This paper presents two models of key determinants in the evolution of the shadow banking system. First of all, a shadow banking measure is built from a European perspective. Secondly, information on several variables is retrieved basing their selection in previous literature. Thirdly, those variables are grouped in: 1) the base model: real GDP, Institutional investors’ assets, term-spread, banks’ net interest margin and liquidity; and 2) the extended model: the former five plus an indicator of systemic stress, an index of banking concentration and inflation. