- Email: [email protected]
A model of bank lending in the global financial crisis and the case of Korea
Journal of Asian Economics 22 (2011) 322–334
Contents lists available at ScienceDirect
Journal of Asian Economics
A model of bank lending in the global financial crisis and the case of Korea§ Larissa Leony a, Rafael Romeu b,* a b
Inter-American Development Bank, United States International Monetary Fund, United States
A R T I C L E I N F O
A B S T R A C T
Article history: Received 1 February 2010 Received in revised form 6 March 2011 Accepted 10 March 2011 Available online 23 March 2011
This paper studies two interrelated banking sector issues in the context of the global financial crisis and its impact on Korea: (i) To what extent did state owned banks expand lending to offset declining credit as private sector bank balance sheets deteriorated? (ii) In the recent crisis, was bank lending constrained because of funding or liquidity factors? To address these issues, a framework of optimal bank lending is presented in which banks face adverse selection in a market for risky investment projects, pay regulatory costs, and borrow in wholesale and traditional funding markets. The model implications are tested on bank-level data with a focus on the global financial crisis. The results point to significant credit expansion by public sector banks in response to deteriorating international credit conditions. Estimates of private sector lending during the crisis suggest that, at best, the credit crunch was less profound than it could have been. Wholesale funding is not found to have been a major driver of deteriorating credit conditions in Korea, reflecting, at least in part, strong policy support from the authorities during the crisis. ß 2011 Elsevier Inc. All rights reserved.
JEL classification: E5 F3 G28 Keywords: Financial crisis Banking Regulation
1. Introduction This paper studies two interrelated banking sector issues in the context of the global financial crisis and its impact on Korea: (i) To what extent did state owned banks expand lending to offset declining credit as private sector bank balance sheets deteriorated? (ii) In the recent crisis, was bank lending constrained because of funding or liquidity factors? To address these issues, a framework of optimal bank lending is presented in which banks face adverse selection in a market for risky investment projects, pay regulatory costs, and borrow in wholesale and traditional funding markets. The model implications are tested on bank-level data with a focus on the global financial crisis. The results point to a significant credit expansion by public sector banks in response to deteriorating international credit conditions. Estimates of private sector lending during the crisis suggest that, at best, the credit crunch was less profound than it could have been. Wholesale funding is not found to have been a major driver of deteriorating credit conditions in Korea, reflecting, at least in part, strong policy support from the authorities during the crisis. The model presented incorporates adverse selection in the lending market and credit growth that depend both on the risk profile of the loans and the risk tolerance of the lending bank. Specifically, a dynamic optimization problem for a bank lending portfolio is developed. In this problem, banks choose between investing in either short-term high return
§ The views expressed herein are those of the author and should not be attributed to the IMF, its Executive Board, or its management, the IADB, its executive Board, or its management. * Corresponding author at: International Monetary Fund, Fiscal Affairs Department, 700 19th Street, N.W. Washington, DC, United States. E-mail address: [email protected] (R. Romeu).
1049-0078/$ – see front matter ß 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.asieco.2011.03.004
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
323
investments or long-term lower return projects, in a market with adverse selection, alternative funding sources (including wholesale funding), and costly regulatory provisioning for risk. The model solution suggests that over time, banks facing higher costs to risk taking (e.g. state-owned banks) will earn lower returns, but also have smaller reduction in loan growth in the wake of economic shocks–even transitory (mean-zero) shocks. While banks optimally speculate by absorbing a part of realized economic shocks, they hold less risk and need to rebalance away from risky assets less as regulatory costs (or institutional risk aversion) increases. The more risk a bank holds, the greater the impact on loan growth from both losses to regulatory costs, and losses incurred from rebalancing their portfolio using expensive wholesale markets. Rebalancing costs increase (further constraining loan growth) with greater marginal liquidity costs in the wholesale funding market or lower market power from traditional intermediation–both of which tend to increase in periods of financial stress.1 The model’s implications are tested using bank-level data from eighteen major banks comprising the majority of the Korean banking sector. The impact of liquidity shocks and wholesale funding costs on loan growth are considered for all banks. In addtion, public and private banks are considered separately with a view that indentifies the implications for public credit growth in the economy. During the global financial crisis, a large decline in export demand alongside financial contagion resulted in seizing credit markets, and a contraction above 5 percent of GDP in the last quarter of 2008 in Korea—among the largest in Asia. In response, the authorities responded with a wide range of policy measures to ensure adequate credit flows, including a cumulative 325 basis points interest rate cut, widening the list of eligible counter parties and collateral in repo operations, and relaxed bank liquidity requirements. The Bank of Korea also set aside US$55 billion to support credit to trade related business, announced support for the corporate and commercial paper markets, and increased lending to small and medium sized enterprises. Nonetheless, many of these measures were announcements to stand behind markets and support confidence, and not actual expenditures.2 As a result, the up-front cost of government support to the financial sector remained below 0.5 percent of GDP, and total support cost less than 15 percent of GDP, which is moderate by international standards, and in particular, compared with the US, the UK, or the G20 average (74 percent, 48 percent, and 28 percent of GDP, respectively).3 Given the sizeable economic shock alongside a mixed public–private banking system typical of Asian economies (and increasingly common in other regions, e.g. Brazil), Korea is a good candidate to study the response of public banks in crisis conditions. The empirical results suggest that the role of the public sector banks in Korea helped counter the effects of the crisis. Estimates of the model’s structural parameters, which reflect the marginal propensity to lend in response to an economic shock, show that public banks became countercyclical during the crisis, while private banks largely did not. On the funding side, the empirical results show that wholesale funding is a driver of loan growth, but the costs of funding – both traditional core deposits and wholesale – increased during the crisis. While prior work has identified exisiting difficulties with state-owned banks, the policy implications of these results to some extent counter convetional banking sector policy advice regarding their role.4 Barth, Caprio, and Levine (2001) and Micco and Panizza, 2004, for example, find poor state-owned bank performance in developing countries, with lower profitability and margins, and higher overhead. The model presented here predicts lower relative returns in public banks— they do not chase high return short-term risky investments, and they face higher costs from greater ‘‘institutional’’ risk aversion. Both the model and the empirical results show a trade-off from public banks that optimally lend countercyclically, in contrast to private banks that optimally curtail lending in a downturn. That is, privately owned banks that are in full compliance of regulatory and risk provisioning requiremets will optimally curtail lenidng during periods of stress. Other studies have presented evidence of the long-term negative effects of public banks on output growth. For example, La Porta, Shleifer, and Lopez-de-Silanes (2002) find significantly lower long-term GDP growth in countries with large public banking sectors. Nevertheless, Ramey and Ramey (1995) show a strong negative correlation between volatility and long-term growth. Reinhart and Rogoff (2008) show the pervasiveness of banking crises, and importantly, the heavy cost borne by the public sector that ultimately undo reductions in the size of the public sector from bank privatizations. Hence, the potential impact of greater banking sector instability associated with private bank incentives suggest a potential tradeoff between volatility and growth from the size of a country’s public banking sector. The evidence that state-owned banks increase the probability of systemic banking crises is tentative (e.g. Caprio & Martinez Peria, 2002) and under increasing review given the recent financial crisis.5 The next section lays out the conceptual framework and the implications from its analytical solution, which is developed in detail in Appendix A. Section 3 gives an overview of the data, and Section 4 estimates the model and presents the empirical evidence. Section 5 concludes.
1
Wholesale funding refers to all financing raised by banks excluding core deposits. See IMF (2009). See Blundell-Wignall (2009). 4 For example, Clarke, Culla, and Shirley (2005), which provide a summary of lessons regarding bank privatization in developing countries and conclude that ‘‘although bank privatization usually improves bank efficiency, gains are greater when the government fully relinquishes control. . .’’, i.e. there is no benefit from direct state ownership of banks. 5 For example, Allen, Babus, and Carletti (2009) argue that a coherent conceptual framework for banking crises has still not emerged and regulation is ad hoc. 2 3
324
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
2. A framework of bank lending behavior This section develops a dynamic inter-temporal model where bankers strategically manage long-term lending portfolios while profiting from short-term speculative returns funded by both deposits and a more costly wholesale funding market. This framework is intended to incorporate the more salient insights of static game-theoretic models of the banking sector in an inter-temporal dynamic programming framework.6 While more constrained in scope, the optimal control problem developed here yields an empirically tractable closed form solution and testable implications for the South Korean data. The conclusions of the model show that bank portfolio allocations between long-term and short-term investments and the cost of wholesale funding impact loan growth in a crisis. Hence, public banks, which face tighter restrictions and are limited in their ability to invest in riskier projects are ex-ante better prepared for a crisis and optimally expand lending faster in downturns. Consider an economy where bankers raise capital every period to invest in long-term assets (denoted K) earning a return normalized to unity, and short-term riskier loans (denoted L) with a liquidation value rt. There exists a non-zero probability (denoted (1 t)) that at period t = T the market for these riskier projects collapses for reasons that are extraneous to the bank, at which time the bank’s portfolio would be liquidated.7 Every period (i) bankers offer to pay funding cost ct for risky projects that have liquidation value rt at time T in an elastic market with adverse selection; (ii) bankers choose to purchase (or liquidate) risky projects through a wholesale funding market, denoted wt, which has increasing marginal costs8; and (iii) bankers pay a capital charge kT directly related to the variance of liquidation value of the portfolio. 2.1. Buying and selling short-term risky investment projects After quoting a schedule of funding costs, ct, bankers are offered a schedule of investment projects pt that is composed of two unobserved funding demands, pt = Pt + Et. The first component, Pt, reflects the adverse selection in a market for risky projects where the seller or ‘‘entrepreneur’’ knows the return and the banker does not. The amount of projects the banker buys for a given funding cost ct is Pt = d(rt ct), where d > 0 reflects the price elasticity faced by the banker, and rt the true liquidation value of the project. Unlike the entrepreneurs, the bankers cannot observe the true liquidation value of the project (rt), but know it is a random walk, so that rt = rt1 + yt and yt Nð0; s 2r Þ. The second component, Et, is a mean zero funding demand that is not strategic and only reflects shocks to the economy, i.e. Et Nð0; s 2E Þ. In forming expectations of rt, bankers extract an informative signal of excess demand: zt ð pt Þ ¼ P t þ Et ¼ Dt d pt ;
(1.1)
With the intercept of excess demand for projects given by Dt = drt + Et. Appendix A shows that observing excess demand allows the bankers to update their unbiased estimates of the rate of return every period given their information set Ct, which contains their past banking transactions and current excess demand. The estimate of rt is denoted rt = E[rt|Ct]. The unbiased estimator of rt also allows for an unbiased estimate of the liquidity demand from economic shocks, denoted et = E[Et|Ct]. 2.2. Tapping wholesale funding Banks can adjust their level of risky projects by changing their funding costs and by buying (or liquidating) risky projects in a wholesale funding market, denoted wt. Hence, when the bank is not financing risky short-term projects directly through zt(pt), it may choose to do so through the wholesale market, wt. Trading costs in this wholesale market are increasing in wt, so that buying/selling wt projects costs wt ðrt þ bwt Þ. Hence, bankers face an increasing marginal cost in the wholesale funding market given by b.9 2.3. Regulatory capital charges Bankers must set aside a capital charge each period directly proportional to the short-term variance of their portfolio. Hence, they must pay kt ¼ us 2 ðA˜ t Þ on the bank’s portfolio of assets, given by A˜ t ¼ K t þ r t Lt , with u scaling the opportunity cost of this capital requirement.
