INGENIERÍA DEL DISEÑO

The dynamics of movement in Credit Spreads over time have been described in detail in the previous section. This section aims to provide further understanding of, on the one hand, the available pieces of information contained in the iBoxx dataset, and on the other hand aims to provide some insight into the dynamics of the corporate bond market it describes. Firstly, the amount of data is investigated in Figure 2.4 using the number of bonds in the dataset (top left), the total notional amount of debt outstanding (top right), the average duration of bonds (bottom left) and the number of ‘new’ bonds (bottom right).

700 800 900 2004 2006 2008 2010 2012 2014 Date Number of Bonds Number of Bonds excluding Sovereigns 3.0e+08 3.5e+08 4.0e+08 2004 2006 2008 2010 2012 2014 Date A v er

age Notional Amount Outstanding

Total Notional Amount Outstanding excluding Sovereigns 6 7 8 9 2004 2006 2008 2010 2012 2014 Date A v er age Dur ation

Average Duration by Rating excluding Sovereigns 0.0 0.1 0.2 0.3 2004 2006 2008 2010 2012 2014 Date P ercentage y

ounger than 12 months

Rating

A AA AAA BBB

Percentage of Bonds younger than 12 months by Rating and excluding Sovereigns

Figure 2.4: Descriptive analysis of market data: number of issuers (top left), total no- tional amount outstanding (top right), average duration by rating (bottom left and the proportion of the bonds universe issued fewer than twelve months ago.)

Based on the two graphs at the top of Figure 2.4, one can see that the number of unique issues (investment grade only) peaked around 2008, when, possibly in response to ongoing financial distress, firms were likely to postpone new issues. The amount of outstanding debt however, has increased over the entire period under study. Figure 2.4 (bottom left) shows how the average duration of the observed bonds varies over time. In general, the figure shows how there appears to be a response to the financial distress leading to a decline in observed durations; this is certainly the case for AAA, A, and BBB-rated bonds, whereas the effect of AA-rated bonds is more difficult to observe. Figure 2.4 (bottom right) gives an indication of the issuance of new bonds, as it shows the percentage of bonds that were issued less than twelve months ago. One can easily observe that across rating categories, the numbers are very similar, with the post-2009 numbers for AAA-rated bonds being very volatile due to the number of bonds in this rating category in general.

From a very high level, Figure 2.5 shows how the market for investment grade corporate bonds has changed during the period under study. For instance, the average Bid-Ask Spread of bonds (Figure 2.5, left) evolves over time and bivariate relationships (based on correlation estimates) have changed (Figure 2.5, centre and left).

0.005 0.010 0.015 0.020 0.030 0.050 2004 2006 2008 2010 2012 2014 Date A v er

age Bid−Ask Spread

Rating

A AA AAA BBB

Average Bid−Ask Spread by Rating

−1.0 −0.5 0.0 0.5 1.0 −1.0 −0.5 0.0 0.5 1.0 Financial Non−Financial 2004 2006 2008 2010 2012 2014 Date P earson Correlation Rating A AA AAA BBB

Pearson Correlation Coefficient for log(Duration) and log(Credit Spread)

−0.8 −0.4 0.0 0.4 −0.8 −0.4 0.0 0.4 Older than 5 Y ears Y ounger than 5 Y ears 2004 2006 2008 2010 2012 2014 Date P earson Correlation Rating A AA AAA BBB

Pearson Correlation Coefficient for log(Age) and log(Bid−Ask Spread)

Figure 2.5: Exploratory Analysis of a selection of available analytical values and their change over time (top), coupled with the way in which some bivariate relationships vary over time (centre, bottom).

Figure 2.5 (top) provides a very high-level overview of a key component to the study of liquidity; Bid-Ask Spreads. Important to note is the log-scale of the y-axis which illustrates the extreme increase in compensation demanded by market makers to take the opposite side of trades. This appears to follow anecdotal evidence that liquidity becomes a major issue when financial uncertainty (the credit crunch) hits. Without trying to over-analyse a simplistic visualization, it does, interestingly ap-

pear as if the A-rated bonds were hit the hardest when looking at pre- and post-crisis numbers for the Bid-Ask Spread. In addition to aggregating over many underlying variables, Figure 2.5 also fails to show the width of the range of observed Bid-Ask Spreads within a rating category. The centre and bottom plot in Figure 2.5 both show Pearson correlation coefficients as a straightforward means to quantifying the bivariate relationship of several key variables. Important to note that no causal rela- tionship is implied to exist, the plots merely show a co-occurrence of values and how this may have changed over time; for simplicity, standard errors of the correlation estimates are omitted. As such, careful to draw conclusions, it is worth noting that;

• The correlation estimates for AAA-rated bonds, particularly for those issued

by Financials, are volatile due to the small number of bonds that fall within this category.

• The correlation between the Duration and Credit Spread is positive on the

whole, which would be indicative of an upward sloping Duration-Spread curve, subject to controlling for non-Duration differences that cause variation in spreads.

• The correlation is highly positive prior to the onset of financial distress in 2007,

when the coefficients drop substantially, across ratings and across Financial and non-Financial firms. From the end of 2010 onwards, the coefficient has been increasing, in general, until the last day in the dataset.

• Comparing across rating categories, a few observations stand out; BBB-rated

Financial firms appear to have a consistently lower correlation over the entire time period, where all the other rating categories move together, with the exception of the period post-2011. After 2011, all the coefficients for Financial firms take a different path; AA recovers to pre-crisis levels very quickly, A recovers a lot more slowly even though pre-crisis coefficients were near-identical to the AA-coefficients and the BBB coefficient stays flat until a slight recovery is visible in 2013. Considering the non-Financials, coefficients are all rising together with the exception of AAA-rated bonds, for which the coefficient is

below pre-2008 levels. The caveat to this descriptive analysis is the effect all other variables have on the credit spread; these attributes may have changed over time and may have changed across rating category.

Figure 2.5 (bottom) shows the bivariate relationship of the age of a bond (mea- sured in years since issuance) and the Bid-Ask Spread, where the universe of bonds is divided into two groups, bonds younger than five years and those older than five years. Previous literature by Houweling et al. (2005) suggests that age itself can be a good proxy of liquidity and suggests that corporate bonds may be (relatively) actively traded when they are first issued, but after some time a very large portion of the market will find its way into institutional portfolios. Based on empirical work in Houweling et al. (2005), a simple cut-off at six or twelve months may capture this effect best; hence the inclusion of this indicator variable in later analyses.