Thailand
Financial Sector Assessment Program-Technical Note-Risk Assessment
Author: International Monetary Fund. Monetary and Capital Markets Department
Publication Date:
24 Oct 2019
eISBN:
9781513517544
ISBN:
9781513517544
Language:
English
Keywords:
ISCR; CR; investment funds; central bank; fund family; liquid asset; fixed income; bond market; IRB bank; hurdle rate; exchange rate
Download PDF (2.4 MB)

This technical note on the risk assessment for Thailand discusses that the Thai banking system shows a substantial resilience to severe shocks. The solvency stress tests indicate that the largest banks can withstand an adverse scenario broadly as severe as the Asian financial crisis. While three banks would deplete their capital conservation buffer (CCB) under the adverse scenario, recapitalization needs would be minimal. A battery of complementary sensitivity stress tests, which allows to cover in more detail certain risk factors, also confirmed the overall picture of a resilient baking system: no particular vulnerability emerged from the analysis of the bond portfolio to an increase in government and corporate spreads, exposure to foreign exchange risk, and concentration risk in the loan portfolio, with the possible exception of one entity with a particular concentration on single-name exposures. The liquidity stress test on investment funds (IFs) showed that they would be able to withstand a severe redemption shock and its impact on the banks and the bond market would be limited.

Abstract

This technical note on the risk assessment for Thailand discusses that the Thai banking system shows a substantial resilience to severe shocks. The solvency stress tests indicate that the largest banks can withstand an adverse scenario broadly as severe as the Asian financial crisis. While three banks would deplete their capital conservation buffer (CCB) under the adverse scenario, recapitalization needs would be minimal. A battery of complementary sensitivity stress tests, which allows to cover in more detail certain risk factors, also confirmed the overall picture of a resilient baking system: no particular vulnerability emerged from the analysis of the bond portfolio to an increase in government and corporate spreads, exposure to foreign exchange risk, and concentration risk in the loan portfolio, with the possible exception of one entity with a particular concentration on single-name exposures. The liquidity stress test on investment funds (IFs) showed that they would be able to withstand a severe redemption shock and its impact on the banks and the bond market would be limited.

Executive Summary

The Thai banking system shows a substantial resilience to severe shocks. The solvency stress tests indicate that the largest banks can withstand an adverse scenario broadly as severe as the Asian financial crisis. While three banks would deplete their capital conservation buffer (CCB) under the adverse scenario, recapitalization needs would be minimal. A battery of complementary sensitivity stress tests, which allows to cover in more detail certain risk factors, also confirmed the overall picture of a resilient baking system: no particular vulnerability emerged from the analysis of the bond portfolio to an increase in government and corporate spreads, exposure to foreign exchange risk, and concentration risk in the loan portfolio, with the possible exception of one entity with a particular concentration on single-name exposures. From a systemic risk perspective, certain risk concentrations can act as shock amplifiers in case of stress, and hence highlight the importance of improving and expanding the range of analytical tools to detect them. The BoT’s solvency stress test exercise, conducted independently based on the same macro scenarios, showed very similar results despite some fundamental differences of approach, providing a mutual check on the overall robustness of the results.

Banks also appear to be resilient to sizable withdrawals of liquidity, though some would face increased funding pressures. Thai banks’ funding maturity structure is front-loaded mostly to sight deposits in the near-term. Under the current regulatory regime, banks have sufficient liquidity buffers to withstand a one-month risk horizon. The aggregate Liquidity Coverage Ratio (LCR) remains above the hurdle rate of 100 percent under the severe scenario, with three banks falling below the hurdle rate with the aggregate liquidity shortfall of 0.7 percent of total assets (1.5 percent of GDP). The cash-flow-based analysis results were broadly consistent with the LCR test over a one-month horizon.

The liquidity stress test on investment funds (IFs) showed that they would be able to withstand a severe redemption shock and its impact on the banks and the bond market would be limited. The exercise covered open-ended daily fixed income funds (daily FI) and money market funds (MMF), accounting for 33 percent of net asset value (NAV). Their cash positions were mostly sufficient to meet redemption demands under the waterfall strategy, while a majority of the IFs retains a good amount of liquid assets under the pro rata strategy despite more aggressive sale of government bonds required. Of the eight individual funds that would see a significant of depletion of their liquidity reserves, all except one would be able to withstand the shocks when the liquidation of corporate bonds is included. Credit lines between banks and asset management companies (AMCs) would provide an additional layer of liquidity buffer. The impact on the bond market could be substantial depending on the type of liquidation strategy.

An analysis of interconnectedness and contagion in the banking sector and in the financial system at large did not find any particular vulnerabilities. Interconnectedness appears to be at its lowest point in the last decade, both within the banking system and across sectors. However, interconnectedness and contagion are inherently difficult to measure and operationalize. In particular, it is challenging to incorporate the potential channels of contagion identified by the analysis into the scenario-based exercises to test the resilience of the system when shocks travel through those channels and get amplified in the process. In this regard, the BoT’s ongoing effort to explore an analysis aimed at capturing the interconnection between the main financial entities and economic sectors as well as across the border is welcome.

The BoT continued to improve its stress testing framework since its first top-down solvency macro stress test in 2017. The activity is based on the joint effort of different units within the BoT, under the coordination and with the active involvement of the Financial Stability Unit. This decentralized, network-like approach appears to be functioning well in ensuring a rich mutual cross-feeding through the exchange between different and complementary skills and ‘cultures’ across the different areas of the bank. The BoT has also addressed many of the recommendations provided by the 2018 IMF technical assistance (TA). Indeed, the BoT has improved its modeling of credit losses and feedback effects under adverse scenarios and introduction of macroprudential liquidity stress test.1 The modeling of Net Interest Income (NII) in times of stress is an area that could be strengthened further, as identified by the 2018 TA.2

The BoT should also invest in improving the quality and granularity of certain datasets. While the BoT has a wide range of well-structured data, there is room for improvement, in particular, on the time series of Internal Ratings-Based (IRB) banks’ Probability of Defaults (PDs) and Loss Given Default (LGD) and data management for liquidity risk to ensure the availability of more granular data including a finer breakdown by type.

The mission would like to express its gratitude to the management and staff of the BoT for their excellent cooperation, hospitality, and openness during the discussions and for effectively managing the logistics to facilitate the mission’s work.

Table 1.

2019 Thailand FSAP: Key Recommendations

article image

“I (immediate)” is within one year; “NT (near-term)” is one–three years; “MT (medium-term)” is three–five years.

Priorities are: H = High-Priority; M = Medium-Priority; L = Lower-Priority

Introduction

1. Thailand’s economy has been resilient to several shocks during the last decade. These shocks included severe floods in 2011, supply shocks in global commodity markets, and political instability in 2013–14 leading to subdued economic activity. The resilience of the economy was supported by ample international reserves, a flexible exchange rate, and a prudent fiscal position. Growth started to pick up in early 2018 underpinned by a recovery of domestic demand led by an improving labor market and investment. However, the momentum appears to be faltering due to the weak external demand, especially from China, and the impact of trade tensions on global supply chains. As a result, the economy grew by 4.1 percent in 2018 and is projected to slow down to around 3.0 percent in 2019 and 2020. Core inflation remains subdued, and average headline inflation (which reached 1.1 percent in 2018) is projected to decline to just below the lower end of BoT’s target band of 1.0–4.0 percent in 2019 (Figure 1).

Figure 1.
Figure 1.

Thailand: Main Macrofinancial Developments

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Sources: Bank of Thailand, Bloomberg, CEIC Data Co. Ltd, Datastream, Haver Data Analytics, IMF Global Data Source and World Economic Outlook databases, and IMF staff estimates and calculations.

2. Financial vulnerabilities appear to be contained, but household indebtedness is relatively high and there are some weaknesses in corporates and Small and Medium Enterprises (SMEs) that the authorities are monitoring closely (Figure 2). On the positive side, the credit cycle started tapering off in 2015, partly due to increased risk aversion by banks, and the increase in equity and house prices has been moderate. While available data indicate that foreign exchange exposures of the financial sector are limited (5–6 percent of the commercial banks and Specialized Financial Institutions (SFIs)’ aggregate assets and liabilities), uneven distribution of Foreign Currency (FX) assets and liabilities across sectors, if any, could be a potential source of risk. The main financial vulnerabilities are:

  • Household vulnerabilities (Figure 3). Credit to households expanded rapidly until 2015, largely due to reconstruction efforts after the 2011 floods and the first-time car buyer program (October 2011–December 2012). As a result, household debt reached 80.8 percent of GDP in 2015 (up from 59.3 percent in 2010). Its growth started to pick up in 2018, driven mainly by hire purchase (auto loans). Moreover, since 2015 households have become increasingly exposed to capital markets through mutual funds.

