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

Author

Listed:
  • Mr. Jorge A Chan-Lau
  • Weimin Miao
  • Mr. Ken Miyajima
  • Mr. Jongsoon Shin

Abstract

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.

Suggested Citation

  • Mr. Jorge A Chan-Lau & Weimin Miao & Mr. Ken Miyajima & Mr. Jongsoon Shin, 2017. "Assessing Corporate Vulnerabilities in Indonesia: A Bottom-Up Default Analysis," IMF Working Papers 2017/097, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2017/097
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    References listed on IDEAS

    as
    1. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    2. Mr. Julian T Chow, 2015. "Stress Testing Corporate Balance Sheets in Emerging Economies," IMF Working Papers 2015/216, International Monetary Fund.
    3. Michael Chui & Ingo Fender & Vladyslav Sushko, 2014. "Risks related to EME corporate balance sheets: the role of leverage and currency mismatch," BIS Quarterly Review, Bank for International Settlements, September.
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    5. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    6. Jin-Chuan Duan & Tao Wang, 2012. "Measuring Distance-to-Default for Financial and Non-Financial Firms," World Scientific Book Chapters, in: Risk Management Institute, Singapore (ed.), Global Credit Review, chapter 6, pages 95-108, World Scientific Publishing Co. Pte. Ltd..
    7. Jin-Chuan Duan & Tao Wang, 2012. "Measuring Distance-to-Default for Financial and Non-Financial Firms," Global Credit Review (GCR), World Scientific Publishing Co. Pte. Ltd., vol. 2(01), pages 95-108.
    8. Duan, Jin-Chuan & Sun, Jie & Wang, Tao, 2012. "Multiperiod corporate default prediction—A forward intensity approach," Journal of Econometrics, Elsevier, vol. 170(1), pages 191-209.
    9. Robert Neil McCauley & Patrick McGuire & Vladyslav Sushko, 2015. "Dollar credit to emerging market economies," BIS Quarterly Review, Bank for International Settlements, December.
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    Cited by:

    1. Humala, Alberto, 2019. "Corporate earnings sensitivity to FX volatility and currency exposure: evidence from Peru," Working Papers 2019-021, Banco Central de Reserva del Perú.
    2. International Monetary Fund, 2017. "Japan: Financial Sector Assessment Program-Technical Note-Systemic Risk Analysis and Stress Testing the Financial Sector," IMF Staff Country Reports 2017/285, International Monetary Fund.

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    More about this item

    Keywords

    WP; interest rate; Corporate sector; bottom-up default analysis; default risk; scenario analysis; simulation; Indonesia; hazard rate models; rollover Needs; FX debt; corporates manage currency risk; BuDA framework; default probability; heightened debt risk; Hedging; Currencies; Global;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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