III. Measuring Sovereign and Banking Sector Risk in Indonesia : An Application of the Contingent Claims Approach 1
1. The balance-sheet approach is a tool developed by economists to help understand the evolution of risks in an economy-wide setting.2 This approach collects information on the size and structure of assets and liabilities of key sectors of an economy, in order to assess the extent of currency and maturity mismatches, or imbalances in the debt and equity structure (such as an excessive reliance on debt). However, balance sheet data do not provide a full picture of all the risks facing a country, because of the contingent nature of many risks. Accounting balance sheets, particularly at the economy-wide level, are also typically valued at full face value (or book value), and are not adjusted to reflect fluctuations in market prices or changes in the likelihood of default. Valuing assets using marked-to-market prices and incorporating contingent liabilities can provide a more complete picture of the risks inherent in a balance sheet.
2. The contingent-claims approach (CCA) provides a methodology to combine balance sheet information with widely used finance and risk management tools to construct marked-to-market balance sheets that better reflect underlying risk. It can be used to derive a set of risk indicators that can serve as barometers of risk and financial sector vulnerability. The CCA has been widely used in financial markets to derive risk indicators for corporations, and its use has been recently extended to sovereign balance sheets and industry-wide balance sheets.3 To date, the CCA has been applied at the sovereign or industry level for illustrative purposes only, since there are numerous challenges in calibrating the methodology without extensive cross-sectional or historical databases like there are for models of the corporate sector.
3. This paper examines the evolution of sovereign and banking-sector risk in Indonesia using the CCA. The first section outlines the methodology and constructs a set of risk-adjusted balance sheets for the period 2000-2005. The second section of the paper assesses the impact of the recent market turbulence in May and June 2006 on risk indicators. The third section of the paper considers the sensitivity of current balance sheets to potential shocks and changes in the structure of debt.
4. The results show a steady improvement in the health of sovereign and bank balance sheets since mid-2001. The recent market volatility has caused risk indicators for the sovereign to show a modest deterioration, with improvements towards the end of June returning indicators to end-2005 levels. For the banking system, risk indicators for state-owned banks deteriorated modestly as a result of recent volatility, reversing some of the gains of 2005.
B. Constructing Risk-Adjusted Balance Sheets for Indonesia
5. To understand changes in the overall level of risk facing a balance sheet, an estimate of the value of total assets and their volatility is needed, since they are typically not observable directly. Because many of the assets on the balance sheet are not traded, and are observed only at infrequent intervals, it is difficult to derive marked-to-market balance sheets. In contrast, many liabilities are traded, and thus can be valued more readily using methods from finance theory to impute the value and volatility of assets using the liability side of the balance sheet. Merton’s (1974) key insight in option pricing theory was that liabilities are contingent claims on total assets, with each liability having a different priority and maturity structure. The most junior liability on the balance sheet can be valued as an implicit call option on total assets. When the value of assets declines relative to the face value of debt, the value of the junior claims declines. Since the liability structure is observed, and many of the liabilities are traded, market prices of different liabilities can be used to derive information on the evolution of total assets. The framework can be applied to individual firms, or at a more aggregated level for an industry or for the sovereign.
6. We can use the CCA to estimate the risks to the combined balance sheet of the Indonesian central government and Bank Indonesia,4 following the process in Gray, Merton, and Bodie (2002), and Gapen, et. al (2005). The main elements on the asset side of the public sector balance sheet include international reserves, the net present value of primary surpluses, and the public sector’s monopoly on the issuance of money. These assets are net of any guarantees the public sector may implicitly or explicitly provide to the private sector. The main elements on the liability side of the public sector balance sheet are domestic currency liabilities (domestic currency debt and base money), and foreign currency debt. Thus, the balance sheet of the public sector can be described in the following highly-stylized manner:
7. Estimating the observed value and volatility of sovereign assets directly is difficult, since only international reserves are directly observable on the asset side of the public sector balance sheet. In contrast, each entry on the liability side of the balance sheet is directly observable on a high-frequency basis for Indonesia.5 The CCA uses observed liabilities together with well-known option pricing techniques to derive implied estimates for sovereign asset value and asset volatility. Figure 1 illustrates the different stages of this process.