  • Corporate vulnerabilities (Figure 4). Corporate debt has been relatively stable and stood at 70.5 percent of GDP in 2017 (similar to the 2009 level). While leverage is relatively low compared to regional peers, debt-at-risk and the rollover risk are somewhat higher. There are signs of weaknesses in the SME sector, with nonperforming loans (NPLs) and special mention loans (SMLs) inching up. NPLs of SMEs related to the construction and real estate sectors appear to be relatively high, exposing banks to an adverse shock in the real estate market.

Figure 2.
Figure 2.

Thailand: Financial Vulnerabilities

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

1 Credit to corporate and household sectors extended by commercial banks and SFIs.Sources: Bank of Thailand, CEIC Data Co. Ltd, Datastream, Haver Data Analytics, and IMF staff calculations.
Figure 3.
Figure 3.

Thailand: Selected Facts of the Household Sector

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

1 Includes loan for business purpose and other categories.2 Based on monthly income.Sources: Bank of Thailand; and IMF staff calculations.
Figure 4.
Figure 4.

Thailand: Selected Facts of the Corporate Sector

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

1 Others include Agriculture, recreation and hotel, electronic and computer; and others.Note: Based on the sample of 459 listed companies with asset size larger than US$25 million.Sources: Bank of Thailand, Capital IQ (covers more than 10,000 firms across major Asian countries with total assets of US$ 25 trillion), and IMF staff calculations.

3. Risks to the macrofinancial outlook have shifted to the downside. Near-term risks have shifted to the downside, reflecting external and domestic headwinds. If trade tensions intensify, export growth could decline and spill over to domestic demand. A sharp rise in risk premia could precipitate capital outflows, adding to FX volatility and higher borrowing costs. Domestically, a difficult transition to a new government could lead to policy paralysis, derailing the Eastern Economic Corridor infrastructure push. Nevertheless, the country’s ample buffers and strong fundamentals should be sufficient to help smooth these shocks. The medium-term growth outlook could be dampened by the high level of household debt, weaker-than-expected fiscal stimulus, and anemic productivity growth.

Financial System Structure

4. While banks continue to account for a sizable share of the financial sector, the role of SFIs, other deposit-taking institutions, and nonbank financial institutions (NBFIs) has grown (Figure 5 and Table 2). Financial sector assets reached 266 percent of GDP at end-2018 (up from 183 percent in 2007). Assets of banks represented 46 percent of total financial sector assets at end-2018, down from 56 percent in 2007. The assets of SFIs (government-owned financial institutions for promoting economic development and supporting credit to specific sectors) and other deposit-taking institutions (e.g., credit unions (CUs) and thrift and credit cooperatives (TCCs)), as well as those of mutual funds and insurance companies (some of which are subsidiaries of the commercial banks), grew faster than banks’ assets.

Figure 5.
Figure 5.

Thailand: Financial System Structure

(In percent of total financial assets)

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Source: Bank of Thailand and Fund staff estimates.
Table 2.

Thailand: Financial System Structure

(In billions of Bahts, unless otherwise stated)

article image
Sources: Bank of Thailand and Fund staff estimates.

Composed of Secondary Mortgage Corporation and Thai Credit Guarantee Corporation for 2007, and also include pawn shops for 2018.

5. Commercial banks appear to be sound, though profitability is weak (Figures 6 and 7, and Table 3). The sector is supervised by the BoT, and consists of 30 institutions, with five domestic systemically important banks (D-SIBs) accounting for 70 percent of assets. The aggregate capital adequacy ratio (CAR) stood at 18.0 percent in the second quarter of 2018, well above the minimum of 10.375 percent in 2018 and 11 percent from 2019 (including the conservation buffer). While the ratio of NPLs to total loans is relatively low at 3.1 percent, the quality of credit to SMEs has deteriorated. Current weaknesses in loan management practices may be understating the level of NPLs, though this is being mitigated by high levels of provisioning and targeted in-depth supervision. Commercial banks rely mostly on retail deposits and have been improving liquidity risk management. While the liquidity coverage ratio (LCR) was almost 170 percent in the third quarter of 2017, higher than in other regions,3 the liquidity metrics of the financial soundness indicators (FSIs) indicate that Thailand is below the median for peer countries. The profitability of the sector remains below peer countries.

Figure 6.
Figure 6.

Thailand: Financial System Soundness Indicators

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Note: SML stands for special mention loans. Peer countries include ASEAN 5 (Indonesia, Malaysia, Philippines, Singapore), Colombia, South Africa, and Turkey.Sources: Bank of Thailand and IMF Financial Soundness Indicators database.
Figure 7.
Figure 7.

Thailand: Balance Sheet Structure of Banks and SFIs

(As of end-2018)

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Sources: Bank of Thailand.
Table 3.

Thailand: Financial Soundness Indicators (2013–2018)

article image
Source: IMF, FSI database.

6. SFIs, TCCs, and CUs play a key role in providing credit to households. The supervisory responsibility for the SFIs was shifted to the BoT in April 2015, as recommended by the IMF TA (2015) and the World Bank FSAP Development module (2011), and a structured framework for prudential supervision is being developed; the oversight of financial cooperatives (FCs, including TCCs and CUs) is under the Ministry of Agriculture Cooperatives. There are eight SFIs (four take retail deposits), 566 CUs, and over 1,400 TCCs. SFIs’ loans and deposits are equivalent to about 40 percent of those of commercial banks, and SFIs, CUs, and TCCs account for about 45 percent of loans to households. SFIs’ asset quality is somewhat weaker than that of commercial banks, with an average NPL ratio at 4.5 percent as of Sep 2017.

7. The assets of the main NBFIs reached 61 percent of GDP in 2018 (up from 33 percent in 2007). Insurance and mutual fund assets doubled as a share of GDP, while private pension funds experienced a moderate increase (text table).

  • Insurance. The insurance sector is supervised by the OIC, created in line with the recommendations of the 2008 FSAP and accountable to the MoF. With gross premiums written growing well above nominal GDP in the last 10 years, the insurance penetration ratio (the ratio of premiums written to GDP) has increased from 3.6 percent in 2008 to 5.6 percent in 2017 (somewhat below the 8.8 percent observed in Singapore, but higher than most other countries in the region including Malaysia, Indonesia, and Vietnam). Of the 23 life (re)insurers operating in Thailand, the top 5 represent 72 percent of total assets in the sector and include a branch of a foreign insurance group (the largest) and 2 insurers owned by domestic banks. The non-life sector is less concentrated. Of the 53 non-life (re)insurers operating in Thailand, the top 5 represent 42 percent of direct premium in the sector (all data end-2017). At the same time, interest of foreign participants in the market is increasing, and the Thai authorities are actively working to increase foreign investment, most immediately, by adopting incentives to encourage foreign reinsurers to make Thailand a business center for their Southeast Asian operations. The industry is well-capitalized, with a diversified asset allocation, and has adjusted to the low interest rate environment by shifting away from endowment products. However, profitability has been weakening, reflecting rising costs, and competition. Asset allocation to equity is relatively high for non-life at around 30 percent, and investments in riskier assets have increased.

  • The pension system. The pension system is fragmented, and coverage is low. The incentive structure of the private pension system is not aligned with the long-term objective of contributors of ensuring an adequate lifetime pension. Instead, the system includes incentives for overly conservative, low-growth investments, and for pay lump-sum payments upon retirement (or occasionally installment payments for a limited number of years) rather than lifetime pensions. This structure of the pension system increases the risk of retirement poverty for Thailand’s fast-aging population.

  • Mutual funds. The Securities and Exchange Commission (SEC) oversees capital markets and investment intermediaries. The top five AMC (all part of conglomerates) accounted for over 70 percent of assets under management (AUM) at end-2017 (Figure 8). Roughly half of the funds are fixed income, while the shares of equity and infrastructure funds have increased in the last few years. Foreign investment funds account for about one fifth of total AUM. Retail clients dominate the investor base for mutual funds, which account for 83 percent of the total, potentially exacerbating liquidity risks.

Assets of Main NBFIs

(In percent of GDP)

article image
Sources: FinStats, The BoT, and Fund staff estimates.

Excludes government pension fund for Thailand.

End-2018 for Thailand.

Figure 8.
Figure 8.

Thailand: Asset Management Industry

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Source: Association of Investment Management Companies.

