It’s essentially IT/data work - the days of sophisticated maths are mostly gone. There always was a lot of code, but these days for most people there’s little to no new maths.
From what I’ve seen, post-2008 the job changed significantly, with more IT, less maths, more standardization - basically the job moved from bespoke everything to super industrialized. You’ll be able to have your model work for one underlying and one product, but what’s really useful is for lots of underlyings and many products - and that’s very hard.
That being said, and that’s important, you must understand the maths behind, otherwise you won’t be able to do anything useful.
You are right. For most people there's little to no new maths.
But not for all. There's still plenty of good quality math to be done in the exotics space. However, there's a bit of Catch 22 that prevents people from doing new math: all the big shops have had exotics libraries since before 2008, and because of the exotics hiatus between about 2008 and maybe 2013, the research momentum was lost. After that, most quants in the space were happy to find ways to use the old stuff, and apply small tweaks at the margins. Most small shops use vendor models (Numerix, Murex) or open source (QuantLib), and people who use vendor solutions or open source are not looking for cutting edge stuff.
But there's still good math left out there.
No matter how much I try to understand the financial system, there seems no end to the nomenclature.
Do you or others know of any good references that help navigate this?
Not sure how exotic he gets but likely the page that sells this book will have other options books.
I think there's one by Espen Hauge about exotics.
Relevant book by Nassim Taleb (before his big break) is Dynamic Hedging, which tells you what to do with your option risk once you have it.
For a structured products introduction you may take a look at this one: https://sspa.ch/en/book/
It's a very simple book, very high level, but explains the most popular structured products in a very simple manner. If you can read a payoff diagram, then this is the simplest intro.
Looking at their website though, they seem to have some nice online material there also. For example this explains the 5 most popular products, and perhaps that's good enough for an introduction (really these 5 products cover 90% of the market anyway, though there's no limit to how exotic some bespoke structures can get): https://sspa.ch/en/lab/?underlying=CH0012221716&final_fixing...
In case you're interested in getting to get to learn about them on a deeper level I would recommend https://www.amazon.com/Exotic-Options-Hybrids-Structuring-Pr.... This book explains not only the products, but also the pricing dynamics and hedging too.
And just a small gem I found recently about volatility trading:https://www.ebay.co.uk/itm/306680584072?chn=ps&_ul=GB&_trkpa...
Despite its appalling Amazon reviews I consider this book to be a real gem when it comes to the introduction to vol trading (basically dynamic hedging of equity derivatives)
https://youtube.com/playlist?list=PLHC72UlhAthBEEAhoQwPdDaL_...
Intellectually, it's interesting when you start. There's all these weird payoffs that you are introduced to, and it feels like a game.
The thing is, there's a limit to how exotic things can get. People have already figured out how to price most of the things you can imagine, including all the things that customers normally ask for. Most of the day goes on looking after your hedges, basically implementing the model.
It's like a zoo. When you arrive there's a bunch of different, interesting animals. After a while, you've met them all. There's no new animals, just variations of existing ones.
However the thing that is really an issue is how the business works. Over time I came to the conclusion that the quants in the derivs space are really secondary to the salespeople. How important is the quant who can get the price right to within 1%, when the sales guy can talk the customer into overpaying by 5%? Sometimes it feels like the customer is not even shopping the structure around at all, he just feels comfortable with his sales guy and is willing to hand over a few million bucks of customer money with barely any thought.
You need quants and sales and trading. Which is why all banks have all three.
> You need quants and sales and trading. Which is why all banks have all three.
I don't think anybody said you can just run without one of those. But it seems the magic is in spotting the fish, not hauling it in.
There is, eventually, a shortage of dumb money. The sustainable way of making money involves competition, and this involves knowing the "right" price within a tight tolerance.
I've witnesses this several times, some trader always uses the same relationship, irrespective of cost. I've seen this both in terms of friends from a long time ago helping each other, family, or backhanders.
And so the real winner is that sales guy. I've known people climb to the very top of well known institutions on the back of relationships with just one hedge fund.
What you're describing is a sort of ideal market from an economics textbook.