Figure 1.Overview of CCA for the Sovereign
8. Domestic currency liabilities of the sovereign can be modeled as junior claims,6 whereby holders of these liabilities have a residual claim on sovereign assets above what is necessary to service foreign currency debt. If sovereign assets fall to a level where foreign currency debt payments cannot be made, then default is the result. This level is referred to as the distress barrier (DB), and is equivalent to the default-free value of debt.7 Therefore, the value of domestic currency liabilities can be viewed as a call option on sovereign assets with a strike price equal to the level of the distress barrier. Holders of such liabilities receive the maximum of either sovereign assets minus the distress barrier, or nothing in default. The Black-Scholes option pricing formulae can be used to estimate sovereign asset value and volatility with only a few select variables: the value and volatility of domestic currency liabilities (VL and σL, respectively), the distress barrier (DB), the risk-free interest rate (rf), and time (t).8 Once the implied asset values and volatilities are calculated, a range of risk indicators can be derived, including the distance to distress (the number of standard deviations away from the distress barrier), the probability of default, and the credit spread on sovereign assets.
The banking sector balance sheet
9. The process of estimating total assets and their volatility for the banking system is similar to that for the sovereign. However, instead of focusing on the value of domestic currency liabilities, the market value of equity (i.e., total market capitalization from stock price data) and its volatility, together with the distress barrier, can be used to calculate implied assets and their volatility.
10. For Indonesia, the largest private and public banks are included in the analysis. Two groups of banks are defined: the 3 large majority state-owned banks (Bank Mandiri, BNI, and BRI), and the 9 largest private banks.9 The daily market capitalization based on traded stock prices is used to calculate the volatility of bank equity for all 12 banks. The book value of short- and long-term obligations10 are used to calculate the distress barrier for the bank. The distress barrier, market capitalization, and volatility of market capitalization can be used to calculate the implied asset value and implied asset volatility. This is then used to calculate the distance-to-distress, the probability of default, as well as the expected losses of the individual banks.11 Aggregated figures for all private banks and for the state-owned banks are then derived by summing the respective balance sheets and calculating the risk indicators for the two groups of banks.
11. The first step in implementing the CCA for Indonesia is to calibrate the baseline, which was set to June 30, 2006. The estimated stock of local currency liabilities (reserve money and domestic debt) on that date was converted to U.S. dollars, then combined with the U.S. dollar value of external liabilities to derive a distress barrier (in dollars).12 In turn, this was used to solve for implied assets and their volatility plus a range of risk indicators, including the distance to distress, risk-neutral default probabilities, and sovereign credit spreads over U.S. dollar risk-free assets. The same procedure was applied for the 9 private and 3 public banks, using stock market capitalization data to solve for implied assets.13 Then, using the historical data available for Indonesia, a time series of the various risk indicators for the sovereign and the banks were produced from 2000 through to June 30, 2006.14
12. The results show a steady improvement in sovereign balance sheets since mid-2001, with a modest deterioration in risk indicators during the turbulence of August/September 2005, and more recently since May 15, 2006. Figure 2 shows the estimated default probability when 100 percent of expected losses of the banks are assumed to be guaranteed by the sovereign (solid line) and when expected losses are excluded (dashed line). The figure shows a gradual decline in default probabilities, with the exception of a spike in March 2001. This spike was caused by heightened volatility in financial markets in Indonesia and abroad.15 For the later period for which daily information is available, the sovereign spread and default probability move in line with the downward trend in CDS and EMBIG spreads on Indonesian government debt quoted by the market (Figure 3).16
Figure 2.Sovereign Default Probability, 1 Year Horizon, 2000-06.
Source: Fund staff calculations.
Figure 3.Sovereign Default Probability and 5 Year CDS Spread, July 2005-June 2006.