Key Risk Factors and Stress Testing Approach

A. Stress Testing under FSAP program

8. The FSAP, established in 1999, is a comprehensive, in-depth assessment of a country’s financial sector. The stability assessment under the FSAP is the main responsibility of the Fund in countries where FSAPs are done jointly with the World Bank (developing and emerging market countries). It is meant to cover, inter alia, the source, probability, and potential impact of the main risks to macrofinancial stability in the near-term.

9. In the context of FSAPs, a stress test is a financial stability tool to assess bank resilience to extreme but possible scenarios. The goal is to provide recommendations to help preserve financial stability, i.e. minimize the probability of financial disruptions and crisis. This is also consistent with the FSAP institutional focus on supervisory ability to monitor and regulate bank risks, crisis management and resolution frameworks.

10. Stress tests in FSAPs aim at assessing the resilience of the banking sector at large, rather than the capital adequacy or financial soundness of individual institutions. They embrace a macrofinancial perspective, as opposed to the microprudential angle adopted by supervisors.

B. Key Risk Factors

11. The Thai financial sector is exposed to several macrofinancial risks stemming from external and domestic factors (Risk Assessment Matrix (RAM), Table 4).

  • External risks. The negative impact on growth from rising protectionism, exacerbated by adverse changes in market sentiment and investment, could lead to weak (even negative) growth in key advanced economies and in China, ultimately depressing Thailand’s exports. This would cause lower GDP growth and higher unemployment, which, coupled with an increase in corporate vulnerabilities and a deterioration in households’ repayment capacity, could lead to a weakening of banks’ asset quality. Sharp rise in risk premia could lead to a reversal of capital flows and a depreciation of the baht that could raise financial sector funding costs and weaken balance sheet of corporates with unhedged foreign currency exposures and currency mismatches.

  • Domestic risks. An increase in real interest rates and the real debt burden could pose balance sheet risks in the private sector. In addition, the outcome of the general elections may lead to a political gridlock which may disrupt public investment projects and lead to higher risk premia for sovereign and corporate yields. In the unlikely event that such uncertainty was to become a crisis of confidence, it could lead to a collapse in equity prices, sharp exchange rate depreciation, and translate into funding pressures if banks experience a sudden withdrawal of retail and wholesale deposits.

Table 4.

Thailand: Risk Assessment Matrix1

article image

The Risk Assessment Matrix (RAM) shows events that could materially alter the baseline path (the scenario most likely to materialize in the view of IMF staff). The relative likelihood is the staff’s subjective assessment of the risks surrounding the baseline (“low” is meant to indicate a probability below 10 percent, “medium” a probability between 10 and 30 percent, and “high” a probability between 30 and 50 percent). The RAM reflects staff views on the source of risks and overall level of concern as of the time of discussions with the authorities. Non-mutually exclusive risks may interact and materialize jointly.

12. An adverse scenario has been designed, which is in line with the RAM. The adverse scenario would be triggered by: (i) a weaker-than-expected growth in the U.S. (due to waning confidence and weaker investment); (ii) a prolonged period of anemic growth and low inflation in the euro area (due to weak foreign demand, Brexit, concerns about some high-debt countries, and faltering confidence); and (iii) lower growth in China due to weaker external demand, the potential reversal of globalization, and the increasing role of the state. This is modeled through a shock to demand in the United States, Euro Area, and China. The global demand shock would lead to a sell-off in emerging markets, which would affect Thailand through weaker exports and imports and through investor uncertainty. This would lead to a rise in corporate and household risk premia, which in turn would lead to a strong decline in investment, consumption, and asset prices. This, in turn, would trigger portfolio outflows and a depreciation of the exchange rate. However, the exchange rate depreciation is limited (to 12 percent in the first year of the shock) due to Thailand’s substantial reserve buffers and the expectation that authorities will step in to support the exchange rate. It is also assumed that, in response to the decline in GDP and inflation, the central bank would lower the policy rate to the zero lower bound. Moreover, since households and corporates are debt constrained, it is assumed that they would sell-off their assets to meet interest payments and other debt obligations leading to further declines in stock prices.

C. Stress Testing Approach for the Thailand FSAP

13. The resilience of the Thailand banking system was assessed under a battery of stress tests:4

  • Solvency stress test and sensitivity tests. The solvency stress test estimated the evolution of banks’ profitability and capitalization under a baseline scenario and one adverse scenario. The sensitivity tests focused on banks’ exposure to risks from shifts in other risk factors, such as interest rates and corporates spreads, and concentration risk.

  • Liquidity stress tests. The tests were based on two frameworks: (i) the Basel III LCR under a severe scenario, combining shocks from the outflow of the retail, wholesale and mutual funds deposits due to a confidence crisis and resulting in a sharp exchange depreciation, and (ii) an implied cash-flow-based analysis by maturity bucket.

  • Test on investment funds’ redemption risk. The test assessed the investment funds’ capacity to withstand a severe redemption shock, their impact on the banking sector, and the bond market.

  • Intereconnecetedness and contagion. Systemic and contagion risks stemming from interlinkages were explored using market based and balance sheet approaches. The team used four approaches: (i) Espinoza and Sole (2009) to simulate credit and funding shocks across the domestic interbank network as well as the potential cross border spillovers; (ii) Diebold and Yilmaz (2012), based on market data, to measure the network interconnectedness between listed banks and nonbanks (with a possible extension to major Thai corporates); (iii) Financial Stability Measures to quantify the impact of systemic risk amplification mechanisms due to interconnectedness across banks, insurance companies, IFs, and other financial intermediaries; and (iv) a balance sheet analysis based on flow of funds data.

Solvency Stress Test

14. A solvency stress test was conducted combining a scenario-based assessment with sensitivity analyses on single risks. The scenario-based assessment was based on full-fledged macroeconomic scenarios comprising a baseline and one severe but plausible adverse scenario. Sensitivity analyses were performed for aspects not covered under the scenarios and/or for further investigation into specific sources of risk.

A. Macroeconomic Scenarios

15. The scenarios span a three-year period from June 2018 to June 2021. The baseline scenario was based on the October 2018 World Economic Outlook (WEO) projections. The projections for the adverse scenario were based on the IMF’s Flexible System of Global Models (FSGM) for the external environment, on previous crisis observations (such as the Global Financial Crisis (GFC)) and on expert judgement (Table 5).5

Table 5.

Thailand: Macroeconomic Scenario Projections

(2018–2021)

article image
Sources: IMF, World Economic Outlook database; and IMF staff estimates.

16. The adverse scenario features a U-shaped GDP profile, resulting in a prolonged decline in GDP, with a path similar to the experienced by Thailand during the Asian Financial Crisis (Figures 9 and 10). A fundamental assumption under the adverse scenario is a deviation of GDP from baseline of -15.6 ppt over the first two years (2019 and 2020). This represents approximately 2.1 standard deviations of GDP growth (as calculated over the 1980–2017 period) and it is broadly in line with recent FSAPs in similar countries and with Thailand’s experience during the Asian crisis.6 The GDP assumption is also consistent with a calibration based on the Growth-At-Risk methodology at low percentiles. 7 Based on current financial conditions, the assumed decline in the growth rate of 5.6 percent in the first year has a likelihood of about 7 percent, which lies between the 5 percent GaR threshold of -6.75 percent and the 10 percent GaR threshold of -3.3 percent. The estimate for the 10 percent GaR for the second year is equal to -2.5 percent, close to the assumed decline in GDP growth of 2.4 percent in the second year.

Figure 9.
Figure 9.

Thailand: Solvency Stress Test: Assumptions on GDP

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Sources: IMF staff estimates.
Figure 10.
Figure 10.

Thailand: Main Macroeconomic Variables under the Adverse Scenario

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Sources: WEO, IMF staff estimates.

17. The exercise involved eight commercial banks, representing 75 percent of banking sector assets. The sample includes the 5 D-SIBs, all of them using the standardized approach for credit risk and 3 banks authorized to use their IRB models for the calculation of their regulatory capital requirements for credit risk.

B. Methodological Approach to Balance Sheet and Income Projections

18. The exercise was based on a quasi-static allocation balance sheet assumption. This means that: (i) interest earning assets and exposures at default grow at a rate consistent with the macro scenario (based on the estimated relationship between total bank credit and domestic demand and unemployment, with a judgmental floor to prevent excessive deleveraging), adjusted by losses suffered in the previous period and by exchange rate changes (for assets denominated in foreign currency); (ii) non-interest earning assets grow at a rate aligned with historical experience; (iii) the evolution of the bank’s equity over the risk horizon depends on the results of the stress tests—in particular on the profits realized, net of the losses incurred; and (iv) interest earning liabilities grow at the rate necessary to equate assets to total liabilities. The asset allocation and the composition of funding sources remain the same throughout the risk horizon.

