Training on SF, June 15–16

Structural Financial Stress Index (ACRA SFSI) Calculation Methodology

1 Definitions and scope of application

The methodology is based on the systemic risk and financial stability concepts. The underlying idea of the methodology is that by connecting various industries of the economy, the financial system may foster payment defaults (regardless of their reasons) spreading from agents in one market to agents in other markets. Massive incidents of that kind (financial crises) may result in malfunctions in the real sectors of the economy (initially, due to local liquidity crises), which highlights the importance of paying attention to such events.

In order to evaluate how close the financial system is to a crisis, ACRA uses two approaches:

  • Financial Stress Index (ACRA FSI) measuring financial instability indirectly based on external signs of a financial crisis: volatility in the key financial markets, interest rate spreads, etc. (see Principles of Calculating the Financial Stress Index for the Russian Federation).
  • Structural Financial Stress Index (ACRA SFSI) that measures potential financial instability directly by aggregating information on the financial position of economic agents and by assessing their vulnerability to specific types of risk (this methodology describes that approach).

In some countries where liquidity in the financial markets is low even in normal times or market participants disregard information on financial position of counterparties and issuers or regard it as secondary due to some reasons, the latter approach may be the only one possible. As in such case we would not be able to obtain information of interest indirectly from the available market data regarding vulnerability of companies and households, and instead of analyzing external signs of stress, we have to analyze the structure of their financial balances directly. Considering the practical complexity of analysis at the level of stand-alone agents and the objective inability to cover the larger share of the economy, for calculation of ACRA SFSI we consider maturity profile and FX structure of assets and liabilities in each particular sector (financial companies, non-financial companies, households, and government). As compared to the indirect approach, these calculations require more structural statistics updated less frequently.

Structural imbalance indicators that we calculate show how strongly and quickly can the aggregate financial sector respond to the materialization of specific risks. Potential damage from materialization of the refinancing risk is captured by the liquidity imbalance indicator, and in case of FX risks – by the FX imbalance indicator. Imbalance indicators are calculated based on information available in the debt market map1.

1 For more information on debt market map please refer to ACRA research “Russian government to become a net borrower and Kazakhstan government to remain a net creditor in 2018” published on July 27, 2017.

Availability of imbalances may lead to the systemic risk materialization if trigger events occur that change the expectations regarding the amount of free cash flow for a large number of economic agents. If large structural imbalances are present, even weak triggers may lead to a system-wide crisis. And vice versa, small structural imbalances lead to only slight revision of expectations as to the future cash flows even in case of massive trigger events. For the purposes of ACRA SFSI calculations, we regard volatility in the FX and interbank lending markets as trigger events2. FX market volatility may adjust expectations as to the value of foreign currency-denominated cash flows, and interbank lending market volatility may do the same with respect to availability of short-term financing.

ACRA SFSI combines information regarding the amount of structural imbalances and the magnitude of trigger events.

Figure 1. Difference in approaching construction of ACRA FSI and ACRA SFSI

Source: ACRA

2 The economic reasons for reduced availability of short-term financing may be different (bank run, lower income, revaluation of obligations, etc.) but, in our opinion, exactly those events that may create volatility in the interbank market, among other things, represent a systemic threat.

2 Sector’s liquidity imbalance indicator

This indicator is intended to assess the amount of additional funds required for the industry’s economic agents in the coming year to completely repay their short-term debt and pay interest on debt obligations, while using (1) maximum available revenues from short-term financial assets, (2) interest income from all financial assets, (3) cash available as at period start as well as (4) a-cyclical share of the free cash flow from non-financial operations.

The resulting figure after the above calculations shows the aggregate liquidity deficit in the industry if the following stress scenario materializes:

  1. Cash flow from non-financial operations reaches the minimum guaranteed level;

  2. Short-term debt obligations are claimed without refinancing options;

  3. Long-term commitments are serviced without any changes in terms and conditions3;

  4. Short-term financial assets are liquid or repaid according to the schedule (counterparties comply with their short-term obligations)4;

  5. The government makes no efforts to support liquidity.

Upon materialization of the stress scenario, the assessment has industry specifics:

Table 1. Comparing industry specifics of a stress scenario

Source: ACRA

3 Long-term deposits are also not claimed for early redemption.
4 The reflection of the “contamination effect” is limited due to this assumption.
5 0% < X < 100%, selected on the basis of averaging previous crisis developments. The operating income includes non-cash flows as well but is the best available approximation to free cash flow indication for a sector.

As initial statistical information regarding cash flows and inventories is aggregated at the sector level, it implicitly implies that, within an industry, a positive free cash flow of one agent may cover short-term obligations of the other. This is a forced assumption improving the assessment in the case where many agents in the industry are members of one financial group, but worsens it if a large share of group relationships take place between the industries.

