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Chapter 6 Macroprudential Policies

Author(s):
Ana Corbacho, and Shanaka Peiris
Published Date:
October 2018
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The use of macroprudential tools to limit systemic financial risk has grown over the past two decades. The Association of Southeast Asian Nations–5 (ASEAN-5) economies have been well ahead of other regions in realizing the value of macroprudential policies for financial stability. However, prudential policy frameworks are still a work in progress, and the ASEAN-5 are striving to develop and build appropriate institutional underpinnings for such policies.

This chapter looks at what macroprudential policies are and why their use has grown in the ASEAN-5. It then analyzes the impact of such policies on maintaining financial stability and whether these tools, if used more preemptively, could have helped prevent the financial excesses during the Asian financial crisis. Chapter 8, in turn, examines macroprudential policy frameworks and the policy agenda ahead to ensure that financial stability is maintained over the medium term.

The increased use of macroprudential policies in the ASEAN-5 since the Asian financial crisis has reduced the incidence of credit boom-bust cycles and their impact on asset prices. Event studies show that macroprudential tools have been an effective policy instrument for moderating credit growth. Since the Asian financial crisis, macroprudential policies have also complemented monetary policy and enhanced the monetary transmission mechanism by altering bank loan profitability. Indeed, the greater use of prudential tools in the ASEAN-5 has been mirrored by more prudent bank balance sheet management.

Although their efficacy has been demonstrated, the argument for using macroprudential policies to mitigate financial vulnerability is further strengthened by the macroeconomic history of the ASEAN-5. Credit and real cycles in these countries have operated at different frequencies, and inflation has not been a reliable indicator of financial excess. The ASEAN-5 have demonstrated since 2000 that dedicated macroprudential policies can help achieve the financial stability objective because policies can be better tailored to financial risks. These countries’ experience with macroprudential policies thus hold lessons for other advanced and emerging market economies beginning to explore macro-financial policymaking.

Why Macroprudential Policies?

The increasing use of macroprudential tools for safeguarding financial stability over the past decade stems from a greater understanding of the limits of monetary policy. These limits include the realization that real and financial cycles operate at different frequencies, that supply-side developments have constrained monetary policy, that external factors have gained importance for domestic financial conditions, and that monetary policy is too blunt an instrument for dealing with asset price bubbles.

The financial sector is inherently procyclical and can amplify the real cycle. This amplification occurs through price and quantity channels. Bank lending is procyclical because liabilities tend to increase by more than assets during a credit boom, thus raising leverage. Moreover, because financial conditions are positively correlated with overall economic activity, price-based market risk indicators tend to be procyclical. An examination of the risks to financial stability arising from excessive procyclicality highlights several coordination issues among policymakers.

The duration and amplitude of the financial cycle are not the same as those of the real cycle, which in real time could lead monetary policymakers astray (Borio 2012). Drehmann, Borio, and Tsatsaronis (2012) note that the financial cycle operates at a much lower frequency than the traditional business cycle, while Borio and Lowe (2002, 2004) show that widespread financial distress typically arises from the unwinding of financial imbalances that build up while disguised by benign economic conditions characterized by stable and low inflation.

Since 1960, the duration of the average business cycle has been smaller than the average credit cycle in the ASEAN-5 (Table 6.1). It is these patterns that cause coordination difficulties for monetary policy and potentially lure a central bank into a looser monetary policy stance than would otherwise be warranted. This concept can also be illustrated by a simple scatterplot of the credit gap, which is one measure of the financial cycle and is defined as the difference between the credit-to-GDP ratio and its long-term trend, and inflation. If inflation is a reliable indicator of excessive financial leverage, there should be a positive relationship between inflation and the size of the credit gap. However, the data for the ASEAN-5 countries show a weak relationship between the financial cycle and inflation (Figure 6.1), and this relationship has generally weakened since the early 2000s. Taken together, these findings parallel the idea that financial and real cycles operate at different frequencies (Borio 2012). For this reason, monetary policy may not be an efficient tool for calming the credit cycle if it is expected to moderate business and inflation cycles at the same time. Moreover, multiple objectives may overburden monetary policy, creating an expectation gap between what the central bank can achieve and what it can deliver. Therefore, moderating real and financial cycles calls for complementary sets of monetary and macroprudential policies. Chapter 9 illustrates the benefits of such complementary policies for addressing both price and financial stability.

Table 6.1.Average Length of Credit and Business Cycles in the ASEAN-5 (1960–2016)(Years)
Business CycleCredit Cycle
Indonesia6.99.6
Malaysia3.66.4
Philippines4.14.9
Singapore4.55.4
Thailand5.47.8
Source: IMF staff calculations.Note: The average length of business and credit cycles is calculated using annual data and a Bayesian Markov chain regression. The business cycle is captured using GDP growth rates.
Source: IMF staff calculations.Note: The average length of business and credit cycles is calculated using annual data and a Bayesian Markov chain regression. The business cycle is captured using GDP growth rates.

Figure 6.1.
Scatterplot—Inflation and Credit Gap

(Percent)

Sources: Authorities’ data; and IMF staff calculations.

Positive supply-side developments in the ASEAN-5 since 2000 have constrained monetary policy flexibility, raising the risk of larger and more prevalent financial booms. These supply-side developments linked to increased global trade and financial integration have contributed to higher growth potential and hence the scope for credit and asset price booms. At the same time, they have put downward pressure on inflation, which, in turn, constrains the room for monetary policy tightening (Juselius and others 2016). As discussed in Chapter 2, most ASEAN-5 countries give preeminence to inflation in guiding monetary policy interest rates. As inflation has declined, monetary policy rates have fallen, which, in turn, has depressed natural interest rates across the region. The natural rate, a convenient benchmark against which to measure the policy rate, is an unobservable equilibrium concept assumed to be determined by real factors. At the heart of this interpretation are two features: first, the natural rate is defined as the rate that would prevail if actual output equaled potential output. Second, inflation is the key signal that output is not at its potential, sustainable level. This view presumes that over the medium term, monetary policy only passively tracks the natural rate. Thus, the observed decline in real interest rates is purely a function of forces beyond the central bank’s control.

