Seismic anatomy of financial crises

Appendix 1: Seismic anatomy of financial crises Source: Author.

Financial crises, in many ways, work like earthquakes. Earthquakes are the result of long period of pressure accumulation (i.e. energy) and by a trigger event, releases strong shock waves suddenly. These extreme vibrations propagate through earth crust and cause widespread surface damages. Financial crises are results of accumulating financial pressure (i.e. solvency imbalance) and also by certain events, releases credit contractions. These crunches propagate through FIs and cause damages to the real economy. Scientists have a pretty good idea of where an earthquake is most likely to hit, but they still cannot tell exactly when it will happen. Not unlike earthquakes, economists have tried lots of different ways of predicting crises, but none has been successful so far. There are also differences, good and bad. The bad side is financial crises often cause much more economic damages than earthquakes. The good side is they much less likely to take a human toll; and since economies are created and operated by human, we can actually have more ways to steer them than changing things under the earth crust. Thus, there are a wealth of regulations for financial systems, each nation is different on its own – all try to protect its economy.

Appendix 2: Financial accelerator effects and macro-financial linkages Source: Author’s compilation.

Unlike in classical dichotomy, the real economy and the financial sector have close ties and can influent each other, at least in the short-to-medium term. It is agreed that monetary policies often take effect on the economy relative quickly compare to fiscal policies; and thus, monitored closely by both financial and non-FIs. This relation creates a loop, which explained as the financial accelerator effect [figure]. A derivative from this idea is systemic crises involve feedback effects between the real economy and the financial sector.

- 63 -

Financial Accelerator Effect Source: Coric (2010)

It also explains “Macro-financial linkages,” which refers to the interaction between the financial sector and the domestic economy. Macro-financial linkages can also be used to examine linkages from international financial flows to an economy, or from an economy’s financial system to the rest of the world.

Appendix 3: BICRA (Banking Industry Country Risk Assessment) score

Source: Standard & Poor’s.

The BICRA methodology has two main analytical components:

Economic risk: The analysis of economic risk of a banking sector takes into account the structure and stability of the country's economy, including the central government's macroeconomic policy flexibility; actual or potential economic imbalances; and the credit risk of economic participants--mainly households and enterprises.

Industry risk: The view of industry risk factors in the quality and effectiveness of bank regulation and the track record of authorities in reducing vulnerability to financial crises, as well as the competitive environment of a country's banking industry--including the industry's risk appetite, structure, and performance - and possible distortions in the market.

Industry risk also addresses the range and stability of funding options available to banks, including the role of the central bank and government.

Appendix 4: Theory of financial instability

Source: Burton and Brown (2009) – The Financial System and the Economy, p. 240

BRUNOĆORIĆ:THEFINANCIALACCELERATOREFFECT: CONCEPTANDCHALLENGES FINANCIALTHEORYANDPRACTICE35 (2) 171-196 (2011)

performance, etc.; change in economic agents’ outstanding obligations can also be caused by a change in the interest rate, under the assumption that outstanding loans are subject to variable interest rates.

FIGURE 1

The fi nancial accelerator effect

The arrows symbolize the mechanisms (channels) through which the financial accelerator opera- tes, and the boxes represent events and their consequences for the economy. Arrow 1 represents a positive relationship between changes in aggregate economic activity and agents’ net worth.

In turn, Arrow 2 represents an inverse relationship between net worth and the size of the exter- nal finance premium. Arrow 3 represents an inverse relationship between the external financial premium and investment, spending and production. Finally, the return arrows represent pro-cyc- lical feedback into aggregate economic activity.

3 MODELLING STRATEGIES

The different modelling approaches to the fi nancial accelerator effect refl ect: the variety of different situations in which the asymmetric information problem between borrowers and lenders may emerge; different fi nancial markets in which this problem can appear; different attitudes toward the question whether participants in fi nancial markets can overcome the problem of asymmetrically distribu- ted information or not; and different types of borrowers whose economic activities can be infl uenced by fi nancial market imperfections.

3.1DIFFERENCES IN MODELLINGINFORMATIONALASYMMETRY

In general, it is possible to distinguish three major types of modelling strategies used by researchers to incorporate the fi nancial accelerator effect into general equilibrium models (the main differences among these approaches are summari- sed at the end of this section in the table 1). The fi rst two approaches, established by Bernanke and Gertler (1989) and Kiyotaki and Moor (1997), consider informa- tional asymmetry on credit markets as the cause of the fi nancial accelerator effect.

