Our results of the risk-adjusted returns and market returns model are subject to under- lying assumptions related to the event-window, the estimation period and normality of abnormal returns. The impact of these underlying assumptions on our results is examined by means of a sensitivity analysis.
5.4.1 Event-window and estimation period
We use an event-window of twelve days to analyse the effect of CoCo issuance on the funding costs of financial institutions. We assume that the first market response of CoCo issuance takes place four days before the official announcement att =−9and all relevant information is incorporated two days after issuance att= 2.
The difference between the risk-adjusted returns and market returns model can be explained by the alpha and beta estimate. Alpha and beta for the risk-adjusted returns model are estimated by Equation3.1. We use an estimation period of 120 trading days and assume that this period is optimal to estimate alpha and beta.
Chapter 5. Results 54 Start of the event-window
The sensitivity of our results is analysed by changing the start of the event-window to t = −11,t =−10, t= −8, ort = −7. The results of these start dates are reported in AppendixG. This analysis is performed for the risk-adjusted returns (TableG.1and TableG.3) and the market returns model (TableG.2and TableG.4). For both models we observe that our results are more stable for larger sample sizes and the P-value of smaller sample sizes is more volatile.
The results of the risk-adjusted returns model related to the overall impact of CoCo issuance on the CDS spread of the issuer are significant at the 5% level for start dates betweent= −11andt =−9. We observe the same impact on significance levels for equity-conversion mechanisms, coupon rates between 5% and 10%, AT1 CoCos, O- SIBs, issuers with total assets below $1.500 billion, contributions to the Tier 1 capital ratio between 0,5% and 1%, and issuers located in the Asia Pacific.
The results of the market returns model related to the overall impact of CoCo is- suance on the CDS spread of the issuer are significant at the 1% level for start dates between t = −11 and t = −8. We observe the same impact on significance levels for equity-conversion mechanisms, trigger levels at 5,125% of the CET1 ratio, coupon rates between 5% and 10%, AT1 CoCos, O-SIBs, issuers with total assets below the median or below $1.500 billion, contributions to the Tier 1 capital ratio between 0,5% and 1%, and issuers located in the Asia Pacific.
Based on the sensitivity analysis regarding the start of the event-window, we con- clude that an earlier start of the event-window (t = −11 and t = −10) results in a stronger negative impact on default probabilities. A later start of the event-window (t=−7) results in a less negative impact on default probabilities.
End of the event-window
The sensitivity of our results is also analysed by changing the end date of the event- window tot= 0,t= 1,t= 3, ort= 4. The results of these end dates are reported in AppendixG. This analysis is performed for the risk-adjusted returns (TableG.5and TableG.7) and the market returns model (TableG.6and TableG.8). We observe that our results are more stable for larger sample sizes and the P-value of smaller sample sizes is more volatile.
The results of the risk-adjusted returns model related to the overall impact of CoCo issuance on the CDS spread of the issuer are significant at the 10% level for end dates betweent= 0andt= 3.
Our results for trigger levels at 5,125% of the CET1 ratio, coupon rates between 5% and 10% and AT1 CoCos are not significant if we change the end date to t = 0, t = 1,t = 3or t = 4. For equity-conversion mechanisms, O-SIBs, issuers with total assets below the median or below $1.500 billion, contributions to the Tier 1 capital ratio between 0,5% and 1%, and issuers located in the Asia Pacific we observe that the impact is significant at the 5% or 10% level for end dates betweent= 1andt= 4.
Chapter 5. Results 55 The results of the market returns model related to the overall impact of CoCo is- suance on the CDS spread of the issuer are significant at the 1% level for end dates between t = 0 and t = 4. We observe the same impact on significance levels for equity-conversion mechanisms, trigger levels at 5,125% of the CET1 ratio, coupon rates between 5% and 10%, AT1 CoCos, O-SIBs, issuers with total assets below the median or below $1.500 billion, contributions to the Tier 1 capital ratio between 0,5% and 1%, and issuers located in the Asia Pacific (significance levels of 1% or 5%).