Time-varying estimates show a decline in the natural interest rate since 1990 among ASEAN-5 economies.1 The natural rate rose in the high-inflation era of the 1980s, but declined persistently during the so-called Great Moderation of the 1990s, when global inflation declined. The natural rate stayed low across the ASEAN-5 during the first decade of the 2000s. Since 2010, the natural rate has hovered around zero. The decline in the natural rate across the ASEAN-5 mirrors global financial trends and reflects, in part, the success of ASEAN central banks in moderating and stabilizing inflation, as documented in Chapters 2 and 7. Since credit booms have not, historically at least, been accompanied by higher inflation in the ASEAN-5 (Figure 6.2)—reflecting positive supply-side developments and improved central bank credibility—a monetary policy focused on price stability has not needed to tighten beyond the natural rate to restrain a buildup in financial imbalances. Therefore, where low policy rates are consistent with low inflation, they may contribute to excessive credit growth and the buildup of asset bubbles and thereby sow the seeds of financial instability (Juselius and others 2016). These factors reinforce the need for prudential policies that mitigate the buildup of financial risk in a low-interest-rate environment.

Figure 6.2.
Implied Real Natural Interest Rate Estimates

(Percent)

Sources: National authorities’ data; and IMF staff calculations.

As a result of increased trade and financial integration, the global financial cycle has come to play a more important role in determining domestic financial conditions in the ASEAN-5 (Chapter 4; Rafiq 2016). This external influence may limit the effectiveness of domestic monetary policy in determining local financial conditions (Chapter 5; Miranda-Agrippino and Rey 2015).

How important are external credit conditions in Asia for domestic credit conditions in the ASEAN-5? The relative importance of the external credit cycle for domestic credit growth in the ASEAN-5 is quantified using a dynamic factor model, following Kose, Otrok, and Whiteman (2003). The framework decomposes observable credit growth ci,t, i = 1,  . . .  , n,t = 1,  . . .  , T into the sum of two unobservable components: one that affects all ci,t, that is, the factor ft, which captures the Asian credit cycle, and one that is idiosyncratic (εi,t and specific to each country i:

Estimates suggest that a significant share of credit growth in the ASEAN-5 is linked to credit growth fluctuations in the rest of Asia. Figure 6.3 plots the exposure of domestic credit growth, measured by bi, to the Asian credit cycle, captured by ft. The results show that about 20–25 percent of the credit growth in Malaysia and Thailand is linked to credit developments in the rest of the region. The comparable estimate for Indonesia is about 30 percent. Because the external environment in the global banking system is a significant determinant of domestic credit conditions and, therefore, a source of vulnerability of the economy to financial excesses, considerations of financial stability cannot be easily separated from the merits of macroprudential policies.2 This interdependence is recognized in the macroprudential policy framework discussed by the international regulatory community (FSB, IMF, and BIS 2011). Because macroprudential policies are less constrained than monetary policy, they can better deal with financial stability issues as a result of shifts in global liquidity.

Figure 6.3.
Variance Decomposition: Asia Credit Factor

(Share of variance explained)

Source: IMF staff calculations.

Finally, despite better understanding of the limitations of monetary policy, a pre–global financial crisis view that central banks should focus on stabilizing inflation and output has, in some circles, given way to the postcrisis view that policymakers should pay attention and eventually respond to developments in asset markets. However, proposals for monetary policy that leans against the wind in response to financial conditions’ perceived deviation from fundamentals rely on the assumption that higher short-term interest rates will be effective in shrinking the size of an emerging financial or asset price bubble. Yet—and despite the popularity of such proposals—the empirical evidence for such a link is far from established (Galí and Gambetti 2015; Rigobon and Sack 2003).

The link between stock prices and monetary policy can be established via a risk-neutral general equilibrium environment, as in Galí 2014. The stock price (Q) is decomposed into fundamental ( Qf ) and bubble (Qb) components, Q = Qf + Qb. In a risk-free environment, the fundamental component is defined as the present discounted value of future dividends

The response of an asset price to a change in monetary policy can be expressed as

in which γ = Qb/Q measures the relative size of the bubble component in the overall asset price. In response to a monetary impulse, the fundamental stock price can be traced out using

in which Θ = d / r < 1 and dt is the gross dividend yield, and rt is the riskless real rate.

Under the conventional view that monetary policy can be used to prick asset price bubbles,

which implies that a tightening of monetary policy should cause a decline in the size of the bubble. Hence, the overall effect on the observed asset price should be unambiguously negative, independent of the relative size of the bubble. The response of the bubble component can be backed out via the gap between the empirical stock price and the fundamental stock price responses (Qb=qt+kfϵtmqb+kϵtm) to a tightening in monetary policy.

This proposition can be tested empirically using a vector autoregression model. The analysis uses monthly Malaysian data for GDP, the GDP deflator, a commodity price index, dividends, the short-term interest rate, and a stock price index from January 2004 to December 2016. The focus is on the dynamic response of stock prices to an exogenous hike in the interest rate. Results show that monetary policy tightening, characterized by a rise in the short-term interest rate, has, on average, been associated with an eventual rise in stock prices and a rise in the bubble component (Qb) of asset prices (Figure 6.4). This effect is persistent and statistically significant. This simple finding casts doubt on the view that monetary policy that leans against the wind, that is, a rise in interest rates, will help deflate an emerging asset and credit market bubble. Moreover, since monetary policy has a broad impact on the economy and financial markets and gets in all the cracks, attempts to raise interest rates to deflate an asset price bubble are likely to have many unintended side effects, such as increased capital flows. And, to the extent that it is diverted to the task of reducing risks to financial stability, monetary policy is not available to help the central bank attain its near-term objectives of full employment and price stability.

Figure 6.4.
Malaysia: Response of Bubble Component of Stock Prices to a Rise in Interest Rates

(Share of variance explained)

Source: IMF staff calculations.

In summary, monetary policy can only do so much, increasing the role of macroprudential policy to prevent financial excesses and build financial resilience. Evidence for the ASEAN-5 implies that financial stability will not necessarily materialize as a natural by-product of a so-called appropriate monetary policy stance. Although the effects of monetary and macroprudential instruments may overlap, they are not perfect substitutes, and achieving the financial stability objective requires dedicated macroprudential policies, which can be better tailored to financial risks, to address specific problems. Well-tailored macroprudential policies can have fewer unintended consequences on other sectors of the economy and other policy objectives.

What are Macroprudential Policies?

The primary aim of macroprudential policy is to secure financial stability by leaning against excess financial conditions. FSB, IMF, and BIS (2011) define macroprudential policy as “a policy that uses primarily prudential tools to limit systemic or system-wide financial risk, thereby limiting the incidence of disruptions in the provision of key financial services that can have serious consequences for the real economy. . . .” Rather than managing the level and composition of aggregate demand or the business cycle, as monetary policy aims to do, macroprudential policy tries to strengthen the financial system’s defenses in the face of economic and financial shocks.