Normally though, such relationships do not involve the sales person charging significantly above market rates. The client usually has very strong incentives to reduce costs. While a single salesperson might build a career on a chummy relationship this isn't a sustainable approach for an entire firm to take because it is too unusual. The majority of the revenue is coming from client/sales relationships where the client is at least somewhat price sensitive and sufficiently savvy to get more than one quote.
Huh? What happened? This is a very interesting claim I would love to hear elaborated
The thing is that there isn’t really strong institutional demand for exotic derivatives, people are happy using existing methods and just applying those to current markets.
The other type of fancy math has to do with deriving alpha, which is also not that complex, from a statistics perspective you’re mostly using linear regression or other basic forms of regression.
The hard part of quant is implementation, making sure your data is right, hunting through poorly understood markets, and managing risks carefully and understanding them.
There’s also ML but that’s equally complex in quant as it is anywhere else.
In my experience I have seen far more division of labor than you describe. Real quants don’t do work like making sure your data is right or even much of implementation; they delegate that to software engineers. But a cheap quant shop might be too cheap to hire SWEs so quants end up doing this work instead. The real quant work is just hunting through poorly understood markets.
Some assets are very easy to price, like bonds. Others are esoteric, like Exotics which are options or other securities with uncommon pricing structures. This is where the "fancy math" kicks in. But as time goes on, more and more firms figure out how to better price all the various assets that they trade, which erodes the competitive edge between market participants.
The assertion is that today, most firms have most things mostly figured out as far as how to value them. There's little to no competitive advantage to be mined from esoteric stochastic calculus. In contrast, it's rumoured that one of the most successful firms of all time, Renaissance, owes a large part of their success to their absolutely pristine data which comes from a massive data ingestion and cleaning pipeline, allowing them to get a clearer statistical picture of what the current market forces at play are, and how they're going to manifest.
Fancy math definitely is part of derivatives pricing. However financial world has become too complicated for simple models. Adding things like the risk of counterparty default to your pricing equation quickly leads you into the world of equations without closed form solutions. The common approach these days is some kind of huge multi factor Monte Carlo model. These are still solving pricing equations but the challenge is more about numerical methods than brilliant algebraic gymnastics.
Unless you happen to be in a place like RenTech, perhaps?
“I joined a hedged fund, Renaissance Technologies, I'll make a comment about that. It's funny that I think the most important thing to do on data analysis is to do the simple things right. So, here's a kind of non-secret about what we did at renaissance: in my opinion, our most important statistical tool was simple regression with one target and one independent variable. It's the simplest statistical model you can imagine. Any reasonably smart high school student could do it. Now we have some of the smartest people around, working in our hedge fund, we have string theorists we recruited from Harvard, and they're doing simple regression. Is this stupid and pointless? Should we be hiring stupider people and paying them less? And the answer is no. And the reason is nobody tells you what the variables you should be regressing [are]. What's the target. Should you do a nonlinear transform before you regress? What's the source? Should you clean your data? Do you notice when your results are obviously rubbish? And so on. And the smarter you are the less likely you are to make a stupid mistake. And that's why I think you often need smart people who appear to be doing something technically very easy, but actually usually not so easy.”
http://www.thetalkingmachines.com/episodes/ai-safety-and-leg...
Trading huge equity portfolios and getting paid a lot? Pretty fun
Pricing structured products all day in a bank that charges you for lunch? Not great
Well take someone who YOLO on a 0 DTE option and makes 80x (not unheard of) vs someone who wins the same amount over, say, four years and 8 000 trades... Well it's not impossible that the YOLOer is more skilled and has an edge (while also being a degen but that's not the point): it is just very unlikely.
Thousands, tens of thousands, hundreds of thousands of trades is not the same as "gambling one night in Vegas".
- a casino is a random game
- stock market is a game of incomplete informatoin
The one cant related to the other by whatever equation.
The statistics of games are understandable, defined and easy to work with.
The statistics of markets, as Soros spent his career investigating, are filled with feedback loops, and as Taleb investigated, fat tails.