The third approach, established by Greenwald and Stiglitz (1993) considers infor- mational asymmetry on equity markets and managers’ risk aversion as the cause of this effect.

The financial instability hypothesis, developed by the late economist Hyman Minsky, refers to the natural tendency of the financial system to undergo periodic waves of crises and bankruptcies. The financial crisis can lead to a general economic decline because lending falls and businesses that rely on borrowing to maintain their general scope of operation deteriorate. During an extended period of prosperity, the seeds of crisis spontaneously germinate as lenders become too overconfident that loans will be repaid.

This overconfidence causes them to make bad loans, which eventually result in defaults, a subsequent reduction in credit extension, and the possibility of system-wide collapse.

Minsky argues that at any moment in time, the economy is composed of a mix of three types of spending units — hedge, speculative, and Ponzi units.

Hedge Spending Unit – A spending unit such as a household or firm where the anticipated revenues (inflows) significantly exceed the anticipated payment obligations (outflows).

Speculative Spending Unit – A spending unit in which the funds coming in may potentially fall short of the payment outflows if there is an increase in interest rates.

Ponzi Spending Unit – A spending unit that must continuously increase its outstanding debt to meet its current obligations or payments.

According to Minsky, it is the mixture of hedge, speculative, and Ponzi spending units at large that determines the overall financial health of the economy.

It is in an extended recovery that the seeds of the downturn are planted. Spending units forget the lessons of the past as the past becomes more distant. They increase their outflows relative to their inflows. Debt-to-income ratios rise throughout the economy. The economy moves to a more vulnerable position, and some random event that would hardly be noticed in a different environment sets off a chain of bankruptcies and defaults that spread throughout the economy.

Minsky believed that the boom-crises cycle repeats itself every 40 to 50 years.

Appendix 5: Network model of systemic risks Source: Acemoglu et al. (2013) with author’s adaptation.

Acemoglu et al. constructed a stylized model of an economy consisting of n FIs that lasts for three periods. In the initial period, banks borrow funds from one another to invest in

projects that yield returns in both the intermediate and final periods. The liability structure that emerges from such interbank loans determines the financial network. In addition to its commitments to other FIs, each bank also has to make other payments with respect to claims that are senior to those of other banks. These claims may correspond to payments due to retail depositors or other types of commitments such as wages, taxes, or claims by other senior creditors. It is assumed that the returns in the final period are not pledgeable, so all debts have to be repaid in the intermediate period. Thus, a bank whose short-term returns are below a certain level may have to liquidate its project prematurely (i.e., before the final period returns are realized). If the proceeds from liquidations are insufficient to pay all its debts, the bank defaults. Depending on the structure of the financial network, this may then trigger a cascade of failures: the default of a bank on its debt may cause the default of its creditor banks on their counterparties, and so on.

The model confirmed Haldane’s (2009) conjecture of robust-yet-fragile nature of financial networks. The authors also introduce a new notion of distance over the financial network, called the harmonic distance, which captures the susceptibility of each bank to the distress at any other. It is shown that, in the presence of large shocks, all banks whose harmonic distances to a distressed bank are below a certain threshold default.

Appendix 6: Group of 30’s set of macroprudential tools Source: G30 (2010)

Group of 30 recommend the set of tools to monitor and adjust the systemic risks as follow:

1. Avoiding systemically risky levels of leverage;

a. Capital Adequacy Requirements

i. Inclusion of Multipliers Reflecting Systemic Importance, Growth of Credit, and Maturity Mismatches

ii. Increases in Capital Required against Trading Books iii. Countercyclical Capital Buffers

iv. Macroprudential Stress Tests b. Gross Leverage Ratio

2. Ensuring a degree of liquidity sufficient for well-functioning markets;

a. Liquidity Buffers

b. Core Funding Ratio

c. Capital Surcharge on Liquidity 3. Preventing excessive credit extension;

4. Regulating market activity that could pose a systemic risk.

Appendix 7: Motivations for macroprudential policy adoption

In this section, the study considers international experiences from two angles: (1) failures that raised the need for macroprudential policy and (2) effectiveness of macroprudential policy application. For the first viewpoint, we take a look at the last two prominent crises that shape much of Asian financial landscape: the Asian financial crisis in 1997 and the global financial crisis in 2008. For the second perspective, Cerutti, Claessens and Laeven (2015) evidenced general positive effectiveness [2.5.1]. Claessens and Ghosh (2012) also argued that emerging markets have greater experiences with macroprudential policies because they have experienced more pronounced business and financial cycles43.

