Reports

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Date : 22 Dec 2011
Methodologies

Financial Stability Map and Indicator

The Financial Stability Map and Indicator depict the overall stability condition in the Indian financial system. The Financial Stability Indicator (FSI) is based on the three major indicators namely, Macro Stability Indicator (MSI), Financial Markets Stability Indicator (FMSI) and Banking Stability Indicator (BSI). FSI was derived using simple average of MSI, FMSI and BSI. The methodologies for calculation of MSI, FMSI and BSI are described below.

Macroeconomic Stability Map and Indicator

The Macroeconomic Stability Indicator (MSI) is based on seven sub-indices, each pertaining to specific area of macroeconomic risk. Each sub-index on macroeconomic risk includes select parameters representing risks in that area. These sub-indices have been validated by assessing their appropriate impact on macroeconomic or financial variable such as GDP, inflation, interest rates or the quality of assets of the banks. The seven sub-indices of the overall macroeconomic stability index and their components are described below:

Global Risk Index

The Global Risk index is based on real output and the prices in the advanced economies. In respect of real output, a composite index based on the weighted average of the growth rate of GDP of U.S., Euro Area and Japan has been constructed. Using the similar procedure, index for inflation in these advanced economies was also constructed. GDP index is ranked in ascending order while that of inflation is ranked in descending order. Global Risk Index is a composite index of these indices having equal weights for each.

External Vulnerability

The index of external vulnerability is based on current account deficit/GDP, current payments/current receipts, average monthly imports/reserve, share of short term debt in total debt, debt stock - GDP ratio and debt service ratio.

Fiscal Vulnerability

Initially, an index of fiscal stress is constructed based on the gross primary deficit (GPD), gross fiscal deficit (GFD) and the total liabilities of the centre and state governments. This is based broadly on the methodology suggested in two IMF Working Papers by Baldacci, McHugh and Petrova (2011) and Baldacci, Petrova, Belhocine, Dobrescu and Mazraani (2011). The weights in respect of GFD and GPD so obtained were applied to recent data on GPD and GFD provided by the Office of the Comptroller General of Accounts to assess the change in fiscal risks.

Growth

For obtaining the outlook on domestic growth, the relationship of growth with a number of variables were attempted, viz. exports/GDP, growth of core industry, GFCF/GDP, real bank credit, PMI and yield curve (difference between the ten-year and one-year yield). Amongst these variables, the yield curve and PMI Manufacturing were found to be the most appropriate indicators of growth.

Inflation

The outlook for inflation is based on the changes in international oil prices, exchange rate, and world Inflation.

Corporate Sector

The health of the corporate sector is captured through profit margin. The risks emanating from the sector is inversely related to it. In order to capture the relationship of the corporate sector with the financial sector, the share of interest in sales is also captured in the index for the corporate sector.

Household Sector

In the absence of frequent data on indebtedness of household, the outstanding credit from the bank to the household sector, viz. retail credit, is taken as a proxy for household indebtedness. Further, in view of the delay in availability of data on personal disposable income, private final consumption expenditure (PFCE) is used as its proxy. Based on these two variables, the index for household sector attempts to capture the risks originating from the household sector.

Financial Markets Stability Map and Indicator

With the objective to measure stability of the financial market, Financial Markets Stability Indicator (FMSI) has been prepared based on the indicators of four sectors/markets, namely, banking sector, foreign exchange market, equity market and debt market. The indicators selected from various sectors/market are followings; i) Banking Sector: Banking Beta of CNXBANK Index and NIFTY Index, CD Rate and CD rate minus Implied Forward rate, ii) Foreign Exchange Market: CMAX of daily Rupee-Dollar exchange rate, which is defined as Xt/Max(Xi, i = 1,2,...upto one year). Where, Xt is Rupee-Dollar exchange rate at time t, and 25 Delta Risk Reversals of foreign exchange rate, iii) Equity Market: Inverse of NIFTY CMAX and India VIX, and iv) Debt Market: Corporate bond which is average return of corporate bonds rated A, AA, and AAA, 10-years Government bond yield and CP Rate.

