Indonesia: Selected Issues

Abstract

Indonesia: Selected Issues

Corporate Vulnerabilities1

While overall corporate sector risks in Indonesia appear manageable, some corporates are facing higher risks including exchange rate, refinancing, or default, with possible spillovers to the banking system. With a slowing economy and a weakened rupiah, corporate balance sheets are expected to provide smaller buffers against negative macroeconomic shocks going forward. This paper assesses corporate sector vulnerabilities. It first describes the current situation in the corporate sector, and then projects corporate default probabilities under different macroeconomic scenarios. Results from the scenario analyses suggest that if economic growth slows sharply and recovers only slowly, the default probabilities of domestic firms could rise to levels comparable to those during the Global Financial Crisis. While this is a low-probability scenario, policy makers should continue to closely monitor vulnerabilities and step up effort to strengthen contingency plans.

A. Introduction

1. This note assesses corporate sector vulnerabilities in Indonesia. It first discusses key facts about the sector exploiting a range of macroeconomic and financial market data. Then, as a way of further assessing corporate vulnerabilities, it projects corporate default probabilities under different macroeconomic scenarios.

B. Corporate Performance and External Debt Risk

2. Indonesia’s corporate sector remains relatively strong and sound compared to its EM peers. Aggregate corporate leverage is comparatively low, with the corporate debt relative to GDP standing relatively small at around 32 percent (compared to around 70 percent on average for Asian EM peers.2 The liability-to-asset ratio is low at less than 50 percent (Figure 1), and profitability is highest among peers (Figure 2). Many corporates in Indonesia also tend to rely on internal cash flows for funding rather than external financing.

Figure 1.
Figure 1.

Peer Comparison: Leverage—Total Liabilities to Total Assets, 2014

(56 percent)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

Sources: Bloomberg LP.; Datastream; and IMF staff estimates.
Figure 2.
Figure 2.

Peer Comparison: Profitability—Return on Assets, 2014 1/

(In percent)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

Sources: Bloomberg LP„ Datastream; and IMF staff estimates1/ Based on capitalization -weighted average of listed corporates.

3. Nonetheless, corporates have been impacted by continuing commodity price falls and a weakened rupiah, exacerbated by rapidly increased external debt. The commodity down-cycle and slowing economy have impacted commodity-related corporates (i.e., coal mining), and corporates in non-tradable sectors, reducing their income stream and ability to pass the costs to consumers due to consumers’ reduced purchasing power. Profitability continues to decline and liquidity remains tight, reflecting a weakening operating environment and tighter financial conditions (Figures 3 and 4). Some corporates have been facing debt repayment problems in recent months, notably on foreign currency denominated (FX) bonds. In the coming periods, as external financing conditions tighten, the corporate sector could face difficulties in servicing their high level of FX debt.

Figure 3.
Figure 3.

Profitability—Return on Assets of Listed Companies

(In percent)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

Sources: Bloomberg LP.; and IMF staff estimates.
Figure 4.
Figure 4.

Peer Comparison: Liquidity—Liquid Assets to Current Liabilities, 2014

(In percent)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

Sources: Bloomberg LP ; Datastrea m; and IMF staff estimates.

4. Foreign currency (FX) denominated debt of corporates grew rapidly over the past years. FX corporate debt (including FX debt to domestic banks) reached around 20 percent of GDP as of June 2015, doubling the level seen in 2010, albeit from a low base. FX debt of corporates accounts for around 60 percent of the total corporate debt. However, FX debt growth moderated in 2015, with issuance affected by general risk aversion towards emerging markets and weak private investment.

Figure 5.
Figure 5.

Corporate Debt Outstanding 1/

(In billions of U.S. dollars; unless otherwise noted)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

Sources: Bank Indonesia; and IMF staff estimates1/ Consolidates External debt and domestic commercial and rural barks’ FX credit to corporates; and excludes credit to individuals.
Figure 6.
Figure 6.

FX Debt by Industry 1/

(In billions of U.S. dolllars; unless otherwise noted)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

Sources: Bank Indonesia ana IMF staff estimates1/ Excludes financial, leasing & business service2/ Includes mining & drilling and agriculture forestry & fishery.3/ Includes housing & building, trading, hotel & Si resturantes, and other services.4/ Excludes the commodity sector and the manufacturing industry.