6 The application of dynamic programming model here is similar to Madhavan and Smidt (1993), Suarez (1994), and Romeu (2010). Repullo (2004), Huang and Ratnovski (2009) are banking models influencing this approach, among others. Allen et al. (2009) provide a survey of recent banking crisis literature. 7 This probability of the market dissolving is similar to Acharya, Gale, and Yorulmazer (2010), and appears in other contexts, such as the probability of death or market shut-downs as in Blanchard (1985), Foucault (1999), Madhavan and Smidt (1993), among others. 8 On the potential difficulties of alternative funding for banks, see Stein (1995) and Huang and Ratnovski (2009). 9 Note that the wholesale transaction price is centered on the banker’s expectation of the true return r rather than the true price, r. This assumption, without loss of generality, eases the model exposition and could be similar to an over the counter forward contract with an uninformed party. This is done for simplicity, as introducing a second unbiased informative signal resulting from such a transaction would be inconsequential to the model conclusions.
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
325
Bankers solve the following problem: ˜ tþ1 ÞjC t g; VðK t ; et ; Lt ; rt Þ ¼ max Efð1 t Þ½K t þ Lt rt kt þ t VðK˜tþ1 ; e˜ tþ1 ; L˜tþ1 ; r ct ;wt
(1.2)
Such that: E½K˜tþ1 jC t ¼ K t þ ct ðdðrt ct Þ þ et Þ wt ðrt þ bwt Þ kt ;
(1.3)
E½˜etþ1 jC t ¼ 0;
(1.4)
E½L˜tþ1 jC t ¼ Lt dðrt ct Þ et þ wt ;
(1.5)
E½r ˜ tþ1 jC t ¼ rt :
(1.6)
Note that in equations (1.3) and (1.5), the framework imposes full collateralization of new projects for banks—an increase in Lt implies an offsetting decrease in Kt regardless of whether its financed by deposits or wholesale funding. This assumption reinforces in the short-term the funding tradeoff between deposits and wholesale markets which exists for long-term expansions of the bank balance sheet.10 The first order conditions are taken for the funding cost ct and the wholesale funding amount wt and yield the optimal bank funding policy: dðrt ct Þ ¼ dbwt þ
et 2
(1.7)
:
The left hand side of (1.7) is the net change in short-term loans financed directly by the bank, which optimally offsets any risky projects financed through the wholesale market, and also accommodates half the speculative demand. A larger d or b implies that banks will offset more of the wholesale funding of risky projects with traditional bank intermediation. As b is larger, the marginal cost of wholesale funding increases faster. As the marginal cost of wholesale funding increases, a bank exposes itself to greater risk if it needs to reverse its position using the wholesale funding market, causing it to hold less short-term assets. For a given demand shock e, the change in funding costs will be smaller as d is larger. Appendix A shows the model solution to the growth rate of risky loans: E½L˜tþ1 jC t ¼ aLt
a 2
et ;
with a ¼
b and f1 < 0: b f1 ð1 þ dbÞ
(1.8)
In (1.8), the parameter f1 reflects the asset losses from the changes of the value of bank’s risky assets, and is a function of both the size of the capital charge and the volatility of the value short-term assets (i.e. f1 ¼ f ðs 2r ; u; . . .Þ). As either of these increases, f1 becomes more negative and banks shift holdings away from risky assets and towards long-term investments, Kt. Expressing loan increases as E½L˜tþ1 jC t ¼ Lt þ E½DL˜tþ1 jC t , the change in holdings of risky asset is given by e a1 1 t L E½DL˜tþ1 jC t ¼ : (1.9) a a 2 Eq. (1.9) shows that the bank holds half of the risk-adjusted economic shock every period, e, thereby speculating on shortterm projects. The speed of adjustment away from risky assets in general, given by a, depends on the cost of unwinding these risky investments, that is, the liquidity conditions. These are reflected in the cost of traditional bank intermediation (d) and wholesale funding (b). Importantly, changes in wholesale funding drive much larger adjustments in holdings of risky assets. Note that consistent the notational convention employed throughout, which is intended to contrast wholesale funding with traditional deposit growth (e.g. Eq. (1.5)), a negative shock (e < 0) implies an increase in lending. Hence, as a ! 1, lending becomes more procyclical. From Eq. (1.8), as a is larger, an economic shock, given by e < 0, implies that greater liquidity and deposits would drive loans higher. Conversely, as a ! 0, lending becomes insensitive to economic shocks. Eq. (1.10) gives the solution for wholesale funding, which is a weighted average of the bank’s need to rebalance risky loan position with its absorption of the economic shock. a1 a1 wt ¼ et : (1.10) Lt 1 þ db 2ð1 þ dbÞ
10 Without loss of generality, the assumption of full collateralization simply forces the tradeoff between wholesale or deposit funding in response to change in investments. It thus avoids unlimited expansions of the asset side of the balance sheet based on only one type of funding that banks cannot do in actuality.
[()TD$FIG]
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
326
7 6
One Quater Loan Growth
Private
(in percent)
Public
400 350
Loan Index (2000=100)
5 300
4 3
250
2
200
1
150
Private Public
0 -1
100 2007/03 2007/06 2007/09 2007/12 2008/03 2008/06 2008/09 2008/12
2000/03
2002/03
2004/03
2006/03
2008/03
Fig. 1. Loan growth by bank type. Note: The right panel shows an average of indexes of loans for public and private banks. The left panel shows the average of one-quarter growth in loans for public and private banks, based on the index, 2000–2008. Source: Korean authorities, authors’ estimates.
2.4. Empirical estimation Simple manipulation of the model solution yields directly estimable equations, including an estimate of the parameter of interest, a, which summarizes the bank lending response to economic shocks, per (1.8). Combining (1.9) with the solution for wholesale funding in (1.10), yields:
að1 þ dbÞwt þ ð2a 1Þð1 aÞLt ¼ ða 1ÞE½DL˜tþ1 jC t :
(1.11)
Similarly, combining the analogous solution for interest rate setting given in (1.38) of Appendix A with (1.9), and recalling that pt = d(rt ct) + et yields:
að1 þ dbÞ pt þ ð1 aÞð1 þ dbða 2ÞÞLt ¼ FE½DL˜tþ1 jC t ;
F ð1 þ 2dbða 1ÞÞ:
(1.12)
Eq. (1.11) can be estimated directly or jointly in a system estimation with Eq. (1.12). The left-hand-side of (1.11) and (1.12) depend solely on deposits, wholesale funding, and loans ( pt ; wt ; and Lt, respectively), which are observable. A proxy for the right-hand-side – expected one-period-ahead loan growth – must be found. This is operationalized here as the updated linear projection of loan growth onto the available information set in time t, and this estimate is used to instrument expected loan growth. 3. Data The data employed in this study is based on the Korean banking system, which is composed of a mix of publicly and privately owned banks.11 This type of mixed banking sector is increasingly common among east Asian economies, as well as Latin America, for example, in Brazil. In these mixed banking systems, state owned banks often differ from private banks due to differences in the incentive structure of their management, the selection of staff, and the business model they follow. All of these factors impact a bank’s portfolio of assets and its ability to respond with new lending during the crisis. Many state owned banks also share implicit/explicit guarantees by the state that confer a level of liability security that can lower funding costs and at times, serve as safe havens. The bank level data used here consists of a panel of quarterly lending and balance sheet data from 8 Korean banks, from 2000 to 2008. Of these, eight state banks are owned either directly or indirectly by the Korean government (including development banks and cooperatives) while the remaining 10 are private (and in some cases, foreign owned). The start of the financial pressure on Korean bank balance sheets from the global financial crisis is associated with three different dates in the estimations (broadly from 2007Q1 to 2008Q4).12 These are, 2007Q1 (the collapse of the subprime mortgage lending industry in the U.S.), 2007Q4 (the start of the recession in the U.S.), and 2008Q3 (the collapse of Lehman in the U.S.) (Fig. 1). Summary statistics for the banking sector are shown in Table 1. The table shows loan and funding growth for public and private sector banks, before and after the crisis. The most pronounced differences between the pre-crisis years and the crisis
11 The authors are grateful to the Bank of Korea for providing the data. Coverage of the banking sector and aggregate statistics are reported on the Bank of Korea website (www.bok.or.kr) along with biannual financial stability reports. 12 This dating of the start of the crisis follows from the March–April 2007 revelation that the subprime industry in the United States was in severe financial distress; several subprime lenders declared bankruptcy announcing significant losses or company sell-offs, e.g. New Century Financial. By July 2007, two Bear Stearns hedge funds declared bankruptcy, by August 2007, broad global financial distress prompted joint interventions by the U.S. Federal Reserve, the European Central Bank, and the Central Banks of Australia, Canada, and Japan.
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
327
Table 1 Summary statistics by type of bank and crisis period. Private
(four quarter percent growth) Deposits Wholesale Loans Gov. paper (percent) NPL ratio Tier 1 cap. Global int. National int.
Public
2000–2006
2007–2008
2000–2006
2007–2008
8.64 11.19 13.14 71.22
7.59 20.45 12.31 7.74
10.76 11.83 13.56 20.02
10.8 21.93 20.59 2.5
2.83 7.56 0.2 0.54
0.77 8.55 0.51 0.56
2.4 8.12 0.2 0.54
0.62 8.22 0.51 0.56
Source: Bloomberg, Haver Analytics, authors’ estimates. Note: The upper panel shows the simple average of four quarter growth for two measures of bank funding (deposits and wholesale funding) and two asset categories (loans and government paper) for the years prior to the crisis and the 2007–2008 crisis period, broken down by public and private banks. The bottom shows the analogous breakout for the non-performing loan and tier 1 capital ratios, as well as global liquidity conditions (proxied by the OIS spread) and national liquidity conditions (proxied by the difference between three month CD and call money rates), all in percentage points.
[()TD$FIG]
1
1
5
2
11
5
12
18
6
6
7
3
8
8
9
13
2
17
3
9
15
4
4
7
10
10
13
12
14
11
17
14
18
Risk and Regulatory Indicators
15
Asset Structure
Fig. 2. Clustering by banking regulatory and risk indicators. Note: The left panel (hierarchically) clusters banks on the basis of their regulatory and supervisory indicators for prudential supervision (SBL ratio, NPL ratio, CAR, and equity ratio). The right panel clusters banks by indicators of their asset and liability structure (government securities, loans, wholesale funding, and deposits, all in percent of assets). Numbers are shown in lieu of bank names, with public banks numbered 2, 10, 13–18, and the rest private banks. Source: Authors’ estimates.
period can be observed in the growth of loans and in the purchase of government debt. After 2007, public banks accelerate lending and increase their sovereign debt holdings, while private banks slow their loan growth and lower their sovereign debt. The growth of wholesale funding accelerates for both public and private banks, however, in the latter it is compensating slower growth in deposits. For public banks, deposit growth remains stable during the crisis period. Notwithstanding this deposit growth in recent years, there has been a broad shift of household assets away from deposits to other assets, mainly securities, that to some extent is reflected in a wider increase in wholesale funding.13 Fig. 2 gives a feel for the differences in banking behavior by hierarchically clustering banks on the basis of their regulatory/supervisory indicators and asset structure. For prudential supervision, banks are clustered according to their similarity across the SBL, NPL,14 capital to asset and equity ratios.15 The right panel clusters banks by indicators of their asset and liability structure (government securities, loans, wholesale funding, and deposits, all in percent of assets). The figures show public banks (numbered 2, 10, 13–18) broadly clustered on one end of the spectrum, and private banks on the
13 Factors driving the rebalancing of household assets away from deposits include: (i) further liberalization of outflows and tax benefits in 2006; and (ii) portfolio rebalancing in the context of declining home-bias, increasing risk tolerance, and search for yield. 14 SBL ratios are a loan classification used which means ‘‘substandard or below (SBLs)—substandard, doubtful, or presumed loss.’’ NPL ratios are nonperforming loans. 15 The right panel clusters banks by indicators of their asset and liability structure (government securities, loans, wholesale funding, and deposits, all in percent of assets). Numbers are shown in lieu of bank names, with public banks numbered 2, 10, 13–18, and the rest private banks.