The monetary value of a potential liquidity deficit in the sector is normalized in order to ensure comparability of the indicator values over time: we divide it by the amount of debt assets of all sectors6.

Transforming maturities (short-term obligations into long-term loans) is sometimes regarded as an economic function of the banks but too large a gap in maturities between liabilities and assets is undesirable even for banks. The world’s experience demonstrates that, prior to a crisis, liquidity imbalance amount at the micro-level has a positive correlation with the depth and speed of decline in share prices of banks, increase of debt and amount of support from the government during the crisis7, and the depth of the ensuing economic crisis.

6 The indicator is constructed in the spirit of Liquidity mismatch index (LMI) as described in Measuring Liqiudity Mismatch in the Banking Sector; Bai, Krishnamurthy, Weymuller; 2016. The approach is applied at the sector level taken as a whole rather than at a company level; and it was summarized for analyzing not only banking industry but also other industries by a-cyclical share of free cash flow from non-financial operations.
Our approach can be regarded as a distant analogue of approaches to analyzing liquidity in the rating analysis but used for macrosectors rather than individual companies. See LTLSI from the Methodology for Credit Ratings Assignment to Banks and Banking Groups under the National Scale of the Russian Federation and short-term liquidity ratio from the Methodology for Assigning Credit Ratings to Non-Financial Companies under the National Scale for the Russian Federation.
7 E.g., see the article referred to in footnote 6 above.

3 Sector’s FX imbalance indicator

This indicator’s objective is to assess the total FX needs of the sector, which are not covered by cash flows from FX debt assets, expected foreign currency-denominated operating revenues or foreign currency reserves. In contrast to liquidity imbalance, long-term assets and liabilities are also of significance in this case. Such needs, even if extended over time according to necessary expenses schedule, may induce additional demand in the domestic foreign exchange market and result in a sharp change or overshooting8 of the exchange rates as soon as the market realizes that shift in needs.

The indicator is calculated by adding together net FX position of the sector under debt instruments, FX liquidity as at period start, the balance of expected interest income/expenses from FX debt instruments, and the share of expected export revenues for the next y-years9.

The monetary value of the sector’s potential FX needs is normalized in order to compare the indicator’s values over time. As the case with the liquidity imbalance is, we divide it by refer to the aggregate debt assets of all sectors.

Any large FX imbalance resulting from changes in the foreign economic environment leads to disparate revaluation of incoming cash flows and funds required to service FX liabilities.

Table 2. General pattern for calculating sector’s imbalance indicators

Source: ACRA

Overshooting of the exchange rate represents a change exceeding the one required to establish a long-term equilibrium.
9 The indicator calculations take into account track record of BIS in constructing Currency Mismatch Index (CMI) for the emerging economies.
10 The y-parameter is taken as equal to the average maturity of FX borrowings (to be determined by expert opinion or based on statistics).

4 Assessing the magnitude of trigger events

In our opinion, expansion of money market spreads indicates a reduced availability of liquid funds, with that reduction increasing the probability of refinancing risk materialization. In the index calculations, the spread is used between the average interest rate for short-term lending and deposit interbank transactions11 and the rate for the key instrument used by the Central Bank to provide liquidity.

where [¯] stands for using the average weighted monthly figure for the last 2 quarters; weights decrease in linear fashion from 1 to 0 with going back in time from the current period. The U-operator normalizes the indicator’s dynamics for it to fluctuate in the range of 0 to 1 in the historic period available at the time of first calculation.

An increase in FX rate volatility may indicate a higher probability of its permanent shift and ultimately lead to FX risk materialization.

where τ means the period for calculation of FX rate gains, or 1 day in our case; σ is the standard deviation calculated using daily values in the series for the last month; ¯ indicates usage of the average weighted daily values for the last quarter; weights decrease linearly from 1 to 0 with going back in time from the current period.

11 Spot or forward, depending on market specifics.

5 Calculation of the aggregate economic index

For index calculations, we aggregate the imbalance indicators for economic sectors and translate them into general economic indicators:

The aggregation method implies that excess liquidity reserves in one sector do not smoothen a liquidity deficit in another sector. This is reasonable as with occurrence of trigger events and expectations revision uncertainty as to the credit quality of counterparties usually increases, and therefore financial markets may fail to facilitate liquidity cross flows.

The resulting structural financial stress index combines liquidity imbalance indicator with the refinancing risk trigger and the FX imbalance index with the FX risk trigger:

where α is the relative importance of liquidity imbalance versus FX imbalance12.

   are triggers for refinancing and FX risks.

Index values are normalized at the time of its first calculation in the range of 0 to 10, where 10 represents the maximum stress. By construction, Index values have no upper limits. An increase above 10 points would indicate a more significant financial stress than the one witnessed in history based on the available data.

