Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund

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1

The authors are grateful to the Indonesian authorities for their thoughtful comments and suggestions. This paper benefitted from comments by L. Breuer, H. E. Khor, S. G. Toh, E. Loukoianova, R. Perrelli, C. Pouvelle, L. Ratnovski and seminar participants at Bank Indonesia and the IMF. The Credit Research Initiative (CRI) at the Risk Management Institute, National University of Singapore, kindly provided the computer programs used in the analysis. The views expressed herein do not necessarily reflect those of NUS and the RMI, the IMF, its Executive Board, or IMF management. Any errors or omissions are the authors’ sole responsibility.

2

This paper extends the analysis in Indonesia Selected Issues Paper (IMF Country Report No. 16/82) issued in March 2016, and uses available data at the time of the publication. Corporate data cover listed corporates.

3

The interest coverage ratio is calculated by dividing a firm’s earnings before interest and taxes (EBIT) by the firm’s interest expenses for the same period. Bank Indonesia’s analysis shows that ICR is above 1 for all economic sectors due likely to differences in methodology and data sources.

4

The BuDA platform serves to support applied economic surveillance work. See for instance, Chapter 3 in IMF (2015), and Chapter 2 in IMF (2016).

5

The firm-specific factors selected for BuDA provide the best fit to the data, among a large number of different firm-specific factors initially tested guided by theory and practice. While the paper focuses on one-year ahead PDs, the model performs well in forecasting default events up to a five-year horizon. The model maximizes a quasi-likelihood function calibrated using data for thousands of firms in emerging economies. Information on interconnectedness, which could be useful to further refine the model, is not available for all the countries and firms included in the estimation.

6

See Duan and Wang (2012) for detail on volatility-adjusted leverage.

7

The use of the country average factor is analogous to the use of the market return in the CAPM model. For instance, in the latter model, the returns of an individual firm are regressed on the returns of the aggregated market, to which the individual firm contributes. In this case, for the risk factors, we use the country average as a common risk factor, and model firm-specific deviations from it.

8

See Duan, Miao, and Wang (2014) for details. The default settings in BuDA, used in our analysis, are twelve-month aggregation, and the use of two lags of the dependent variable in equations (1) and (2).

6

The “LASSO-OLS hybrid” is originated from the “LARS-OLS hybrid” proposed by Efron, Hastie, Johnstone, and Tibshirani (2004), with the variable selection in the first step replaced from LARS to LASSO. LARS is short for least angle regression, an efficient model selection algorithm; while LASSO is short for least absolute shrinkage and selection operator, a model selection method. A simple modification of the LARS implements the LASSO. BuDA uses AR (3) by default.

9

The predictive accuracy of the PD model for corporate defaults in emerging markets over a one-year horizon is 77 percent, if the accuracy ratio is used, and 89 percent, if the area under the receiver operating characteristic curve is used. A perfect predictive model would score 100 percent under both measures, and an uninformative model 50 percent.

Assessing Corporate Vulnerabilities in Indonesia: A Bottom-Up Default Analysis
Author: Mr. Jorge A Chan-Lau, Weimin Miao, Mr. Ken Miyajima, and Mr. Jongsoon Shin