What is the current status of NDF algorithm adoption?
So far, uptake has been limited and uptake can be said to be slow. There aren’t many providers that offer true algo products. Rather, it’s more like a smart order router that wipes out liquidity rather than internalizing flows.
Many people are spending significant time and resources on NDF algorithms, but the final product is not yet complete. This is a combination of lack of customer demand and lack of liquidity. However, I expect the NDF algorithm to grow in popularity over time. This is what clients should seek and what LPs should continue to strive for.
Is a lack of liquidity hindering the adoption of NDF algorithms?
Yes, the lack of liquidity is absolutely hurting the adoption of NDF algorithms, but so is the availability of market data.
Exchange data repositories require NDF trades to be reported, but currently the data feeds are not real-time enough for algos to efficiently estimate market volumes. Without accurate real-time market volume data, the predictive value of under-implemented and VWAP algorithms is reduced.
However, I believe this is a major hurdle, although data availability should improve over time as more and more customers request NDF algorithms. Some customers are talking about it, and this is a conversation that’s being repeated over and over again in the market, and banks are definitely investing time and resources to make sure they’re working on it – but the market There is no clear provider leading the way, or a clear bank group leading the way. Fee.
It’s something everyone is working on and many people claim to have it. However, it is much easier to run algos on G10 or artifact EM than it is to run algos in NDF space.
How are companies evolving to improve trading efficiency in the NDF market?
Many people run electronic NDF businesses, whether on a single dealer platform or for market making in the one-month space, and undoubtedly both in banks and non-banks. Masu. However, there is a disconnect because the market trades on a monthly basis and many end users want to trade on the IM date. So basis risk is definitely there and the market needs people to step up to solve that problem.
Many major market makers currently operate electronic NDF businesses. The company’s existing eFX spot business and market makers internalize NDF risk and add liquidity to the market. Although the additional liquidity improves trading efficiency, some market participants may argue that the subsequent fragmentation of ECN liquidity actually reduces trading efficiency.
Most of the focus was on enabling Swap Execution Facility (SEF) for users in North America. I see places like CBOE setting up SEF 1M pools, but the problem is that locals don’t want to trade in SEF, and as a result, except in North American time zones where SEF EBS is predominant. , meaning there is not a ton of liquidity in these venues (SEF dominates during Asian time zones and London mornings). So far, progress in this area has been limited.
What is the depth of the NDF market in the Latin American market/currency? When can we expect the development of market data and electronic pricing in the Latin American market?
Brazilian (and Asian) NDF is much better than Andean FX, but the key challenge for customers is that they want to trade to the IMM date and have inter-dealer liquidity trading at 1M (BRL’s BMF) is. Not all NDF algo providers have built the capacity to trade up to these full dates due to lack of significant demand.
Chile and Colombia currently do not have enough liquidity to have a solid algo business. We hope that liquidity will improve enough to enable a robust algo market in the coming years. Much less is traded regularly while in Peru. I’m sure some would like there to be enough demand, but the market seems too small to tap into.
Not all undespairs have good data input for 1 million points. For APAC, there is a central limit order book (CLOB) and an electronic communication network (ECN) with good liquidity. Therefore, one of the main challenges for clients is to justify the adoption of NDF algorithms when trading results are not very predictable. You have to balance risk and reward. In general, the less liquid a market is, the more principled the market is, so we see that happen in NDF, where banks and customers trade more frequently in streams and RFQs.