5. FX corporate debt is concentrated in the commodity and some non-tradable sectors, driven by FX debt securities.

  • The share of the commodity sector steadily rose to around 30 percent in 2014 from 20 percent in 2007. A group of non-tradable sectors, notably the transport and telecommunication industries, accounts for around 40 percent of FX corporate debt, while these sectors are running a growing risk of currency mismatches between rupiah incomes and FX debt service.

  • FX debt securities (i.e., syndicated loans) were the major driver. Around 90 percent of debt securities issued in 2014 were FX denominated. The heavy reliance on FX syndicated loans is in contrast with EM peers where local currency bond markets have increasingly substituted bank loans to corporates (Figure 8).

  • The rise in FX debt has been led by SOEs (e.g., energy-related SOEs), while FDI-related corporates’ borrowing accounts for half of external borrowing (i.e., foreign private corporates and joint-venture private corporates). The expected rise in infrastructure spending in the coming years suggests that external debt borrowing may continue to rise at a brisk pace.

Figure 7.
Figure 7.

Annual Gross Issuances of Syndicated Loans and Bonds

(In billions of U.S. dollars; unless otherwise noted)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

Sources: Dialogic; and IMF staff estimates.1/ Corporates either operating in Indonesia or whose parents’ nationality is Indonesia.
Figure 8.
Figure 8.

Share of FX Debt Securities 1/

(In percent of total issuances of debt securities)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

Sources: Dealogic; and IMF staff estimates1/ consists of syndicated loans and bonds; grouped by residence of subsidiaries.

6. The rapid increase in corporate FX borrowing has been driven by both pull and push factors. Corporates tapped low-cost external borrowing under the U.S. Fed monetary easing, which helped create ample liquidity in EM debt markets. With favorable interest rate spreads and commodity booms in 2010-13, corporates borrowed actively from global bond and syndicated loan markets. More structurally, shallow domestic financial markets, particularly thin corporate bond markets, have led corporates to tap offshore debt markets.

7. Some corporates has been facing rising FX exposure, refinancing risk, or default risk.

  • A portion of the FX debt is estimated to be unhedged, making it vulnerable to currency depreciation. Rupiah depreciation has exposed corporates to losses from the revaluation of their FX debt. Bank Indonesia (BI)’s hedging regulations have helped corporates to manage currency risk (Box 1). However, some corporates do only partial hedging to reduce hedging costs. Since plain vanilla hedging instruments have a high cost, some corporates use hedging instruments with built-in ceiling options. If the rupiah depreciates substantially, FX exposure is likely to jump, causing losses.

  • Refinancing risk is likely to rise, as maturing FX debt securities are set to rise in 2016 (Figure 9). Maturing FX syndicated loans and bonds have a large proportion of debt categorized as leveraged or high-yield, whose ease of rollover could be affected by BI’s new requirement for corporate that wants to issue FX debt to be of investment grade credit rating starting from 2016. Still, there are some mitigating factors. Two-thirds of non-bank private corporates’ external debt maturing within a year was borrowed from affiliates, which could help mitigate the refinancing risk. Also, the overall amount of maturing debt (including financials) within a year appears manageable (Figure 10).

  • Corporates face higher default risks. The interest coverage ratio has fallen sharply to the level seen during the global financial crisis (Figure 11). Corporates in the resource sector are under the most pressure, with the interest coverage ratio3 below 1.5 for a third of the sector, followed by corporates in the telecommunication industry (Figure 12). Some corporates are running a heightened default risk, stemming from eroding liquidity, worsening revenue and margin compression, while others face an unfavorable debt maturity profile and growing refinancing risks. According to Moody’s KMV Credit Edge model, default probability has picked up, especially in the weakest deciles (Figures 13 and 14). This is mirrored in a recent rise in nonperforming loans (NPLs) and special mention loans in the banking system.

Figure 9.
Figure 9.

Maturing Syndicated Loans or Bonds of Corporates 1/

(In billions of U.S. dollars)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

Sources: Dealogic; and IMF staff estimates1/ Includes Indonesian parent corporates or debt in Indonesia; and exculdes government, banks, and finance companies.
Figure 10.
Figure 10.