Financial stability risks can occur in several guises:

  • Aggregate weakness: This weakness arises when the financial sector becomes overexposed to the same risks. Examples include credit (borrower may default), market (collateral values may decline), or liquidity (assets may be hard to sell or debts refinanced) risks. Such risks are particularly acute when credit becomes increasingly tied to the value of asset prices. When asset prices collapse, lenders become exposed both to market risk, because the value of assets declines, and to credit risk, because borrowers are less able to repay their loans. In addition, banks that expand credit by borrowing from wholesale markets and that rely less on traditional deposits from customers are at risk if market funding dries up, making it harder to refinance expiring debt.

  • Systemic financial risk: Failure of an individual institution can give rise to systemic risk and cripple the financial system. These spillovers can occur through several channels: (1) increases in funding costs and runs on other institutions in the wake of the failure of the systemic institution, (2) direct exposure to another financial institution, and (3) fire sales of assets by the stricken institution that cause the value of all similar assets to decline, forcing other institutions to take losses on the assets they hold.

Macroprudential policy makes two active contributions to limit these risks to the wider economy:

  • First, it can preempt aggregate weakness by limiting the buildup of risk, thereby reducing the occurrence of crises. By building buffers, macroprudential policy helps maintain the ability of the financial system to provide credit to the economy, even under adverse conditions.

  • Second, it can reduce systemic vulnerability by increasing the resilience of the financial system. Macroprudential policies can reduce the procyclical feedback between asset prices and credit and contain unsustainable increases in leverage and volatile funding.

For a macroprudential policy framework to operate it needs to include a system of early warning indicators that signal increased vulnerabilities to financial stability. Excessive asset growth is at the core of increased financial sector vulnerabilities. The challenge is knowing when asset growth is “excessive.” Simple rules of thumb such as the ratio of total credit to GDP are often used. The liabilities side of banking sector balance sheets also offers clues to financial vulnerabilities. The ratio of noncore to core liabilities of the banking sector is useful for gauging the stage of the financial cycle. Monetary aggregates and other banking sector liability measures may also be usefully developed to track potential vulnerabilities.

Macroprudential tools can be grouped in many ways. The different prudential tools overlap, and there is no hard-and-fast boundary between monetary and macroprudential measures. One useful way to group the tools is to distinguish between those that prevent financial excess from building up and those that increase financial sector resilience. Several macroprudential policy tools are useful for addressing the buildup of financial vulnerabilities (IMF 2014a; IMF, FSB, and BIS 2016):

  • Bank capital–oriented tools can limit loan growth by altering bank incentives. Such tools affect all credit exposures of the banking system and aim primarily to increase resilience, but some of them may also have a moderating effect on credit in buoyant times. Such policies include credit growth and sectoral limits and loan-to-value and debt-to-income ratios. Countercyclical capital buffers and dynamic loan loss provisioning requirements can help build buffers to absorb losses. A static leverage ratio limit, such as the one envisaged in Basel III, can constrain the buildup of excessive leverage in the context of capital inflows.

  • Sectoral tools target specific credit categories to help mitigate systemic risk arising from excessive credit growth. Sectoral capital requirements (risk weights) on specific loans, such as mortgages, can be raised to induce banks to hold extra capital and protect against unexpected losses that arise when default rates increase because of an economic downturn. Constraints on household lending, such as limits on loan-to-value and debt-service-to-income ratios, increase resilience to asset price and income shocks and reduce demand for housing loans. Loan restrictions and guidance on underwriting standards are often targeted at mortgages but can also be applied to other segments, including commercial property and loans to the corporate sector (IMF 2014b; IMF, FSB, and BIS 2016).

  • Liquidity tools can help contain vulnerabilities related to volatile funding structures. The Basel III liquidity tools—minimum standards for the liquidity coverage ratio and the net stable funding ratio—can do much to improve resilience to liquidity shocks. Liquid asset requirements (such as the liquidity coverage ratio) make banks hold more liquid assets, hold fewer illiquid assets, or lengthen funding maturities, making it less likely that funding pressure will lead to a fire sale.

To mitigate financial vulnerabilities, macroprudential policy tools should be designed to fit closely with early warning indicators, and it is unlikely that a single prudential tool can address the various sources of systemic risk. Policies must be tailored to specific macroprudential instruments to lessen the vulnerabilities identified by analysis. The macroprudential toolkit should be broad enough to prevent boom-bust credit cycles and should include tools to address the interplay between market and credit risks—such as maximum loan-to-value ratios for home mortgages—and the buildup of liquidity risks as credit surges. Moreover, macroprudential policies could also aim to tackle financial imbalances in individual financial institutions, which could also deal with the aggregate credit cycle. This method may be appropriate because bank-specific actions sometimes internalize spillovers that arise across banks over the credit cycle. Table 6.2 provides a simple schema of macroprudential tools. Table 6.3 provides a more detailed description of these tools.