19. Interest income was derived from the evolution of interest-bearing assets and liabilities and of interest rates applied by banks (Figure 11). To capture the impact of the general level of interest rates on banks’ interest margin, the effective interest rate on deposits was projected based on a panel data model with an autoregressive component and the short-term rate as an exogenous explanatory variable. The effective interest rates on loans were estimated bank by bank, using a system of seemingly unrelated regressions (SUR). The impact of idiosyncratic increases in funding costs was estimated via a nonlinear feedback effect mechanism based on the interaction between solvency (total capital ratio) and liquidity (spread paid by the banks on their wholesale borrowings, i.e., interbank funding and issued debt).

Figure 11.
Figure 11.

Thailand: Solvency Stress Test: Methodological Approach

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Source: IMF staff.1 In particular: (i) rLOANS are estimated through separate bank-by-bank equations in a Seemingly Unrelated Regression with the current short-term rate and lagged interest rate on loan as explanatory variables; (ii) rBONDS change according to the initial composition of each bank’s bond portfolio (i.e., assuming constant roll-over of maturing bonds over the risk horizon) and the changes in domestic sovereign, domestic corporate, and foreign sovereign spreads assumed in the scenario; (iii) rD are estimated as dynamic panel data with the short-term interest rate as exogenous variable and floored at 0 percent plus the fee paid to the Financial Institutions Development Fund (47 bp); (iv) rWL is set equal to the short-term rate plus a spread based on a function that links the average spread for wholesale funding across banks to its lagged value plus the (lagged) average capital ratio and the reciprocal of the current average capital ratio (to capture the nonlinear impact of solvency on liquidity); (v) NPL ratios are estimated as explained in ¶22; (vi) PDs and LGDs are estimated as explained in ¶21.

20. The bulk of fees and commissions was assumed to evolve in line with the growth of assets, adjusted for certain categories to take into account impact from competition. For example, some e-banking fees have already been slashed down by the largest banks, while the remuneration of other digital services has been exposed to competition from FinTech companies. Given their current sensitivity to competitive pressures from within and outside the banking sector, the income from certain digital (or ‘digitizable’) services was assumed to be impacted by the compounded effect of the crisis scenario and the materialization of increasing competitive pressures. Operating expenses and other non-interest expenses were assumed to grow in line with the growth of interest-bearing assets. Taxes were conservatively set at the marginal tax rate (30 percent) in case of positive net income and zero otherwise.8 Dividends were also assumed to be paid out only in case of positive income, at a flat 30 percent payout ratio, consistent with historical experience in Thailand, and subject to restrictions in case of erosion of the CCB.9

21. The calculation of risk-weighted assets (RWAs) took into account the Basel regulatory framework under which banks operate. For banks adopting the standardized approach for credit risk, RWAs under stress were adjusted for asset growth in the current year, including impairments accrued in the past year, and by changes in the exchange rate for those exposures denominated in foreign currency. For IRB banks, RWAs were recalculated according to the projections of probability of default (PDs), LGDs,10 and exposure at default (EADs) in the adverse scenario. For banks under the IRB approach, satellite models were used to estimate (bank by bank and portfolio by portfolio) the link between PDs and LGDs and macro variables; then, the forecasts of PDs and LGDs under the adverse scenario were used to estimate RWAs and expected losses.

22. For banks under the standardized approach (and for exposures of IRB banks treated as standardized), credit loss estimates were based on a satellite model linking NPLs to macro variables. NPL ‘inflows’ (i.e., the transition of performing loans to nonperforming status, quarter by quarter) were modeled separately, as a SUR system, for each of the 11 sectors for which public data are available.11 NPL ‘outflows’ (i.e., the exit from nonperforming status for different reasons) were calibrated bank by bank based on their recent experience and under the assumption of a reduced outflow under stress. Based on the estimated coefficients, NPL inflow ratios were forecasted over the risk horizon—year by year and sector by sector—and applied to the stock of performing assets existing at the beginning of each year. The resulting new NPLs (net of the share of old NPLs leaving the non-performing status) determined the amount of additional provisions to be expensed against the profit and loss account. The net flow of NPLs (for exposures under the standardized approach) and expected losses (under the IRB approach) were assumed to be fully provisioned. This means that the full amount of new NPLs and expected losses enter the income statement and that losses cannot be distributed over time. Also, existing ‘excess’ provisions are not allowed to be used to absorb the emerging losses, implicitly assuming that they cover existing losses and are hence not available to cover new ones.12

23. The evolution of financial variables under the adverse scenario determines the impact on market risk exposures in the trading book and—for FX risk—in the whole balance sheet. The impact of shocks to (risk-free) interest rates and credit spreads was captured via a duration gap analysis. Shocks to the major foreign currencies (USD, CNY, JPY, and EUR) directly affect the banks’ net open positions. Similarly, the assumed shock to the stock exchange index was applied to all equity holdings.

24. The outcome of the exercise is measured in terms of capital ratios, against the current and future requirements and buffers. In particular, three distinct hurdle rates were used: Common Equity Tier 1 (CET1) ratio, Tier 1 (T1) ratio, and Total Capital ratio (CAR). Each of these is considered with and without buffers. The BoT Regulation on Supervision of Capital for Commercial Banks introduced a CCB and the possibility of introducing also a CounterCyclical Buffer (CCyB). The CCyB is currently set at 0 percent, while the CCB was subject to a phase-in and has now reached its final level of 2.5 percent of RWAs. The buffers are meant to amortize the impact of negative (idiosyncratic or systemic) developments, granting a bank (and its supervisor) time to react and prevent a breach of the minimum requirements. A reduction of the CCB below 2.5 percent triggers specific limitations to earning distribution. Finally, D-SIBs are subject to a capital surcharge of 0.5 percent of RWAs in 2019 and 1 percent from 2020 onwards (Box 1).

Hurdle Rates

(in percent)

article image
Source: Bank of Thailand and IMF staff estimates

C. Results of the Solvency Stress Test

25. Under the adverse scenario, credit growth would slow down and then turn negative while NPLs accumulate rapidly (Figure 12). While under the baseline banks’ loans to customers would grow and accelerate (from +8.5 to +12.8 percent between 2019 and 2021), credit growth under the adverse scenario would slow down in the first year (+3.9 percent) and decrease in the following two (-1.4 and -5 percent in 2020 and 2021, respectively). There would be a widespread increase of NPLs across the financial system as a result of the very high unemployment rate, the consequent impact on domestic demand, and the increase in the weight of debt (via higher interest rates) against dwindling incomes in the corporate and household segments. The picture is similar for IRB banks, though the estimation of the relationship between PDs (and LGDs) and the relevant macroeconomic variables is more challenging due to either short time-series or poor data quality.13 The estimation was satisfactory only for a limited number of bank-portfolio pairs, and the results of the estimation were extrapolated, when possible, to the remaining bank-portfolio pairs as a fallback option. The increase in NPLs and PDs (and, hence, losses) would likely be larger if FX depreciation were included; however, the portion of FX loans is small and the negative effect is already captured to a large extent by the decline in GDP. Losses would be larger also if the policy rate were to increase instead of decrease. Nonetheless, it is assumed that the central bank would privilege restoring growth—by cutting the policy rate—over defending the currency, given the high level of international reserves and current account surplus in the current situation and likely fall of imports under the adverse scenario.

Figure 12.
Figure 12.

Thailand: Credit growth and Evolution of NPLs Under the Adverse Scenario

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Sources: Bank of Thailand; and IMF staff estimates.

26. Banks show substantial resilience to the adverse scenario even though significant losses are accumulated and capital ratios decline sharply, and the recapitalization needs would be minimal (Figure 13). Most banks would incur negative net income throughout the horizon of the exercise. Three banks would experience a depletion of their CCB, but of modest quantity and the shortfall would occur in the last year (2021). The resources needed by the three banks to restore their capital buffers would be approximately THB 5 billion, equivalent to about 0.03 percent of Thailand’s GDP and easily covered by one quarter of ‘normal’ profits for the three banks (measured with respect to their average profits earned in the previous 5 years).

Figure 13.
Figure 13.

Thailand: Main Results of the Solvency Stress Test

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Sources: Bank of Thailand and IMF staff estimates.

27. Credit losses are the main factor behind the decline in capital ratios, with an additional impact from the following factors:

  • The compression of the net interest margin. The adverse scenario assumes significant monetary accommodation as a reaction to the pronounced slump in economic activity, with the policy rate dropping to zero and remaining at that level through the horizon of the exercise (i.e., no negative rates). As the rates on deposits would approach the lower bound rapidly following the benchmark rate and stop declining while lending rates would continue to decline, the net interest margin would shrink from 3.3 to less than 2.6 percent, on average, for the eight banks.

