How hedging market risk is changing

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Kate Finlayson: Hello. You listening to Market Matters? Our market series here on J.P.  Morgan's Making Sense Podcast Channel. I'm Kate Finlayson from the FICC Market Structure Liquidity Strategy Team. So as electrification continues across various asset classes, it will be interesting to understand how this has impacted the way that market risk is hedged. In today's episode, I'm joined by my two colleagues who are very, very well placed to discuss this topic. Eddie Wen, global head of Digital Markets and Chi Nzelu, global head of FICC E-Trading. Welcome, Eddie and Chi.

Chi Nzelu: Thanks, Kate.

Eddie Wen: Great to be here.

Kate Finlayson: So let's perhaps start from the basics, right? How do we as a market maker, hedge risk and has that evolved over time?

Chi Nzelu: So if we go back I mean, way back, we would typically manage market risk on an instrument basis. So we trade potentially into a cash products and we manage on the same cash flow or possibly cash flow future on money basis risk. And it was typically about simple. Nowadays we tend to consider more portfolio risk and the idea is we are market making across many instruments, contributing risk into a portfolio distributed across clients all around the globe. So it's necessary to be able to quantify that analytics and be very clear on the precise measure of the factors of risk that we accumulate in our portfolio. This also enables us to operate at scale as we distribute products.

Kate Finlayson: Okay. Interesting. Eddie, would you say that the culture of how we approach market risk has changed at all?

Eddie Wen: Yeah, say so. I mean, there's definitely the movement towards a from the qualitative to the quantitative. We look at the traditional risk manager who looked at risk previously. It was qualified flows as being good or bad. The reality is there is a lot of gray in between and with the amount of data that we have, a lot of analytics that we provide, we can now measure the quality of those quantitatively and make decisions much more objectively. And with that, there is a culture of experimentation, right? Looking at things not just by the word of conventional wisdom, but rather does the data tell the story that we're looking for? And some of the things that we have learned over the years through the data analysis that we have has taught us a lot of how electronic trading has been a very different way of conducting business and how we've done it traditionally.

Kate Finlayson: Interesting. Chi. what do you think is driving this change? What are the perceived benefits from your site?

Chi Nzelu: Sure. So so we think there are a few factors that are working. Complementary there is the rise of electronic trading. The more electronic trading you get, the more competition you have. Clients want to engage very similar services, multi dealer single dealer competition by providers in a single dimension. Liquidity and price leads to margin compression. And as a result, you have to be very precise in your measures of risk understanding what works for your market, making strategy, properly understanding client flow. Also, resource constraints, the cost of doing business, the balance sheets, etc. So I would say margin compression is a consequence of competition and a result of margin compression. There's a lot more precision in risk management.

Kate Finlayson: I see. Eddie, if we take a step back for a moment and look at the broader market structure, you and I have spoken about the development of the network centric E trading model with the establishment of bi-lateral channels of connectivity via APIs, the move away from the club model. We've seen that with effects increasingly more with U.S. treasuries and credit is an interesting space to watch too. How is the development of this network centric E trading model shape the ability to scale, which is what she was mentioning before?

Eddie Wen: Look, there's no doubt that the digital distribution process has fundamentally changed how we service our customers. The reality is that most of our liquidity is actually coming from the customers, from the network effect that we have, from the servicing through the various different channels that we provide liquidity through. And increasingly, these platforms that we service are creating more of a concentration effect where we can service our customer access to liquidity from them the same way they access it from us. How we distribute our liquidity is fundamental to how we portfolio manage the risk that we've got.

Kate Finlayson: I see. And Chi, how does this change in model feed into your analytics when assessing performance?

Chi Nzelu: Sure. So we see the network model as a rich datasets on the star clients to now engage with us through a variety of channels. Each one has its own attributes in terms of flow quality, transaction cost, and that enables us to customize liquidity. Ultimately, our objectives are still the same. We want to be able to distribute sustainably, scale the franchise, subject the cost and understand how protocols work. So a very good example is in the stream model, we could distribute access effectively across our risk factors in the RFQ model that operates somewhat differently. So we can vary the services that we provide.

Kate Finlayson: Okay. Interesting. So we've been talking right now about how we as a market maker hedge market risk. What about other market participants? And in terms of how our clients on the buy side might be approaching it, is that vastly different?

Chi Nzelu: I think we have similar interests and concerns on the client side in general, They also care about transaction cost and minimize the market impact. If we consider the real money asset manager category, they typically have some risk they need to acquire. They've gradually evolved from trade in risk transfer to using algorithmic execution so they can understand the cost of the transaction. If you move a bit further to the systematics, for example, then they look quite similar to us. They're managing risk somewhat centrally. They understand through risk models how they should manage the portfolio and they have electronic services for distribution. And then you have the hedge funds and the banks who also try to do similar friends to what we do.

Kate Finlayson: I see. Okay. So if we're talking about them moving from a single Strat model to multiple parts multi Strat, they arguably one would see the emergence of centralization, of execution, perhaps from an efficiency perspective. Does that mirror how we approach it?

Chi Nzelu: I think so. We've approached this similarly with a central risk model where we try to internalize products that can trade on dealer-to-dealer venues or exchanges net down risk before we put that out on the markets. This obviously reduces our footprint, saves us on transaction costs as well as market impacts on the multistate model. We see very similar things happening with execution desks, and I believe that also allows them to achieve the same objectives as well as potentially operate risk models similar to what we described using a portfolio-based approach.

Kate Finlayson: Right. And Eddie, your thoughts on that?

Eddie Wen: I think there's an interesting parallel between how the buy side is forming execution desks, whereas similarly, if you look at FICC E-trading, a team that she runs oversees trading across all the FICC businesses. I think the reality is as these business moves toward electronic trading, they look more similar than different. And you're seeing that the investments you make in technology or the expertise you develop in these areas are transcending across the various different asset classes that we have. And I think those parallels are very similar in nature. A good example that I've seen with clients is that you look at one client, they have combined their equities execution with the execution and the traditional approach of looking at TCAS for equity algos versus FX algos are now being applied equally. So you can see those knowledge sharing and expertise sharing across the different areas. The same thing can be said in foreign exchange where majority of their transactions are done on streaming prices. You're seeing treasuries following the same pattern. So if you have execution desk, they're familiar with that paradigm. It's very easy to take the ideas from one area and apply it to another.

Kate Finlayson: And Chee looking to more of a future state. We know we have the development of artificial intelligence continuing at pace, large language models as well. Do you think this development has any impact on how market risk is hedged at all?

Chi Nzelu: So I would say in the large language model category, possibly unlikely for portfolio risk management. However, we already see interest in ML techniques in trying to understand the important features as we continue to expand the assets on our particular portfolio. We think those are typically be done offline. So identify what's interesting is hedge or the best way to manage risk in a portfolio and maybe in a future stage. And I'll say long term future that might evolve into something more active.

Kate Finlayson: Interesting. Okay, Fascinating. Well, listen, thank you both, Eddie and Chi for your thoughts today. There are some really, really dynamic developments in this area. So something for us to keep an eye out for sure. And thank you to our listeners. Stay tuned for more fic market structure content on this channel.

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