The case of two crises

The needs for macroprudential policy are demonstrated via crises and instabilities. When systemic risks are not addressed and treated properly, the risk factors will accumulate and eventually roll out as cascaded failures. The two large international crises (that more or less affected Vietnam) provided insights on how unnoticed systemic risks could bring harms to financial systems.

In the global financial crisis (2008), systemic risks were able to bypass regulators scrutiny for several reasons, including: the use of complex derivatives to distribute risk; via the unregulated shadow-banking system; excessive reliance on wholesale funding by banks;

under-capitalization of banks; and lack of understanding regarding the risk profile of innovative financial product (Kawai & Morgan, 2012). It indicated that the regulators were

43 “This greater pro-cyclicality is due to their greater exposures to international capital flow volatility, commodity price shocks, and other risks, and external and internal transmission channels that operate more adversely.” Source: Claessens and Ghosh (2012)

not able to keep these risks in check by missing insights (i.e. indicators and awareness) of them.

In the Asian financial crisis (1997), the context is different. It is characterized by systemic risks associated with so-called “double-mismatches” associated with borrowing short-term in foreign currencies and lending longer-term in domestic currencies (Kawai & Morgan, 2012). The problem was getting more severe when compounding with other systemic risks:

moral hazard comes from foreign banks that are insured directly by governments (or indirectly via an IMF bail-out); government subsidization and/or support of favored firms/industries in the name of economic growth and decades of account deficits, pegged exchange rate and declined export prospect (Corsetti, Pesenti and Roubini, 1999).

The case of two crises shows that despite of vastly differences in size and development level, countries can face with financial difficulties if systemic risks are not treated properly.

The case of Korea

The situation of South Korea in 199744 is not much different to Vietnam (financial system) a decade later, with specific characteristics of a credit-based financial system: (1) the pricing mechanism is influenced more by administrative guidance than market forces; (2) a major objective of monetary policy is resource allocation rather than financial system stability; (3) main tools of monetary policy are administrative guidance and regulations rather than monetary aggregates; (4) industrial adjustment is often led by government rather than by firms; (5) government, banks and firms formed close ties rather than staying at arm’s length; (6) main supplies of long-term funds are from specialized banks instead of investment institutions (Sang Yong Park, 2003).

After the crisis happened, liquidity ratio regulations have been put in place. Furthermore, as the housing market warmed up in the 2000s, loan-to-value and debt-to-income control ratios were also enacted. However, Korea did not have specific measures aimed at the time-series risk dimension. It left loopholes for banks to raise excessive leverage through funding with “non-core liabilities” leading to a round of crisis-like events in 2008 (Canuto,

44Right before the Asian financial crisis (1997)

2013). Beginning from 2010, Bank of Korea introduced several macroprudential tools to address the volatility of cross-border capital flows, which includes: macroprudential stability levy (levy on non-core FX liabilities), ceilings on FX derivative positions (leverage caps), taxation on foreign bond investment.

In 2013, Bruno and Shin (2013) did an empirical study assessing the sensitivity of capital flows to global financial conditions after macroprudential policies deployed in Korea in 2010. There is evidence from a variety of approaches that the sensitivity of capital flows to global factors reduced substantially in the case of Korea from 2010. Remarkably, the dampening of sensitivity in Korea sits side-by-side with evidence that Korea’s experience is different from group of comparable Asian countries (including Vietnam). For these Asian countries, they saw an increase in the sensitivity of their capital flows to global factors after 2010.