12 Imbalance figures include a k-lag as publication of official consolidated balance sheet statistics is delayed.

Appendix. Structural Financial Stress Index calculation for Kazakhstan

We calculated the Structural Financial Stress Index (see Figure 1) for Kazakhstan for the period from mid-2008 to November 2017. The need to apply the structural approach can be explained by the fact that domestic financial markets are relatively thin. The calculation was possible owing to availability of detailed statistics in the public domain (see Table 3) with respect to financial balance sheets of both financial and non-financial sectors and households as well as sufficiently long history of balance sheet indicator data. Based on this information, we assess the structure of debt relationships in the Kazakhstan economy13 as well as structural imbalances.

13 For more details on the debt market map, please see ACRA research titled Russian government to become a net borrower and Kazakhstan government to remain a net creditor in 2018, published on July 20, 2017.

Figure 2. Structural Financial Stress Index for Kazakhstan (ACRA SFSI KZ)

Source: ACRA estimates

We highlight three periods of increased financial stress in Kazakhstan in the last ten years: from late 2008 to early 2009, early 2015, and from late 2015 to early 2016. The last two periods could be fundamentally combined into one as both of them initially resulted from a slump in global crude oil prices in 2014. Nevertheless, we think they are quite interesting (albeit related), and we consider them separately. Analysis based on the Index data allows to mark three events in 2014-2016 that generated stress as well as to understand crisis mechanics in more detail (see Figure 3).

Figure 3. Trigger events in Kazakhstan’s economy

Source: ACRA estimates

The 2009 crisis

As in many other mineral-exporting countries alike, a drop in export revenues and FX risk materialization accompanied the 2009 crisis in Kazakhstan. Substantial currency imbalance of households exacerbated this shock (see Figure 7). It arose on the back of foreign currency lending growth in 2005-2008 (4.6x) significantly outpacing the increase of foreign currency liquidity reserves of households and foreign currency deposits (3.3x). Banks were driving up foreign currency lending while being aware that foreign currency-denominated income of the households does not cover their future loan payments. FX risk was underestimated, and regulations put no pressure on banks to consider this risk in full: net FX position of banks including the expected interest income/expenses was negative.

The exchange rate policy and the related expectations likely contributed to the underestimation of the FX risk. In fact, from mid-2000 to early 2016, KZT/USD exchange rate played a role of a short-term operating target of the exchange rate policy, and the KZT/RUB exchange rate – as a long-term target thereof (see Figure 4). The above is related to close economic ties between Russia and Kazakhstan: a little less than 50% of imported goods are Russian-produced, and Eurasian Economic Union integration aggravates interdependence of labor and capital markets. In addition, the similarity of economic and export profiles of the countries explains the presence of many common factors in play for fundamental exchange rates of national currencies against major global currencies. Retaining the tenge peg to the US dollar at the time of Russian ruble devaluation resulted in a substantial strengthening of tenge against the ruble, sharp increase of imports, and capital outflow amid future depreciation expectations.

Keeping the peg to dollar in such environment is possible only at a very high cost (which would be lower if the main trade partner of Kazakhstan were a country not involved in oil or gas exports). Nevertheless, the short-term target seems to have been regarded as relatively stable.

In 2010, upon occurrence of the trigger event the imbalance intensified the surge in overdue loans: as at early 2011, BTA Bank had 48% of its lending operations booked on off-balance sheet accounts, and loans overdue for more than 90 days (NPL90+) totaled 36.5% of the portfolio. Another systemically important bank with similar difficulties was Alliance Bank with NPL90+ at 65.5%. The government was forced to become a shareholder of large banks. The share of overdue retail loans increased on average from 3% to 12%.

Foreign currency liquidity was supported by the National Bank of the Republic of Kazakhstan (NBRK) mainly in form of FX market interventions. As sterilization of interventions has not been done to a full extent, they intensified the domestic currency liquidity squeeze and contributed to a concurrent materialization of the refinancing risk.

Figure 4. Nominal tenge FX rate

Source: ACRA estimates

By late 2009, FX imbalance of households declined to zero by virtue of FX market interventions and reduced availability of external foreign currency loans for banks as well as changes in regulatory approaches in the country.

The period of 2010-2013

During this period, the Structural Financial Stress Index (ACRA SFSI KZ) was around zero. No trigger events occurred in this period (Figure 3): interest rates on the interbank market were below or close to the rates of the NBRK (structural liquidity surplus), and the tenge exchange rate was fixed to the US dollar. By following the managed exchange rate policy, the regulator was holding the USD/KZT exchange rate close to 150, with the RUB/KZT exchange rate staying in the range of 4.4-5.2, which was possible in a favorable external environment.