Table 6.2.Schema of Macroprudential Policies
Preventing Financial ExcessBuilding Financial Resilience
Credit SupplyLending rate ceilingsCapital requirements
Leverage capsDynamic and forward-looking provisioning
Reserve requirementsRisk weights
Credit growth limitsReserve requirements
Exposure limitsLiquidity requirements
Levy on noncore liabilities
Sectoral limits
Credit DemandLoan-to-value ratios
Debt-service-to-income ratios
Tax policies and incentives
Source: IMF staff calculations.
Source: IMF staff calculations.
Table 6.3.Macroprudential Policies and Aims
Preventing Financial ExcessBuilding Financial Resilience
Monetary MeasuresReserve RequirementsWith reserve requirements, banks are required to hold at least a fraction of their liabilities as liquid reserves. These are normally held either as reserve deposits at the central bank or as vault cash.
Liquidity RequirementsLiquidity requirements typically take the form of a minimum ratio for highly liquid assets, such as government securities and central bank paper, as a proportion of certain types of liabilities. These prudential regulations ensure that banks can withstand severe cash outflows under stress. However, liquidity requirements act similarly to reserve requirements in that they influence the amount of funds available for lending to the private sector.
Limits on Credit GrowthWhen an economy experiences rapid credit growth, the central bank may impose a quantitative ceiling on the rate of credit growth per month or year, or a maximum per-month or per-quarter increase in lending. Such limits to credit growth include actions that specify a quantitative limit on the rate of credit growth and penalties for exceeding this limit.
Capital RequirementsThe rise in asset values that accompanies a boom results in higher capital buffers in financial institutions, supporting further lending in the context of an unchanging benchmark for capital adequacy. During a bust, the value of this capital can drop precipitously, possibly even necessitating a cut in lending. Current capital requirements can therefore amplify the credit cycle, making a boom and bust more likely. However, capital requirements that lean against the credit or business cycle instead—rise with credit growth and fall when it contracts—can play an important role in promoting financial stability and reducing systemic risk.
Prudential MeasuresRisk-Weighting AssetsUnder Basel I, II, and III, housing loans are subject to risk weights that differ from those applied to corporate or sovereign exposures. Raising the risk weight on housing loans makes it costlier for banks to extend them and, at the same time, banks are induced to build up buffers against potential losses. Often, risk weights are differentiated by the actual LTV ratio for individual loans. For example, the portion of a housing loan’s LTV ratio that exceeds a certain threshold (for example, 80 percent) may carry a higher risk weight.
Forward-Looking ProvisioningForward-looking provisioning requires the buildup of a loss-absorbing buffer at the time the loan is made, sharing similarities with the countercyclical capital buffer. However, there is a key difference between provisioning and equity in accounting treatment. The forward-looking provision is not counted as bank capital and hence is less likely to influence a bank’s business focus—which targets a specific return on equity. To the extent the bank uses its capital as the base for constructing its total balance sheet, the larger the equity base, the larger the balance sheet, and hence the greater its use of debt to finance assets. During a credit boom, the buildup of assets using debt financing will contribute to a buildup of vulnerabilities.
Prudential MeasuresLimits on Credit GrowthWhen an economy experiences rapid credit growth, the central bank may impose a quantitative ceiling on the rate of credit growth per month or year, or a maximum per-month or per-quarter increase in lending. Such limits to credit growth include actions that specify a quantitative limit on the rate of credit growth and penalties for exceeding this limit.
Leverage LimitsCaps on bank leverage can limit asset growth by tying total assets to bank equity. The rationale rests on the role bank capital plays as a constraint on new lending rather than the Basel approach of using bank capital as a buffer against loss. The main mechanism is the cost of bank equity, regarded by banks as more expensive than short-term debt. By requiring a larger equity base to fund the total size of the balance sheet, a regulator can slow asset growth.
Sectoral LimitsDesigned to be less blunt than dynamic capital buffers, sectoral limits force institutions to add capital to cover new loans in sectors that are building up excessive risks.
Loan-to-Deposit LimitsFor domestic banks, the loan-to-deposit ratio cap has two effects: First, it restrains excessive asset growth by tying loan growth to growth in deposits. Second, it directly affects the growth of noncore liabilities and hence the buildup of vulnerabilities that arise from the liability side of the balance sheet. In this respect, there are similarities between the loan-to-deposit cap and the levy on noncore liabilities.
Prudential MeasuresLoan-to-Value and Debt-Service-to-Income LimitsLimits on bank lending, such as caps on LTV and DSTI ratios, may be a useful complement to traditional tools for bank supervision. LTV regulations restrict the amount of a loan to a maximum percentage of the value of collateral. DSTI caps operate by limiting a borrower’s debt service costs to some fixed percentage of verified income. The macroprudential rationale for imposing LTV and DSTI caps is to limit bank lending to prevent both the buildup of noncore liabilities in funding these loans as well as to lean against eroding lending standards associated with rapid asset growth.
Levy on Noncore LiabilitiesExcessive asset growth and greater reliance on noncore liabilities are closely related to systemic risk and interconnectedness between banks. In a boom when credit is growing rapidly, the growth of bank balance sheets outstrips available core funding, and asset growth is mirrored in the greater cross-exposure across banks.
Source: IMF staff calculations.Note: DSTI 5 debt-service-to-income; LTV 5 loan-to-value.
Source: IMF staff calculations.Note: DSTI 5 debt-service-to-income; LTV 5 loan-to-value.

Macroprudential Policies in the ASEAN-5

This section explores two key questions: how macroprudential policies have evolved in the ASEAN-5 and whether macroprudential policies have complemented monetary policy. The following section, in turn, analyzes the impact of macroprudential policies on credit and asset price cycles and whether such policies could have dampened some of the adverse fallout from the Asian financial crisis.

How Have Macroprudential Policies Evolved in the ASEAN-5?

Since the Asian crisis, the ASEAN-5 countries have adjusted their policy frameworks to address financial booms and busts more systematically, embarking on an ambitious and broad-ranging program of economic and financial sector reforms. The ASEAN-5 have been well ahead of the rest of the world in recognizing the value of macroprudential policies for financial stability; they have routinely responded to emerging systemic risks by deploying a variety of instruments, such as loan-to-value ratios, reserve requirements, limits on currency and maturity mismatches, and adjustments in risk weights, to contain excessive financial imbalances. Following the global financial crisis, the ASEAN-5 financial systems were much healthier than those of many advanced economies because policymakers across the ASEAN-5 had routinely responded to emerging systemic risks and preserved financial resilience through a variety of prudential instruments.

The past 30 years have witnessed a shift in the types of macroprudential tools used by the ASEAN-5 to safeguard financial stability. A database of macroprudential policies by the Bank for International Settlements (BIS 2013) shows a move away from monetary macroprudential tools to broader prudential instruments for the ASEAN-5 (Figure 6.5). What might explain the shift from monetary to prudential measures over time? First, reserve requirements lost their importance as monetary policy tools after many ASEAN-5 central banks started to adopt interest rate policy and inflation targeting.3 Second, there is growing recognition that financial cycles, such as housing credit and house prices, have become longer, larger, and less synchronized with real and inflation cycles (Drehmann, Borio, and Tsatsaronis 2012). In response, policymakers in the ASEAN-5 increasingly resorted to prudential measures to moderate credit and asset price cycles. Third, there was a shift toward explicit macroprudential objectives following the Asian financial crisis (Figure 6.6).

Figure 6.5.
Use of Macroprudential Tools across ASEAN-5

(Number of policy changes)

Source: Bank for International Settlements data. Note: ASEAN-5 = Indonesia, Malaysia, Philippines, Singapore, Thailand.

Source: Bank for International Settlements data.

Figure 6.6.
ASEAN-5 Macroprudential Policies: Monetary Measures

(Index)

Although monetary prudential tools are deployed less often, they have continued to be used to safeguard financial stability in the face of turbulent economic events. Monetary prudential tools have been employed in a countercyclical fashion since 2003. Mirroring a loosening in monetary policy rates, in 2008 Indonesia, Malaysia, and the Philippines lowered banks’ reserve requirements and expanded liquidity provision measures to preserve orderly money market conditions. All ASEAN-5 economies expanded depositor insurance guarantees. In 2011, coinciding with a large increase in capital flows, reserve requirements were tightened. During the taper tantrum in 2013 Indonesia gave stability priority over supporting economic activity by tightening reserve requirements and loan-to-value ratios to contain credit growth. During summer 2015, reserve requirements were left unchanged, but they were reduced in December 2015 in Indonesia and in February 2016 in Malaysia.