To aggregate all the indicators, variance-equal transformation has been used.

At first level, four indicators for the four selected sectors/markets were prepared based on simple average of elementary indicators and thereafter FMSI was derived based on simple average of the four indicators derived at first level. FMSI was estimated based on daily data.

Further, projection of FMSI was done based on monthly FMSI, which is monthly average of daily FMSI, credit growth, WPI-Manufactured Products inflation and REER using following regression equation:

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Banking Stability Map and Indicator

The Banking Stability Map and Indicator (BSI) were introduced in FSR December 2010 to present an overall assessment of changes in five dimensions that have a bearing on stability of the banking sector. The methodology was further enhanced by including other variables for each of the five dimensions, which were presented in FSR June 2011. The following ratios are used for construction of each composite index:

Table : Indicators used for construction of Banking Stability Map and Banking Stability Indicator

Dimension

Ratios

Soundness

CRAR

Tier-I Capital to Tier-II Capital

Leverage ratio as Total-Assets to Capital and Reserves

Asset-Quality

Net NPAs to Total-Advances

Gross NPAs to Total-Advances

Sub-Standard-advances to gross NPAs

Restructured-Standard-Advances to Standard-Advances

Profitability

Return on Assets

Net Interest Margin

Growth in Profit

Liquidity

Liquid-Assets to Total-Assets

Customer-Deposits to Total-Assets

Non-Bank-Advances to Customer-Deposits

Deposits maturing within-1-yearto Total Deposits

Efficiency

Cost to Income

Business (Credit + Deposits) to total employees

The five composite indices represent the five dimensions of Soundness, Asset-quality, Profitability, Liquidity and Efficiency. Each index, representing a dimension of bank functioning, takes values between zero (minimum) and 1 (maximum). Each index is a relative measure during the sample period used for its construction, where a high value means the risk in that dimension is high. Therefore, an increase in the value of the index in any particular dimension indicates an increase in risk in that dimension for that period as compared to other periods. For each ratio used for a dimension, a weighted average for the banking sectors is derived, where the weights are the ratio of individual bank asset to total banking system assets. Each index is normalised for the sample period as 'Ratio-on-a-given-date minus Minimum-value-in-sample-period divided by Maximum-value-in-sample-period minus Minimum-value-in-sample-period'. A composite measure of each dimension is calculated as a weighted average of normalized ratios used for that dimension, where the weights are based on the marks assigned for assessment for CAMELS rating. Based on individual composite indices for each dimension, the Banking Stability Indicator is constructed as a simple average of these five composite sub-indices.

For the current map and indicator, the sample period for assessment was taken from March 2006 to September 2011. Projection of BSI was done using Auto Regressive Moving Average (ARMA) method.

Single Factor Sensitivity Analysis - Stress Testing

As a part of quarterly surveillance, stress tests are conducted covering credit, interest rate, equity price, foreign exchange and liquidity risk. Resilience of the commercial banks in response to these shocks is studied. The analysis covers all scheduled commercial banks. Single factor sensitivity analysis on credit risk of scheduled urban co-operative banks and non-banking financial companies are also conducted.

Credit Risk

To ascertain the resilience of banks, the credit portfolio was shocked by increasing NPA levels, for the entire portfolio as well as for select sectors, along with a simultaneous increase in provisioning requirements. For testing the credit concentration risk, default of the top individual borrowers and the largest group borrower is assumed. The estimated provisioning requirements so derived were first adjusted from the profit of the banks and the residual provisioning requirements, if any, were deduced from banks' capital.

The analysis was carried out both at the aggregate level as well as at the individual bank level, based on quarterly supervisory data for September 2011. The scenario assumed increase in the existing stock of NPAs by 50, 100 and 150 per cent and enhanced provisioning requirements of 1 per cent, 30 per cent and 100 per cent for standard, sub-standard and doubtful/loss advances, respectively. The assumed increase in NPAs was distributed across sub­standard, doubtful and loss categories in the same proportion as prevailing in the existing stock of NPAs. The additional provisioning requirement was applied to the altered composition of the credit portfolio.

Equity price, foreign exchange and interest rate risk

The fall in value of the portfolio or income losses due to change in equity prices, appreciation/ depreciation of INR, shifting of INR yield curve are accounted for as the total loss of the banks because of the assumed shock. The estimated total losses so derived were first adjusted from the profit of the banks and the residual provisioning requirements, if any, were deduced from banks' capital.

For Interest rate risk in the Banking Book, two kinds of approaches were considered: (1) Income Approach, which impacts the earnings of banks because of shift in INR yield curve and (2) Duration Gap Analysis, which computes the valuation impact (portfolio losses). The income losses, on interest bearing exposure gap, are calculated for one year for each time bucket separately. The portfolio losses, on interest bearing exposure gap, are calculated for each time bucket, using duration gap analysis. For interest rate shocks in trading book, the valuation losses are calculated for each time bucket on the interest bearing assets using duration approach.

Liquidity Risk

The aim of liquidity stress tests is to assess the ability of a bank to withstand unexpected liquidity drain without taking recourse to any outside liquidity support. The analysis is done as at end-September 2011. The scenario depicts different proportions (depending on the type of deposits) of unexpected deposit withdrawals on account of sudden loss of depositors' confidence and assesses the adequacy of liquid assets available to fund them. Two different definitions of liquid assets have been assumed.

  • As per the first definition, liquid assets consist of cash funds, excess CRR balances with the Reserve Bank, balances with other banks and all SLR investments.

  • The second definition assumes that the liquid assets would include cash funds, excess CRR balances with the Reserve Bank, balances with other banks payable within one month and investments maturing within one month.

  • It is assumed that banks would meet stressed withdrawal of deposits through sale of liquid assets.

  • The sale of investments is done with a hair cut of 10 per cent on their market value.

  • The stress test is done on a static mode.

Urban Co-operative Banks - Credit Risk

Stress tests on credit risk were conducted on Scheduled Urban Co-operative Banks (SUCBs) using their asset portfolio as at end-June 2011. The tests were based on single factor sensitivity analysis. The impact on CRAR was studied under three different scenarios. The assumed scenarios were as under:

Scenario I:

  • Shock applied: 50% increase in gross NPAs.

  • Provisioning requirement is increased by 50%.

  • Capital (Tier I & II) is reduced by additional provisions.

Scenario II:

  • Shock applied: 100% increase in gross NPAs.

  • Provisioning requirement is increased by 100%.

  • Capital (Tier I & II) is reduced by additional provisions.

Scenario III:

  • Shock applied: Loss or Zero profit by all SUCBs due to adverse macroeconomic conditions.

  • Capital (Tier I & II) is reduced by amount of profits in respect of those banks that reported profit (no change if reported loss).

Non-Banking Financial Companies (ND-SI) - Credit Risk

Stress tests on credit risk were conducted on Non-Banking Financial Companies (Non-Deposit taking and Systemically Important) using their asset portfolio as at end-March 2011. The tests were based on single factor sensitivity analysis. The impact on CRAR was studied under two different scenarios. The scenario assumed increase in the existing stock of NPAs by 200 and 500 per cent. The assumed increase in NPAs was distributed across sub-standard, doubtful and loss categories in the same proportion as prevailing in the existing stock of NPAs. The additional provisioning requirement was adjusted from the current capital position.

Systemic Liquidity Index

The SLI uses the following four indicators representing various segments of the market:

  1. Weighted Average Call Rate - RBI Repo Rate

  2. 3 Month Commercial Paper (CP) Rate - 3 Month Certificate of Deposits (CD) Rate

  3. 3 Month CD Rate - 3 Month Implied Deposit Rate

  4. Weighted Average Call Rate - 3 Month Overnight Index Swap (OIS) Rate

In order to create the Systemic Liquidity Index (SLI), four different methodologies viz., Relative Distance, Standard Normal, Principal Component Analysis, Percentile Ranks, were considered. The Standard normal or Variance-equal weighted method has been found to be most suitable for India.

