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

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INTRODUCTION

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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.

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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

RESEARCH

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CENTRAL QUESTION

“What are the key determinants for the shadow banking system?”

“Are those key determinants consistent among countries?”

SUBQUESTION

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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.

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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.

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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

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MEASURE

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MEASURE

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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RESULTS & CONCLUSIONS

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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

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RESULTSCore 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.20

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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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