The broadening of the macroprudential toolkit—with greater focus on the real estate sector and credit-specific domestic prudential tools—was an attempt to address financial stability risks marked by rising household debt and the real estate price cycle. The use of housing-related macroprudential tools across the ASEAN-5 has grown significantly since early 2010 (Figure 6.7). Updated policy indices compiled by Zhang and Zoli (2014) also show increasing use of macroprudential policies, particularly housing-related measures, in the ASEAN-5 economies in the wake of the global financial crisis. Tighter real estate–related macroprudential policies reflected an attempt to control high real estate loan growth attributed to speculative activities in Indonesia, Malaysia, the Philippines, and Singapore, and to tame real estate price inflation in Indonesia and the Philippines. To build financial resilience in the event of a large correction in asset prices, risk weights, particularly for bank assets linked to real estate, rose in Malaysia and Thailand, and loan-to-value ratios were tightened in Indonesia, Malaysia, and Singapore (Figure 6.8). These policies were complemented by a rise in stamp duties, particularly in Malaysia and Singapore.

Figure 6.7.
ASEAN-5 Macroprudential Policies–Real Estate

(Index)

Source: Bank for International Settlements data.

Note: LTV = loan-to-value ratio.

Figure 6.8.
ASEAN-5 Macroprudential Policies: Risk Weighting

(Index)

Source: Bank for International Settlements data.

Note: LTV = Loan-to-value ratio.

The increased use of macroprudential tools has also mirrored shifts in the management of bank capital across the ASEAN-5. Capital requirements are a central part of the macroprudential toolkit, and few issues in the aftermath of the global financial crisis have been more contentious than the level of bank capital. Raising capital requirements serves both goals of macroprudential policy: preemption and resilience. Higher bank capital requirements have several benefits from a financial stability perspective and provide a buffer that absorbs losses—in principal, bank capital plays a preventive role through greater incentives for better risk management (Perotti, Ratnovski, and Vlahu 2011).4

Bank capital in the ASEAN-5 has risen progressively since the Asian financial crisis and comfortably exceeds the Basel I minimum requirement of 8 percent in all countries (Figure 6.9). Prudent policy would dictate that when output and credit gaps are large and positive banks should have larger capital buffers. As part of Basel III regulatory reform, banks are required to hold a capital conservation buffer.5 While the countercyclical buffer is a relatively new tool, simulations can be used to illustrate the appropriate level of capital given the stages of financial and real cycles both before and after the Asian and global financial crises.6

Figure 6.9.
Implied Countercyclical Capital Adequacy Ratios

(Percent)

Sources: World Bank; and authorities’ data.

Note: CAR = capital adequacy ratio.

A countercyclical capital buffer rule would have raised banks’ capital in the run-up to the Asian financial crisis (Malaysia and Thailand) and the more recent global financial crisis (Indonesia, Malaysia, Thailand, Singapore) (Figure 6.9). Although this exercise is purely illustrative and the focus is on the actual level of bank capital rather than minimum capital requirements, a countercyclical capital buffer would have lowered bank capital in the aftermath of the Asian financial crisis (Malaysia, Thailand) and following the global crisis (Indonesia, Singapore). Despite the lack of an explicit countercyclical capital buffer policy rule, capital adequacy in Malaysia and Thailand since the Asian financial crisis has closely mirrored the level of capital adequacy implied under such a rule. In other ASEAN-5 countries, the actual capital ratio has not veered substantially from the level of capital implied by a simple countercyclical capital buffer rule. This finding suggests that, whether explicitly realized or not in real time, the management of bank capital has become more macroprudential, adjusting somewhat to excesses in real and financial cycles.7

The ASEAN-5 have also taken measures to manage capital inflow and outflow surges (Figure 6.10). These measures have overlapped with macroprudential policies to address systemic risks at times.8 Capital flows can give rise to financial stability risks through various channels (IMF 2014a), including increases in short-term wholesale funding of the banking system, growth in foreign currency funding of the financial system, contributions of capital inflows to local credit booms and asset price appreciation, and credit risks from foreign-currency-denominated loans. A database compiled by the Bank for International Settlements suggests that Malaysia, the Philippines, and Thailand took advantage of the loosening in the global financial cycle between 2004 and 2008 to implement measures that liberalized capital inflows and outflows, with an emphasis on bank, bond, and equity flows, and capital inflows grew significantly. The era following the global financial crisis also saw some measures to tighten outflows.

Figure 6.10.
ASEAN-5 Use of Capital Flow Measures

(Number of policy actions)

Source: Bank for International Settlements data.

Note: GFC = global financial crisis.

Have Monetary Policy and Macroprudential Policies Been Complementary?

Macroprudential policies work most successfully when monetary policy is pulling in the same direction. Bruno, Shim, and Shin (2015) find that macroprudential policies are not particularly effective when they lean in a direction opposite to monetary policy. Effective monetary and macroprudential policies that complement each other yield better outcomes than monetary—or macroprudential—policy pursued separately. Tightening macroprudential policy tools can dampen real economic activity. However, the authorities can counter these effects by loosening monetary policy at the margin. Moreover, macroprudential policy can give monetary policy more room to pursue its primary objective and can help build buffers that can be relaxed in periods of financial stress, as shown in Chapter 9. Such a policy can help keep monetary policy transmission open, preserving its effectiveness in the event of financial stress.

Monetary and macroprudential policies in the ASEAN-5 have complemented each other since the turn of the century. Table 6.4 shows the degree of complementarity between monetary policy and macroprudential policies for the ASEAN-5, calculated using a pairwise correlation of various policy cycles—specifically, the policy rate cycle and the macroprudential policy cycle as represented by cumulative variables for macroprudential policy tightening or loosening. The correlation between the monetary policy cycle and the macroprudential policy cycle is positive, with a slightly stronger outcome for nonmonetary prudential tools.

Table 6.4.Average Correlation of Monetary and Macroprudential Changes in the ASEAN-5
Policy Rate Change
Policy Rate Change1
Monetary Prudential Tools0.21
Nonmonetary Prudential Tools0.27
Source: IMF staff calculations.
Source: IMF staff calculations.