Maturity of Private Sector’s External Debt 1/

(In billions of U.S. dollars; as of end-September 2015)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

Source: Bank Indonesia; and IMF staff estimates.1/ Includes the financial sector; the corporate sector makes up three-quarters of the private debt outstanding; and as of end-September 2015.
Figure 11.
Figure 11.

Corporate Debt-at-Risk

(In percent of the total debt)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

Sources: Orbis Database; and IMF staff estimates.1/ Interest coverage ratio (ICR = EBIT/interest expense) < 1.5
Figure 12.
Figure 12.

Share of Corporate Debt-at-Risk by Industry 1/

(In percent of total debt-at-risk; as of end 2014)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

Sources; Orbis; and IMF staff estimates.1/ Interest Coverage Ratio [ICR) < 1.52/ Includes the primary sector, wood & paper, and metals & metal products.3/ Textiles & textile products, machinery, and chemicals.
Figure 13.
Figure 13.

Default Probability of Corporates by Country 1/2/

(In percent)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

Sources Moody’s KMV Credit Edge.1/ average default probability of publicly traded corporates within one year: 369 firms for Indonesia; 826 firms for Malaysia; 501 firms for Thailand; and 166 firms for the Philippines.2/ (i) The model estimates asset value and asset volatility from equity value and equity volatility from stock markets (assuming a firm’s stock price (changes reflect its asset’s future cash flow generation); (ii) calculates a default point (when the market value of assets falls below a firm’ labilities) and the distance to default, an index measure of default risk (using the ratio of the default point to the market value of assets); and (iii) then, scales the distance to default to actual probabilities of default using a default database.
Figure 14.
Figure 14.

Default Probability of Corporates by Group

(In percent)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

Sources: Moody’s CreditEdge.

8. A disorderly default of a large systemically-connected corporate could create negative spillovers to the banking system and weaken confidence. Given the banking sector’s large exposure to corporates, tightening of corporate FX borrowing conditions could have an impact on domestic banks’ loan quality and liquidity, while forcing corporates to borrow from domestic banks. Risks are mainly on linkages with mid-sized banks, which are vulnerable to shocks. Also, an abrupt downgrade of corporate credit rating could quickly weaken investor confidence in the corporate sector.

C. Bottom-Up Scenario Analysis of Corporate Default Probability in Indonesia

9. This section provides a forward-looking assessment of corporate sector vulnerabilities.

In particular, it projects corporate default probabilities under different macroeconomic assumptions in several steps as explained below.

10. The model maps macroeconomic scenarios to probabilities of default (PDs) of individual firms (Figure 15).4 A forward intensity model is a reduced form model in which the PD is computed as a function of different input variables. The model accounts for exits of firms due both to defaults and reasons other than defaults.5 Two sets of independent factors—common risk factors and firm specific factors—are used as input variables. Common and firm specific factors are assumed to be influenced by a set of macroeconomic factors.

Figure 15.
Figure 15.

Schematic of Bottom Up Scenario Analysis

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

Source: IMF staff estimates.

11. The variables used for the scenario analysis are summarized in Table 1. Macroeconomic conditions are characterized by variables commonly used in the literature of stress testing. GDP growth proxies for the growth in incomes and earnings of firms. Unemployment rate affects the consumption and spending of households and, in turn, corporate sales. Inflation can signal macroeconomic uncertainty. High inflation raises costs and impairs credit quality but also reduces real debt burden. Exchange rate performance affects firms through net exports and balance sheet channels. Short-term interest rates are an indicator of the cost of funding for corporates. Common risk factors are the domestic equity price index and short-term interest rates, which define the market conditions and in turn affect the state of individual firms. Firm specific factors for more than 400 corporates (both financial and nonfinancial) capture characteristics including liquidity, profitability, and size.6

Table 1.

Indonesia: Data for Simulating Corporate Probability of Default

article image
Sources: NUS and IMF.