Source: Fund staff calculations, Bloomberg.
13. Balance sheet indicators for the banking system also show a strong improvement in the underlying health of banks over the past five years (Figure 4). The distribution of default risk by assets shown in Figure 5 confirms the general improvement in banking system indicators, with the riskiest banks (those with the highest default probability) accounting for a smaller percentage of total assets over time. Expected losses for the banking system have declined steadily, with sharp falls since end-2003 (Figure 6). This positive trend reflects rising equity valuations and declining volatilities, as balance-sheet structures have improved and non-performing assets have declined. The measure of expected losses for the 12 largest banks moves quite closely with the overall NPL ratio,17 and tends to lead changes in the NPL ratio by as much as two quarters.18
Figure 4.Banking Sector Default Probability, 1 Year Horizon, 2000-06.
Source: Fund staff calculations.
Figure 5.Banking Sector Distribution of Credit Risk by Default Probability, 2003-2005.
Source: Fund staff calculations.
Notes: Figures based on averages for four quarters.
Figure 6.Banking Sector Expected Losses and NPL Ratio, 2000-2005.
Source: Bank Indonesia, Fund staff calculations
C. May-June 2006 Global Market Turbulence
14. Having calibrated the set of risk-adjusted balance sheets, they can be used to understand the impact of recent market turbulence on sovereign and banking sector risk. The estimated sovereign spreads and cumulative default probabilities from the model can be used as the basic metric for comparing and analyzing sensitivities. Spreads provide a valuable and real-time measure of the cost of new borrowing or refinancing. The cumulative probability of default can also be directly related to various rating categories, providing an intuitive benchmark for comparisons. For the banking sector, expected losses provide a convenient measure of the impact of a change in the market environment.
15. The recent volatility experienced by emerging markets since mid-May 2006 provides a natural experiment to illustrate the impact of increased volatility on a variety of risk indicators.Figure 7 shows the market capitalization and volatilities for banks, while Figure 8 shows the recent jump in volatilities for key Indonesian asset markets (overnight interest rate volatility and the bid-ask spread on the rupiah/dollar rate, along with a developed market indicator—the VIX—often used as a proxy for global risk aversion).19 These figures show the increases in volatilities beginning in mid-May, and the subsequent declines in late June, albeit to modestly more elevated levels.
Figure 7.Banking Sector Capitalization and Volatilities, June 2005-June 2006.
Source: Bloomberg, Fund staff calculations.
Figure 8.Key Volatilities, 2005-2006.
Source: Fund staff calculations, Bloomberg.
16. Risk indicators for the sovereign show a modest increase in default probabilities and credit spreads as a result of recent market volatility, then declines toward the end of June.Figure 9 shows the estimated default probabilities for the sovereign on May 8, 2006—just prior to the increased turbulence in emerging markets—which coincided with the low-point for Indonesian default probabilities. The figure also shows the default probabilities on June 15, and then on June 30. As expected, this figure shows that default probabilities increased by about 3 percentage points since the turbulence began (to June 15), but then settled down to around 1¼percent above the May 8 level. Figure 10 shows the model spreads for Indonesian sovereign debt for the baseline on June 30, 2006, as well as the estimated spreads at their low point on May 8, together with their level on June 15. Again this figure shows how the estimated spreads increased across the maturity spectrum for Indonesian sovereign debt by a cumulative 10 basis points to June 15, but then returned to close to their May 8 level by end-June. The actual increase in 5-year CDS spreads observed from May 8 to June 15 was 61 basis points, while EMBIG spreads rose by 44 basis points. On June 30 CDS spreads remained about 76 points above their May 8 level, while EMBIG spreads remained about 44 basis points above May 8 levels. This suggests that the model may underestimate the extent of deterioration in underlying credit risks from May to June. Another explanation may be that CDS and EMBIG spreads overestimate the extent of deterioration in underlying risk, or that they were too low relative to fundamentals in early May, and the recent increase in spreads simply reflects a market correction to more normal levels.