A link between macroprudential and monetary policies in the ASEAN-5 should not be surprising given the similarities and complementarities between these types of policies. Both affect credit demand, albeit in different ways. Monetary policy works by intertemporal allocation of spending, bringing forward spending from the future or pushing it into the future. One way to bring spending forward is to lower interest rates so that economic agents can borrow more sooner. In contrast, macroprudential policy works by restraining borrowing. Monetary policy and macroprudential tools also affect risk taking by banks: monetary policy works through the so-called risk-taking channel, whereas macroprudential regulation affects financial risk taking by imposing equity constraints. Finally, monetary and prudential policies affect bank funding costs through the net interest margin.

The ASEAN-5 have progressively altered their monetary policy stances to complement existing macroprudential measures. This process can be observed by examining the data during the taper tantrum episode of 2013. All countries raised their policy rates during the Asian financial crisis to support their external positions, but eased these rates in the aftermath of the global financial crisis to support growth (Figure 6.11). Only Indonesia raised policy rates during the taper tantrum to support its external position; Malaysia and the Philippines subsequently tightened modestly for domestic stability purposes. Singapore and Thailand gradually eased their monetary policy stances during 2011–12, reflecting the weakening economic outlook. During the summer 2015 turbulence, policy rates were left unchanged in all ASEAN-5 economies because policymakers had to weigh concerns about capital flow reversals that were largely confined to portfolio equity flows against worries about slowing economic activity. However, not until January 2016 did Indonesia start loosening monetary policy to support domestic demand. These episodes demonstrate the increased degree of coordination between monetary and macroprudential policies in the ASEAN-5, which eased the burden on monetary policy to lean against unfavorable financial developments. As a result, the monetary authorities had more flexibility to pursue price and output stability objectives while preserving the established independence and credibility of monetary policy (see IMF 2016).

Figure 6.11.
ASEAN-5: Policy Interest Rates

(Percent)

Sources: Haver Analytics; and IMF staff estimates.

Note: GFC = global financial crisis; NEER = nominal effective exchange rate.

The Macroeconomic Impact of Macroprudential Policies in the ASEAN-5

Empirical evidence supports the effectiveness of macroprudential tools for building resilience (IMF, FSB, and BIS 2016). Studies of macroprudential tools’ potential to reduce the procyclicality of credit or contain excessive credit growth find sizable economic effects. However, the strength of the effects depends on capital market openness and financial market development (for example, Lim and others 2011; Cerutti, Claessens, and Laeven 2017). Strength also differs across tools: loan restrictions and borrower eligibility tools (such as loan-to-value and debt-service-to-income ratios) affect credit more, based on their historical calibration, than capital or liquidity tools (for example, Akinci and Olmstead-Rumsey 2015). On the other hand, borrower-based tools are generally found to have measurable effects on credit. Tools that impose limits based on borrower income, such as debt-service-to-income ratios, do more to contain increases in credit than limits based on asset prices (such as loan-to-value ratios).

Canonical correlations and model simulations using data for the ASEAN-5 suggest that more active use of macroprudential policies has resulted in less risk taking and a decline in boom-bust cycles. Three methodologies for calculating the credit gap identify credit booms before the Asian financial crisis in all ASEAN-5 economies (see Table 6.5). Except in the case of Singapore, none of the methodologies show that the ASEAN-5 economies experienced credit booms in the run-up to the global financial crisis or thereafter. In addition, for Singapore, the first two approaches should receive more weight given the country’s high GDP ratio as a result of its role as a financial center.

Table 6.5.Heat Map on the Evidence of Credit Booms14
Pre-AFC (1996–97)Pre-GFC (2007–08)Post-GFC/UMP (2009–2012)Post-Taper Tantrum (2013–15)
M&TD&OGFSRM&TD&OGFSRM&TD&OGFSRM&TD&OGFSR
Indonesia0.09-0.98-0.53-0.305.221.18-0.293.760.89-0.293.651.06
Malaysia0.0516.5820.87-0.231.381.22-0.192.662.73-0.172.272.59
Philippines0.1020.988.95-0.362.140.58-0.352.960.86-0.247.512.57
Singapore-0.027.896.90-0.1211.169.25-0.123.983.84-0.044.435.06
Thailand0.0712.2717.26-0.251.601.40-0.224.834.74-0.173.494.05
Source: IMF (2016).Note: AFC = Asian financial crisis; D&O = Dell’Ariccia and others 2012; GFC = global financial crisis; GFSR = IMF, Global Financial Stability Report, September 2011; M&T = Mendoza and Terrones 2008; UMP = unconventional monetary policy.

Shades of green indicate lower threshold/early warning of credit boom; shades of red indicate that credit is above upper threshold/evidence of a credit boom.

Figures under M&T refer to the deviations of log real credit per capita from its Hodrick-Prescott trend times 1.75 the trend’s standard deviation. The deviations are averaged for the subperiods identified. Positive figures shaded in red indicate evidence of a credit boom.

Figures under D&O refer to the average growth of credit-to-GDP ratio for the subperiods identified. Figures shaded in green and red show ratio above the lower cutoff at 10 percent ratio and upper threshold at 20 percent ratio.

Figures under the IMF’s GFSR refer to the annual change in credit-to-GDP ratio in percentage points, averaged for the subperiods identified. Figures shaded in green and red identify change in credit-to-GDP ratio above 3 percentage points and 5 percentage points, respectively.

Source: IMF (2016).Note: AFC = Asian financial crisis; D&O = Dell’Ariccia and others 2012; GFC = global financial crisis; GFSR = IMF, Global Financial Stability Report, September 2011; M&T = Mendoza and Terrones 2008; UMP = unconventional monetary policy.

Shades of green indicate lower threshold/early warning of credit boom; shades of red indicate that credit is above upper threshold/evidence of a credit boom.

Figures under M&T refer to the deviations of log real credit per capita from its Hodrick-Prescott trend times 1.75 the trend’s standard deviation. The deviations are averaged for the subperiods identified. Positive figures shaded in red indicate evidence of a credit boom.

Figures under D&O refer to the average growth of credit-to-GDP ratio for the subperiods identified. Figures shaded in green and red show ratio above the lower cutoff at 10 percent ratio and upper threshold at 20 percent ratio.

Figures under the IMF’s GFSR refer to the annual change in credit-to-GDP ratio in percentage points, averaged for the subperiods identified. Figures shaded in green and red identify change in credit-to-GDP ratio above 3 percentage points and 5 percentage points, respectively.