  • A solvency-liquidity feedback. Spreads paid by the banks on their market funding (interbank funds and bonds issued, in particular) would increase in the three-year horizon for almost all the banks, as a result of their perceived weakness—proxied by the falling capital ratios. This solvency-liquidity feedback includes a nonlinear component that amplifies the effect as capitalization declines.

  • Equity investments. Banks with important equity investments are significantly penalized under the adverse scenario, reflecting the sizable drop in the Thai stock exchange index (SET) assumed in the first year (55 percent) and a partial recovery in the following year (20 and 10 percent in 2020 and 2021, respectively).

  • The increase in the RWAs. For IRB banks, the deterioration in borrowers’ financial conditions and in the recovery rates on defaulted loans determine an increase in PDs and LGDs that translates into larger RWAs.14

D. The BoT Stress Tests Results

28. The BoT has conducted a top-down macro (solvency) stress test in parallel with the FSAP team, based on the same scenarios and cut-off date.15 The impact of the macroeconomic environment on bank-level variables (via satellite models) has been estimated independently by the BoT and IMF staff. While at a broad level the fundamental approach is very similar, differences arise in the more granular methodological decisions and in the numerous assumptions needed—beyond the statistical evidence—to operationalize the stress test exercise. In particular, in the BoT approach, banks, on average, would not experience a compression of their net interest margin. This can be ascribed at differences in the way effective rates on loans and deposits are estimated, and is an area where the BoT could increase the severity of its assumptions. Protracted periods with the policy rate at or next to the zero lower bound can seriously jeopardize banks’ net interest margin, as experienced by banks in several advanced economies in the post-GFC period and also relevant for Thailand given its experience in the last decade (weak growth and persistently low inflation).

29. Notwithstanding the methodological differences, the IMF and the BoT results are very similar. As in the IMF-run stress test, no bank would experience, over the risk horizon and under the adverse scenario, breaches of their capital requirements; two banks would see their capital buffers partially eroded (marginally in one case, slightly more substantially in the other one).

E. Sensitivity Tests

Concentration Risk in the Loan Portfolio

30. Sensitivity tests incorporating capital surcharges for single-name and sectoral concentration affect only one bank that has enough capital buffers to comfortably absorb the shock. The surcharges have been estimated by calculating the Herfindahl–Hirschman Index (HHI)16 on the top 20 exposures—for single name concentration—and total exposures by sector—for sectoral concentration. The HHIs have been translated into capital surcharges by applying the multipliers developed and adopted by the U.K. Prudential Regulation Authority as part of their methodologies for setting Pillar 2 capital.17 The concentration risk adjustment materially impacts the RWAs of only one bank, which however has enough excess capital to comfortably absorb it. A reverse stress test on the top 20 exposures, assuming the default (and 100 percent loss) of the largest borrower, followed by the next largest and so on, indicates that the default of the five largest borrowers would cause two banks to breach their Tier-1 capital requirements, and one bank would breach the required threshold with the default of the top three borrowers, indicating a significant concentration risk.

31. There is room for improvement in the BoT’s analytical approach to concentration risk. While the BoT already adopts the fundamental elements of concentration risk from a supervisory angle (e.g., large exposures regime and limits on investments), it could further develop its analytical tools for the assessment of this type of risk, including its implications on systemic risk: asset concentration typically impacts the tail of the distribution of losses, manifesting itself more acutely in times of stress and potentially acting as a shock amplifier. It is then important to estimate as accurately as possible the weight that concentration has on the risk inherent in banks’ loan portfolios—as well as other forms of credit concentration, such as the bond portfolio and interbank market. This could also help, on the supervisory side, to estimate the capital surcharge for IRB banks (whose Pillar 1 requirements are based on the unrealistic hypothesis of infinitely granular portfolios) and to calibrate an add-on to be applied to all the other banks.

Interest Rate Risk in the Banking Book

32. A sensitivity test was run to gauge the exposure of the structure of banks’ assets and liabilities to changes in interest rates (Interest Rate Risk in the Banking Book (IRRBB)). These tests are meant to complement the moderate policy rate assumption in the macro scenario. The Basel Committee defines IRRBB as the “current or prospective risk to the bank’s capital and earnings arising from adverse movements in interest rates that affect the bank’s banking book positions.” While IRRBB does not attract a Pillar 1 requirement in the Basel framework, it needs to be adequately addressed, measured, and managed by banks, as specified in a Basel standard.18 IRRBB can be analyzed from two different perspectives: (i) Economic Value of Equity (EVE), i.e., the change in the net present value of a bank’s assets and liabilities under a stressed interest rate scenario, representing a ‘stock’ perspective; and (ii) NII, i.e., the difference between total interest income and total interest expense within a one-year horizon, given a certain scenario, representing a ‘flow’ perspective.

33. The EVE and NII measures depend on the assumptions about the evolution of the term structure of interest rates. The sensitivity test is based on the derivation of six interest rate shock scenarios for the Thai economy according to the methodology proposed in the Basel standard.19 The scenarios are the following: (i) parallel shock up; (ii) parallel shock down; (iii) steepener shock (short rates down and long rates up); (iv) flattener shock (short rates up and long rates down); (v) short rates shock up; and (vi) short rates shock down. The calibration of the shocks is based on daily zero-coupon sovereign rates and money market rates over a 16 year-time span, as suggested in the Basel methodology.20 The shocks have been applied to the aggregate assets and liabilities of the banks as of end-June 2018, broken down by maturity band. The impact is approximated via modified duration and convexity for the median tenor in each time band.

34. The results point to a relatively contained exposure of the banks to IRRBB (Figure 14). The parallel shocks give rise to larger impacts on EVE than the non-parallel shocks and all banks are exposed to upward shocks to interest rates, as expected.21 All banks would experience an implicit drop in EVE, as a percent of Tier 1 capital, lower than the -15 percent level identified by Basel as the threshold for the identification of “outlier banks.”22

Figure 14.
Figure 14.

Thailand: Interest Rate Risk in the Banking Book—Impact on EVE and NII

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Sources: Bank of Thailand and IMF staff estimates.

35. An alternative analysis, based on a historical simulation Value-at-Risk (VaR) run on the same data, broadly confirms the previous results, but also highlights the importance of using alternative tools in the assessment of IRRBB. The historical simulation was conducted by revaluing the current portfolio of assets and liabilities according to the year-on-year changes in the yield curve for each day over the same period used for the Basel calibration (2002–2018). The comparison of the 99th percentile obtained from the simulation with the results of the previous exercise indicates a broad alignment between the methodologies, but also, in some cases, slightly more extreme results: for example, at the 99 percent confidence level the largest negative impact of a parallel shock would be -13.8 percent, instead of the -12.6 percent in the Basel methodology. In general, the results of the Basel methodology correspond to percentiles of the historical simulation lower than the 99th and as low as the 96th, underlining the opportunity to use a wide range of tools in monitoring banks’ interest rate risk.23

36. The analysis in terms of NII points to a limited impact across banks. While the direction of the impact is not the same across banks, implying differentiated asset and liability structures at the shorter tenors, the negative impacts are overall quite small, and would not, per se, dent the banks’ profitability in such a way as to compromise their capitalization.

Trading Book

37. The fixed income instruments categorized as Held For Trading (HFT) and Available For Sale (AFS) determine an immediate impact on capital—unlike those held to maturity. HFT instruments impact capital via profit and loss, while AFS hit capital via other comprehensive income. The sensitivity test focused specifically on two asset classes: own sovereign and corporate bonds. In both cases, a historical simulation was run to estimate the VaR of the portfolio at the 99th confidence level. However, the assumed liquidity horizon differs:24

  • Government bonds. The data source for the term structure of interest rates is the same as per the IRRBB test, i.e., 16 years of daily zero-coupon sovereign rates and money market rates; the liquidity horizon is 20 business days, i.e., double the minimum liquidity horizon available in Basel’s market risk framework.

  • Corporate bonds. The test is based on 10 years of monthly yields on THB-denominated BBB-rated corporate bonds; the liquidity horizon is 60 business days, in consideration of the significantly lower liquidity of corporate vs. sovereign instruments, especially under stress.

38. The results indicate a small impact both for government bonds and corporate bonds, with a single exception. For government bonds, the 99th percentile VaR represents around 1 percent of Tier 1 capital or less for all banks except for one bank, for which it represents more than 5 percent of Tier 1 capital and a potential reduction of it Tier 1 capital ratio by up to two percentage points, suggesting a non-negligible exposure to sovereign risk.25 For corporate bonds, the 99th percentile VaR represents less than 0.4 percent of Tier 1 capital for all banks.