[()TD$FIG]
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
328
0, 3
0, 4
0, 5
0, 6
0, 7
0, 8
0, 9
0, 11
0, 12
1, 2
1, 10
1, 13
Loans
0, 1
Loans Wholesale
1, 14
1, 15
1, 16
1, 17
1, 18
2007q1 2007q32008q1 2008q3 2009q1 2007q1 2007q3 2008q1 2008q3 2009q1 2007q1 2007q3 2008q1 2008q3 2009q1 2007q1 2007q3 2008q1 2008q3 2009q1 2007q1 2007q3 2008q1 2008q3 2009q1
date Loans (solid), left scale, Wholesale funding (dashed), right scale.
Fig. 3. Lending and wholesale funding during the crisis 2007–2008. Note: The upper panel shows loans (left scale, values not shown) against wholesale funding (right scale, values not shown) for 10 private banks (labeled 0), while the lower panel shows lending and wholesale funding for public banks (labeled 1), for the 2007–2008 crisis period. Source: Authors’ estimates.
Table 2 Model estimates based on all banks. All banks Start crisis: Constant Loans
Dwholesale a Dcrisis lending R-squared N
Full
Public 2007Q1
2007Q4
2008Q3
Full
Private 2007Q1
2007Q4
2008Q3
Full
2007Q1:
2007Q4
2008Q3
0.05 0.27 0.03 0.01* 0.14 0.04*
0.37 0.71 0.04 0.01* 0.09 0.06
1.00 1.33 0.05 0.02* 0.08 0.09
1.57 6.54 0.06 0.11 0.07 0.3
0.05 0.36 0.04 0.01* 0.15 0.05*
0.67 1.1 0.05 0.02* 0.08 0.09
1.97 2.1 0.07 0.03* 0.04 0.12
0.85 12.61 0.05 0.19 0.03 0.43
0.45 0.44 0.03 0.01* 0.13 0.07*
0.31 0.94 0.03 0.02* 0.09 0.09
0.52 1.73 0.03 0.03 0.11 0.12
1.89 7.26 0.06 0.13 0.24 0.53
0.49
0.48 0.14 0.97 136
0.48 0.40 0.97 85
0.47 0.72 0.98 34
0.48
0.48 0.30 0.96 56
0.47 0.81 0.97 35
0.47 0.43 0.94 14
0.48
0.49 0.07 0.97 80
0.48 0.02 0.98 50
0.47 0.76 0.98 20
0.93 561
0.93 231
0.94 330
Source: Country authorities, authors’ estimates. Note: Panel GMM estimation of equation expected loan growth regressed on loans and wholesale funding, and for all (left), public (center), and private (right) banks, quarterly data, 2000–2008. Estimates are presented for the pre-crisis sample (up to 2008Q2) and sub-samples based on crisis starting dates (top row) for 2007Q1 (subprime lender bankruptcy), 2007Q4 (start of U.S. recession start), and 2008Q3 (Lehman). Standard errors in italics, a is the estimated speed of portfolio adjustment. Dcrisis lending is the percentage point change in a during the crisis relative to the full sample estimate, negative indicates countercyclical lending growth in response to negative economic shock. Fixed effects not reported. * Signifies at least 10 percent significance.
other end. Moreover, the most pronounced differences are driven by regulatory indicators, consistent with the model assumption of differences in prudential management by bank type.16 Fig. 3 shows each bank’s lending graphed against its drawing of wholesale funding for the 2007–2008 crisis period. Towards the end of the period, when the crisis is most acute, wholesale funding declines in roughly half of all the banks shown. Nonetheless, for five of eight public banks, wholesale
16 Note that this does not imply a different regulatory and oversight regime, but different management within a shared regime, which can be due to a number of factors such as mandate, compensation schemes, etc. See, for example, La Porta, Lopez de Silanes, and Schleifer (2002) on public banks.
[()TD$FIG]
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
Private Banks
Public Banks 12
329
12
DDEPOSITS
DDEPOSITS
8
8
4 4 0 0 -4 -4 00 12
01
02
03
04
05
06
07
08
-8 00 6
DWHOLESALE
01
02
03
04
05
06
07
08
02
03
04
05
06
07
08
02
03
04
05
06
07
08
02
03
04
05
06
07
08
DWHOLESALE
4
8
2 4
0 -2
0
-4 -4 -6 -8 00
200
01
02
03
04
05
06
07
08
-8 00 250
LOANS
160
200
120
150
80
100
40
50
0 00 10
01
02
03
04
05
06
07
08
LOANS
0 00 6
EDLOANS
01
8
5
6
4
01
EDLOANS
3
4
2 2 1 0
0
-2 00
01
02
03
04
05
06
07
08
-1 00
01
Fig. 4. Data used in estimation. Note: The left panel shows the change in deposits, the change in wholesale funding, loans, and the estimated expected loan growth (the dependent variable in the estimations) for eight public banks, and the right panel show the same for ten private banks, quarterly, 2000–2008. All series except loans are stationary. Source: Authors’ estimates.
funding is broadly stable or increasing even during the quarters that observed the most acute global financial pressures (2008 Q3–Q4). 4. Empirical results This section presents GMM estimates of Eq. (1.11) and GMM system estimation of Eqs. (1.11) and (1.12). The estimations follow closely from the model solution and use employ observable bank-level data. Only expected loan growth is not directly observed. Thus, consistent with rational expectations, it is operationalized as the updated GMM projection of loan growth on
[()TD$FIG]
330
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334 0.40 0.20 0.00 -0.20 -0.40 -0.60 -0.80
Public All Banks Private
-1.00
2007Q1
2007Q4
2008Q3
Fig. 5. Change in lending response during the crisis, by bank type. Note: The graph shows the difference in the estimated lending response parameter, a, when estimating on the full sample (labeled full) versus the crisis sub-samples (beginning in 2007Q1, 2007Q4, and 2008Q3). An increase in the estimate of a (a positive bar) implies a greater direct response to expected loan growth in the wake of an economic shock. A decline in the estimate of a implies that during the crisis, the bank type on average responded countercyclically. Source: Authors’ estimates.
the available information set.17 Having generated (E½DL˜tþ1 jC t ), the estimated equations follow directly from (1.11) and (1.12), and are E½DL˜tþ1 jC t ¼ g 10 þ g 11 wholesalei;t þ g 13 loanst þ e1it ;
(1.13)
E½DL˜tþ1 jC t ¼ g 20 þ g 21 Dde positsi;t þ g 23 loanst þ e2it :
(1.14)
From Eq. (1.11), the estimated coefficients are non-linear functions of the model parameters. In solving for E[DLi,t+1], its parameter coefficients in the solution are implicitly dividing the estimated coefficients in (1.13) and (1.14). Nonetheless, identification of a, the loan reaction parameter, is achieved in (1.13), since E½gˆ 11 ¼ ð1 2aÞ and hence E½1=2ð1 gˆ 11 Þ ¼ a: The other parameters cannot be directly identified but they are not critical to interpreting the model implications. Table 2 reports estimates of Eq. (1.13) for all banks (left), public banks (center), and private banks (right). The estimates are presented for the full sample and sub-samples based on differing crisis start dates: 2007Q1 (subprime lender bankruptcy), 2007Q4 (U.S. recession start), and 2008Q3 (Lehman collapse). The equation is estimated via fixed effects panel GMM to control for the potential endogeneity from the generated regressor E[DLi,t+1]. The results show broadly stable and statistically significant estimates across the different start dates (the last crisis sub-sample is intended as indicative as it includes only two quarters in the panel data). In particular, the coefficient on loans ðgˆ 11 Þ is stable, significant and signed as expected. While the high goodness of fit could potentially stem from a spurious regression of two non-stationary series, this is not the case. The model solution include loans, which are non-stationary, however, all other variables are stationary, and are shown in Fig. 4. The bottom of Table 2 reports the estimated speed of portfolio adjustment, a. The estimates based on all banks suggest that bank lending on average became countercyclical during the crisis: as economic shocks materialized, the propensity to respond with lending increased, evidenced by the declining a in the reported as Dcrisis lending row. The estimate in this row is interpreted as follows: given a negative economic shock, the bank rebalances its portfolio by reducing its exposure to risky lending. The estimate reported in the Dcrisis lending row is the decline in this marginal propensity to cut quarterly loan growth in response to shocks, in percent of the shock. Hence, a Dcrisis lending of 0.4 implies that from 2007Q4 on loan growth would have been an aggregate 2 percent lower in the economy, but for countercyclical measures taken by banks. Comparing public and private banks, the estimates suggest private banks observed almost no change in their cyclical lending pattern, while public banks increased lending countercyclically at an average of 0.6 percent quarterly. Fig. 5 graphically shows the change in lending by bank type, where public banks drive the countercyclical lending in the overall banking sector based on the first two crisis start dates (2007Q4 and 2008Q1). The data for Korea shows that total lending from 2007Q4 onwards grew by 20 percent, with public banks increasing lending by 26 percent and private banks increasing lending by 16 percent. Table 3 confirms the estimated values of a via panel GMM system estimation of Eqs. (1.13) and (1.14), and shows pffiffiffiffiffiffi estimates of wholesale and traditional funding costs, in the row labeled db. The estimates cannot identify d (the traditional funding costs) or b (wholesale funding costs) separately, it can identify the product of the two parameters. Hence, the row reports the geometric average of the parameters, and compares estimates of these costs across public and private sector banks. The estimates suggest that private banks faced higher funding costs throughout the sample. This evidence also suggests that financial turmoil exarcebates liquidity risks for some banks, those banks that have implicit or explicit
17 Panel GMM estimation, at time t, instruments include lagged loan growth, domestic and external interest rates, prudential and regulatory risk measures, and current and lagged bank balance sheet variables, e.g. deposits, equity, etc.
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
331
Table 3 System estimation.