After the Kazakhstan economy recovered following the 2009 crisis, liquidity imbalances were small over four consecutive years, and FX imbalances were non-existent. The expected export revenues were more than enough to cover all FX liabilities of the sectors. At the same time, external foreign currency liabilities of banks were gradually being replaced by domestic ones, while the share of US dollar-denominated deposits was relatively stable (30%-40%). A substantial liquidity imbalance was seen only in banks, but the liquidity imbalance level was in line with the role of the banking sector in the economy. Increased dependence of the non-financial sector on short-term financing could have been a cause for concern: a slight positive imbalance emerged as early as in 2013, and was only increasing thereafter. Largely, the above was caused by growth in lending to the trade industry outpacing lending to other industries.

Figure 5. Liquidity imbalances by sectors (positive values correspond to a liquidity deficit)

Source: ACRA estimates

Figure 6. FX imbalances by sectors (positive values correspond to potentially uncovered share of foreign currency liabilities)

Source: ACRA estimates

Figure 7. FX imbalances by sectors: a zoom-in of the positive portion of Figure 6.

Source: ACRA estimates

The period of 2014-2016

Kazakhstan’s financial system was relatively stable when the decline in crude oil prices started in 2014. The first devaluation of tenge to the US dollar by 19% (in February 2014) did not significantly aggravate the expected financial standing of banks and non-financial companies even if it represented a potentially important trigger event (see Figure 3). For the lack of an acute phase of the crisis, devaluation expectations resulted in a shift toward predominance of foreign currency deposits of households and non-financial companies (even including the currency revaluation effect); these changes were incremental.

Within almost entire 2014, no surge was seen in information asymmetry and uncertainty threatening the system’s stability. However, in December 2014 lending operations on the Russian interbank market virtually stopped and a significant liquidity deficit emerged in the banking system, while the shift in exchange rate expectations resulted in a panic foreign currency demand. In responding to these developments, the Central Bank of the Russian Federation decided to sharply hike the key interest rate and to abandon its policy aimed at mitigation of FX market fluctuations. Keeping the tenge peg to the dollar target failed to curb the increase in devaluation expectations in Kazakhstan. Resulting from the tenge appreciation against the ruble, dollarization of deposits accelerated, and households and legal entities raised their propensity to import. Amid regulator’s interventions and increased preference for liquidity (including in form of cash tenge), certain liquidity deficit arose in Kazakhstan’s financial system as well, which constituted the second trigger event after crude oil prices started their slide. The effect of this trigger event on the financial stress level was tangible: banks and non-financial companies with dependence on short-term financing found themselves close to refinancing risk materialization and payment defaults.

By early 2015, uncertainty increased substantially: the financial system exhibited high volatility in terms of transaction amounts, interest rates, and yields of various instruments. According to ACRA SFSI KZ, this period was less acute but more long-lived as compared to the period of early 2009.

The FX imbalance in the financial sector has increased notably driven by the response from households and banks. Finally, when the NBRK abandoned the fixed exchange rate policy in late 2015 the changes that have occurred in the expected standing of companies were found to be much stronger than those in early 2014. The index marks this period as comparable in terms of acuteness and as a more long-lived one than the 2009 crisis.

The situation in the financial system turned into a challenge for the regulator. The key monetary policy tool for the NBRK – as well as for the Bank of Russia – was its base interest rate (versus FX market interventions). By virtue of a surge in short-term rates, deposit dollarization level declined, and tenge-denominated assets became more attractive, which smoothened the liquidity issues as compared to the late-2014 period. Monetary financing of budget deficit continues supporting the financial sector’s liquidity in December 2017.

The current status

As of December 2017, the slowly declining FX imbalance in the banking sector is, in ACRA’s opinion, the key structural issue for Kazakhstan’s financial system (Figure 7). Amid free-floating exchange rate, this factor may become the source of a systemic risk.

The liquidity imbalance of 2013-2016 appears to have materialized through increase in overdue loans provided to trade and construction industries, which we are witnessing in 2017. Contraction of households’ real disposable income that started in 2015 could have been the reason for the above. As this issue’s scope is limited to two subsectors, its importance is not that high as that of an FX imbalance.

For the lack of new trigger events as of December 2017, the financial system of Kazakhstan is relatively stable: ACRA SFSI KZ is now at 15% of its maximum registered levels. However, it is worth noting that the situation may change rapidly due to the relatively high remaining liquidity and FX imbalances.

Table 3. Key information sources for calculation of ACRA SFSI KZ

Source: ACRA estimates

14 Non-profit institutions servicing households.

Table 3. Key information sources for calculation of ACRA SFSI KZ (continued)

Source: ACRA estimates

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