The case of Thailand

Thailand has used elements of macro-prudential toolkits since 2002 and extended their usage continuously. A report from Bank of Thailand (BoT, 2012) stated that these policies helped contain foreign exchange risk in the banking sector; build up non-performed loan buffer during good time; reduce sectoral imbalance in credit-card and personal loans;

impose withholding tax on foreign investment in bonds. On the challenges side, the report also emphasizes on the difficulty of coordination and appropriate policy mix and the importance of putting macro-prudential policy in context (i.e. it is not an all-powerful solution and/or a replacement for existing monetary policies)

[Figure] Macroprudential tools employed by Thailand.

Source: Bank of Thailand (2012)

The case of China

Wang & Sun (2013) performed an empirical study on the effectiveness of macroprudential policies in China on 171 banks. It concludes that both form of systemic risks: time and &

cross-sectional are presented in China. Some macroprudential policy tools (e.g., the reserve requirement ratio and house-related policies) are useful but not guarantee protection.

Further, macroprudential policies have even greater potential to contain systemic risk if better targeted.

Consider the similarities between Vietnam and China; it is an interesting subject for further study.

Appendix 8: Cost of prudential regulations Source: Author’s compilation.

There is available theoretical and empirical evidence on the positive effect of finance on long-term economic growth. Accordingly, concerns have been raised about the impact of macroprudential policies on the dynamism of financial markets and, in turn, on investment and economic growth. Popov and Smets (2012) thus recommend that macroprudential

tools be employed more forcefully during costly booms driven by over borrowing, targeting the sources of externalities but preserving the positive contribution of financial markets to growth.

In analyzing the costs of higher capital requirements implied by a macroprudential approach, Hanson et al. (2011) report that the long-run effects on loan rates for borrowers should be quantitatively small.

Some theoretical studies indicate that macroprudential policies may have a positive contribution to long-run average growth. Jeanne and Korinek (2011), for instance, show that in a model with externalities of crises that occur under financial liberalization, well- designed macroprudential regulation both reduces crisis risk and increases long-run growth as it mitigates the cycles of boom and bust.

Due to the wealth of tools and the associated cost of implementation, it is not always beneficial to apply macroprudential policy aggressively. In fact, each country, depend on circumstances may pick different set of tools. Bank of England (2011) and Blancher et al.

(2013) gave recommendations regarding the selection criteria. The two set of recommendation are not matched precisely but reinforcing each other. When combined, the overall strategy to select macroprudential tools can be summarized below:

Firstly, the selection of tools should be country-specific, driven by its effectiveness and efficiency: tools are likely to differ in their effectiveness of tackling sources of systemic risk, and their effectiveness may vary over time and according to circumstances. It also needs to consider the efficiency of tools by comparing the benefits against the cost of implementation, including enforcement cost and adverse consequence stems from the new policy.

Secondly, tools should be combined to exploit their complementarities: to cross check and confirm the materiality of sources of systemic risk stem from domestic macro-financial imbalances (e.g. credit boom, asset price bubble, unsustainable public debt) and cross- border linkages, or individual institution exposures (e.g. size, leverage, interconnectedness). They help practitioners to avoid overreacting to a single signal, or being lulled into a false sense of security.

Thirdly, the tools should be considered based on their inherent quality, including:

coverage, independence & simplicity.

Fourthly, tools usage should reflect the typical phases of systemic risk: see [2.3.2].

Finally, in emerging countries, data gaps are posing as obstacles to building key systemic risk indicators (e.g. interlinkages and common exposures), therefore, tools selection must consider of data availability within the country (to build if needed).

Appendix 9: Capital inflow and the potential build-up of vulnerability Source: Claessens and Ghosh (2013)

Appendix 10: Cross shareholdings, interbank lending and investment vehicles.

Source: FETP (2013)

Appendix 11: Shareholding structure in Vietnam’s banking sector Source: VELP (2012)

Structural Reform for Growth, Equity, and National Sovereignty A Policy Discussion Paper for the Vietnam Executive Leadership Program Page 21 of 44 Diagram 1. Shareholding structure in Vietnam’s banking sector

Note: Shareholding information is as of 30 June 2011. Shareholdings by individuals are not depicted in the diagram. Shareholdings by institutions of less than 5 percent are not depicted except for those having representation in boards of directors or strategic partner status. Mekong Housing Bank and five joint-stock banks are not included in the diagram.

