EBA欧洲银行-Silva-Strategic-complementarity-in-banks2720funding-liquidity-choices-and-financial-stability-Presentation_70页_1mb

Summary of "Strategic complementarity in banks' funding liquidity choices and financial stability"

Core Content

This document presents a research paper by Andre Silva, presented at the 4th EBA Policy Research Workshop on November 19, 2015. The paper focuses on the strategic complementarity in banks' liquidity choices and its implications for financial stability. It argues that banks' liquidity decisions are not made in isolation but are influenced by the behavior of their peers, which has important consequences for systemic risk and individual bank risk.

Main Research Questions

1. Why and how are liquidity holding choices of a bank influenced by the behavior of its peers?

   • Possible reasons:
     • Learning effect: Banks may free-ride on the information acquired by their peers.
     • Collective moral hazard: Banks may take on more risk if they believe they will be bailed out by the Last Resort Lender (LOLR).
   • The paper explores whether this influence occurs through direct responses to peers' liquidity decisions or through changes in other peer characteristics.

2. Do strategic funding liquidity risk management decisions have an impact on financial stability?

   • The paper suggests that collective risk-taking increases the likelihood of systemic bank failures due to higher correlation in defaults, as noted by Allen et al. (2012).

Key Findings and Contributions

• Strategic liquidity decisions increase both individual banks' default risk and overall systemic risk.
• Peer effects are significant: The coefficient β in the model captures the influence of peer banks' liquidity choices on a given bank.
• Empirical gap: No prior study has empirically examined the impact of banks' strategic balance-sheet decisions on financial stability.
• Two channels of influence are identified:
  • Learning effect: Banks may adjust their liquidity choices based on the actions of their peers.
  • Collective moral hazard: The expectation of a bailout from LOLR can lead to riskier liquidity behavior.
• Banks' liquidity choices are influenced directly by competitors, as well as indirectly by other characteristics of the peer group.

Data Description

• Sample size: 17,831 bank-year observations from 2,058 commercial banks across 32 OECD countries between 1999 and 2013.
• Data sources:
  • Banks' balance sheets and income statements: Bankscope
  • Bank ownership data: BvD ownership database, annual reports, websites, and newspaper articles. Cross-checked with Claessens and van Horen (2014).
  • Daily stock prices and number of shares outstanding: Datastream
  • Country/sector equity market indices: MSCI
  • Country-level data: World Bank WDI, Doing Business database, and IMF International Financial Statistics
• Sample restriction: Only the largest 100 commercial banks in each country are included, excluding smaller regional banks in the US and Japan.

Peer Group Specification Criteria

1. Country and Year:
   • Banks within the same country are more likely to mimic peers due to shared LOLR and easier access to information.
2. Business Model:
   • Only commercial banks are included in the sample. Cooperative and savings banks are typically domestically owned.
3. Bank Size:
   • Each peer group in a country and year has a maximum of 20 banks.
   • At least one foreign-owned subsidiary is required in the group to identify the remaining 19 banks.
   • Bizjak et al. (2011) suggest that the average peer group size for executive compensation decisions is 17.3 for S&P 500 firms.

Empirical Model

Model 1: Baseline model to capture peer effects

$$
L i q _ {i, j, t} = \omega + \beta \overline {{L i q}} _ {- i, j, t} + \lambda^ {\prime} \bar {X} _ {- i, j, t - 1} + \gamma^ {\prime} X _ {i, j, t - 1} + \eta^ {\prime} Z _ {j, t - 1} + \mu_ {i} + v _ {t} + \varepsilon_ {i, j, t}
$$

• Liq is measured using either the Liquidity Ratio (Acharya and Mora, 2015) or the Liquidity Creation measure (Berger and Bowman, 2009).
• The coefficient β represents the peer effect, i.e., the influence of peer banks' liquidity decisions on a given bank.
• The model accounts for endogeneity by using an identification strategy based on the "peer's peer" approach, leveraging the liquidity risk profile of a bank-holding group as an exogenous instrument.

Model 2: Baseline model to examine impact of peer effects on financial stability

• The model allows β to vary across countries and over time.
• Example: In the UK in 2010, the model is modified to include a time and country interaction term.
• This model helps assess whether peer effects have a broader impact on financial stability by influencing the correlation of defaults.

Identification Strategy

• Solution: Use systematic differences in peer group composition to identify peer effects.
• Heterogeneity: The liquidity choices of a bank-holding group (e.g., Bank X in country f) are used as an instrument for banks in country j that are part of the peer group of its foreign subsidiary (e.g., Bank A).
• Reflection problem: The strategy addresses this by using the "peer's peer" as an instrument, thus isolating the exogenous component of peer influence.
• Angrist (2014): This strategy helps reduce potential bias from weak instruments.

Conclusion

The paper highlights the importance of strategic complementarity in banks' liquidity choices, showing that peer behavior significantly affects individual banks' liquidity risk management. It also emphasizes the role of liquidity decisions in increasing both individual and systemic risk, suggesting that current regulatory frameworks (e.g., Basel III) may not fully account for these interdependencies. The study provides a novel empirical approach to examine the impact of peer effects on financial stability, contributing to the understanding of how banks' strategic behavior influences the broader financial system.