Constant Loans
Dwholesale Constant Loans
Ddeposits a ffiffiffiffiffiffi p
db
R-squared
All
Private
Public
0.05 0.02* 0.03 0* 0.17 0.01* 0.02 0.01* 0.03 0* 0.28 0.01*
0.11 0.01* 0.03 0* 0.34 0.01* 0.05 0* 0.02 0* 0.18 0*
0.11 0.01* 0.04 0* 0.12 0* 0.04 0.01* 0.04 0* 0.22 0*
48.7 0.42 0.69
48.9 0.60 0.66
48.2 0.36 0.83
Source: Country authorities, authors’ estimates. Note: Panel GMM system estimates of the model solution for quarterly banking data, 2000–2008, presented for 10 private, eight public, and all banks. Standard errors in italics. The estimated speed of portfolio adjustment, a gives the estimated sensitivity of expected loan growth to an economic shock. Fixed effects not reported. * Signifies at least 10 percent significance.
government guarantees, more conservative ex-ante risk management, or lower ex-ante asset allocation, benefit during the crisis from lower wholesale funding costs.18 5. Conclusion This study addresses three banking interrelated banking issues in the context of the recent global financial crisis and its impact on the Korean banking sector. First, public banks were able to offset some of the impact of the global financial crisis that began in 2007 by significantly expanding lending. As the financial crisis spread to Korea, state owned banks expanded lending, offsetting credit contractions resulting from private sector banks’ efforts to raise capital on their balance sheets. The portfolio management optimization framework presented in this study suggests public sector banks are able to expand lending in a downturn because as a negative shock is realized, they are less affected since they hold less risk ex-ante, and hence, need to rebalance risk (i.e. raise capital) less in a crisis. As a result, they are in a stronger position to lend in a downturn. Private banks were able to expand credit by much less during the crisis. Secondly, for Korea, wholesale funding markets largely continued to function during the crisis, though with slightly higher costs. To some extent, the smooth functioning of the wholesale funding markets reflects the significant policy support on the part of the Korean authorities in support of financial stability, alongside Korea’s peripheral role in the current crisis. These caveats notwithstanding, wholesale funding continued to expand and support lending, and indicators of wholesale funding costs do not suggest that this channel of funding was a significant impediment to countercyclical lending. Finally, this study shows that there could be potential benefits to having state owned banks as a natural hedge against the incentives towards risk taking inherent in private sector banking system. Private banks in the model presented here take risks not in violation of prudential norms, but rather in full compliance of prudential norms. Moreover, the need to rebalance – i.e. raise bank capital or liquidate risky or distressed assets in the midst of a financial crisis – is driven by prudential concerns, and hence countercyclical lending during a downturn is curtailed. In practice and in line with the model, the fact that private banks in Korea were well capitalized and complying with prudential norms did not prevent natural reduction in credit as their balance sheet deteriorated. This makes sense for banks, but is detrimental to the wider banking system, the real economy, and to output volatility. Hence, the impact of greater instability associated with private bank incentives suggest a tradeoff between volatility and output growth related to the the size of a country’s public banking sector. An area for future research is to investigate the cost of public banks against the potential stabilizing benefits that they could provide. Acknowledgements The authors thank Gabriel Di Bella, Hali Edison, Robert Flood, Subir Lall, Charan Singh, Jay Surti, Francisco Va´zquez, and an anonymous referee for helpful comments.
18 Hence, the ex-ante lower profitability of public banks may imply less exposure to the ‘‘dark side’’ of wholesale funding put forth Huang and Ratnovski (2009).
332
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
Appendix A. Model solution A.1. Learning the liquidation value from excess demand The banker’s expectations of rt are based on observed excess demand: zt ð pt Þ ¼ P t þ Et ¼ Dt d pt ;
(1.15)
where observing Dt = drt + Et is equivalent to observing d1Dt = rt + d1Et, an unbiased signal of the true liquidation value, with the variance of this signal denoted s 2f , and the signal to noise ratio given by Y ¼ s 2r =s 2f . Every period the banker updates rt1, the prior expectations of rt based on the new signal as qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 2 Y þ Y þ 4Y : (1.16) rt ¼ Qd1 Dt þ ð1 QÞrt1 ; with Q ¼ 2
A.2. Capital charge Bankers pay kt ¼ us 2 ðA˜ t Þ on the bank’s portfolio of assets, given by A˜ t ¼ K t þ r t Lt , with u scaling the opportunity cost of this capital requirement. The variance of the portfolio is given by s 2 ðA˜ t Þ ¼ varðK t þ r t Lt Þ ¼ L2t s 2r , so that the capital charge becomes:
kt ¼ uL2t s 2r :
(1.17)
A.3. Model solution The control problem faced by bankers is: ˜ tþ1 ÞjC t g; VðK t ; et ; Lt ; rt Þ ¼ max Efð1 t Þ½C t þ Lt rt kt þ t VðK˜tþ1 ; e˜ tþ1 ; L˜tþ1 ; r ct ;wt
(1.18)
such that: E½K˜tþ1 jC t ¼ K t þ ct ðdðrt ct Þ þ et Þ wt ðrt þ bwt Þ kt ;
(1.19)
E½˜etþ1 jC t ¼ 0;
(1.20)
E½L˜tþ1 jC t ¼ Lt dðrt ct Þ et þ wt ;
(1.21)
˜ tþ1 jC t ¼ rt : E½r
(1.22)
The first order conditions are (abbreviating the expectation of the value function by Vt+1): @V tþ1 @V tþ1 ðdrt þ et 2dct Þ td ¼ 0; c:t @K @L w : t
@V tþ1 @V tþ1 ðrt þ 2bwt Þ þ t ¼ 0: @K @L
(1.23)
(1.24)
Combining (1.23) and (1.24) (note that @Vt+1/@K 6¼ 0 shown below) yields the optimal bank funding policy: ct ¼ rt þ bwt þ ðet =2dÞ: Taking the envelope conditions for (1.18) through (1.22) yields: dV t @V tþ1 ¼ ð1 t Þ þ t ; dK @K
(1.25)
(1.26)
dV t @V tþ1 @V tþ1 ct ; ¼ t de @L @K
(1.27)
dV t @V tþ1 @V tþ1 @V tþ1 td þt ¼ ð1 t ÞLt þ t ðdct wt Þ ; dr @K @L @r
(1.28)
dV t @V tþ1 @V tþ1 ¼ ð1 t Þrt þ t 2Lt us 2r ð1 t Þ þ t : dL @L @K
(1.29)
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
333
This problem is solved by verifying a conjectured functional form of the value function. The envelope conditions suggest the form of the value function is Vˆt ¼ f0 þ rt Lt þ K t þ f1 L2t þ f2 et Lt þ f3 e2t :
(1.30)
In (1.30), f0 through f3 are unknown constants. Taking derivatives with respect to K, L, and r and updating yields: ! dVˆtþ1 E (1.31) ¼ Eðrt þ 2f1 Ltþ1 þ f2 etþ1 Þ; dLtþ1
E
dVˆtþ1 dK tþ1
E
dVˆtþ1 drtþ1
E
dVˆtþ1 detþ1
! ¼ 1;
(1.32)
¼ Eðrt Þ;
(1.33)
¼ Eðf2 Ltþ1 þ 2f3 etþ1 Þ:
(1.34)
!
!
Using (1.31) and (1.32), the first order condition for the wholesale market, (1.24), becomes: wt ¼
f1 L : b tþ1
(1.35)
Using (1.35) in the optimal funding policy derived in (1.25) yields: e t : ct ¼ rt þ f1 Ltþ1 þ 2d
(1.36)
Substituting (1.36) – which captures the optimal cost and wholesale funding policies – into the evolution equation for the stock of loans given by (1.21) to solve for Lt+1: E½L˜tþ1 jC t ¼ aLt
a 2
et ;
with a ¼
b bða 1Þ , f1 ¼ ; b f1 ð1 þ dbÞ að1 þ dbÞ
Optimal funding costs and wholesale funding purchase/sale are given by bða 1Þ 1 þ 2dbð2 aÞ Lt þ c t ¼ rt þ et ; ð1 þ dbÞ 2dð1 þ dbÞ wt ¼
a1 a1 et : Lt 1 þ db 2ð1 þ dbÞ
(1.37)
(1.38)
(1.39)
To confirm the conjecture in (1.30), solve for f1 through f3 using the envelope conditions for given along with the updated derivative of the conjecture and the optimal policy functions. Using the envelope condition for loans, (1.29), the derivative of the conjecture (1.31), and the optimal policy functions yields f1 ¼ ls 2r =ða 1Þ and f2 ¼ aðus 2r f1 Þ. A meaningful economic solution with f1 < 0 implies 0 < a < 1 which requires:
a
ða 1Þb > 0; ða 1Þb ð1 þ dbÞus 2r
with b; d > 0:
(1.40)
As a ! 0 from above, the left hand side of (1.40) is negative. As a ! 1 from below, the left hand side of (1.40) approaches unity from below. Since the left hand side of (1.40) is a continuous function, by the intermediate value theorem there exists and a such that 0 < a < 1 and f1 < 0.
References Acharya, V., Gale, D., & Yorulmazer, T. (2010), Rollover risk and market freezes. Vol. w15674, NBER Working Paper Series. Allen, F., Babus, A., & Carletti, E. (2009). Financial crises: Theory and evidence. Annual Review of Financial Economics, 1, 97–116. Barth, J., Caprio, G., & Ross, L. (2001). The regulation and supervision of banks around the world: A new database. University of Minnesota Financial Studies Working Paper No. 0006. Blanchard, O. (1985). Debt, deficits, and finite horizons. Journal of Political Economy, 93, 223–247. Blundell-Wignall, A. (2009). Restoring confidence in financial systems. OECD Forum. www.oecd.org/dataoecd/15/4/43319875.pdf. Caprio, G., & Martinez Peria, M. S. (2002). Avoiding disaster: Policies to reduce the risk of banking crises. In E. Cardoso & A. Galal (Eds.), Monetary policy and exchange rate regimes: Options for the Middle East (pp. 193–230). Cairo, Egypt: Egyptian Center for Economic Studies. Clarke, G., Cull, R., & Shirley, M. (2005). Bank privatization in developing countries: A summary of lessons and findings. Journal of Banking & Finance, 29(August– September (8–9)), 1905–1930.
334
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
Foucault, T. (1999). Order flow composition and trading costs in a dynamic limit order market. Journal of Financial Markets, 2, 99–134. Huang, R., & Ratnovski, L. (2009). The dark side of bank wholesale funding. Working Paper No. 09-3, Federal Reserve Bank of Philadelphia. IMF (2009). Republic of Korea: Article IV Consultation—Staff Report. IMF Country Report No. 09/262, International Monetary Fund, Washington, DC. La Porta, R., Lopez de Silanes, F., & Shleifer, A. (2002). Government ownership of banks. Journal of Finance, 57, 265–301. Madhavan, A., & Smidt, S. (1993). An analysis of changes in specialist inventories and quotations. The Journal of Finance, 48(December (5)), 1595–1628. Micco, A., Panizza, U., 2004. Bank ownership and lending behavior. Research Working Paper # 520. Inter-American Development Bank. Forthcoming in Economics Letters. Ramey, G., & Ramey, V. A. (1995). Cross-Country Evidence on the Link between Volatility and Growth, American Economic Review. American Economic Association, 85(December (5)), 1138–1151. Reinhart, C.M., & Rogoff, K.S., 2008. This Time is Different: A Panoramic View of Eight Centuries of Financial Crises, NBER Working Papers 13882, National Bureau of Economic Research, Inc. Repullo, R. (2004). Policies for banking crises: A theoretical framework. CEPR Discussion Paper No. 4727 (November). Romeu, R. (2010). Price discovery in markets with multiple dealers. IMF Staff Papers Vol. 57, pp. 450–483. Stein, J. C. (1995). An adverse-selection model of bank asset and liability management with implications for the transmission of monetary policy. The RAND Journal of Economics, 29(Autumn (3)), 466–486. Suarez, J. (1994). Closure rules, market power and risk-taking in a dynamic model of bank behavior. LSE Paper 196, SSRN: http://ssrn.com/abstract=6930.
