Data cleansing and preparation

When historic price series are requested, Bloomberg provides a series for each and every ISIN code among the selection, irrespective of whether the series holds zeros, non-available or real price values. Even if the series contains real price values, this may not be for the entire period and/or the same price value may reoccur for more than one trading day. When a bond is issued, it is allocated with a unique ISIN security code and Bloomberg then captures this security for its system (to allow their users to apply its quantitative tools to it) and starts to build a historic time series. But not all eurobonds are traded every day and even if they are traded, the trading desk of the intermediary may not give the price input to Bloomberg. The latter is not so much a problem anymore nowadays because the links (called “feeds”) between trading systems and the Bloomberg terminal are automated, but in the beginning of my time period Bloomberg was much more reliant on manual inputs from traders. If a price input is provided for a particular bond on a particular

trading day, but not for the subsequent day(s), then Bloomberg automatically copies the value over from the previous day. This is an important difference with Morgan Stanley’s proprietary database, which does not show a value if no input has been given for that trading day.

To extract end-of-month prices from the daily price series from Bloomberg which may or may not contain prices that have been held constant for a number of days, the monthly prices series are constructed according to the following rule: replace a value that has been held constant from the previous trading day(s) with a linearly interpolated value and allow for such interpolation only if the value has been left constant by less one calendar month and otherwise to eliminate the price value. The assumption is that bond prices move every trading session, even if the specific bond did not trade that day. Therefore, if on day t=0 a fresh price input is given but then not for x number of days, and then on day t=x+1 a new price input is given, then if the end of the month date falls within that range and provided that x+1 is not longer than one calendar month, it is preferable to take the linear interpolation between the prices on days t=0 and t=x+1 rather than the constant price value from day t=0.

The data sets from Bloomberg and Morgan Stanley are integrated according to the following rule: give preference to Bloomberg as a source and add a Morgan Stanley eurobond price series to the data set only if it is not already incorporated from the Bloomberg series. These would only be eurobonds from the Morgan Stanley source that are not initially part of the Bloomberg set or are among the Bloomberg series but drop out because it contains all zeros or non-available values.

Bloomberg’s downloads picks up government bonds in the corporate eurobond portfolios. This is acceptable if it concerns other sovereigns than the one presiding over the respective currency (e.g. the Kingdom of Belgium issuing in DEM is a ‘corporate’ eurobond) but otherwise not and need to be eliminated (e.g. Kingdom of Belgium issuing in BEF is a domestic government bond). In the Morgan Stanley portfolio this problem does not exist, but it does have the occasional USD and CHF bond which too are eliminated. Three final adjustments are made in the descriptive information that Bloomberg provides for each individual corporate eurobond. This concerns the identification of the country base and industry sector of the issuer, and missing issue dates. Bloomberg populates the country field with the country of the issuing entity. There are several occasions where the mother company is based in one particular country but issues a eurobond through its entity in a different country (often for tax reasons) and then the latter country is recorded. For example, Deutsche Bank AG from Germany issues through its finance vehicle Deutsche Finance N.V. in the Netherlands. The country base indicator is manually overridden to reflect where the mother company as the ultimate guarantor of the issuer is based. Equally, Bloomberg gives the industry sector of the issuing entity. Again, if a company issues through it finance vehicle, the industry indicator provided by Bloomberg would be ‘financial’ even though the corporate business of the mother company is better described as something else. Also in the case of the industry indicators, it is manually overridden to

that of the mother company. The country and industry fields are used to create the country and industry bond portfolios in the next phase of my empirical analysis and thus quite important.

For a number of eurobonds from the Bloomberg source certain descriptive information is missing. Missing fields invariably include amount issued, the issue date and final maturity date. An issue date can be manually inserted, since it does otherwise not matter for the calculation of returns. The applied rule is to consider the first date a price record appears in the historic price series of the respective eurobond and then to work back from the first price record date to the next anniversary of the maturity date. For example, if for a eurobond the final maturity date is 1/1/1998 and the first price record date is 28/2/1995, then an issue date of 1/1/1995 is inserted. This manual input is performed for 153 eurobonds. As regards the missing fields of amount issued and final maturity date no manual inputs can justifiably be given. These eurobonds are excluded from the data set.

Thus, I arrive at a data set of 6,440 eurobonds whose price series cover the period May 1990 to March 2008 though starting and ending at different times within that period. Table 4.1 lists how the total number of eurobonds is divided over the ten currencies of issuance that make up the sample. The largest currency market is not actually GBP, because the EUR market will also in fact incorporate the eurobonds originally issued in Euro legacy currency that continue to run after 1st January 1999. The GBP eurobond market is nevertheless a very sizeable market because of its long history of corporate and financial institution issuance. Within the Euro zone, the DEM market is the largest in the sample, followed by

Table 4.1

Database of eurobonds by original currency of issuance, from domestic and Euro zone issuers

Currency of issuance

Number of eurobonds

% of total From domestic issuers* % of total in respective currency From Euro zone issuers** % of total in respective currency BEF/LUF 466 7% 125 27% 360 77% DEM 607 9% 528 87% 558 92% ESP 105 2% 6 6% 36 34% EUR 1,274 20% 802 63% 802 63% FRF 483 8% 222 46% 316 65% GBP 1,927 30% 716 37% 525 27% ITL 399 6% 65 16% 226 57% NLG 526 8% 424 81% 509 97% SEK 529 8% 348 66% 98 19% XEU 124 2% 44 35% 44 35% Total 6,440 *

with ‘domestic issuer’ is meant the issuing entity based in the country belonging to the respective currency. In the case of the EUR and XEU issuing entities based in any of the eleven countries that first enter EMU have been taken. The supranational issuers have been ignored in all figures in this column.

**

with ‘Euro zone issuer’ is meant the issuing entity based in any of the eleven countries that first enter EMU. The supranational issuers have

the NLG, which is larger in terms of number of eurobonds to both the FRF and ITL markets. The second largest currency market outside the Euro zone is the SEK market. Table 4.1 also shows that some currency eurobond markets are more “domestic” by the nature of their supply than others. The Deutschemark and Dutch Guilder markets are both for more than 80% populated with eurobonds from issuing entities from the home country. This contrasts with the ESP and ITL markets where the majority of issuers are foreign. For the Euro currency, 63% originates from issuers with the Euro zone. For the non-Euro markets, the GBP market is clearly more open to non-EU issuers than the SEK market.