12. The simulation starts by assuming two different paths of quarterly macroeconomic variables through 2017 (Figure 16).7 The trajectories of macroeconomic variables are in turn used to project common risk factors and firm specific risk factors. Finally, these risk factors are used as inputs to a forward intensity model, which is simulated to generate a distribution of PDs.

  • The baseline scenario assumes GDP growth would moderately increase to around 5.3 percent. The unemployment rate would decline gradually to 5.7 percent, while inflation would fall to 4.4 percent. The rupiah’s movement would range between −3 percent to 4 percent year-on-year (y/y) every quarter and the one-month JIBOR interest rate decline moderately to 6.7 percent.

  • The downside scenario is characterized by a sharp drop in GDP growth to below two percent y/y and a vigorous V-shape recovery. The unemployment rate would jump to nine percent and return to somewhat above eight percent. Inflation would surge to above ten percent but return to 6 percent. The rupiah would depreciate by 14–20 percent y/y for three quarters. The JIBOR interest rate would jump to exceed 12 percent for three quarters and return to 9 percent.

Figure 16.
Figure 16.

Projected Macroeconomic Variables

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

13. Several key observations emerge from estimated results.8

  • First, the firm-specific factors may have recently taken less supportive values than in previous periods, after growth slowdown and rupiah depreciation have weakened corporate balance sheet conditions amid rising corporate foreign currency leverage. Under the baseline scenario, the median corporate PD is projected to rise to levels somewhat higher than those during the taper tantrum in 2013 and moderate somewhat toward the end of 2017 (Figure 17, upper panel, red broken line).9 This is the case despite projected macro fundamentals being broadly comparable to those in 2013—GDP growth is somewhat lower, but the rupiah’s performance is more favorable and inflation is lower.

  • Second, weaker macroeconomic performance would naturally lift corporate PD to higher levels. The median PD under the downside scenario would rise to about one half of the maximum registered during the Lehman crisis (Figure 17, upper panel, green solid line). This reflects a sharp GDP growth slowdown and deterioration in other macro variables. However, the PD would decline as economic activity regains momentum.

  • Third, corporate distress can worsen materially if weak macroeconomic performance is accompanied by severe financial market jitters. Under the downside scenario, the 95th percentile estimate, with remote chance of occurrence, rises to very close to the maximum registered during the global financial crisis (Figure 17, lower panel, light green broken line). Meanwhile, cross-border spillovers of a negative shock could be large in an environment of elevated uncertainty and financial market volatility. Under such circumstances, what is considered as a low-probability outcome (with a high impact) could become a real threat.

Figure 17.
Figure 17.

Indonesia: GDP Growth and Corporate Default Probability

(Lehman peak = 100)

Citation: IMF Staff Country Reports 2016, 082; 10.5089/9781513584409.002.A002

D. Concluding Remarks

14. Overall, the risk from the corporate sector remains manageable, and the authorities have strengthened the monitoring framework. The aggregate corporate debt-to-GDP ratio remains small, and on a system wide basis, near-term refinancing risk appears manageable. The authorities are monitoring corporate vulnerabilities closely, and the implementation of the BI’s hedging regulations has helped corporates manage currency risks. Authorities’ ongoing work to upgrade the framework and inter-agency coordination on corporate surveillance is also in the right direction.

15. Nonetheless, close monitoring and granular analysis on maturing FX debt are warranted. Even though the overall risk of the corporate sector is manageable, a group of corporates is facing heightened debt risks, some of which are connected to large business groups. The results of the empirical analysis in Section B confirm these observations. Close monitoring, therefore, is required for FX debt of corporates with rupiah income, as well as unhedged, non-affiliated, or maturing FX debt, together with bank linkages. Strengthening policy coordination should also continue, coupled with data analysis to assess the dimensions of the debt problems of specific corporates in vulnerable groups. The authorities should consider reviewing the corporate resolution framework (including the bankruptcy regime) to ensure that it is capable of dealing with large and systemically connected conglomerates. In the medium-term, deeper financial markets will help reduce the costs of hedging and develop domestic corporate bond issuance and trading.

Bank Indonesia’s Foreign Exchange Regulations on Corporates

FX regulations. To encourage corporates with external debt to enhance risk management, BI introduced a set of prudential measures in October 2014.