Contents lists available at ScienceDirect
Journal of Asian Economics
A model of bank lending in the global financial crisis and the case of Korea§ Larissa Leony a, Rafael Romeu b,* a b
Inter-American Development Bank, United States International Monetary Fund, United States
A R T I C L E I N F O
A B S T R A C T
Article history: Received 1 February 2010 Received in revised form 6 March 2011 Accepted 10 March 2011 Available online 23 March 2011
This paper studies two interrelated banking sector issues in the context of the global financial crisis and its impact on Korea: (i) To what extent did state owned banks expand lending to offset declining credit as private sector bank balance sheets deteriorated? (ii) In the recent crisis, was bank lending constrained because of funding or liquidity factors? To address these issues, a framework of optimal bank lending is presented in which banks face adverse selection in a market for risky investment projects, pay regulatory costs, and borrow in wholesale and traditional funding markets. The model implications are tested on bank-level data with a focus on the global financial crisis. The results point to significant credit expansion by public sector banks in response to deteriorating international credit conditions. Estimates of private sector lending during the crisis suggest that, at best, the credit crunch was less profound than it could have been. Wholesale funding is not found to have been a major driver of deteriorating credit conditions in Korea, reflecting, at least in part, strong policy support from the authorities during the crisis. ß 2011 Elsevier Inc. All rights reserved.
JEL classification: E5 F3 G28 Keywords: Financial crisis Banking Regulation
1. Introduction This paper studies two interrelated banking sector issues in the context of the global financial crisis and its impact on Korea: (i) To what extent did state owned banks expand lending to offset declining credit as private sector bank balance sheets deteriorated? (ii) In the recent crisis, was bank lending constrained because of funding or liquidity factors? To address these issues, a framework of optimal bank lending is presented in which banks face adverse selection in a market for risky investment projects, pay regulatory costs, and borrow in wholesale and traditional funding markets. The model implications are tested on bank-level data with a focus on the global financial crisis. The results point to a significant credit expansion by public sector banks in response to deteriorating international credit conditions. Estimates of private sector lending during the crisis suggest that, at best, the credit crunch was less profound than it could have been. Wholesale funding is not found to have been a major driver of deteriorating credit conditions in Korea, reflecting, at least in part, strong policy support from the authorities during the crisis. The model presented incorporates adverse selection in the lending market and credit growth that depend both on the risk profile of the loans and the risk tolerance of the lending bank. Specifically, a dynamic optimization problem for a bank lending portfolio is developed. In this problem, banks choose between investing in either short-term high return
§ The views expressed herein are those of the author and should not be attributed to the IMF, its Executive Board, or its management, the IADB, its executive Board, or its management. * Corresponding author at: International Monetary Fund, Fiscal Affairs Department, 700 19th Street, N.W. Washington, DC, United States. E-mail address: [email protected] (R. Romeu).
1049-0078/$ – see front matter ß 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.asieco.2011.03.004
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
323
investments or long-term lower return projects, in a market with adverse selection, alternative funding sources (including wholesale funding), and costly regulatory provisioning for risk. The model solution suggests that over time, banks facing higher costs to risk taking (e.g. state-owned banks) will earn lower returns, but also have smaller reduction in loan growth in the wake of economic shocks–even transitory (mean-zero) shocks. While banks optimally speculate by absorbing a part of realized economic shocks, they hold less risk and need to rebalance away from risky assets less as regulatory costs (or institutional risk aversion) increases. The more risk a bank holds, the greater the impact on loan growth from both losses to regulatory costs, and losses incurred from rebalancing their portfolio using expensive wholesale markets. Rebalancing costs increase (further constraining loan growth) with greater marginal liquidity costs in the wholesale funding market or lower market power from traditional intermediation–both of which tend to increase in periods of financial stress.1 The model’s implications are tested using bank-level data from eighteen major banks comprising the majority of the Korean banking sector. The impact of liquidity shocks and wholesale funding costs on loan growth are considered for all banks. In addtion, public and private banks are considered separately with a view that indentifies the implications for public credit growth in the economy. During the global financial crisis, a large decline in export demand alongside financial contagion resulted in seizing credit markets, and a contraction above 5 percent of GDP in the last quarter of 2008 in Korea—among the largest in Asia. In response, the authorities responded with a wide range of policy measures to ensure adequate credit flows, including a cumulative 325 basis points interest rate cut, widening the list of eligible counter parties and collateral in repo operations, and relaxed bank liquidity requirements. The Bank of Korea also set aside US$55 billion to support credit to trade related business, announced support for the corporate and commercial paper markets, and increased lending to small and medium sized enterprises. Nonetheless, many of these measures were announcements to stand behind markets and support confidence, and not actual expenditures.2 As a result, the up-front cost of government support to the financial sector remained below 0.5 percent of GDP, and total support cost less than 15 percent of GDP, which is moderate by international standards, and in particular, compared with the US, the UK, or the G20 average (74 percent, 48 percent, and 28 percent of GDP, respectively).3 Given the sizeable economic shock alongside a mixed public–private banking system typical of Asian economies (and increasingly common in other regions, e.g. Brazil), Korea is a good candidate to study the response of public banks in crisis conditions. The empirical results suggest that the role of the public sector banks in Korea helped counter the effects of the crisis. Estimates of the model’s structural parameters, which reflect the marginal propensity to lend in response to an economic shock, show that public banks became countercyclical during the crisis, while private banks largely did not. On the funding side, the empirical results show that wholesale funding is a driver of loan growth, but the costs of funding – both traditional core deposits and wholesale – increased during the crisis. While prior work has identified exisiting difficulties with state-owned banks, the policy implications of these results to some extent counter convetional banking sector policy advice regarding their role.4 Barth, Caprio, and Levine (2001) and Micco and Panizza, 2004, for example, find poor state-owned bank performance in developing countries, with lower profitability and margins, and higher overhead. The model presented here predicts lower relative returns in public banks— they do not chase high return short-term risky investments, and they face higher costs from greater ‘‘institutional’’ risk aversion. Both the model and the empirical results show a trade-off from public banks that optimally lend countercyclically, in contrast to private banks that optimally curtail lending in a downturn. That is, privately owned banks that are in full compliance of regulatory and risk provisioning requiremets will optimally curtail lenidng during periods of stress. Other studies have presented evidence of the long-term negative effects of public banks on output growth. For example, La Porta, Shleifer, and Lopez-de-Silanes (2002) find significantly lower long-term GDP growth in countries with large public banking sectors. Nevertheless, Ramey and Ramey (1995) show a strong negative correlation between volatility and long-term growth. Reinhart and Rogoff (2008) show the pervasiveness of banking crises, and importantly, the heavy cost borne by the public sector that ultimately undo reductions in the size of the public sector from bank privatizations. Hence, the potential impact of greater banking sector instability associated with private bank incentives suggest a potential tradeoff between volatility and growth from the size of a country’s public banking sector. The evidence that state-owned banks increase the probability of systemic banking crises is tentative (e.g. Caprio & Martinez Peria, 2002) and under increasing review given the recent financial crisis.5 The next section lays out the conceptual framework and the implications from its analytical solution, which is developed in detail in Appendix A. Section 3 gives an overview of the data, and Section 4 estimates the model and presents the empirical evidence. Section 5 concludes.
1
Wholesale funding refers to all financing raised by banks excluding core deposits. See IMF (2009). See Blundell-Wignall (2009). 4 For example, Clarke, Culla, and Shirley (2005), which provide a summary of lessons regarding bank privatization in developing countries and conclude that ‘‘although bank privatization usually improves bank efficiency, gains are greater when the government fully relinquishes control. . .’’, i.e. there is no benefit from direct state ownership of banks. 5 For example, Allen, Babus, and Carletti (2009) argue that a coherent conceptual framework for banking crises has still not emerged and regulation is ad hoc. 2 3
324
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
2. A framework of bank lending behavior This section develops a dynamic inter-temporal model where bankers strategically manage long-term lending portfolios while profiting from short-term speculative returns funded by both deposits and a more costly wholesale funding market. This framework is intended to incorporate the more salient insights of static game-theoretic models of the banking sector in an inter-temporal dynamic programming framework.6 While more constrained in scope, the optimal control problem developed here yields an empirically tractable closed form solution and testable implications for the South Korean data. The conclusions of the model show that bank portfolio allocations between long-term and short-term investments and the cost of wholesale funding impact loan growth in a crisis. Hence, public banks, which face tighter restrictions and are limited in their ability to invest in riskier projects are ex-ante better prepared for a crisis and optimally expand lending faster in downturns. Consider an economy where bankers raise capital every period to invest in long-term assets (denoted K) earning a return normalized to unity, and short-term riskier loans (denoted L) with a liquidation value rt. There exists a non-zero probability (denoted (1 t)) that at period t = T the market for these riskier projects collapses for reasons that are extraneous to the bank, at which time the bank’s portfolio would be liquidated.7 Every period (i) bankers offer to pay funding cost ct for risky projects that have liquidation value rt at time T in an elastic market with adverse selection; (ii) bankers choose to purchase (or liquidate) risky projects through a wholesale funding market, denoted wt, which has increasing marginal costs8; and (iii) bankers pay a capital charge kT directly related to the variance of liquidation value of the portfolio. 2.1. Buying and selling short-term risky investment projects After quoting a schedule of funding costs, ct, bankers are offered a schedule of investment projects pt that is composed of two unobserved funding demands, pt = Pt + Et. The first component, Pt, reflects the adverse selection in a market for risky projects where the seller or ‘‘entrepreneur’’ knows the return and the banker does not. The amount of projects the banker buys for a given funding cost ct is Pt = d(rt ct), where d > 0 reflects the price elasticity faced by the banker, and rt the true liquidation value of the project. Unlike the entrepreneurs, the bankers cannot observe the true liquidation value of the project (rt), but know it is a random walk, so that rt = rt1 + yt and yt Nð0; s 2r Þ. The second component, Et, is a mean zero funding demand that is not strategic and only reflects shocks to the economy, i.e. Et Nð0; s 2E Þ. In forming expectations of rt, bankers extract an informative signal of excess demand: zt ð pt Þ ¼ P t þ Et ¼ Dt d pt ;
(1.1)
With the intercept of excess demand for projects given by Dt = drt + Et. Appendix A shows that observing excess demand allows the bankers to update their unbiased estimates of the rate of return every period given their information set Ct, which contains their past banking transactions and current excess demand. The estimate of rt is denoted rt = E[rt|Ct]. The unbiased estimator of rt also allows for an unbiased estimate of the liquidity demand from economic shocks, denoted et = E[Et|Ct]. 2.2. Tapping wholesale funding Banks can adjust their level of risky projects by changing their funding costs and by buying (or liquidating) risky projects in a wholesale funding market, denoted wt. Hence, when the bank is not financing risky short-term projects directly through zt(pt), it may choose to do so through the wholesale market, wt. Trading costs in this wholesale market are increasing in wt, so that buying/selling wt projects costs wt ðrt þ bwt Þ. Hence, bankers face an increasing marginal cost in the wholesale funding market given by b.9 2.3. Regulatory capital charges Bankers must set aside a capital charge each period directly proportional to the short-term variance of their portfolio. Hence, they must pay kt ¼ us 2 ðA˜ t Þ on the bank’s portfolio of assets, given by A˜ t ¼ K t þ r t Lt , with u scaling the opportunity cost of this capital requirement.
