Key determinants of shadow banking

KEY DETERMINANTS OF THE SHADOW
BANKING SYSTEM.
THE CASES OF EURO AREA, UNITED KINGDOM AND
UNITED STATES.
Álvaro Álvarez-Campana Rodríguez

STRUCTURE
• INTRODUCTION
 RELEVANCE
 DEFINITION
 DISTRIBUTION
• RESEARCH
 AIM OF THE STUDY
 METHODOLOGY
 MAIN CONTRIBUTIONS
• RESULTS & CONCLUSIONS
2

RELEVANCE
• NOVELTY: Shadow banking is at the vanguard of financial research. The term was first coined by
McCulley (2007).
• FINANCIAL INSTITUTIONS CONCERN ABOUT SHADOW BANKING:
 Risk build-up promotion in the financial system.
 Hinders financial stability and foster potential spill-overs.
 Optimal point complements positively traditional banking system.
• SHIFT IN BANKING PROCESS: from the originate-to-hold to the originate-to-distribute model.
• “GREAT RECESSION”:
 2000s housing boom US -> financed through originate-to-distribute model.
 Credit boom. Investors’ need to satisfy risk desires.
4

DEFINITION
• SHADOW BANKING: activities related to credit intermediation, liquidity and maturity
transformation that happen outside the regulated banking system. (ECB)
 Main implication: lack of “formal safety net”.
• SECURITIZATION PROCESS: Originator -> Issuer -> Investors.
• INTERCONNECTEDNESS: the traditional banking system (TBS) and the shadow banking
system (SBS) are deeply linked.
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Source: International Monetary Fund (2014).
Traditional and Shadow Banking credit intermediation.
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DEFINITION

Shadow Banking assets share per country
Note: CA = Canada; CN = China; DE = Germany; EMEs ex CN = Argentina, Brazil, Chile, India, Indonesia, Mexico, Russia, Turkey, Saudi
Arabia, South Africa; FR = France; IE = Ireland; JP = Japan; KR = Korea; NL = Netherlands; UK = United Kingdom; US = United States.
Source: Global Shadow Banking Monitoring Report 2015. Financial Stability Board (FSB).
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DISTRIBUTION

CENTRAL QUESTION
“What are the key determinants for the shadow
banking system?”
“Are those key determinants consistent among countries?”
SUBQUESTION
9
AIM OF THE STUDY

METHODOLOGY
• DATA
 Selection -> Literature review.
 Main sources: ECB, FRB, FRED, OECD and WB-IFS.
 Limitations: availability, definition, granularity, homogeneity and periodicity.
• MEASURE: (sbsratio) Ratio of Other Financial Institutions (OFI) over Monetary Financial Institutions (MFI).
 From ECB Shadow Banking Overview. Bakk-Simon et al. (2012).
 Most appropriate measure for European data availability.
• VARIABLES: 8 independent variables selected and adjusted from a wider database.
10

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METHODOLOGY
DEFINITION OF VARIABLES:
• realgdp: inflation adjusted value of the goods and services produced by in a country.
• instinv: total assets of insurance corporations and pension funds.
• tspread: difference between interest rates of 10y Treasury bond and 3m Treasury bill.
• margin: difference between interest rates on deposits and loans.
• liquidity: total monetary reserves.
• ciss: composite indicator of systemic stress.
• hhi: Herfindahl index of traditional banks’ concentration.
• inflation: measure of the variation of the increase in the general price level.

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• MODELS: Analysed with STATA software.
𝐬𝐛𝐬𝐫𝐚𝐭𝐢𝐨𝒊,𝒕 = 𝛃 𝟎 + 𝛃 𝟏 𝐫𝐞𝐚𝐥𝐠𝐝𝐩𝒊,𝒕 + 𝛃 𝟐 𝐢𝐧𝐬𝐭𝐢𝐧𝐯𝒊,𝒕 + 𝛃 𝟑 𝐭𝐬𝐩𝐫𝐞𝐚𝐝𝒊,𝒕 + 𝛃 𝟒 𝐦𝐚𝐫𝐠𝐢𝐧𝒊,𝒕 + 𝛃 𝟓 𝐥𝐢𝐪𝐮𝐢𝐝𝐢𝐭𝐲𝒊,𝒕 + 𝜸𝒊 + 𝜸 𝒕 + 𝛆𝒊,𝒕
𝐬𝐛𝐬𝐫𝐚𝐭𝐢𝐨𝒊,𝒕 = 𝛃 𝟎 + 𝛃 𝟏 𝐫𝐞𝐚𝐥𝐠𝐝𝐩𝒊,𝒕 + 𝛃 𝟐 𝐢𝐧𝐬𝐭𝐢𝐧𝐯𝒊,𝒕 + 𝛃 𝟑 𝐭𝐬𝐩𝐫𝐞𝐚𝐝𝒊,𝒕 + 𝛃 𝟒 𝐦𝐚𝐫𝐠𝐢𝐧𝒊,𝒕 + 𝛃 𝟓 𝐥𝐢𝐪𝐮𝐢𝐝𝐢𝐭𝐲𝒊,𝒕 + 𝛃 𝟔 𝐜𝐢𝐬𝐬𝒊,𝒕 +
𝛃 𝟕 𝐡𝐡𝐢𝒊,𝒕 + 𝛃 𝟖 𝐢𝐧𝐟𝐥𝐚𝐭𝐢𝐨𝐧𝒊,𝒕 + 𝜸𝒊 + 𝜸 𝒕 + 𝛆𝒊,𝒕
BASE MODEL
EXTENDED MODEL
METHODOLOGY

Cross-sectional data Panel data
Whole-
sample
Core-
sample
Robustness-
sample
Base
model
Extended
model
Base
model
Base
model
Extended
model
Base
model
Extended
model
Base
model
Extended
model
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METHODOLOGY - APPROACH

MAIN CONTRIBUTIONS
• Adaptation of a European-based SBS measure and its replication for the US.
• Categorization of Euro area countries: the south-mediterranean and the central-north.
• Construction of two new models based on well-known indicators which have not been
grouped together before.
• Implementation of a fixed effects approach to study the shadow banking system
controlling for time or country differences.
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Core base model results. (Panel).
Note: ***, **, * indicate significance at the 1 percent, 5 percent and 10 percent levels, respectively
C-N S-M
RESULTS
18

RESULTS
Core extended model results. (Panel).
Note: ***, **, * indicate significance at the 1 percent, 5 percent and 10 percent levels, respectively
C-N S-M
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CONCLUSIONS
“What are the key determinants for the shadow banking system?”
• Most variables analysed are statistically significant for shadow banking.
 Real GDP, liquidity and banking concentration (hhi) are quantitatively more important.
 Systemic stress indicator (ciss) has no relevance for the Euro area.
“Are those key determinants consistent among countries?”
• NO
 Shadow banking and key determinants show opposite relations for C-N than for S-M.
 Models have higher fit for Euro area than for UK and US.
 Shadow banking system behaviour in US is more similar to C-N than to S-M.
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CONCLUSIONS – ECONOMIC RELEVANCE
• Policy makers should be aware of the performance of these indicators to monitor and control
for shadow banking.
• In the case of Euro area, opposite behaviours of determinants in C-N and S-M pose two
relevant questions:
“Which is the part of the Euro area that deserves further resources allocation to overcome
shadow banking risks?”
And, in case that area-specific policies could be implemented:
“How to keep these policies independent between areas in a common monetary and economic
environment?”

THANK YOU FOR YOUR ATTENTION!
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Key determinants of shadow banking

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