Figure 9.Sovereign Cumulative Default Probabilities, 1-5 Years
Fund staff calculations, S&P.
Figure 10.Estimated Sovereign Spreads, 1-5 Years.
Source: Fund staff calculations.
17. The global market turbulence experienced since May 15 also caused an up-tick in risk indicators for the banking system. Bank capitalization declined by around 15 percent for private banks and 17 percent for state-owned banks (to June 30), while volatilities increased from 29 percent to 33 percent for private banks (reaching as high as 42 percent) and from 34 percent to 46 percent for state-owned banks (reaching as high as 63 percent).20 The reduction in market capitalization and increase in its volatility (particularly for state-owned banks—Figure 7) decreased implied assets and increased their volatility, leading to a decline in distance-to-distress measures and increases in expected losses. These developments reflect increases in interest rate volatilities, as well as a softening of bank earnings for the first quarter of 2006 in the wake of weaker growth and rising NPL ratios. As shown in Figure 11, the recent market turbulence had a larger impact on state-owned banks, reflecting a larger increase in asset volatility (Figure 7). One possible explanation for this result is that state-owned banks have higher NPL ratios, which may imply greater sensitivity of earnings to future growth prospects and interest rate volatility. As uncertainty rises, the volatility of projected earnings is likely to rise more for state-owned banks. Furthermore, the higher debt levels of state-owned banks would imply that higher interest rate volatility would have a greater impact on future debt-servicing costs relative to private banks.
Figure 11.Banking Sector Expected Losses, 1-5 Years, $mn.
Source: Fund staff calculations.
D. Understanding Sensitivities
18. The risk-adjusted balance sheets produced with the CCA can be used to illustrate the sensitivity of Indonesian balance sheets to changes in key parameters and balance sheet structures.Table 2 summarizes the effects of changing different parameters or balance sheet components on the overall distance to distress (D2D) and default probabilities (DP). The previous section demonstrated the impact of increased volatility on a variety of risk indicators, while this section focuses on the impact of changes in exchange rates and the currency composition of sovereign debt.
|Change in Input:||Effect in Model:||Change in Output:|
|Primary Surplus ↑||ASovereign ↑||D2D ↑, DP ↓|
|Reserves ↑||ASovereign ↑||D2D ↑, DP ↓|
|Nominal Ex. Rate ↑||ASovereign ↑||D2D ↑, DP ↓|
|Dom. Interest Rate ↓||ASovereign ↑||D2D ↑, DP ↓|
|DB (Foreign) ↓||DB ↓||D2D ↑, DP ↓|
|Ex. Rate Vol. ↑||σA ↑||D2D ↓, DP↑|
|Other Vol. ↑||σA ↑||D2D ↓, DP↑|
|Stock Mkt. ↓||ACorp ↓ ⇒ ABanks ↓ ⇒ Guarantee ↑ ⇒ ASovereign↓||D2D ↓, DP↑|
19. Exchange rate sensitivity can be considered by revaluing the balance sheets for a distribution of different exchange rates to show how the risk indicators might vary with a change in this parameter. This is equivalent to assessing the “partial derivative” of the risk-adjusted balance sheet with respect to the exchange rate.21 Conceptually, the exchange rate is a key price in marking-to-market the sovereign balance sheet, because of the importance of external assets and liabilities in the overall balance sheet. When the exchange rate depreciates, its volatility rises, and the cost of servicing foreign debt increases, increasing the overall level of risk. The distress barrier (measured in dollars) also decreases, as the stock of domestic debt declines in dollar terms, decreasing the overall level of risk. There is thus a non-linear relationship between the level of the exchange rate and the level of implied assets as these two factors (higher volatility and a lower distress barrier) move in different directions, with sovereign assets declining with higher levels of the exchange rate (i.e., a more depreciated exchange rate).