Other Risks

39. The exposure of Thai banks to foreign exchange risk is moderate. Commercial banks in Thailand are subject to a net open position rule that limits their exposure to foreign currencies in either direction (long or short) to no more than 15 percent of total capital (or US$5 million, if greater) per single currency and 20 percent (or US$10 million, if greater) for the aggregate exposure to all currencies. A historical simulation of FX losses based on 10 years of daily changes shows that over a 2-week horizon the current (as of cut-off date) banks’ exposure in foreign currency would generate losses that represent less than 0.3 percent of Tier 1 capital, at a 99 percent confidence level.

40. Risks from the residential property market are difficult to assess due the lack of data.26 House prices have risen almost continuously over the past 10 years, with limited price corrections.27 While not necessary the sign of an asset bubble, this long and almost uninterrupted growth raises concerns about the possibility of a more pronounced price correction. The share of new mortgage loans with a Loan-To-Value (LTV) ratio above 90 percent has increased from 33 to 46 percent since end-2012. However, no data is available with the needed granularity and updated LTVs to allow an assessment of the impact that a decline in house values could have on the adequacy of banks’ collateral. Staff estimate based on flows of mortgage loans by income bracket point to a likely steady increase in the debt-service-to-income (DSTI) ratios in the past 5 years across all income brackets, with the lowest bracket probably recording, on average, a DSTI in excess of the conventional wisdom threshold of one-third (and without considering other possible debt incurred by the same households).

Liquidity Stress Tests for the Banking Sector

41. Liquidity risk in the banking system was assessed using various stress tests. The first test measures bank’s capacity to meet its liquidity needs in a 30-day stress scenario by using a stock of unencumbered high-quality liquid assets (HQLA). The second test is a cash-flow-based analysis by maturity buckets. It involves a more granular analysis of bank’s liquidity buffers cash flows generated by different assets and liabilities with varying maturities (ranging from seven days to more than one year). For AMCs, their resilience to meet redemption shocks was assessed, as well as its impact on banks (given the asset management company bank nexus) and on government bonds (since a majority of these funds hold sovereign securities).

A. Banks’ Current Liquidity Conditions and Liquidity Profiles

42. Liquidity risks appear limited as banks rely mostly on retail deposits. For the eight banks, 50 percent of banks funding comes from retail depositors (Figure 15). Most deposits are placed in demand and savings accounts (66 percent of total deposits for the eight banks), with term deposits accounting for 34 percent. Stable deposits (deposits that are fully insured) are about a quarter of total retail deposits. Retail depositors in Thailand are perceived to be more stable: in fact, deposits rose by 9 percent in 1998, during the Asian Financial Crisis.

Figure 15.
Figure 15.

Thailand: Funding Structure

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Source: Bank of Thailand; and IMF staff estimates.

43. HQLA comprise mainly government securities. The eight banks appear highly liquid, with 93 percent of HQLA in level 1 unencumbered assets (consisting mostly of government securities). The HQLA assets of the 5 D-SIBs includes both level 1 and 2 assets, while the 3 IRBs hold mainly level 1 assets. Within Level 1 assets, 5 D-SIBs hold a larger amount of cash, and central bank reserves than the 3 IRB banks. In addition, the holdings of government and central bank securities are largely domestic and those issued in foreign currency represent less than one percent of total HQLA.

44. IRB banks rely mainly on wholesale funding and nonfinancial corporations (NFCs) are the largest source of these funds. Wholesale funding accounts for 57 percent of total funding for the 3 IRBs (which are largely subsidiaries of foreign banks), higher than the share of 47 percent for the 5 D-SIBs. The competitive retail market dominated by the 5 D-SIBs could be the reason contributing to the IRB’s reliance on wholesale funding.

45. Reliance on foreign funding is limited. Foreign exchange exposures represent less than 8 percent of total liabilities of the banking system (mainly in the form of loans and repos), and the net open FX position is less than 2 percent of capital. In the event of a market-wide USD liquidity stress, the BoT can step in to ease the stress by supplying USD liquidity to the market via its FX swap window, which is part of the BoT’s regular open market operations.

46. Thailand’s liquidity metrics are mixed when compared to peer countries or other regions. Thailand’s LCR ratio of 170 percent is well above the regulatory threshold of 80 percent and significantly higher when compared with other regions such as Europe (130 percent), the Americas (126 percent) and rest of the world (128 percent). For the eight banks covered in this exercise, the aggregate LCR was 188 percent as of end-June 2018. While liquid assets to total assets of all Thai banks have remained relatively stable at around 19.5 percent in September 2018, this ratio is moderately below the median of peer countries. Thai banks tend to rely more on short-term liabilities, with liquid assets to short-term liabilities accounting for 32 percent. This may be due to the different definition between financial soundness indicator metrics and the LCR methodology where the latter only looks at a one-month horizon.

47. Top-down liquidity stress tests were conducted on a consolidated basis, jointly by the FSAP team and the BoT. The LCR-based test and cash-flow based analysis were carried out using June-2018 data, covering the eight largest banks (five domestically owned and systemically important banks and three IRB banks). For the LCR-based test, the BoT adopted a gradual implementation of the LCR on January 1, 2016, with the initial minimum requirement starting at 60 percent (currently 80 percent LCR for banks) and subsequently increasing by 10 percentage points to reach 100 percent by January 1, 2020. For the FSAP, the hurdle rate was set at 100 percent.

B. LCR Based Tests

48. The LCR test considered a severe scenario against a baseline LCR scenario. The severe scenario reflects deposit outflows due to a confidence crisis and results in a sharp exchange depreciation, which incorporates the sensitivity analysis on retail deposits, wholesale, and mutual funds:

  • A baseline LCR scenario. The analysis applied the same parameters as required by the authorities under the LCR implementation. This was done at the aggregate currency level including local and foreign currencies (Table 6 and 7).

  • A severe scenario. The authorities and the IMF team calibrated a one-month severe scenario, premised in the unlikely event that extreme external volatility and political uncertainty could become a crisis of confidence, leading to a collapse in equity prices and a sharp exchange rate depreciation that would translate into panic and funding pressures, with banks faced with sudden withdrawal of deposits. Under these circumstances, there would likely be an increase in yields for government and corporate bonds. The changes in the yields underpinned the assumptions for haircut rates.

  • A retail shock. The shock assumed a run on deposits by assuming higher run-off rates for insured and uninsured demand deposits to 10 and 20 percent, respectively; for savings accounts, the insured and uninsured rates were raised to 15 and 30 percent, respectively. All other rates remained the same as in the baseline.

  • The wholesale shock. Based on the assumptions of the one-month severe scenario envisaging higher corporate yields and a collapse in equity prices, higher run-off rates were applied to the operational non-operational deposits of NFCs, government, banks and other financial institutions. Specifically, the run-of rates for insured and uninsured non-operational deposits were increased to 30 and 50 percent, respectively.

  • The investment funds shock. The shocks assumed increased funding pressure on other financial institutions due to a large withdrawal by investment funds (IFs) such as savings and time deposits, resulting from large unexpected redemption shocks. The key assumptions included higher run-off rates of up to 15 and 50 percent for insured and uninsured operational deposits of other financial institutions, respectively, reflecting some feedback loop effect. In addition, a parent bank is assumed to provide liquidity assistance up to 10 percent (5 percent in the baseline) if an affiliated subsidiary is unable to meet redemption demands due to the maturity structure of the fund.

Table 6.

Thailand: LCR—Based Stress Test Assumptions on Run-off Rates

(In percent)

article image
Sources: Bank of Thailand; and IMF staff estimates.
Table 7.

Thailand: LCR—Based Stress Test Assumptions on Roll-off Rates and Haircuts

(In percent)

article image
Sources: Bank of Thailand; and IMF staff estimates.

Calibration of Run-off Rates and Data Issues

49. Countries that have not faced a major banking crisis tend to rely more on expert judgement and international benchmarks as inputs in the calibration of run-off and roll-off rates for the LCR analysis. Ideally, the run-off rates should be based on withdrawals of funding experienced during historical stress episodes. However, except for the Asian Financial Crisis, Thai banks have not faced a major liquidity crisis. Given such limitation, BoT employed the 11 years of historical data and calculated the negative outflow rates for different percentiles before choosing the combination of the 90th to 92nd percentile of the outflow rate. This yielded, on average, the 27 percent outflow rate experienced by finance companies during the 1997 Asian Financial Crisis, supplemented by expert judgement. The loan maturing inflow rates were pinned by stressed NPLs from the solvency stress test with an additional layer of expert judgement.