Greater use of macroprudential measures since the Asian financial crisis has coincided with lower risk taking by banks and less reliance on noncore funding. The ratio of total credit to broad money is a useful signal of the stage of the financial cycle: an increase implies greater dependence on noncore bank liabilities to finance credit expansion (see Borio and Zhou 2008; Shin and Shin 2011). Broad money signals changes in the size of the banking sector’s aggregate balance sheet. It implicitly conveys the degree of risk taking in the economy along with information on the vulnerability of the financial system to a reversal of available funding. The ratio of credit to broad money—exceeding 100 percent—rose sharply in the years preceding the Asian financial crisis (Figure 6.12) for Indonesia, Malaysia, the Philippines, and Thailand, implying that this rapid credit growth was increasingly financed by noncore sources. Following the crisis, as banks shrank their balance sheets, the ratio of credit to broad money declined to less than 100 percent for Indonesia, the Philippines, and Thailand by 2001. The ratio has remained flat for Malaysia, the Philippines, and Thailand since then, despite the global asset price boom and the global financial crisis.

Figure 6.12.
Ratio of Credit to Broad Money

Sources: Authorities’ data; and IMF staff calculations.

Greater use of prudential tools has also led to more prudent bank balance sheet management and is reflected in acyclical bank leverage. Gourinchas and Obstfeld (2012) report leverage as a consistent and significant predictor of a financial crisis because bank leverage is typically procyclical, with aggregate consequences for the financial system via aggregate volatility and the price of risk (Adrian and Shin 2009). A rise in asset prices strengthens bank balance sheets and—without adjusting asset holdings—leads to a decline in their leverage; banks that hold surplus capital find ways to employ that surplus, leading to a rise in bank leverage. Figure 6.13 shows that bank leverage for Indonesia, Malaysia, Singapore, and Thailand was not procyclical, but rather appears to have been acyclical for much of the asset price boom period of the 2000s. Data for Malaysia show that in the year before the Asian financial crisis, on average, every unit of capital was associated with an 11 percent increase in credit growth. This rate declined to about 7.5 percent by 2010 and has stayed relatively flat since then despite strong capital inflows. A similar pattern is also observed for Thailand, with bank leverage declining in Indonesia and Singapore since 2010.

Figure 6.13.
Bank Leverage

(Unit of bank capital)

Sources: Authorities’ data; and IMF staff calculations.

These developments in the ASEAN-5 are not only consistent with their greater use of aggregate and sectoral macroprudential policies, but also with their efficacy in moderating credit and asset price cycles since the Asian financial crisis. By means of an event study, Figures 6.14 and 6.15 illustrate dynamic estimates using local projections drawn from a robust panel regression model linking credit growth and various macroprudential policy tools: bank capital, reserve requirements, loan-to-value ratio, property taxes, and risk-weighted assets.9 These tools operate differently on credit demand and loan supply. The dynamic responses show that, on average across the ASEAN-5, macroprudential measures have had an effective impact on the credit cycle (Figure 6.14). Changes in loan-to-value limits and reserve requirements appear, with a lag, to have the largest impact on the credit cycle. This outcome is perhaps not surprising, since loan-to-value limits work directly to limit credit demand. Dynamic estimates also show that real estate–specific measures, such as raising real estate–related taxes or tightening the loan-to-value ratio, help reduce real estate price inflation (Figure 6.15). The lagged effect of some prudential measures on credit and asset prices suggests that macroprudential policies need to be forward looking to preempt financial excesses. Taken at face value, the empirical evidence indicates that, on average, macroprudential measures effectively dampened the procyclicality of credit and asset price growth in the ASEAN-5 since the Asian financial crisis.10

Figure 6.14.
Response of Credit Growth to Macroprudential Measures

(Percent)

Sources: Authorities’ data; and IMF staff calculations.

Figure 6.15.
Response of Real Estate Prices to Macroprudential Measures

(Percent)

Sources: Authorities’ data; and IMF staff calculations.

Macroprudential policies in the ASEAN-5 have also enhanced the monetary policy transmission mechanism. Macroprudential policy can affect the transmission mechanism because the interest rate margin is a function of the compensation taken by banks for items such as administrative costs, capital costs, risk premiums, and the banks’ profit margins. Nondynamic macroprudential instruments, such as increased capital or reserve requirements, affect the net interest margin because they tend to increase banks’ costs, which, to a certain extent, are passed on to customers in the form of an increased interest margin. The rule for regulation through the bank lending interest rate equation, which describes the relationship between monetary policy and macroprudential policy, is expressed as follows:

Equation (6.5) expresses banks’ lending rate as a function of the policy interest rate and the interest margin (δt). The interest margin is influenced by regulation (zt), which is itself determined by non-time-varying regulations (z¯), the credit gap, and the output gap (Ingves, Apel, and Lenntorp 2010; Shin 2011).

Model simulations suggest that the impact of macroprudential policies on the monetary transmission mechanism via the banking system has grown since the 2000s. From estimating equation (6.5), which links macroprudential policies and the net interest margin, dynamic responses are extracted using local projection methods that show that a tightening in macroprudential policies leads to a rise in the net interest margin (Figure 6.16). The impact of tightening macroprudential regulations on banks’ net interest margin has grown for the ASEAN-5 since the Asian financial crisis. The influence of macroprudential policies on financial intermediaries in the ASEAN-5 has grown, reflecting their more aggressive use, improved credibility, and increased financial deepening since the Asian crisis, all of which have increased the sensitivity of the financial system to policy changes.

Figure 6.16.
Median Impact of Macroprudential Policies on ASEAN-5 Net Interest Margin

(Percent)

Sources: Authorities’ data; and IMF staff calculations.

Policy simulations suggest that a modest prudential intervention would have helped curtail the pre–Asian financial crisis credit and asset price booms in Malaysia and Thailand. Given the lessons learned since the crisis, the question is whether macroprudential tools, if deployed more aggressively and preemptively in the years leading up to the crisis, could have done more to preserve financial stability during 1996–99. Given limited data availability for the other countries, a counterfactual experiment is performed for Malaysia and the Philippines by simulating a set of modest macroprudential policy interventions in the years preceding the Asian financial crisis.11 The responses of credit growth and asset prices are then traced out based on these modest macroprudential policy simulations to produce a counterfactual series that can be compared with the realized data. On a technical level, following Leeper and Zha (2003) the model is a special case of Kalman smoothing and is estimated using a Bayesian VAR(3) model with Minnesota priors, which in basic terms can be expressed as

in which Yt is a vector containing a set of monthly macroeconomic and policy variables, including credit growth, reserve money, industrial production, and a macroprudential policy index; B is a matrix of reduced-form coefficients; and A0 captures the contemporaneous relationships between the macro time series and policy variables. To produce forecasts in the years leading up to and during the Asian financial crisis (1993–98), equation (6.7) is iterated over this forecast period h:

The forecast Yt+h+h in equation (6.7) is essentially a decomposition of two components: an unconditional forecast and a component with structural shocks. Equation (6.7) can be rearranged as

Policy simulations show that a more aggressive macroprudential policy stance in the years leading up to the Asian financial crisis would have helped moderate credit and asset price cycles. The simulation assumes that the macroprudential index for Malaysia, constructed from the Bank for International Settlements macroprudential database, would be progressively tightened throughout 1995 until mid-1996. Figure 6.17 reports the actual time series, the out-of-sample forecasts conditional on a tightening in the macroprudential policy index, and 68 percent probability bands for the forecasts. The estimates for Malaysia show that credit growth, allowed to expand at a brisk pace of between 11 and 15 percent from 1995 to early 1997 under the policy scenario, would have grown more slowly than the level realized during this time; actual private sector credit growth averaged about 30 percent between the middle of 1995 and early 1997. In general, these findings illustrate that macroprudential policies would have been useful in containing systemic vulnerabilities.

Figure 6.17.
Macroprudential Policy Simulations on Credit Growth before and after the Asian Financial Crisis

Source: IMF staff calculations.

Although the use of macroprudential policy tools has grown, prudential policy frameworks remain a work in progress, and the ASEAN-5 are striving to develop and build appropriate institutional underpinnings for such policies. Although the ASEAN-5 remain much more capable of weathering external shocks than when the Asian financial crisis struck, the taper tantrum turmoil exposed several vulnerabilities policymakers had not fully recognized. There is concern about policymakers’ ability to provide detailed advice on macroprudential policies—considering information gaps—and there is still only limited experience with the instruments. Moreover, further evolution of monetary policy frameworks is likely in the “new normal” (Bayoumi and others 2014). Part III of the book delves into the challenges ahead for upgrading policy frameworks for price and financial stability in the ASEAN-5.

Conclusions

The ASEAN-5 economies have been well ahead of other regions in realizing the value of macroprudential policies for supporting financial stability. The more active use of macroprudential policies by the ASEAN-5 since the Asian financial crisis is a sign that policymakers across the region have not been lulled into complacency by apparent macroeconomic stability. They recognize that financial imbalances can materialize during periods of economic tranquility and benign inflation pressure. Structural financial risks have grown as ASEAN-5 credit cycles have become increasingly influenced by external conditions, while a low (natural) interest rate environment over the past decade resulting from persistent low inflation and supply-side improvements has increased the probability of excessive credit growth and the buildup of asset bubbles. Evidence for the ASEAN-5 implies that financial stability will not necessarily materialize as a natural by-product of a so-called appropriate monetary policy stance. With this in mind, macroprudential policies have been effective in stemming the buildup of financial risks. Event studies for the ASEAN-5 show that macroprudential tools have been useful in containing systemic vulnerabilities and procyclical dynamics between asset prices and credit over the past two decades. Macroprudential policies have also complemented monetary policy and enhanced the monetary transmission mechanism via the bank lending channel. The greater use of prudential tools has been mirrored by a lower incidence of credit booms and more prudent bank balance sheet management since 2000.

Macroprudential policies alone cannot prevent financial crises. The findings in the chapter imply that central banks have strong incentives to pursue macroprudential policies to safeguard financial stability. However, effective measures are also needed to ensure that macroprudential policy does not become overburdened. These measures must be complemented by strong macroeconomic policies to build a stable environment conducive to a healthy financial system. Policymakers should be mindful that macroprudential policy is not free of costs and that there may be trade-offs between the stability and the efficiency of financial systems. For instance, when policymakers impose high capital and liquidity requirements on financial institutions, they may enhance the stability of the system, but they also drive up the price of credit. Balancing benefits and costs of macroprudential policies will often require difficult judgments. For macroprudential policy to contribute to financial stability and social welfare, its objectives need to be defined clearly and in a manner that can form the basis of a strong accountability framework.

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The natural rate cannot be directly observed, and the rate is model dependent. Estimates in this chapter are drawn using ex post real short-term interest rate data in a local-level model estimated using a Kalman smoother. Chapter 7, in turn, presents alternative estimates using a time-varying vector autoregression framework.

Chapter 10 discusses the benefits and risks of regional financial integration in the ASEAN-5.

See IMF 2016. All the countries have low inflation as an objective of monetary policy, with some of them (Indonesia, Philippines, Thailand) adopting an inflation-targeting regime.

Bank capital can limit excesses by increasing shareholders’ so-called skin in the game, which prevents excessive risk taking, especially under conditions of asymmetric information.

This is specified as 2.5 percent of total risk-weighted assets.

An implied countercyclical capital buffer is calculated using the following equation: Capital Adequacy = Banks’ Long-Run Capital Adequacy Ratio + 0.5 × Credit Gap + 0.3 × Output Gap.

It has been argued that higher capital ratios are associated with a higher probability of a crisis. This mechanism suggests that banks raise capital in response to higher-risk lending choices rather than as a buffer against a potential systemic crisis event in the economy. Such a finding is consistent with an empirical reverse causality mechanism reported in the data: the more risks the banking sector takes, the more markets and regulators are going to demand that banks hold higher buffers. See Jordà and others 2017.

The policy frameworks for capital flow management measures and macroprudential policy can overlap (IMF 2012, 2013). Capital flow management measures are designed to limit capital flows by affecting the scale or composition of these flows. Macroprudential measures are designed to limit systemic vulnerabilities, including those associated with capital inflows. To the extent that capital flows are the source of systemic financial risks, the different prudential tools overlap.

See Jordà 2005 for an explanation of the local projection method.

The confounding effect of the endogeneity of the policies should be kept in mind when interpreting the results. The introduction of macroprudential policies often reflects the external environment and the perception that surges in bank or bond capital flows may lead to destabilizing capital outflows in any subsequent reversal. To the extent that new macroprudential policies happen only after a period of discussion within the government, central bank, and other public authorities (such as financial regulators), the introduction of such policies often coincides with the late stages of the boom. To the extent that the boom subsides under its own weight, the introduction of the macroprudential policy and the subsequent slowdown of capital flows and credit growth would be a coincidence, not a causal effect. Thus, the results reported herein should be taken with some caution.

It is worth noting that such simulations may suffer from the Lucas critique, which predicts that the coefficients of a macroeconometric model will change when there is a change in policy actions. However, without quarreling with the logic of the Lucas critique, Leeper and Zha (2003) have shown that “modest” policy interventions are unlikely to bias the results, since policy changes tend to be small and do not resemble the once-and-for-all changes in policy rules that underlie the Lucas critique.

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