20. Credit spreads are quite sensitive to variations in the exchange rate.Figure 12 shows the distribution of exchange rates that were used to revalue the baseline balance sheet, centered on Rp. 9,263 per U.S. Dollar, with a 95 percent confidence interval from 8,580 to 10,625. This distribution is centered on the exchange rate level at the baseline on June 30, 2006, and is calibrated according to the observed distribution of exchange rates over the last 5 years. Figure 13 shows the associated distribution of credit spreads. For each value of the exchange rate distribution in Figure 12, there is an associated level of credit spread estimated by the model. Plotting all of these combinations together provides a distribution of credit spreads. The 95 percent confidence interval of credit spreads ranges from 185 to 351 basis points, with a mean of 239. Thus, there is a 5 percent chance that spreads could rise above 351 basis points under this exchange rate distribution, and a 5 percent chance that spreads could fall below 185 basis points.
Figure 12.Distribution of Exchange Rates for Simulations.
Source: Bloomberg, Fund staff calculations.
Figure 13.Distribution of 5 Year Credit Spreads From Exchange Rate Simulations.
Source: Fund staff calculations.
21. The next balance sheet sensitivity to be considered is the sensitivity to changes in the structure of debt. The risk-adjusted balance sheets can be used to show the impact of a debt reduction of $10 billion on default probabilities and sovereign spreads, under two different states of the world. First, a reduction in external (dollar-denominated) debt is considered under the conditions prevailing in the baseline, then with a more depreciated and volatile rupiah. The results of this scenario can be compared to an alternative of repaying an equivalent amount of domestic (rupiah-denominated) sovereign debt under the baseline conditions, and then with a higher level and volatility of the exchange rate.22 The impacts of these two scenarios are shown in Table 3.
|5 Year||5 Year|
|Repayment of $10bn External||−0.1%||0.0|
|Repayment of $10bn External, Higher Volatility||1.6%||12.4|
|Repayment of $10bn, Domestic||−0.7%||−10.9|
|Repayment of $10bn, Domestic, Higher Volatility||0.6%||−6.6|
22. Repaying the more costly domestic debt brings a greater benefit to the overall sovereign balance sheet.Table 3 shows that sovereign spreads and default probabilities are largely unchanged when long-term foreign debt is repaid, but a reduction in domestic debt has a larger impact. This is because the average interest rate on foreign liabilities is lower than the interest rate on domestically-issued debt, due to the high proportion of external debt that is on concessional terms. Reducing the more expensive liabilities reduces the level of risk in the sovereign balance sheet, as the remaining creditors are more likely to be repaid. This will be reflected in higher values for the remaining liabilities, and lower spreads. In a less favorable state of the world (where volatilities and the level of the exchange rate are 25 percent higher), domestic debt reduction reduces sovereign spreads and default probabilities relative to the baseline and relative to the repayment of external debt.
E. Summary and Conclusions
23. This paper has developed a set of risk-adjusted balance sheets for Indonesia using the CCA. Using data from the liabilities side of the balance sheets of the sovereign and banking sectors, is has been possible to impute the value and volatility of assets and produce a range of risk indicators. These risk indicators are based on forward-looking, marked-to-market balance sheets, and provide information about the distribution of risks and sensitivities to different shocks.
24. Risk indicators for the sovereign and banking system show a steady improvement in balance sheets since mid-2001, with a modest deterioration during the turbulence of August/September 2005. The risk-adjusted balance sheets were also used to assess the impact of recent market volatility since mid-May 2006 on sovereign and banking sector risk indicators. The results for the sovereign show a modest increase in default probabilities and credit spreads as a result of recent market volatility, followed by declines toward the end of June. For the banking system, risk indicators suggest a more significant, though still relatively modest deterioration in expected losses, particularly for state-owned banks. Finally, the paper examined the sensitivity of sovereign risk by considering the impact of changes in exchange rates and debt levels. The results show that credit spreads are quite sensitive to changes in the exchange rate, and that reducing domestic debt levels has a more beneficial impact on risk indicators than reducing foreign debt levels.
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