Credit defaults in Asia rose in 2019 while ratings remained indicators of relative risk: S&P

A total of 10 companies rated by S&P Global Ratings including seven confidential issuers defaulted in Asia last year, up from three corporate defaults in 2018.

A total of 10 compes rated by S&P Global Ratings including seven confidential issuers defaulted in Asia last year, up from three corporate defaults in 2018.

Meanwhile, the total number of rated issuers in Asia increased to 911 in 2019 from 831 in 2018, S&P said on Wednesday.

The newly rated issuers in the region decreased by over 39 per cent to 87 in 2019 from 143 in 2018 amid growing concerns about the potential for a turning credit cycle, curb in economic growth or rise in interest rates.

Issuance rose dramatically in the same period owing to a number of compes taking advantage of comparatively low interest rates, adding on leverage to their balance sheets in the process.

"Asia's speculative-grade default rate increased to 2.5 per cent in 2019 from 0.5 per cent in 2018. Globally, the 2019 speculative-grade default rate was also 2.5 per cent, up from 2.1 per cent in 2018," said Sudeep Kesh, Head of S&P Global Credit Markets Research.

"The COVID-19 pandemic and the knock-on impact on global economic growth have been major dampers in 2020 and have already led to an increase in defaults globally. While default risk is less apparent in Asia, with its higher balance of investment-grade issuers, riskier borrowers in Asia are still vulnerable to global issues."

The 10 defaults in the region in 2019 with a total debt of 800 million dollars were just a fraction of the 118 defaults globally with $183.2 billion in debt. The regional average (since 1993) number of annual defaults is 3.6 in Asia, compared with a global average (since 1981) of 73.6.

The Gini coefficient for Asia was 86.8 per cent, the three-year Gini was 82.4 per cent and the five-year was 76 per cent. Gini coefficients measure the correspondence between ratings and defaults.

Gini ratios measure the rank-ordering power of ratings over a given time horizon and compare the ratio of actual rank-ordering performance with theoretically perfect rank ordering.

( With inputs from ANI )