Macrofinancial Stress Testing

To ascertain the resilience of banks, the credit risk was modeled as functions of macroeconomic variables. Credit risk stress tests have been computed using several econometric models that relate banking system aggregates to the macroeconomic variables, such as (i) multivariate logit regression on aggregate systems' NPAdata; (ii) multivariate regression in terms of the slippage ratio (inflow of new NPAs); (iii) aggregate VAR using slippage ratio; (iv) multivariate panel regression on bank wise slippage ratio data, which was later aggregated into bank-groups; and (v) multivariate regressions for aggregate sectoral NPAs. The banking system aggregate includes current and lagged values of aggregate NPAs (NPA ratio) and inflow of new NPAs (slippage ratio), while macroeconomic variables include GDP growth,

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The Modeling Framework

The following multivariate models were run to estimate the impact of macroeconomic shocks on the aggregate NPA (npa) / slippage ratio (SR):1

• Aggregate banking system multivariate logit2 regression:

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• Aggregate banking system multivariate regression:

The analysis was carried out on slippage ratio at the aggregate level for the commercial banking system as a whole.

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• Vector Autoregression (VAR):

In order to judge the resilience of banks on various macroeconomic shocks, Vector Autoregressive (VAR)3 approach has been adopted. The advantage of VAR model is that, it allows to fully capture the interaction among macroeconomic variables and banks' stability variable. It also captures the entailed feedback effect.

In notational form, mean-adjusted VAR of order p (VAR(p)) can be written as

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The appropriate order of VAR has been selected based on minimum information criteria as well as other diagnostics and suitable order was found to be two. Accordingly, VAR of order 2 (VAR(2)) was estimated and stability of the model was checked based on roots of AR characteristic polynomial. Since, all roots are found to be inside the unit circle, this selected model was found to be fulfilling the stability condition. The impact of various macroeconomic shocks was determined using impulse response function of the selected VAR.

• Bank wise panel fixed-effect regression:

Bank wise panel regression was modeled where slippage ratio was considered as function of macroeconomic variables. The bank effect were identified along with the overall model specifications. Bank group-wise results were obtained by grouping bank-wise estimates.

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where, ai is the bank specific parameter.

• Sectoral multivariate regression:

The impact of macroeconomic shocks on various sectors was assessed by employing multivariate regression models using aggregate NPA ratio for each sector separately. The dependent variables consisted of lagged NPAs, sectoral GDP growth, inflation, and short-term interest rate.

Derivation of the NPAs and CRAR from the slippage ratios, which were projected from the above mentioned credit risk econometric models, were based on the following assumptions: credit growth of 18 per cent; recovery rate of 6 per cent; zero write-offs; risk weighted assets growth of 20 per cent; and profit growth of 10 per cent. The regulatory capital growth is assumed to remain at the minimum by assuming minimum mandated transfer of 25 per cent of the profit to the reserves account. The distribution of new NPAs in various sub-categories was done as prevailing in the existing stock of NPAs. Provisioning requirements for various categories of advances are 0.4 per cent for standard advances, 10 per cent for sub-standard advances, 75 per cent for doubtful advances, and 100 per cent for loss advances. The projected values of the ratio of the non-performing advances were translated into capital ratios using the 'balance sheet approach', by which capital in the balance sheet is affected via the provisions and net profits. It is assumed that the existing loan loss provisioning coverage ratios remain constant for the future impact.


1 Slippage ratio, exports/GDP, and the call rate are seasonally adjusted.

2 For detailed model specifications, please refer to FSR-December 2010. The logit transformation of NPA ratio is define as:

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3 For detailed VAR model specifications, please refer to FSR-June 2011


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