6 The application of dynamic programming model here is similar to Madhavan and Smidt (1993), Suarez (1994), and Romeu (2010). Repullo (2004), Huang and Ratnovski (2009) are banking models influencing this approach, among others. Allen et al. (2009) provide a survey of recent banking crisis literature. 7 This probability of the market dissolving is similar to Acharya, Gale, and Yorulmazer (2010), and appears in other contexts, such as the probability of death or market shut-downs as in Blanchard (1985), Foucault (1999), Madhavan and Smidt (1993), among others. 8 On the potential difficulties of alternative funding for banks, see Stein (1995) and Huang and Ratnovski (2009). 9 Note that the wholesale transaction price is centered on the banker’s expectation of the true return r rather than the true price, r. This assumption, without loss of generality, eases the model exposition and could be similar to an over the counter forward contract with an uninformed party. This is done for simplicity, as introducing a second unbiased informative signal resulting from such a transaction would be inconsequential to the model conclusions.
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
325
Bankers solve the following problem: ˜ tþ1 ÞjC t g; VðK t ; et ; Lt ; rt Þ ¼ max Efð1 t Þ½K t þ Lt rt kt þ t VðK˜tþ1 ; e˜ tþ1 ; L˜tþ1 ; r ct ;wt
(1.2)
Such that: E½K˜tþ1 jC t ¼ K t þ ct ðdðrt ct Þ þ et Þ wt ðrt þ bwt Þ kt ;
(1.3)
E½˜etþ1 jC t ¼ 0;
(1.4)
E½L˜tþ1 jC t ¼ Lt dðrt ct Þ et þ wt ;
(1.5)
E½r ˜ tþ1 jC t ¼ rt :
(1.6)
Note that in equations (1.3) and (1.5), the framework imposes full collateralization of new projects for banks—an increase in Lt implies an offsetting decrease in Kt regardless of whether its financed by deposits or wholesale funding. This assumption reinforces in the short-term the funding tradeoff between deposits and wholesale markets which exists for long-term expansions of the bank balance sheet.10 The first order conditions are taken for the funding cost ct and the wholesale funding amount wt and yield the optimal bank funding policy: dðrt ct Þ ¼ dbwt þ
et 2
(1.7)
:
The left hand side of (1.7) is the net change in short-term loans financed directly by the bank, which optimally offsets any risky projects financed through the wholesale market, and also accommodates half the speculative demand. A larger d or b implies that banks will offset more of the wholesale funding of risky projects with traditional bank intermediation. As b is larger, the marginal cost of wholesale funding increases faster. As the marginal cost of wholesale funding increases, a bank exposes itself to greater risk if it needs to reverse its position using the wholesale funding market, causing it to hold less short-term assets. For a given demand shock e, the change in funding costs will be smaller as d is larger. Appendix A shows the model solution to the growth rate of risky loans: E½L˜tþ1 jC t ¼ aLt
a 2
et ;
with a ¼
b and f1 < 0: b f1 ð1 þ dbÞ
(1.8)
In (1.8), the parameter f1 reflects the asset losses from the changes of the value of bank’s risky assets, and is a function of both the size of the capital charge and the volatility of the value short-term assets (i.e. f1 ¼ f ðs 2r ; u; . . .Þ). As either of these increases, f1 becomes more negative and banks shift holdings away from risky assets and towards long-term investments, Kt. Expressing loan increases as E½L˜tþ1 jC t ¼ Lt þ E½DL˜tþ1 jC t , the change in holdings of risky asset is given by e a1 1 t L E½DL˜tþ1 jC t ¼ : (1.9) a a 2 Eq. (1.9) shows that the bank holds half of the risk-adjusted economic shock every period, e, thereby speculating on shortterm projects. The speed of adjustment away from risky assets in general, given by a, depends on the cost of unwinding these risky investments, that is, the liquidity conditions. These are reflected in the cost of traditional bank intermediation (d) and wholesale funding (b). Importantly, changes in wholesale funding drive much larger adjustments in holdings of risky assets. Note that consistent the notational convention employed throughout, which is intended to contrast wholesale funding with traditional deposit growth (e.g. Eq. (1.5)), a negative shock (e < 0) implies an increase in lending. Hence, as a ! 1, lending becomes more procyclical. From Eq. (1.8), as a is larger, an economic shock, given by e < 0, implies that greater liquidity and deposits would drive loans higher. Conversely, as a ! 0, lending becomes insensitive to economic shocks. Eq. (1.10) gives the solution for wholesale funding, which is a weighted average of the bank’s need to rebalance risky loan position with its absorption of the economic shock. a1 a1 wt ¼ et : (1.10) Lt 1 þ db 2ð1 þ dbÞ
10 Without loss of generality, the assumption of full collateralization simply forces the tradeoff between wholesale or deposit funding in response to change in investments. It thus avoids unlimited expansions of the asset side of the balance sheet based on only one type of funding that banks cannot do in actuality.
[()TD$FIG]
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
326
7 6
One Quater Loan Growth
Private
(in percent)
Public
400 350
Loan Index (2000=100)
5 300
4 3
250
2
200
1
150
Private Public
0 -1
100 2007/03 2007/06 2007/09 2007/12 2008/03 2008/06 2008/09 2008/12
2000/03
2002/03
2004/03
2006/03
2008/03
Fig. 1. Loan growth by bank type. Note: The right panel shows an average of indexes of loans for public and private banks. The left panel shows the average of one-quarter growth in loans for public and private banks, based on the index, 2000–2008. Source: Korean authorities, authors’ estimates.
2.4. Empirical estimation Simple manipulation of the model solution yields directly estimable equations, including an estimate of the parameter of interest, a, which summarizes the bank lending response to economic shocks, per (1.8). Combining (1.9) with the solution for wholesale funding in (1.10), yields:
að1 þ dbÞwt þ ð2a 1Þð1 aÞLt ¼ ða 1ÞE½DL˜tþ1 jC t :
(1.11)
Similarly, combining the analogous solution for interest rate setting given in (1.38) of Appendix A with (1.9), and recalling that pt = d(rt ct) + et yields:
að1 þ dbÞ pt þ ð1 aÞð1 þ dbða 2ÞÞLt ¼ FE½DL˜tþ1 jC t ;
F ð1 þ 2dbða 1ÞÞ:
(1.12)
Eq. (1.11) can be estimated directly or jointly in a system estimation with Eq. (1.12). The left-hand-side of (1.11) and (1.12) depend solely on deposits, wholesale funding, and loans ( pt ; wt ; and Lt, respectively), which are observable. A proxy for the right-hand-side – expected one-period-ahead loan growth – must be found. This is operationalized here as the updated linear projection of loan growth onto the available information set in time t, and this estimate is used to instrument expected loan growth. 3. Data The data employed in this study is based on the Korean banking system, which is composed of a mix of publicly and privately owned banks.11 This type of mixed banking sector is increasingly common among east Asian economies, as well as Latin America, for example, in Brazil. In these mixed banking systems, state owned banks often differ from private banks due to differences in the incentive structure of their management, the selection of staff, and the business model they follow. All of these factors impact a bank’s portfolio of assets and its ability to respond with new lending during the crisis. Many state owned banks also share implicit/explicit guarantees by the state that confer a level of liability security that can lower funding costs and at times, serve as safe havens. The bank level data used here consists of a panel of quarterly lending and balance sheet data from 8 Korean banks, from 2000 to 2008. Of these, eight state banks are owned either directly or indirectly by the Korean government (including development banks and cooperatives) while the remaining 10 are private (and in some cases, foreign owned). The start of the financial pressure on Korean bank balance sheets from the global financial crisis is associated with three different dates in the estimations (broadly from 2007Q1 to 2008Q4).12 These are, 2007Q1 (the collapse of the subprime mortgage lending industry in the U.S.), 2007Q4 (the start of the recession in the U.S.), and 2008Q3 (the collapse of Lehman in the U.S.) (Fig. 1). Summary statistics for the banking sector are shown in Table 1. The table shows loan and funding growth for public and private sector banks, before and after the crisis. The most pronounced differences between the pre-crisis years and the crisis
11 The authors are grateful to the Bank of Korea for providing the data. Coverage of the banking sector and aggregate statistics are reported on the Bank of Korea website (www.bok.or.kr) along with biannual financial stability reports. 12 This dating of the start of the crisis follows from the March–April 2007 revelation that the subprime industry in the United States was in severe financial distress; several subprime lenders declared bankruptcy announcing significant losses or company sell-offs, e.g. New Century Financial. By July 2007, two Bear Stearns hedge funds declared bankruptcy, by August 2007, broad global financial distress prompted joint interventions by the U.S. Federal Reserve, the European Central Bank, and the Central Banks of Australia, Canada, and Japan.
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
327
Table 1 Summary statistics by type of bank and crisis period. Private
(four quarter percent growth) Deposits Wholesale Loans Gov. paper (percent) NPL ratio Tier 1 cap. Global int. National int.
Public
2000–2006
2007–2008
2000–2006
2007–2008
8.64 11.19 13.14 71.22
7.59 20.45 12.31 7.74
10.76 11.83 13.56 20.02
10.8 21.93 20.59 2.5
2.83 7.56 0.2 0.54
0.77 8.55 0.51 0.56
2.4 8.12 0.2 0.54
0.62 8.22 0.51 0.56
Source: Bloomberg, Haver Analytics, authors’ estimates. Note: The upper panel shows the simple average of four quarter growth for two measures of bank funding (deposits and wholesale funding) and two asset categories (loans and government paper) for the years prior to the crisis and the 2007–2008 crisis period, broken down by public and private banks. The bottom shows the analogous breakout for the non-performing loan and tier 1 capital ratios, as well as global liquidity conditions (proxied by the OIS spread) and national liquidity conditions (proxied by the difference between three month CD and call money rates), all in percentage points.
[()TD$FIG]
1
1
5
2
11
5
12
18
6
6
7
3
8
8
9
13
2
17
3
9
15
4
4
7
10
10
13
12
14
11
17
14
18
Risk and Regulatory Indicators
15
Asset Structure
Fig. 2. Clustering by banking regulatory and risk indicators. Note: The left panel (hierarchically) clusters banks on the basis of their regulatory and supervisory indicators for prudential supervision (SBL ratio, NPL ratio, CAR, and equity ratio). The right panel clusters banks by indicators of their asset and liability structure (government securities, loans, wholesale funding, and deposits, all in percent of assets). Numbers are shown in lieu of bank names, with public banks numbered 2, 10, 13–18, and the rest private banks. Source: Authors’ estimates.
period can be observed in the growth of loans and in the purchase of government debt. After 2007, public banks accelerate lending and increase their sovereign debt holdings, while private banks slow their loan growth and lower their sovereign debt. The growth of wholesale funding accelerates for both public and private banks, however, in the latter it is compensating slower growth in deposits. For public banks, deposit growth remains stable during the crisis period. Notwithstanding this deposit growth in recent years, there has been a broad shift of household assets away from deposits to other assets, mainly securities, that to some extent is reflected in a wider increase in wholesale funding.13 Fig. 2 gives a feel for the differences in banking behavior by hierarchically clustering banks on the basis of their regulatory/supervisory indicators and asset structure. For prudential supervision, banks are clustered according to their similarity across the SBL, NPL,14 capital to asset and equity ratios.15 The right panel clusters banks by indicators of their asset and liability structure (government securities, loans, wholesale funding, and deposits, all in percent of assets). The figures show public banks (numbered 2, 10, 13–18) broadly clustered on one end of the spectrum, and private banks on the
13 Factors driving the rebalancing of household assets away from deposits include: (i) further liberalization of outflows and tax benefits in 2006; and (ii) portfolio rebalancing in the context of declining home-bias, increasing risk tolerance, and search for yield. 14 SBL ratios are a loan classification used which means ‘‘substandard or below (SBLs)—substandard, doubtful, or presumed loss.’’ NPL ratios are nonperforming loans. 15 The right panel clusters banks by indicators of their asset and liability structure (government securities, loans, wholesale funding, and deposits, all in percent of assets). Numbers are shown in lieu of bank names, with public banks numbered 2, 10, 13–18, and the rest private banks.
