For many OTC derivative products, everyone only see a corner of the whole market. While still unlikely to be comprehensive, the more connected market players have a much better view upon the market. This creates a degree of information asymmetry that successive regulatory reforms aimed to address. One of the policy initiative is to transaction data repository for public dissemination. This blog is a taster for the microstructure analysis of credit index that we can conduct upon these post-trade records.

Itraxx Europe and Crossover indices (denoted as ITX and XO) are the two credit indices under study. The raw data consists of SDR and MIFID post trade reports from Oct 2020 to Mar 2021 for ITX and XO series 34 5-year indices republished via Bloomberg. The underlying reference entities of these credit indices are European corporate entities and they are being actively traded by both US Person [1] and non-US Person [2]. Depending on the location of the trading venue and the jurisdiction of the trader, some of the regulatory trade reporting obligations fall under EU rules and some other under US rules. While regulatory regime is not a topic far from my interest or specialise area, I am going to briefly dabble in some of these matters when trying to garner all the relevant information.

OTC Trade Reports Aim for Public Dissemination

The collapse of Lehman Brothers in 2008 was a wake up call for the regulators. They were caught off-guard by the hidden scale of the OTC derivative exposure amongst major financial institutions and thus demanded more timely and detailed reporting post crisis. The reform was implemented in phases. In the earlier stage, it was about submitting transaction record to global trade repositories or warehouses. In the second stage, it was about mandatory clearing for the more liquid product, more timely reporting to relevant regulators and release of post trade data to the investing public. It is the latter that pique my interest.

Yet when I started looking into the reports, they do not seem to be straightforward to follow. I think there are two main reasons. First, the regulatory regimes have not been harmonised across the Atlantic. Each regulator may choose an agency or principal reporting regime. Along the same line, there are inconsistency for some basic issues e.g. who should report (buyer, seller or the trading venue). Second, many OTC markets are not very liquid or dominated by large players. Forcing instantaneous disclosure of all information for every order could upset the normal functioning of the markets. To mitigate the reporting burden, the regulators introduce deferral mechanisms for data release along with various exemptions. In do some, the reporting becomes more complicated.

One complication is a US Person can trade on MTF. Similarly an EU financial institution can trade on SEF. As reporting framework has not been harmonised, duplicate disseminate of the same transaction at different times can happen. The ISDA guide highlights that a US Person trades on MTF would have its trade reported through the venue to the EU side but it still has to send the same transaction info to the US side as “off-facility” trade. On the other hand, a EU Person trades on SEF the requirement to republishing the transaction via APA is waived. The implication is we should ignore the off-facility trades in SDR report when aggregating the trades in order to avoid double counting.

While reporting aiming for public dissemination are collected at real-time as prescribed by regulators across the Atlantic, only SDR on the US side releases such info (execution time, traded size and price) to the public right away. However, SDR withholds the exact trade size for any trade with a notional of larger than \$100M. On the EU side, credit indices are deemed as not sufficiently liquid and trade-by-trade data can be deferred for public dissemination. On each Tue, the aggregate trading volume for the prior week is released with the trade-by-trade data are made available to the public only after four extra weeks. In other words, tick data is not real-time but would be delayed for weeks. This could be an interesting machine learning project to train a model to spot actual transaction across EU trading venues.

For Itraxx Europe and Crossover indices, there are three sets of reports in the data set (SDR, MTF, APA – both MTF and APA refer to trades covered under MIFID rules but APA are those traded outside recognised market). The average daily volumes were €7.4bn and €2.9bn for Itraxx Europe and Crossover respectively across different trading venues. On average 160 and 190 Itraxx Europe and Crossover trades were being done per day. Assume an 9-hour trade day, there is a transaction roughly average 3 minutes. No wonder many credit index trading desks can still cope with the workload when they are only semi-automated. In terms of trading venue, SEF is the most popular and is followed by MTF. While many US financial institutions are very active in both sides of the market and SEF is not exclusively traded by US Persons, the predominance of SEF is still a bit of a surprise.

Trade statistics for the Itraxx Europe & Crossover S34-5Y on-the-run indices between Oct20-Mar21

Brexit can be a counter-intuitive factor accounting for the popularity of SEF. The market share of SEF jumped from about 40% to above 60% when the Brexit transition ended on 31 Dec 2020. Most of the MTF and APA trades used to be booked in London. While the leading operators of MTF and APA operators have opened new facilities in EU to accommodate their EU based clients post Brexit, liquidity could fall as the market is split into two. Since many clients operating in Europe already have access to SEF, shifting more trading activities into the already popular SEF can be a rational response.

Change in market share of SEF trading venue between Oct20-Mar21

Comparing the average trading volume to number of trades per day gives us an indication of the relative trade size. The trades reported through APA tended to be the largest and the SEF ones be the smallest. It is conceivable as many packaged index trades (e.g. as a part of CDS-index basis package, the index delta leg of options or tranche trades) are booked through APA and they tend to be large trades. In the case of SEF, the price transparency and level of automation is the highest amongst different trade venues. And it may thus attract traders running more nimble and higher frequency strategies.

