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Assessing Corporate Vulnerabilities in Indonesia: A Bottom-Up Default Analysis

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Under adverse macroeconomic conditions, the potential realization of corporate sector vulnerabilities could pose major risks to the economy. This paper assesses corporate vulnerabilities in Indonesia by using a Bottom-Up Default Analysis (BuDA) approach, which allows projecting corporate probabilities of default (PDs) under different macroeconomic scenarios. In particular, a protracted recession and the ensuing currency depreciation could erode buffers on corporate balance sheets, pushing up the probabilities of default (PDs) in the corporate sector to the high levels observed during the Global Financial Crisis. While this is a low-probability scenario, the results suggest the need to closely monitor vulnerabilities and strengthen contingency plans.

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Fig. 1

Source: Haver Analytics; and authors’ calculations. 1/ Period average \(=\) 100 for export price and nominal exchange rate index

Fig. 2

Sources: Worldscope; Bloomberg L.P.; Datastream; and authors’ calculations. 1/ Net income of listed companies, capitalization-weighted average

Fig. 3

Sources: Bank Indonesia; CEIC; and authors’ calculations

Fig. 4

Sources: Bank Indonesia; Dealogic; Orbis; and authors’ calculations

Fig. 5

Source: Authors

Fig. 6

Sources: CEIC; and authors’ estimates

Fig. 7

Sources: Authors’ estimates


  1. The interest coverage ratio is earnings before interest and taxes (EBIT) over interest expenses for the same period. Bank Indonesia’s analysis showed that interest coverage ratio was above one for all economic sectors, due likely to differences in methodology and data sources.

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

  3. 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 1-year ahead PDs, the model performs well in forecasting default events up to a 5-year horizon. The model maximizes a quasi-likelihood function calibrated using data for thousands of firms in EMs. 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.

  4. See Duan et al. (2012) for detail on volatility-adjusted leverage.

  5. The country average factor in this paper is analogous to the market return in the CAPM model. For instance, in the CAPM 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, this paper uses the country average as a common risk factor, and model firm-specific deviations from it.

  6. See Duan et al. (2014) for details. The default settings in BuDA, used in our analysis, are 12-month aggregation, and the use of two lags of the dependent variable in Eqs. (1) and (2).

  7. The “LASSO-OLS hybrid” is originated from the “LARS-OLS hybrid” proposed by Efron et al. (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.

  8. See Duffie and Singleton (2003), Lando (2004) and Bielecki and Rutkowski (2004) for an extensive treatment of mathematical credit risk.

  9. Default and other exits are competing as opposed to independent risks, as both events are mutually exclusive. When modeled as two independent processes, the probability of their joint occurrence happens to equal zero, which blurs the distinction between competing and independent risks. Hence, the modeling assumption of two doubly stochastic Poisson processes appears acceptable, as first introduced by Duffie et al. (2007); and adopted by Duan et al. (2012), whose model this paper uses. The interested reader is referred to these two publications for further details.

  10. The predictive accuracy of the PD model for corporate defaults in EMs over a 1-year horizon is 77%, 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% under both measures, and an uninformative model 50%.


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Correspondence to Ken Miyajima.

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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. We would like to also thank the editor and the anonymous referees for their helpful comments. The views expressed herein are those of the authors and do not necessarily represent those of NUS and the RMI, the IMF, its Executive Board, or IMF management. Any errors or omissions are the authors’ sole responsibility.

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Miyajima, K., Chan-Lau, J.A., Miao, W. et al. Assessing Corporate Vulnerabilities in Indonesia: A Bottom-Up Default Analysis. Asia-Pac Financ Markets 24, 269–289 (2017).

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