Indonesia: BT’s FX regulations on corporates 1/

article image
Source: Bank of Indonesia
  • Hedging ratio. The hedging ratio is defined as the ratio between the total value hedged and the net short-term foreign liability position. The minimum hedging ratio is 20 percent for 2015 and 25 percent for 2016, and is applied to the net foreign currency liabilities with a maturity period up to three months, and those that mature between three and six months. Exemptions are made for export-oriented corporates—corporates with a ratio of export revenue to total revenue exceeding 50 percent of the previous calendar year—with financial statements issued in U.S. dollars.

  • Liquidity ratio. The liquidity ratio is defined as the ratio between short-term foreign currency assets and short-term foreign currency liabilities. The minimum ratio is 50 percent for 2015 and 70 percent for 2016.

  • Credit rating requirement. Nonbank corporates should have a credit rating of no less than BB or equivalent issued by an authorized rating agency, including Moody’s (Ba3), S&P (BB-), and Fitch (BB-). The validity of the credit rating is up to 2 years. Corporates can use a parent company’s credit rating for the external debt of parent companies or external debt secured by parent companies. Exemptions are made for external debt related to infrastructure projects, external debt secured by multilateral institutions, refinancing, and trade credit.

Reporting requirement. BI has also strengthened monitoring on external borrowing of corporates. Corporates with external borrowing should submit quarterly reports to BI regarding their hedging and liquidity ratios for each quarter, starting from 2015. The report covers a corporate’s hedging ratio, liquidity ratio, and credit rating, and all supporting documentation.

Sanctions. To implement these regulations effectively, BI will impose administrative sanctions from 2015:Q4, in the form of warning letters to “related parties” in the transactions, including to the lenders which are providing the non-compliant debt, the Ministry of Finance, the Minister of State Owned Enterprises (in the case of borrowers that are state-owned enterprises), the Financial Services Authority (OJK) and the Indonesia Stock Exchange (in the case of listed-company borrowers).

Appendix 1. Technical Background

This appendix provides a brief description of two steps for the scenario analysis: (i) project common risk factors and firm-specific risk factors given the assumed paths of macroeconomic variables, and (ii) map these risk factors to PDs.

Generating the Paths of Common Risk Factors and Firm-Specific Factors

Given the assumed and projected paths of macroeconomic variables Zk,t (k = 1,2,3), both common risk factors Xm,t (m = 1,2) and risk factors specific to firm j, Yj,t (J = 1,2 …,6) can be predicted.

ΔXm,t=βm,0X+k=1nβm,kXZk,t+γm,1XXm,t1+γm,2XXm,t2+ɛm,kX,(A1)
ΔY¯i,j,t=βi,j,0Y+k=1nβi,j,kXZk,t+γi,j,1YY¯i,j,t1+γi,j,2YY¯i,j,t2+ɛi,j,kX,(A2)

The equations above include first and second order lags to capture auto-correlation. Subscript i represents country, which in our case is Indonesia.

Mapping Risk Factors to PDs

Given the paths of risk factors Xm,t(m = 1, 2) and Yi,t(j = 1, 2 …, 6), multivariate regressions are used to map them to PDs. The PD of firm i at time t for the prediction horizon of) can be written as:

pi,t(τ)=pτ(Xt,Yi,t)(A3)

where Pτ(·) is the PD function for horizon τ, Xt, is the common risk factors at time t, and Yi,t is the firm specific risk factors for firm i at time t. By simulating the model for many times (10,000 times in our case) one can create a distribution of each pi,t(τ). The results presented in the main text represent the average of the observations corresponding to the specific percentile (median, 75th and 95th) of the individual firms’ probability distributions.

References

  • Arman, H., 2015, “Indonesia Macro View, Short-term External Debt: Gauging the Roll-Over Risk.” Citi Research.

  • Duan, J. C., J. Sun, and T. Wang, 2012, “Multiperiod Corporate Default Prediction - A Forward Intensity Approach,Journal of Econometrics, Vol. 170, pp. 191209.

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  • Duan, J. C., A. Fulop, 2013, “Multiperiod Corporate Default Prediction with Partially-Conditioned Forward Intensity,RMI Working Paper No. 12/04 (Singapore: National University of Singapore, Risk Management Institute).