The histogram for the arrival time between successive trades shows an exponential-like drop off. The log frequency plot shows that the relationship is reasonably linear at least for am arrival time of less than around 10 minutes. The pattern is similar for both ITX34 and XO34. Exponential distribution is thus the right model for the arrival time between successive trades.

Arrival Time Distribution for ITX34 and XO34

For an arrival time of beyond about 10 minutes, the rate of decrease becomes slower. One possibility is there exists two sub-populations representing the busier and quieter trading days. If we define a cut-off arrival time of 10 minutes, there were only 9 trading days of which more than 30% of all trades are above the cut-off and these days were the Thu and Fri around Thanksgiving and in the second half of Dec. A more advance arrival time model might include classifying dates into busier and quieter days and fit a separate exponential distribution model to each subset of data.

The distribution of trade size is not smooth. Most still prefer trading at multiples of 5MM or 10MM with 25MM and 10MM being the most popular trade size for Itraxx EUR and XO respectively. Note that in the case of SEF, the exact trade size of larger than USD100MM is not publicly disclosed and is capped to the EUR equivalent in reports. Depending on the EURUSD exchange rate, the trade size of these large trade might report as €82MM+, €84MM+, €90MM+ etc. This would slightly distort the results when we aggregate the data (esp for ITX which tend to be traded in larger sizes).

Trade Size Distribution for ITX34 and XO34

Seasonality Effect – Intraweek and Intraday

The trading activities in many markets often follow some cyclical patterns. This is known as the seasonality effect. For those who intend to design a trade execution model, the intraweek or intraday time scales are the most relevant. First, we examine the intraweek seasonality effect. Tue tended to be the quietest and got busier towards the end of the week. Nevertheless, the difference is less than 10-15% measured either by the average number of trades or volume. This effect was not that apparent for credit index trading during the observation period.

Seasonality Effect – Intraweek for ITX34 and XO34

Unless there are some major economic events, there are not much trading in ITX or XO before 0730 or after 1730 (London time). OTC credit trading does not have official open or close time. I thus pick 0730 and 1730 as the quasi trading hours and aggregate the trades into half hour slots. In terms of intraday pattern, there is an early peak around 0800 to 0830 and the trading activities gradually slow down towards noon. The market heats up again when the US traders come back to office and reach the peak between 1530 to 1630. The intraday seasonality pattern is quite pronounced with average trading volume easily be twice or more than in the peak hours.

Seasonality Effect – Intraday for ITX34 and XO34

The transactions are aggregated into a number of different time intervals with length ranging from 7.5, 15, 30, 60 to 120 minutes. The spread movement in each time interval is taken as a proxy for the return. The first 4 moments for the returns (mean, standard deviation, skewness and excess kurtosis) are calculated for each return time interval. A time series of less than 5 months is not that long even for intraday return analysis. The reader should bear in mind this is more of a taster rather than a robust study.

For the period under examination (Oct20 to Mar21), the market rallied on the back of the successful launch of the first covid vaccine. Spread tightened during the period. Naturally, the means are negative. Also, the large movements of the market during this period tended to be risk-on related news and thus explains the negative skewness.

For the standard deviations, they tend to increase as a power to the time interval. The underlying random process matters and it is a topic to be discussed in the scaling law section later on.

Moment for Different Return Intervals for ITX34 and XO34

Excess kurtosis is often (extremely) unstable and much larger than zero (ie fatter tail than the normal distribution). In the case of ITX34, the excess kurtosis falls from very high level when the time interval for return calculations increases. This is similar is similar to many other financial series.

In the case of XO34, the kurtosis seems to be all over the places with kurtosis calculated using 15-minute and hourly return much higher than the rest.

It was caused by a genuine sharp movement in spread when examining the actual data. At around 11:30 am on 9Nov20, Pfizer announced the success third stage covid vaccine trial. XO34 was tightened by more than 23bp (or 10x hourly standard deviation) in the subsequent hour. As the market rally was driven by incessant buying order for high yield risk (rather than as a sudden jump), the kurtosis calculated at the shorter return interval is not affected by as much. If this data point was being removed from XO34, the kurtoses would fall substantially across board. Perhaps a data set with longer sampling period should be used. Alternatively, the kurtosis is just that unstable by its nature.

Effect of Removal of Just One Extreme Data Point Upon Kurtosis

Scaling Law

There is an element of randomness in return. Based upon Mandelbrot’s initial analysis, [3] suggests an empirical scaling law between return volatility (as measured by ${ E (|r|)}$) and time intervals ${\Delta t ^{D}}$. ${D}$ is termed as the drift exponent. If the return follows a Gaussian random walk, the drift exponent would be 0.5. If it follows a more more trend following process with a large movement tend to be clustered with other large movements, the drift exponent would be larger than 0.5 with ${0.5 . For more mean-reverting processes, the drift exponent would be smaller than 0.5 with ${0 . Since the focus is intraday behaviour, the time interval is limited to a maximum of 240 min.

