Counterparty Radar - Overview

Overview

Counterparty Radar provides extra transparency to OTC derivatives market participants, by aggregating data from the regulatory filings of individual US mutual funds, exchange-traded funds and life insurers.

Crucially, this data doesn’t just describe the trades that are on the funds’ books – the size, underlying and settlement date – it also identifies the dealer counterparty in each case. This allows us to rank and sort the market’s participants, offering a unique snapshot of the biggest dealers,managers and life insurers, and tracking their activity over time.

It’s important to be clear, though, about what the data shows – and what it doesn’t. The filings are required every month for all mutual funds and ETFs regulated by the US Securities and Exchange Commission – about 12,000, in total – but the data is only released publicly 60 days after the end of each quarter.

Similar filings following an identical quarterly schedule are required for US life insurers, who are regulated, however, on state and not by the SEC.

The filings, both for mutual funds/ETFs and life insurers, collects basic information about each trade that is on the books of each entity at the reporting date. Trades that start and end between these dates will not show up in the public record – and trades with a longer lifespan will be captured repeatedly, when adding up the figures for multiple quarters.

So, the quarterly snapshots don’t capture all trades within that quarter; and adding up the quarterly figures means grossing up some of the same trades.

We think the best way to use the data is as a gauge of quarterly activity, and to look at each quarter as part of a time-series, revealing trends, patterns and anomalies. Summing the quarters gives an all-in measure of the biggest managers, life insurers and dealers, but with double- or triple-counting of some trades.

There are other things to be wary of. Although the filings we take the data from contain the same set of fields, funds do not always interpret them in quite the same way. As one example, they do not all use the same conventions when identifying the dealer counterparty – in the case of at least one manager, prime brokers are listed as the counterparty instead of the executing broker. Flaws of this kind mean a small proportion of trades cannot be tied to a specific dealer. The trades have still been included in the dataset, grouping them with the ‘counterparty not specified’ label. (Other decisions we make when collecting and cleaning the data are described in the more detailed Methodology document).

Despite these caveats, the data is unusually extensive and rich, lending itself to a range of possible insights.

The data can be used by dealers to benchmark themselves, to monitor the behaviour of existing clients, or to identify prospective new clients. For buy-side firms – not just US firms, but any buy-sider trading these instruments – the data can be used to identify the most popular liquidity providers for trades of a specific currency, size and maturity.

With a lag, the disclosures also say something about buy-side positioning. You can see whether the market – or a specific manager/insurer, or group of managers/insurers – has been gradually ramping up, or cutting back, its activity in specific instruments. This could be helpful to other managers or life insurers when considering asset allocation – or to traders and risk managers when looking for crowding.

Something similar applies to dealers. Because the disclosures reveal line-level detail, they can also provide an insight into a liquidity provider’s axes – a dealer that is a popular counterparty for Mexican peso might be facilitating balanced flows, or have a franchise that is skewed to buy or sell peso. It may be regularly axed to offer better prices one way or the other.

Our journalists are excited to have access to this data, and have already been using it for popular, regular analysis of FX forwards and options trading on our sister site, FX Markets. We will be providing the same kind of analysis on Risk.net for an expanded range of instruments.

The response to those articles – and the market research that followed – is what convinced us that our readers would benefit from having access to the underlying data, and being able to run their own analysis. It’s a significant departure from our traditional coverage and we’re keen to continue gathering feedback. Do let us know how you’re using it, and what we can do to make the service better: cr@risk.net