[()TD$FIG]
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
328
0, 3
0, 4
0, 5
0, 6
0, 7
0, 8
0, 9
0, 11
0, 12
1, 2
1, 10
1, 13
Loans
0, 1
Loans Wholesale
1, 14
1, 15
1, 16
1, 17
1, 18
2007q1 2007q32008q1 2008q3 2009q1 2007q1 2007q3 2008q1 2008q3 2009q1 2007q1 2007q3 2008q1 2008q3 2009q1 2007q1 2007q3 2008q1 2008q3 2009q1 2007q1 2007q3 2008q1 2008q3 2009q1
date Loans (solid), left scale, Wholesale funding (dashed), right scale.
Fig. 3. Lending and wholesale funding during the crisis 2007–2008. Note: The upper panel shows loans (left scale, values not shown) against wholesale funding (right scale, values not shown) for 10 private banks (labeled 0), while the lower panel shows lending and wholesale funding for public banks (labeled 1), for the 2007–2008 crisis period. Source: Authors’ estimates.
Table 2 Model estimates based on all banks. All banks Start crisis: Constant Loans
Dwholesale a Dcrisis lending R-squared N
Full
Public 2007Q1
2007Q4
2008Q3
Full
Private 2007Q1
2007Q4
2008Q3
Full
2007Q1:
2007Q4
2008Q3
0.05 0.27 0.03 0.01* 0.14 0.04*
0.37 0.71 0.04 0.01* 0.09 0.06
1.00 1.33 0.05 0.02* 0.08 0.09
1.57 6.54 0.06 0.11 0.07 0.3
0.05 0.36 0.04 0.01* 0.15 0.05*
0.67 1.1 0.05 0.02* 0.08 0.09
1.97 2.1 0.07 0.03* 0.04 0.12
0.85 12.61 0.05 0.19 0.03 0.43
0.45 0.44 0.03 0.01* 0.13 0.07*
0.31 0.94 0.03 0.02* 0.09 0.09
0.52 1.73 0.03 0.03 0.11 0.12
1.89 7.26 0.06 0.13 0.24 0.53
0.49
0.48 0.14 0.97 136
0.48 0.40 0.97 85
0.47 0.72 0.98 34
0.48
0.48 0.30 0.96 56
0.47 0.81 0.97 35
0.47 0.43 0.94 14
0.48
0.49 0.07 0.97 80
0.48 0.02 0.98 50
0.47 0.76 0.98 20
0.93 561
0.93 231
0.94 330
Source: Country authorities, authors’ estimates. Note: Panel GMM estimation of equation expected loan growth regressed on loans and wholesale funding, and for all (left), public (center), and private (right) banks, quarterly data, 2000–2008. Estimates are presented for the pre-crisis sample (up to 2008Q2) and sub-samples based on crisis starting dates (top row) for 2007Q1 (subprime lender bankruptcy), 2007Q4 (start of U.S. recession start), and 2008Q3 (Lehman). Standard errors in italics, a is the estimated speed of portfolio adjustment. Dcrisis lending is the percentage point change in a during the crisis relative to the full sample estimate, negative indicates countercyclical lending growth in response to negative economic shock. Fixed effects not reported. * Signifies at least 10 percent significance.
other end. Moreover, the most pronounced differences are driven by regulatory indicators, consistent with the model assumption of differences in prudential management by bank type.16 Fig. 3 shows each bank’s lending graphed against its drawing of wholesale funding for the 2007–2008 crisis period. Towards the end of the period, when the crisis is most acute, wholesale funding declines in roughly half of all the banks shown. Nonetheless, for five of eight public banks, wholesale
16 Note that this does not imply a different regulatory and oversight regime, but different management within a shared regime, which can be due to a number of factors such as mandate, compensation schemes, etc. See, for example, La Porta, Lopez de Silanes, and Schleifer (2002) on public banks.
[()TD$FIG]
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
Private Banks
Public Banks 12
329
12
DDEPOSITS
DDEPOSITS
8
8
4 4 0 0 -4 -4 00 12
01
02
03
04
05
06
07
08
-8 00 6
DWHOLESALE
01
02
03
04
05
06
07
08
02
03
04
05
06
07
08
02
03
04
05
06
07
08
02
03
04
05
06
07
08
DWHOLESALE
4
8
2 4
0 -2
0
-4 -4 -6 -8 00
200
01
02
03
04
05
06
07
08
-8 00 250
LOANS
160
200
120
150
80
100
40
50
0 00 10
01
02
03
04
05
06
07
08
LOANS
0 00 6
EDLOANS
01
8
5
6
4
01
EDLOANS
3
4
2 2 1 0
0
-2 00
01
02
03
04
05
06
07
08
-1 00
01
Fig. 4. Data used in estimation. Note: The left panel shows the change in deposits, the change in wholesale funding, loans, and the estimated expected loan growth (the dependent variable in the estimations) for eight public banks, and the right panel show the same for ten private banks, quarterly, 2000–2008. All series except loans are stationary. Source: Authors’ estimates.
funding is broadly stable or increasing even during the quarters that observed the most acute global financial pressures (2008 Q3–Q4). 4. Empirical results This section presents GMM estimates of Eq. (1.11) and GMM system estimation of Eqs. (1.11) and (1.12). The estimations follow closely from the model solution and use employ observable bank-level data. Only expected loan growth is not directly observed. Thus, consistent with rational expectations, it is operationalized as the updated GMM projection of loan growth on
[()TD$FIG]
330
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334 0.40 0.20 0.00 -0.20 -0.40 -0.60 -0.80
Public All Banks Private
-1.00
2007Q1
2007Q4
2008Q3
Fig. 5. Change in lending response during the crisis, by bank type. Note: The graph shows the difference in the estimated lending response parameter, a, when estimating on the full sample (labeled full) versus the crisis sub-samples (beginning in 2007Q1, 2007Q4, and 2008Q3). An increase in the estimate of a (a positive bar) implies a greater direct response to expected loan growth in the wake of an economic shock. A decline in the estimate of a implies that during the crisis, the bank type on average responded countercyclically. Source: Authors’ estimates.
the available information set.17 Having generated (E½DL˜tþ1 jC t ), the estimated equations follow directly from (1.11) and (1.12), and are E½DL˜tþ1 jC t ¼ g 10 þ g 11 wholesalei;t þ g 13 loanst þ e1it ;
(1.13)
E½DL˜tþ1 jC t ¼ g 20 þ g 21 Dde positsi;t þ g 23 loanst þ e2it :
(1.14)
From Eq. (1.11), the estimated coefficients are non-linear functions of the model parameters. In solving for E[DLi,t+1], its parameter coefficients in the solution are implicitly dividing the estimated coefficients in (1.13) and (1.14). Nonetheless, identification of a, the loan reaction parameter, is achieved in (1.13), since E½gˆ 11 ¼ ð1 2aÞ and hence E½1=2ð1 gˆ 11 Þ ¼ a: The other parameters cannot be directly identified but they are not critical to interpreting the model implications. Table 2 reports estimates of Eq. (1.13) for all banks (left), public banks (center), and private banks (right). The estimates are presented for the full sample and sub-samples based on differing crisis start dates: 2007Q1 (subprime lender bankruptcy), 2007Q4 (U.S. recession start), and 2008Q3 (Lehman collapse). The equation is estimated via fixed effects panel GMM to control for the potential endogeneity from the generated regressor E[DLi,t+1]. The results show broadly stable and statistically significant estimates across the different start dates (the last crisis sub-sample is intended as indicative as it includes only two quarters in the panel data). In particular, the coefficient on loans ðgˆ 11 Þ is stable, significant and signed as expected. While the high goodness of fit could potentially stem from a spurious regression of two non-stationary series, this is not the case. The model solution include loans, which are non-stationary, however, all other variables are stationary, and are shown in Fig. 4. The bottom of Table 2 reports the estimated speed of portfolio adjustment, a. The estimates based on all banks suggest that bank lending on average became countercyclical during the crisis: as economic shocks materialized, the propensity to respond with lending increased, evidenced by the declining a in the reported as Dcrisis lending row. The estimate in this row is interpreted as follows: given a negative economic shock, the bank rebalances its portfolio by reducing its exposure to risky lending. The estimate reported in the Dcrisis lending row is the decline in this marginal propensity to cut quarterly loan growth in response to shocks, in percent of the shock. Hence, a Dcrisis lending of 0.4 implies that from 2007Q4 on loan growth would have been an aggregate 2 percent lower in the economy, but for countercyclical measures taken by banks. Comparing public and private banks, the estimates suggest private banks observed almost no change in their cyclical lending pattern, while public banks increased lending countercyclically at an average of 0.6 percent quarterly. Fig. 5 graphically shows the change in lending by bank type, where public banks drive the countercyclical lending in the overall banking sector based on the first two crisis start dates (2007Q4 and 2008Q1). The data for Korea shows that total lending from 2007Q4 onwards grew by 20 percent, with public banks increasing lending by 26 percent and private banks increasing lending by 16 percent. Table 3 confirms the estimated values of a via panel GMM system estimation of Eqs. (1.13) and (1.14), and shows pffiffiffiffiffiffi estimates of wholesale and traditional funding costs, in the row labeled db. The estimates cannot identify d (the traditional funding costs) or b (wholesale funding costs) separately, it can identify the product of the two parameters. Hence, the row reports the geometric average of the parameters, and compares estimates of these costs across public and private sector banks. The estimates suggest that private banks faced higher funding costs throughout the sample. This evidence also suggests that financial turmoil exarcebates liquidity risks for some banks, those banks that have implicit or explicit
17 Panel GMM estimation, at time t, instruments include lagged loan growth, domestic and external interest rates, prudential and regulatory risk measures, and current and lagged bank balance sheet variables, e.g. deposits, equity, etc.
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
331
Table 3 System estimation.