50. The lack of granular data, and the absence of long time series and higher frequency data, is another constraint on the calibration of run-off rates. Indeed, the robustness of the LCR tests depends on the quality of the data, historical availability and granularity. Higher frequency data helps in identifying significant outflows or anomalies in a stressed environment. The BoT provided bank-by-bank deposit data segmented by guaranteed and non-guaranteed accounts on a quarterly basis starting from 2009 and monthly from 2017.

51. Current data reporting requirements are inadequate to assess the impact of unexpected exchange rate shocks. A comprehensive liquidity stress test would require undertaking an LCR analysis on a currency specific basis. Often, in a stressed scenario, domestic currency is subject to significant depreciation, which would undermine the capacity of liquidity surpluses in domestic currency to offset shortfalls in FX. In most instances, FX positions face a liquidity crunch. However, the LCR analysis in Thailand cannot be separated by domestic currency and FX, as banks are not required to report their LCR by currency unless they have a significant outstanding position (banks only provide the sum of inflows and outflows by significant currency), corresponding to a generally-low net FX position due to the legal restriction on the net FX position that Thai banks can hold. Out of the eight banks included in the stress test, only one bank reports LCR in significant currency in detail.

LCR Stress Test Results

52. Results from the LCR stress test show that banks have sufficient liquidity buffers to withstand the severe scenario, which has a much larger impact on the aggregate LCR ratio than the sensitivity analysis (Figure 16).

  • Under the current regulatory regime, all banks have sufficient liquidity buffers to withstand a one-month risk horizon. In the baseline scenario, the 30-day weighted average LCR ratio stood at 188 percent in June 2018. The 5-DSIBs banks have a higher LCR ratio than the IRB banks partly due to their size and higher holdings of liquid assets to total assets (18 percent compared to IRB’s 2 percent).

  • In the severe scenario, the aggregate LCR ratio declines to 104 percent but remains above the hurdle rate of 100 percent. Three banks fall below the 100 percent hurdle rate, of which one falls below the Basel III transitional threshold of 80 percent. The aggregate liquidity shortfall of the three banks amounts to 0.7 percent of total assets (1.5 percent of GDP).

  • The shock on retail deposits indicates that banks can meet the prevailing 80 percent regulatory rate. The aggregate LCR for the eight banks would fall to 138 percent. Two (one D-SIB and one IRB bank) out of the eight banks fall below the hurdle rate of 100 percent, representing a liquidity shortfall of THB 25 billion or 0.2 percent of total assets of the eight banks, but are able to meet the current regulatory threshold of 80 percent.

  • The wholesale scenario shows a similar impact with the aggregate LCR ratio falling to 140 percent. The aggregate LCR falls to 139 percent, leaving one D-SIB and one IRB bank below the 100 percent. The total liquidity shortfall would reach THB 54 billion or 0.4 percent of total banks’ assets.

  • All banks remain above the 100 percent mark following the shock on investment funds. It is still however useful to analyze the linkages between banks and IFs given the bank-AMC nexus.

Figure 16.
Figure 16.

Thailand: Liquidity Stress Test Results

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Source: Thailand Securities and Exchange Commission; and IMF staff estimates.1 Liquidity shortfall is the amount required so that the liquidity ratio in each bank in the system be equal to or above 100 percent; the ratio effective as of June 2018.Note: The analysis of the impact of IFs deposit withdrawal partially took into account the feedback effects between commercial banks, investment funds, and the financial market.

C. Cash-Flow Analysis

53. The cash-flow analysis captures the comprehensive time structure of banks’ cash inflows and outflows. The maturity ladder is composed of five time buckets: one day to one week, one week to one month, 1–3 months, 3–6 months, and over six months. The analysis was conducted for all currencies due to data constraints. The data consists of projected contractual cash flows generated by type of liabilities and distributed across maturity buckets. Banks’ resilience to severe funding shocks is characterized by the same severe scenario in the LCR analysis resulting in higher run-off rates on funding sources calibrated by type, lower roll-off rates and liquidation of assets subject to a 3 percent haircut since the counterbalancing capacity only includes HQLA assets.

54. The cash-flow analysis assesses banks’ resilience to liquidity risk based on net cash balance following the funding outflow shock. In a stressed scenario, a portion of the deposits is withdrawn generating a cash outflow in each maturity bucket, while the rest of the deposits is rolled over. Within each maturity bucket, the net outflows are compared with the liquid assets available for sale (AFS) to counterbalance funding gaps. In the analysis, banks would have liquidity shortfalls if they experience a negative net cash balance after fully using their counterbalancing capacity. The net cash balance consists of the existing cash position, the counterbalancing capacity (i.e., the ability to obtain additional liquidity in secondary markets by selling securities or through standard central bank facilities, and the amount of net funding inflows). In such situations, the central bank can provide Emergency Liquidity Assistance (ELA) under stringent conditions. These include that the bank is adequately capitalized (in essence, that it complies with BoT capital requirements or is on path back to compliance) and that it has sufficient collateral to cover any borrowings from the BoT under the ELA facility. In situations where the standard collateral has already been used, the BoT can consider accepting alternative forms of collateral, such as parts of a bank’s loan portfolio. However, despite its ability to be used as collateral, the alternative form of collateral, such as the loan portfolio, was not considered as counter-balancing capacity under the FSAP liquidity stress test, since it was not part of the HQLA under the LCR requirement.

55. The robustness of the analysis is somewhat affected by the lack of granular data, and the results should be interpreted carefully. In the context of Thailand, deposits cannot be differentiated by type of depositor (retail and wholesale) and maturity. As a proxy, the analysis uses the ratio of each banks’ share of retail and wholesale deposits and applies a constant ratio across the five maturity buckets. In addition, data is unavailable for deposits by type and maturity (sight, term, stable, unstable) (Table 8). Given these data constraints, the analysis assumes the proportion of retail deposits stable vs unstable to be a weighted average of 60 percent and 40 percent. This is the first time the authorities are conducting the test, and there is room for further refinement and sourcing of datasets for the analysis.

Table 8.

Thailand: Cash Flow—Based Stress Test Assumptions on Run-off Roll-off Rates and Haircuts

(In percent)

article image
Sources: Bank of Thailand; and IMF staff estimates.

56. The results of cash flow analysis were consistent with the LCR test over a one-month horizon. All banks, except two, have a positive funding over all the time horizons (“1–7 days” and up to “more than 180 days”) (Figure 17). The counterbalancing capacity is mostly utilized in the “1–7 days” window and “180 days and beyond” window as most banks experience shortfalls based on their cash inflows and outflows. However, two banks would have a negative cash balance in “180 days and beyond” horizon even after utilizing their existing required reserves. The nominal amount of the cash shortfall for each bank is small, 6 percent and 7 percent respectively, of each banks’ total assets during the “180 days and beyond” window.

Figure 17.
Figure 17.

Thailand: Maturity Structure of Cash Flow Analysis

(Billions of baht)

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Sources: Bank of Thailand; and IMF staff estimates.

57. Banks should address the maturity mismatch between assets and liabilities, particularly at the long-end. The maturity structure of the funding is more front loaded compared with cash inflows. In the analysis, sight deposits are treated to have instantaneous maturity. Based on the available data (which lacked a detailed decomposition of assets and liabilities by maturity) the maturity structure of the cash flow of banks seems to point to a possible significant maturity mismatch.

Liquidity Stress Tests on Investment Funds

A. Overview of the Industry

58. The investment fund industry has grown during the last 10 years (Figure 18). Total net AUM have increased from 18 percent of GDP in 2007 to slightly over a third of GDP in 2018, with growth averaging 11 percent per annum. As of September 2018, there were 1,411 funds covering bonds, equity, property, infrastructure, money market, and retirement fund. Funds such as property and infrastructure funds are more long-term and most of the investment funds are listed in the stock exchange. While half of the funds are in fixed income (51 percent of total AUM), the share of equity and infrastructure funds have increased in recent years. Foreign investment funds account for one-fifth of total funds. Most of the investment funds are open ended funds accounting for 90 percent of the total industry.

Figure 18.
Figure 18.

Thailand: Investment Fund Industry, 2007–2018

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Source: Thailand Securities and Exchange Commission; and IMF staff estimates.

59. Investment Funds (IFs) in Thailand are less sensitive to global financial shocks, while the dominance of the retail segment could elevate liquidity risks. A larger proportion of foreign participation in an investment fund industry has financial stability risk implications. The inflow of funds by foreign investors does not pose a risk during normal times, but these investors could abruptly withdraw their funding when faced with a global financial shock. Such an event is unlikely in Thailand since the investor base is largely domestic with a 98 percent share of total value of investment funds. Retail investors account for 83 percent of the total AUM, followed by corporates (11 percent) and institutional (6 percent). Empirical research suggests that retail investors are more inclined towards momentum investing and reactive to global shocks. The behavioral aspect of retail investors is considered in the calibration of the redemption rates which were based on the first and fifth percentile of distribution of flow rates of funds.