$\displaystyle E(|r|)=c\,\Delta t ^{D}$

The expected absolute return (${E(|\Delta t|)}$) is plotted against the time interval ${\Delta t}$ in a log-log plot. It seems that it is more appropriate to fit the data into a shorter end and and longer end model. Picking a transition point at 900 seconds (15minute). Below that, the drift exponent is above 0.8 for both indices, suggesting the spreads tend to be trending when the time interval is short. Beyond this point, the drift exponent falls to nearly 0.5, suggesting the return is not too different from a random walk in longer horizon. This seems to be collaborated with the empirical observation – after an actual transaction goes through, the market quotes tend to trend until the orders from other participants who are in the same direction but with higher private reserve prices all get filled.

Scaling Law Analysis for ITX34 and XO34

Note 1 US Person is defined as a US Resident, partnership or corporate formed under US laws, various types of accounts held for the benefit of a US Person

Note 2 Non-US Person: largely refer to market participants fall within EU jurisdiction. Brexit can be a complication here. Given there still is no agreement on equivalence of financial regulation between the EU and the UK, UK’s FCA temporarily adopt EU rules after the end of Brexit transition period on 31 Dec 2020. Situation could change pending for further negotiations.

Note 3 “An Introduction to High-Frequency Finance, Dacorogna, Gencay, Muller, Olsen”, 2001. Ch 5.5

Does Taking Higher Risk Lead to More Return In Bonds?

The low volatility anomaly is well-known in equity.  Holding a basket of shares with the highest beta does not generate the highest return.  It has been shown in many different regions and periods.  A similar mechanism may be in action in bonds as well.  The yield is higher when going down the rating spectrum.  But that does not fully compensate the credit quality deterioration beyond a certain point.  Examined 20 years of Bloomberg Barclays bond indices for US and European corporate, buy-and-hold the riskiest credit did not generate a good return.  There seems to be a sweet spot when going down the credit spectrum. Continue reading “Does Taking Higher Risk Lead to More Return In Bonds?”

Callable Bond – Part 3: Perpetual Subordinated Capital Note

Perpetual subordinated capital note does not have a maturity date. It has a pre-negotiated coupon (which can be fixed, floating or switches from fixed-to-float in its lifetime) to the holder periodically but the coupon can be switch off if no dividend is being distributed to the ordinary shares at the time. The deferred coupon might be cancelled (non-cumulative) or pay back all at the same time in arrear (cumulative). Continue reading “Callable Bond – Part 3: Perpetual Subordinated Capital Note”

Callable Bond – Part 2: Callable HY in Practice

In this article, I focus on bonds comes with callable features when issued and look into why the bonds are structured the way it is. The callable bonds tend to be from HY issuers. Bond options structured thru fixed income desk of investment bank would not be considered here as these are largely interest rate investment and hedging derivative products with government bond or highly liquid investment bonds as underlying instrument.   The consideration can be different when compared with the cash HY bonds (e.g. the payoff of bond derivatives follow mechanical rule whereas the strategic financing decision at the company level would determine whether a HY bond be called – not just bond price in comparison with the strike). Continue reading “Callable Bond – Part 2: Callable HY in Practice”

Callable Bond – Part 1: YTW vs OAS

Callable bond: a credit perspective – Part 1: YTW vs OAS

Bonds with callable feature are very common in the HY space with close to 65% and 35% of all new US and European HY bonds are callable. These bonds tend to have a call schedule (rather than a single call date and price) with credit component more of a concern than the fluctuations in interest rate. This is a topic falls in an area somewhere between quants and fundamental analysts and tends to ignore by many. I intend to look closer to it in this series of articles. Yield-to-worst (YTW) and option-adjusted spread (OAS) are the commonest analytics being used. In part one, I will explain how to calculate YTW and OAS and how should we interpret them. Continue reading “Callable Bond – Part 1: YTW vs OAS”

Benchmarking with Euro Bond ETF

Bond ETF is now an important investment vehicle for US based high yield investors with the top 5 HY Bond ETF accounting for a total market cap of exceeding \$40bn at the time of writing (24/6/14). While it is still just a small fraction of the \$1.5tn US high yield market, the leading ETF iShares iBoxx \$HY Corp Bond ETF (HYG – \$13.5bn market cap) and SPDR Barclays HY Bond ETF (JNK– \$9.8bn market cap) are closely following by many investors as the passive investment style of the ETFs makes them ideal benchmark (either when comparing with other funds or other asset classes). Moving across the Atlantic, HY bond ETF also enjoys phenomenal growth. The market cap of the biggest iShares iBoxx Euro HY Corp Bond ETF (IHYG) has grown more than 10-fold since Dec 2010 and reaches a market cap of EUR3.2bn. Continue reading “Benchmarking with Euro Bond ETF”

Credit migration matrix – use and misuse

A rating is supposed to be a stable long term predictor of the creditworthiness of a borrower. Every once in a while, the Credit Rating Agencies (CRAs) would release their analysis upon rating migration and, more importantly, how their ratings compare with realised default frequencies. The information is often summarised in form of a credit migration/transition matrix.  It is a useful tool but sometimes be misused. Continue reading “Credit migration matrix – use and misuse”