Constant Loans
Dwholesale Constant Loans
Ddeposits a ffiffiffiffiffiffi p
db
R-squared
All
Private
Public
0.05 0.02* 0.03 0* 0.17 0.01* 0.02 0.01* 0.03 0* 0.28 0.01*
0.11 0.01* 0.03 0* 0.34 0.01* 0.05 0* 0.02 0* 0.18 0*
0.11 0.01* 0.04 0* 0.12 0* 0.04 0.01* 0.04 0* 0.22 0*
48.7 0.42 0.69
48.9 0.60 0.66
48.2 0.36 0.83
Source: Country authorities, authors’ estimates. Note: Panel GMM system estimates of the model solution for quarterly banking data, 2000–2008, presented for 10 private, eight public, and all banks. Standard errors in italics. The estimated speed of portfolio adjustment, a gives the estimated sensitivity of expected loan growth to an economic shock. Fixed effects not reported. * Signifies at least 10 percent significance.
government guarantees, more conservative ex-ante risk management, or lower ex-ante asset allocation, benefit during the crisis from lower wholesale funding costs.18 5. Conclusion This study addresses three banking interrelated banking issues in the context of the recent global financial crisis and its impact on the Korean banking sector. First, public banks were able to offset some of the impact of the global financial crisis that began in 2007 by significantly expanding lending. As the financial crisis spread to Korea, state owned banks expanded lending, offsetting credit contractions resulting from private sector banks’ efforts to raise capital on their balance sheets. The portfolio management optimization framework presented in this study suggests public sector banks are able to expand lending in a downturn because as a negative shock is realized, they are less affected since they hold less risk ex-ante, and hence, need to rebalance risk (i.e. raise capital) less in a crisis. As a result, they are in a stronger position to lend in a downturn. Private banks were able to expand credit by much less during the crisis. Secondly, for Korea, wholesale funding markets largely continued to function during the crisis, though with slightly higher costs. To some extent, the smooth functioning of the wholesale funding markets reflects the significant policy support on the part of the Korean authorities in support of financial stability, alongside Korea’s peripheral role in the current crisis. These caveats notwithstanding, wholesale funding continued to expand and support lending, and indicators of wholesale funding costs do not suggest that this channel of funding was a significant impediment to countercyclical lending. Finally, this study shows that there could be potential benefits to having state owned banks as a natural hedge against the incentives towards risk taking inherent in private sector banking system. Private banks in the model presented here take risks not in violation of prudential norms, but rather in full compliance of prudential norms. Moreover, the need to rebalance – i.e. raise bank capital or liquidate risky or distressed assets in the midst of a financial crisis – is driven by prudential concerns, and hence countercyclical lending during a downturn is curtailed. In practice and in line with the model, the fact that private banks in Korea were well capitalized and complying with prudential norms did not prevent natural reduction in credit as their balance sheet deteriorated. This makes sense for banks, but is detrimental to the wider banking system, the real economy, and to output volatility. Hence, the impact of greater instability associated with private bank incentives suggest a tradeoff between volatility and output growth related to the the size of a country’s public banking sector. An area for future research is to investigate the cost of public banks against the potential stabilizing benefits that they could provide. Acknowledgements The authors thank Gabriel Di Bella, Hali Edison, Robert Flood, Subir Lall, Charan Singh, Jay Surti, Francisco Va´zquez, and an anonymous referee for helpful comments.
18 Hence, the ex-ante lower profitability of public banks may imply less exposure to the ‘‘dark side’’ of wholesale funding put forth Huang and Ratnovski (2009).
332
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
Appendix A. Model solution A.1. Learning the liquidation value from excess demand The banker’s expectations of rt are based on observed excess demand: zt ð pt Þ ¼ P t þ Et ¼ Dt d pt ;
(1.15)
where observing Dt = drt + Et is equivalent to observing d1Dt = rt + d1Et, an unbiased signal of the true liquidation value, with the variance of this signal denoted s 2f , and the signal to noise ratio given by Y ¼ s 2r =s 2f . Every period the banker updates rt1, the prior expectations of rt based on the new signal as qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 2 Y þ Y þ 4Y : (1.16) rt ¼ Qd1 Dt þ ð1 QÞrt1 ; with Q ¼ 2
A.2. Capital charge Bankers pay kt ¼ us 2 ðA˜ t Þ on the bank’s portfolio of assets, given by A˜ t ¼ K t þ r t Lt , with u scaling the opportunity cost of this capital requirement. The variance of the portfolio is given by s 2 ðA˜ t Þ ¼ varðK t þ r t Lt Þ ¼ L2t s 2r , so that the capital charge becomes:
kt ¼ uL2t s 2r :
(1.17)
A.3. Model solution The control problem faced by bankers is: ˜ tþ1 ÞjC t g; VðK t ; et ; Lt ; rt Þ ¼ max Efð1 t Þ½C t þ Lt rt kt þ t VðK˜tþ1 ; e˜ tþ1 ; L˜tþ1 ; r ct ;wt
(1.18)
such that: E½K˜tþ1 jC t ¼ K t þ ct ðdðrt ct Þ þ et Þ wt ðrt þ bwt Þ kt ;
(1.19)
E½˜etþ1 jC t ¼ 0;
(1.20)
E½L˜tþ1 jC t ¼ Lt dðrt ct Þ et þ wt ;
(1.21)
˜ tþ1 jC t ¼ rt : E½r
(1.22)
The first order conditions are (abbreviating the expectation of the value function by Vt+1): @V tþ1 @V tþ1 ðdrt þ et 2dct Þ td ¼ 0; c:t @K @L w : t
@V tþ1 @V tþ1 ðrt þ 2bwt Þ þ t ¼ 0: @K @L
(1.23)
(1.24)
Combining (1.23) and (1.24) (note that @Vt+1/@K 6¼ 0 shown below) yields the optimal bank funding policy: ct ¼ rt þ bwt þ ðet =2dÞ: Taking the envelope conditions for (1.18) through (1.22) yields: dV t @V tþ1 ¼ ð1 t Þ þ t ; dK @K
(1.25)
(1.26)
dV t @V tþ1 @V tþ1 ct ; ¼ t de @L @K
(1.27)
dV t @V tþ1 @V tþ1 @V tþ1 td þt ¼ ð1 t ÞLt þ t ðdct wt Þ ; dr @K @L @r
(1.28)
dV t @V tþ1 @V tþ1 ¼ ð1 t Þrt þ t 2Lt us 2r ð1 t Þ þ t : dL @L @K
(1.29)
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
333
This problem is solved by verifying a conjectured functional form of the value function. The envelope conditions suggest the form of the value function is Vˆt ¼ f0 þ rt Lt þ K t þ f1 L2t þ f2 et Lt þ f3 e2t :
(1.30)
In (1.30), f0 through f3 are unknown constants. Taking derivatives with respect to K, L, and r and updating yields: ! dVˆtþ1 E (1.31) ¼ Eðrt þ 2f1 Ltþ1 þ f2 etþ1 Þ; dLtþ1
E
dVˆtþ1 dK tþ1
E
dVˆtþ1 drtþ1
E
dVˆtþ1 detþ1
! ¼ 1;
(1.32)
¼ Eðrt Þ;
(1.33)
¼ Eðf2 Ltþ1 þ 2f3 etþ1 Þ:
(1.34)
!
!
Using (1.31) and (1.32), the first order condition for the wholesale market, (1.24), becomes: wt ¼
f1 L : b tþ1
(1.35)
Using (1.35) in the optimal funding policy derived in (1.25) yields: e t : ct ¼ rt þ f1 Ltþ1 þ 2d
(1.36)
Substituting (1.36) – which captures the optimal cost and wholesale funding policies – into the evolution equation for the stock of loans given by (1.21) to solve for Lt+1: E½L˜tþ1 jC t ¼ aLt
a 2
et ;
with a ¼
b bða 1Þ , f1 ¼ ; b f1 ð1 þ dbÞ að1 þ dbÞ
Optimal funding costs and wholesale funding purchase/sale are given by bða 1Þ 1 þ 2dbð2 aÞ Lt þ c t ¼ rt þ et ; ð1 þ dbÞ 2dð1 þ dbÞ wt ¼
a1 a1 et : Lt 1 þ db 2ð1 þ dbÞ
(1.37)
(1.38)
(1.39)
To confirm the conjecture in (1.30), solve for f1 through f3 using the envelope conditions for given along with the updated derivative of the conjecture and the optimal policy functions. Using the envelope condition for loans, (1.29), the derivative of the conjecture (1.31), and the optimal policy functions yields f1 ¼ ls 2r =ða 1Þ and f2 ¼ aðus 2r f1 Þ. A meaningful economic solution with f1 < 0 implies 0 < a < 1 which requires:
a
ða 1Þb > 0; ða 1Þb ð1 þ dbÞus 2r
with b; d > 0:
(1.40)
As a ! 0 from above, the left hand side of (1.40) is negative. As a ! 1 from below, the left hand side of (1.40) approaches unity from below. Since the left hand side of (1.40) is a continuous function, by the intermediate value theorem there exists and a such that 0 < a < 1 and f1 < 0.
References Acharya, V., Gale, D., & Yorulmazer, T. (2010), Rollover risk and market freezes. Vol. w15674, NBER Working Paper Series. Allen, F., Babus, A., & Carletti, E. (2009). Financial crises: Theory and evidence. Annual Review of Financial Economics, 1, 97–116. Barth, J., Caprio, G., & Ross, L. (2001). The regulation and supervision of banks around the world: A new database. University of Minnesota Financial Studies Working Paper No. 0006. Blanchard, O. (1985). Debt, deficits, and finite horizons. Journal of Political Economy, 93, 223–247. Blundell-Wignall, A. (2009). Restoring confidence in financial systems. OECD Forum. www.oecd.org/dataoecd/15/4/43319875.pdf. Caprio, G., & Martinez Peria, M. S. (2002). Avoiding disaster: Policies to reduce the risk of banking crises. In E. Cardoso & A. Galal (Eds.), Monetary policy and exchange rate regimes: Options for the Middle East (pp. 193–230). Cairo, Egypt: Egyptian Center for Economic Studies. Clarke, G., Cull, R., & Shirley, M. (2005). Bank privatization in developing countries: A summary of lessons and findings. Journal of Banking & Finance, 29(August– September (8–9)), 1905–1930.
334
L. Leony, R. Romeu / Journal of Asian Economics 22 (2011) 322–334
Foucault, T. (1999). Order flow composition and trading costs in a dynamic limit order market. Journal of Financial Markets, 2, 99–134. Huang, R., & Ratnovski, L. (2009). The dark side of bank wholesale funding. Working Paper No. 09-3, Federal Reserve Bank of Philadelphia. IMF (2009). Republic of Korea: Article IV Consultation—Staff Report. IMF Country Report No. 09/262, International Monetary Fund, Washington, DC. La Porta, R., Lopez de Silanes, F., & Shleifer, A. (2002). Government ownership of banks. Journal of Finance, 57, 265–301. Madhavan, A., & Smidt, S. (1993). An analysis of changes in specialist inventories and quotations. The Journal of Finance, 48(December (5)), 1595–1628. Micco, A., Panizza, U., 2004. Bank ownership and lending behavior. Research Working Paper # 520. Inter-American Development Bank. Forthcoming in Economics Letters. Ramey, G., & Ramey, V. A. (1995). Cross-Country Evidence on the Link between Volatility and Growth, American Economic Review. American Economic Association, 85(December (5)), 1138–1151. Reinhart, C.M., & Rogoff, K.S., 2008. This Time is Different: A Panoramic View of Eight Centuries of Financial Crises, NBER Working Papers 13882, National Bureau of Economic Research, Inc. Repullo, R. (2004). Policies for banking crises: A theoretical framework. CEPR Discussion Paper No. 4727 (November). Romeu, R. (2010). Price discovery in markets with multiple dealers. IMF Staff Papers Vol. 57, pp. 450–483. Stein, J. C. (1995). An adverse-selection model of bank asset and liability management with implications for the transmission of monetary policy. The RAND Journal of Economics, 29(Autumn (3)), 466–486. Suarez, J. (1994). Closure rules, market power and risk-taking in a dynamic model of bank behavior. LSE Paper 196, SSRN: http://ssrn.com/abstract=6930.