60. The close nexus between the banking sector and investment funds exacerbates the risks of transmission of redemption shocks from the fund industry to the banking sector. The top six AMCs, which are part of banking conglomerates, accounted for 80 percent of AUM as of September 2018, and there could potentially be reputational risks from branding in case of a panic redemption. In addition, banks engage in the cross-selling of investment products (accounting for 73 percent of total AUMs), which further elevates reputational risks for the banking sector.

61. Daily fixed income (daily FI) fund and MMF are the largest segments of fixed-income funds. There are 446 funds, totaling US$81 billion as of September 2018. Daily FI fund and MMF account for 72 percent of the total fixed income funds. Term funds—which are another type of fixed income funds—have seen their share falling in the last two years as some funds experienced a default causing investors to realign their risk preferences.

62. Within the fixed income segment, daily FI funds and MMFs are identified as potential sources of systemic risk. Daily FI funds have increased more than four-fold from US$12 billion in 2012 to US$51 billion in September 2018 while MMFs have remained stable. Daily FI funds and MMFs are distributed mainly through the branches of AMCs’ parent banks as substitutes for bank deposits. Thai retail investors perceive these funds to be risk free and liquid. Misperceptions of risks by retail investors and unexpected distresses in funds or panic redemptions can amplify liquidity shocks for investment funds.

63. Assets of daily FI and MMF are liquid and largely short-term. Daily FI funds consist mainly of cash, holdings of sovereign bonds (mostly short-term government bonds), and corporate bonds. MMFs are more liquid as their asset composition only consists of cash, short-term government and corporate bonds with majority of the maturities less than a year (Figure 19). For the 6 largest AMCs, asset with maturities of less than 6 months accounts for 60 percent of Daily FI and 96 percent of MMFs.

Figure 19.
Figure 19.

Thailand: Asset Profile of Investment Funds, 2018

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Source: Thailand Securities and Exchange Commission; and IMF staff estimates.

64. The liquidity stress test on IFs assess their capacity to withstand a severe redemption shock, their impact on the banking sector and the bond market. The exercise assumes that: (i) the redemption shock will transmit through a liquidation of assets at fire sale prices to meet redemption demands; and (ii) fire sale of assets by a captive fund, impacting banks through step-in support, and government bonds through higher yields; and (iii) the analysis assumes a static balance sheet (no new inflows are considered).28

65. The scope of the analysis is limited to open-ended Daily FI and MMF funds and uses monthly data from 2015–2018 to capture historical fund flow patterns. Compared with other funds, Daily FI funds has come to dominate the fixed income market in recent years. This makes it more susceptible to financial stability risks, especially liquidity mismatches and spill-overs to the banking sector. As for MMF, even though the segment’s share has fallen, and it focuses largely on the less risky part of the investment universe, it is still vulnerable to redemption risks. Both these funds account for 33 percent of total AUM and they are open-ended and are representative of types of funds in Thailand. The historical time frame of the analysis represents a stable macrofinancial environment without substantial shocks, and this could affect the results of the liquidity stress test on IFs.

B. Methodology and Results

Methodology: Calibration of Redemption Shocks

66. A redemption shock is defined as net outflows in percent of total net asset value of a fund:29

  • The redemption shock was calibrated by looking at the first percentile of the distribution of flow rates of all fund monthly observations in each fund family. Depending on data availability, the redemption shock was calibrated in three ways.30

  • The first calibration approach was premised on fund-homogeneity. For each fund, a common size redemption shock was applied regardless of their differences.

  • The second calibration approach was premised on fund heterogeneity. This implies that each individual fund experiences an outflow equivalent to the first percentile of its own historical flow rate. In this case, the size of each redemption shock impacting each fund is different.

  • The third calibration approach was based on the type of fund using the first percentile distribution of combined outflows by type of fund-daily FI and MMF.

67. Funds’ redemption patterns also seem to depend on fund-specific factors such as size and returns. A regression model was estimated to determine the sensitivity of redemptions to returns and size of the fund, suggesting that smaller funds and funds that have higher returns in the previous month experience lower outflows (Box 2). However, additional results indicate that there are possibly nonlinearities with respect to size, suggesting that up to a certain level, larger flows attract more inflows. Furthermore, size and returns also seem to interact significantly, as for a given size, funds with better returns seem to be associated with higher inflows (and vice versa, for a given return, larger funds seem to be associated with higher inflows). In addition, momentum seems to be an important factor in fund redemption, given the significance of the lagged dependent variable. These findings seem to support calibration approaches that take into account fund heterogeneity.

Regression Analysis on Sensitivity of Investors to Returns and Size of Fund

article image
Sources: Thailand Securities and Exchange Commission: and IMF staff estimates.

68. The results of the stress test depend on the type of calibration. The redemption rate for daily FI funds and MMFs was 22 percent under the fund homogeneity calibration. Under this approach, the distribution of net outflows was calculated for the fund sector as a whole, and the redemption shock was based on the first percentile of the outflows (Box 3, Figure 20). The data showed that the net flows follow a normal distribution with a large number of net flows of zero percent.31 This approach was used in other FSAP assessments for liquidity stress tests on IFs such as United States, Sweden Luxembourg, and Brazil with average redemption rates ranging between 11.5 percent and 25 percent.

Redemption Rates by Type of Calibration Approach

article image
Sources: Thailand Securities and Exchange Commission; and IMF staff estimates.
Figure 20.
Figure 20.

Thailand: Historical Distribution of Flow Rates, 2015–2018

(In percent of net outflows)

Citation: IMF Staff Country Reports 2019, 318; 10.5089/9781513517544.002.A001

Source: Thailand Securities and Exchange Commission; and IMF staff estimates.

69. Under the fund heterogeneity calibration, the redemption rate was 14 percent for daily FI funds and 19 percent for MMFs on average. However, the redemption rate varied substantially across funds and fund types. This approach takes differences in individual funds’ characteristics into account, evidenced by the large standard deviations of their individual redemption rates (amounting to 8 for Daily FI funds and 15 for MMFs) and the broad dispersion of redemption rates across funds. MMFs generally have higher outflow rates, ranging between 7 and 60 percent for MMFs and between 0.1 and 36 percent for Daily FI funds, since the Daily FI funds are more liquid.

70. The calibration by fund-type shows a slightly high outflow rate for MMFs when compared to the fund-homogeneity calibration, and a slightly lower outflow rate for daily FIs. The redemption rate for the first percentile for daily FI fund was 19.9 percent and 22.7 percent for MMFs compared to an overall rate of 21.9 percent and at the one percent extreme.

Methodology: Asset Sales

71. In the event of a redemption shock, an investment fund is assumed to sell its assets to meet the redemption demand using either of two possible strategies. The analysis looks at the composition of asset holdings of investment funds and estimated the expected total value of assets that must be sold.32 Two different strategies can be followed in the estimation of redemption-induced assets sales since these assets have to be sold at fire-sale prices, haircuts are also applied.33

  • Strategy 1: Waterfall approach. In this sales strategy, a fund is assumed to cover redemptions by liquidating its most liquid asset in an orderly manner. The assets are assumed to be sold in the following order: (i) cash; (ii) reverse repo; (iii) bank deposits; (iv) short-term government bonds; (v) medium-term government bonds; (vi) long-term government bonds; and (vii) corporate debt.34

  • Strategy 2: Pro rata approach. In pro rata selling of assets, assets are sold to meet the redemptions by making sure that the structure of assets is intact. As a result, redemptions are met by liquidating a common fraction of all assets held by each fund.

72. Under the waterfall strategy, the liquidation of governments bonds is relatively small as cash is able to meet most of the redemption demand. IFs have to sell THB 26.7 billion and THB 11 billion of government bonds under the fund-heterogeneity and fund-type calibration approaches, respectively, while under the fund-homogeneity approach, cash alone is sufficient to meet the redemption value (Table 9).35 The cash liquidity position of funds would be sufficient to meet 100 percent, 85 percent, and 96 percent of total value of redemptions, respectively, under the fund-homogeneity, fund-heterogeneity, and fund-family calibration approaches.

Table 9.

Thailand: Asset Composition and Asset Sales After the Redemption Shock

(In billion of Bahts)

article image
Sources: Securities Exchange of Thailand; and IMF staff estimates.