Based on the sensitivity analysis regarding the end of the event-window, we con- clude that our results are stable for end dates betweent = 0andt= 3. t = 4as end date of the event-window results in a less negative impact on default probabilities. Beta estimates
For each CoCo issuance we estimate beta during the estimation period. The average beta estimate of the full CoCo sample and all the sub-samples is between 0,164 and 0,587. To analyse the sensitivity of our beta estimate we change the estimation period to 110 and 130 trading days.
An estimation period of 110 trading days results in an average beta estimate be- tween 0,161 and 0,589. An estimation period of 130 trading days results in an average beta estimate between 0,159 and 0,585.
Based on these results we conclude that the impact of changing the number of trading days in the estimation period is limited. In Section6.2we further discuss the beta estimates.
5.4.2 Normality assumption
The statistical significance of our results is tested using the standardised cross-sectional test. This parametric test assumes that the abnormal returns of individual CoCos are normally distributed.
To analyse the influence of the normality assumption on the result of our research, we use both a parametric and a non-parametric test. The Wilcoxon signed-rank test is a non-parametric statistical test used to analyse whether the difference between two measurements is significant, without assuming that the sample is normally dis- tributed. The results of the Wilcoxon signed-rank test are reported in Appendix G
TableG.9.
Based on the results of the Wilcoxon signed-rank test, we conclude that the nor- mality assumption used in the standardised cross-sectional test does not significantly influence our results. We do observe that the results of both tests are more stable for larger sample sizes.
56
Chapter 6
Conclusion
In the previous chapters we (i) analysed literature related to CoCo structures, regu- lation and the effect of CoCo issuance on funding costs; (ii) performed a qualitative analysis on developments and trends in the CoCo market, between December 2009 and April 2016; and (iii) performed a quantitative analysis on the effect of different CoCo structures and issuer characteristics on the CDS spread of the issuer.
All the foregoing is required to answer our main research question:
What is the effect of different CoCo structures on the funding costs of a financial institution? The answer to this question is threefold, and includes: (i) the overall impact of CoCo issuance; (ii) the effect of different CoCo structures; and (iii) the effect of issuer characteristics on the funding costs of a financial institution (Section6.1).
Our research design, used to perform the quantitative analysis, contains several assumptions and limitations. In Section6.2, we describe these limitations and provide recommendations for further research.
6.1 Conclusion
The overall impact of CoCo issuance on the CDS spread of the issuer is negative and significant. Indicating that the default probability of financial institutions that issue a CoCo decreases.
The average decrease of the CDS spread for the full CoCo sample is equal to 4 bps. If the decrease in the CDS spread is fully passed on to the yields on the corresponding debt, the bank can reduce the total funding costs. The reduction in funding costs can be achieved by rolling-over existing debt instruments, where the replaced debt instruments require lower coupon rates. Decreasing the annual interest rate of $10 billion of non-CoCo debt with 1 bp reduces the annual interest cost, and hence the funding costs, with $1 million.
The decrease in default probabilities is highly dependent on the specific CoCo structure used and characteristics of the issuer. In the sections below we describe the main findings of our research regarding these CoCo structures and issuer charac- teristics.
Chapter 6. Conclusion 57 CoCo structures
Our analysis regarding the effect of different CoCo structures distinguishes between (i) conversion mechanisms; (ii) trigger events; (iii) coupon rates; and (iv) regulatory tiering. We conclude that the effect of CoCo issuance on the funding costs of a financial institution is highly dependent on the specific CoCo structure used.
Our main findings for each of these CoCo structure characteristics indicate that equity-conversion mechanisms, trigger levels at the regulatory minimum of 5,125% of the CET1 capital ratio, coupon rates between 5% and 10% and AT1 CoCo structures are the most effective in reducing default probabilities.
