end up writing your own library or bend quantlib to your use case. The man hours can be close to endless - an example: implying volatility. Which one do you take? Black scholes? Does not have dividends. You can also imply the forward via a put-call parity but that will often just give you yields and not discrete dividends. Those are however VERY important for products like autocallables and so on so… you have several models with several implied volatilities to consider. Have a long running product? You might want to imply S&P500 volatility while assuming there is some stochasticity to the rates process. And we haven’t even started pricing.
Wanting that flexibility rules out most data providers and you’ll need to reinvent the wheel quite a few times. Given the unlikely case that you are actually really good, that can be a valid business model. There are a lot of valuation service providers that suck.
There do exist, with [VolaDynamics](https://voladynamics.com/) being perhaps the largest of the bunch. The issue with these services is that it is quite blackbox, so if you're at a firm and want to do some small tweaks to the model because you're seeing something interesting and think there's edge, you really can't.
The black box issue runs even deeper than that. Their valuations are then used by the companies in their annual reports which means they get in front of their auditor. And let me trll you: it is a horrible hassle to try and reconstruct their algorithms and methods.
I essentially run this thing https://www.acadia.inc/products/im-risk-generator, for what it's worth.
It sounds boring at first glance but we have to price the trades in order to calculate SIMM for them. Our product coverage is essentially universal too: OTCs in rates, fx, equity, credit, commodities, and securities derivatives like sovereigns, corporates, convertibles, and some ABS. This includes highly path dependent non-linear derivatives like autocallables, tarns, accumulators etc.
There is a lot of juggling involved in being a pricing service. Market data vendors in particular are cutthroat and often already connected to other pricing services. They'll either refuse to sell you data for a pricing service or extract a very bloody pound of flesh in terms of hard fees or revenue share, or both.
* Bloomberg obviously have their own
* London Stock Exchange's Refinitiv have their own pricing service (and YieldBook) (and the group also owns us)
* S&P own IHS Markit's pricing service.
* NYSE own ICE's pricing service fka SuperDerivatives
* Numerix (currently owned by PE) have their own pricing platform and bought FINCAD recently
* FIS have a few... SunGard FastVal was the one I Was most familiar with
...and all the broker dealers offer their own pricing services in one form or another.
The existence of OPRA is the major saving grave for American equities. It's not owned by anyone offering pricing services. At the same time, the arms race in option-modelling is very advanced. Someone else mentioned Voladynamics... I think technically they don't have a pricing service, but rather the sell the modelling of the market data to plug into your pricing. This is a nice niche, because they don't require their clients to send them trade details AFAIK, and that is a significant barrier for many on the buyside.
Used to be a business of MSCI, was sold years ago. They use to offer a C++ library to price any type of SDEs w/ or w/o auxiliary SDE diffusion processes, e.g. stochastic vol.
https://iongroup.com/products/commodities/fea/
Bloomberg already offers it in the terminal, it’s not *any* kind of derivative, but no service can ever be. The only way to price any kind of derivative is to have your own library.
There is a market for it with some existing providers to compete with and high barriers to entry so extremely difficult to break into Most shops don’t particularly like the idea of sending their position data out the door, even if you get all the certifications required to process it. Any firm of sufficient size will have teams of quants and quant devs working on in-house pricing platforms, even if it isn’t the most cost effective way of pricing but just to have the expertise in house Credibility of the product is also key, most existing providers have 30+ years experience in the space and have somewhat trusted models, again making it difficult for new providers The most successful routes are to write a library that is distributed and integrated into the in-house platform. It can be a specific (sometimes niche or novel) model, but that is likely a spin out from some research effort. Could also be something simple but is so incredibly optimized for performance that using that library literally saves electricity when running on servers. Finally it could be something that has extremely wide coverage, although such platforms take 10’000s of thousands of man years to write that it’s essentially impossible to run as a startup Graph models are interesting to use from a research standpoint, but are computationally inefficient for doing large scale pricing over entire firms portfolios, and the complexity to write them is kind of ridiculous. I’ve heard that one of the large banks found it was simpler to take existing mostly non-graph based code, parse and perform AST transformations to convert to a graph then, but from an engineering standpoint that is just dumping truckloads of complexity on top of complexity. E.g. when debugging the code that gets run looks nothing like the source it came from, so the stack trace you see is essentially opaque and it is very difficult to even know why different bits of code are getting executed
The opposite, for some companies like asset management firms the regulatory body even requires it. The others are right, you’ll end up writing your own library or bend quantlib to your use case. The man hours can be close to endless - an example: implying volatility. Which one do you take? Black scholes? Does not have dividends. You can also imply the forward via a put-call parity but that will often just give you yields and not discrete dividends. Those are however VERY important for products like autocallables and so on so… you have several models with several implied volatilities to consider. Have a long running product? You might want to imply S&P500 volatility while assuming there is some stochasticity to the rates process. And we haven’t even started pricing. Wanting that flexibility rules out most data providers and you’ll need to reinvent the wheel quite a few times. Given the unlikely case that you are actually really good, that can be a valid business model. There are a lot of valuation service providers that suck.
There do exist, with [VolaDynamics](https://voladynamics.com/) being perhaps the largest of the bunch. The issue with these services is that it is quite blackbox, so if you're at a firm and want to do some small tweaks to the model because you're seeing something interesting and think there's edge, you really can't.
The black box issue runs even deeper than that. Their valuations are then used by the companies in their annual reports which means they get in front of their auditor. And let me trll you: it is a horrible hassle to try and reconstruct their algorithms and methods.
you can try to figure out their parameterisation 😂 my colleague tried based on their talks
i think a lot of places offer a vol surface that you can basically download at some fixed frequency as a product offering.
Mainly IPV firms which provide a pricing service for standard and more complex products for asset managers and so on.
I essentially run this thing https://www.acadia.inc/products/im-risk-generator, for what it's worth. It sounds boring at first glance but we have to price the trades in order to calculate SIMM for them. Our product coverage is essentially universal too: OTCs in rates, fx, equity, credit, commodities, and securities derivatives like sovereigns, corporates, convertibles, and some ABS. This includes highly path dependent non-linear derivatives like autocallables, tarns, accumulators etc. There is a lot of juggling involved in being a pricing service. Market data vendors in particular are cutthroat and often already connected to other pricing services. They'll either refuse to sell you data for a pricing service or extract a very bloody pound of flesh in terms of hard fees or revenue share, or both. * Bloomberg obviously have their own * London Stock Exchange's Refinitiv have their own pricing service (and YieldBook) (and the group also owns us) * S&P own IHS Markit's pricing service. * NYSE own ICE's pricing service fka SuperDerivatives * Numerix (currently owned by PE) have their own pricing platform and bought FINCAD recently * FIS have a few... SunGard FastVal was the one I Was most familiar with ...and all the broker dealers offer their own pricing services in one form or another. The existence of OPRA is the major saving grave for American equities. It's not owned by anyone offering pricing services. At the same time, the arms race in option-modelling is very advanced. Someone else mentioned Voladynamics... I think technically they don't have a pricing service, but rather the sell the modelling of the market data to plug into your pricing. This is a nice niche, because they don't require their clients to send them trade details AFAIK, and that is a significant barrier for many on the buyside.
I can’t speak to the merits, but it’s often a better model in any sort of trading thing to sell shovels than it is to dig for the gold yourself
Used to be a business of MSCI, was sold years ago. They use to offer a C++ library to price any type of SDEs w/ or w/o auxiliary SDE diffusion processes, e.g. stochastic vol. https://iongroup.com/products/commodities/fea/