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  • Duan, J. C., W. Miao, and T. Wang, 2014, “Stress Testing with a Bottom-Up Corporate Default Prediction Model,RMI Working Paper. Available via the Internet: http://www.rmi.nus.edu.sg/duanjc/index_files/files/CreditStressTesting Aug-5-2014.pdf

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  • Laryea, T., 2010, “Approaches to Corporate Debt Restructuring in the Wake of Financial Crises,IMF Staff Position Note 10/02 (Washington: International Monetary Fund).

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  • Standard & Poor’s Rating Service, 2015, “15,000 Rupiah to One U.S. Dollar Could be the Level to Watch For Rated Indonesian Companies,September.

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1

Prepared by Ken Miyajima and Jongsoon Shin.

2

Includes India, Malaysia, Thailand, and the Philippines.

3

Interest coverage is EBIT/Interest Expense, where EBIT stands for Earnings Before Interest and Tax.

4

The model was developed by staff of National University of Singapore (NUS) in collaboration with IMF staff with the active support by NUS Risk Management Institute’s Credit Research Initiative team. For further information see Duan et al (2012), Duan and Fulop (2013) and Duan et al (2014).

5

In the forward intensity model, a firm’s default is signaled by a jump in a Poisson process. The probability of a jump in the Poisson process is determined by the intensity of the Poisson process. With forward intensities, PDs for any forecast horizon can be computed knowing only the values of the input variables at the time of prediction, without needing to simulate future values of the input variables.

6

Idiosyncratic volatility represents the standard deviation of the residuals obtained from a regression of the daily returns of the firm’s market capitalization on those of the economy’s stock index, for the previous 250 days. Firms with more variable cash flows and therefore more variable stock returns relative to a market index are likely to have a higher probability of bankruptcy.

7

The actual simulation is based on quarter-on-quarter percent or percentage point changes implied by the year-on-year data presented in Figure 16.

8

The model performs generally well. For the ASEAN–5 economies, macroeconomic variables explain a large share of variation in the common risk factors (R^2 is around 0.6) and the firm specific risk factors (R^2 is around 0.3–0.4). Accuracy of in-sample prediction of PDs is high.

9

Based on 10,000 simulations.

Indonesia: Selected Issues
Author: International Monetary Fund. Asia and Pacific Dept
  • View in gallery

    Peer Comparison: Leverage—Total Liabilities to Total Assets, 2014

    (56 percent)

  • View in gallery

    Peer Comparison: Profitability—Return on Assets, 2014 1/

    (In percent)

  • View in gallery

    Profitability—Return on Assets of Listed Companies

    (In percent)

  • View in gallery

    Peer Comparison: Liquidity—Liquid Assets to Current Liabilities, 2014

    (In percent)

  • View in gallery

    Corporate Debt Outstanding 1/

    (In billions of U.S. dollars; unless otherwise noted)

  • View in gallery

    FX Debt by Industry 1/

    (In billions of U.S. dolllars; unless otherwise noted)

  • View in gallery

    Annual Gross Issuances of Syndicated Loans and Bonds

    (In billions of U.S. dollars; unless otherwise noted)

  • View in gallery

    Share of FX Debt Securities 1/

    (In percent of total issuances of debt securities)

  • View in gallery

    Maturing Syndicated Loans or Bonds of Corporates 1/

    (In billions of U.S. dollars)

  • View in gallery

    Maturity of Private Sector’s External Debt 1/

    (In billions of U.S. dollars; as of end-September 2015)

  • View in gallery

    Corporate Debt-at-Risk

    (In percent of the total debt)

  • View in gallery

    Share of Corporate Debt-at-Risk by Industry 1/

    (In percent of total debt-at-risk; as of end 2014)

  • View in gallery

    Default Probability of Corporates by Country 1/2/

    (In percent)

  • View in gallery

    Default Probability of Corporates by Group

    (In percent)

  • View in gallery

    Schematic of Bottom Up Scenario Analysis

  • View in gallery

    Projected Macroeconomic Variables

  • View in gallery

    Indonesia: GDP Growth and Corporate Default Probability

    (Lehman peak = 100)