CoCos containing equity-conversion mechanisms are more effective than principal write-down mechanisms in reducing the funding costs of financial institutions. Based on our analysis regarding the specific conversion mechanism used, we conclude that none of the analysed CoCo structures contain a conversion mechanism that leads to a value transfer from shareholders to CoCo holders. The difference between the impact of equity-conversion and principal write-down is therefore not the result of dilution effects as described by Chan and Wijnbergen (2016). The simple fact that conversion to equity leads to additional shares and therefore reduces the stake of initial share- holders might explain the observed effect of issuing equity-conversion CoCos instead of principal write-down CoCos.
CoCos containing mechanical triggers are more effective than discretionary trig- gers in reducing the funding costs of financial institutions. Moreover, trigger levels at the regulatory minimum of 5,125% of the CET1 capital ratio are the most effective. The lower impact of trigger levels above the regulatory minimum is counter-intuitive, because higher trigger levels indicate that the loss absorbing mechanism is activated at an earlier stage.
Coupon rates between 5% and 10% are more effective than lower or higher coupon rates in reducing the funding costs of financial institutions. The ability of financial institutions to influence the required coupon rate is however limited and depends on several other factors.
AT1 CoCos are more effective than Tier 2 CoCos in reducing the funding costs of financial institutions. We explain this effect by the fundamental difference between these CoCo structures on default probabilities. AT1 CoCos contain additional options to preserve the bank as a going-concern, Tier 2 CoCos can only absorb losses on a gone-concern basis.
Our findings regarding the effect of different CoCo structures are different from those of Avdjiev et al. (2015). We do not attribute the stronger negative impact of equity-conversion mechanisms to dilution effects and our results indicate that the im- pact of CET1 triggers at the regulatory minimum of 5,125% is stronger than trigger levels above this minimum.
Chapter 6. Conclusion 58 Issuer characteristics
Our analysis regarding the effect of issuer characteristics distinguishes between (i) systemic importance; (ii) total assets at issuance; (iii) contribution to capital ratios; and (iv) region of the issuer. We conclude that the effect of CoCo issuance on funding costs is highly dependent on the characteristics of the issuer.
Our main findings for each of these issuer characteristics indicate that the negative impact of CoCo issuance on default probabilities is the strongest for O-SIBs, issuers with total assets below $1.500 billion, contributions to the Tier 1 capital ratio between 0,5% and 1% of RWA, and issuers located in the Asia Pacific or EU Euro area.
The funding costs of O-SIBs that issue CoCos decline more than the funding costs of G-SIBs that issue CoCos, and the decline is primarily caused by AT1 CoCos. This may be explained by the market perception that G-SIBs are still too-big-to-fail and will receive government support to prevent wide spread disruption of the financial system. Additional loss absorbing capital therefore has less impact on G-SIBs.
The reduction in funding costs is stronger for issuers with total assets below $1.500 billion than for issuers with total assets above this threshold. Dividing the sample based on total assets instead of systemic importance emphasizes the difference be- tween small and large financial institutions.
The funding cost of issuers that issue an AT1 CoCo decline more if the contribution to the Tier 1 capital ratio is between 0,5% and 1% of RWA, than for contributions below 0,5% and above 1% of RWA.
The reduction in funding costs is stronger for issuers located in the Asia Pacific and EU Euro area than for issuers located in the EU non-Euro area.
Our findings regarding the effect of different issuer characteristics are different from those of Avdjievet al.(2015). We showed that the impact of CoCo issuance on funding cost is stronger for O-SIBs, where Avdjievet al.(2015) concluded that the fund- ing cost of both G-SIBs and O-SIBs decreased. In addition, we showed that for both the median and $1.500 billion threshold of total assets at issuance, the negative impact on default probabilities is stronger for smaller banks. Related research by Avdjievet al.(2015) showed that the effect is stronger for larger financial institutions.