1 Shareholding by Mizuho Bank at Vietcombank is not yet official.

2 Shareholdings by a group of related companies including Sacomreal, Thanh Thanh Cong, Bourbon Tay Ninh, Ninh Hoa Sugar.

3 Indirect shareholding through Agribank Securities Company (Agriseco).

4 Includes shareholdings by Vinalines and its subsidiaries such as Hai Phong Port and Vosco.

5 Includes shareholdings by HCMC Party Office, Ky Hoa Tourism and Trading, and Phu Nhuan Construction and Housing Trading.

6 Includes shareholdings by HCMC Party Office, Sunimex and Savico.

7 Includes shareholdings by HCMC Party Office, Ky Hoa Tourism & Trading, Saigon Petro, and Saigon Tourist Holding.

8 Does not reflect the VND1000 billion capital increase announced in November 2011 and Viet Capital’s acquisition of a controlling stake.

9 Does not reflect the VND500 billion capital increase announced in November 2011 by Phu Nhuan Construction and Housing Trading Co. Ltd.

10 Indirect shareholding through VMS (Mobifone). 14 Indirect shareholding through PV Gas.

11 Indirect shareholding through Vietnam Post. 15 Indirect shareholding through ACB Securities.

12 Indirect shareholding through FPT Fund Management Company. 16 Including shareholding by Tin Nghia Petroleum.

13 Indirect shareholding through PVFI. 17 Including shareholding by Eurofinance.

Source: Compiled by Fulbright Economics Teaching Program (FETP) from banks’ financial reports.

Agribank BIDV Vietinbank Vietcombank

Eximbank ACB

8.2%

Military Bank Sacombank 11%

Sai Gon Bank9 5.3%

Viet A Bank 8.5%

Gia Dinh Bank8 Phuong Dong

Bank

Bao Viet Bank

Dong A Bank Maritime Bank

4.7%

11%

8.9%

15%3

An Binh Bank

VP Bank

Techcombank Tien Phong Bank

Lien Viet Post Bank Vinasiam

Bank Indovina

Bank Vietnam-

Russia Bank Viet-Lao Bank VID Public

Bank

Saigon-Hanoi

Bank Nam Viet

Bank EVN 25.4%

Bao Viet Group HCMC 52%

Party & related firms

12.8%5 Viettel

10%

TKV

13.2%

Vinatex

VNPT 12.5%

6%11

Mizuho 15%1

Standard Chartered

Sumitomo 15%

Dragon Capital

63.6%7

ANZ

Ocean Bank

9.3%

Petro Viet Nam

20%

FPT

16.9%

Global Petro Bank 3.2%13 5.8%12

Nam A Bank

Petrolimex Group

Bank

Petrolimex 40%

9.8%

BNP Paribas

20%

Maybank 20%

Habubank 10% Deutsche Bank OCBC 14.9%

Vietnam International

Bank

Commonwealth Bank of Australia 50%

34%

50%

Gemadept VN 8.4%

Rubber

Group 9.3%

HSBC 19.6%

Masan Group 19.7%

VN Airlines 2.7%

Him Lam 9.9%

5.3%

VN Helicopter

7.2%

Saigon New Port

5.7%

15.3%6

Geleximco

8.3%

HD Bank

Sovico 40%

HFIC Dai A Bank

10.8%

Vietbank 10%

Phuong Nam

Bank United

Overseas Bank Mekong

Development Bank 10.2%

Tin Nghia 14.4%16

Dong Nai Lottery

5.8%

SeABank Société

Générale 20%

6.1%10

1.5%14

6.8%

3.7%

IFC 10%

SJC

15%

Vina Capital 5.0%

2.1%

3.5%

REE

3.2%

SCR, TTC, BTN,

NHS 5%2

Connaught Investors

7.3%

PNJ

7.7%

Vinalines

5.3%4

2.4%

6.8%

3.7%

Vinamilk 8%

7.1%10

Vinare

10%

Dai Duong Group

20%

T&T Group 6.9%

Saigon -Binh Dinh

Power 11.9%

SSI 10%

Temasek Holdings

20%

15%

Eurowindow Holding 7.2%17 Western Bank

9.8%

Kien Long Bank 6.1%15

CMC

Chau Tho 9.9%

15%

5.3%15

Viet Phuong Group Hoa Binh Securities 9.8%

13.6%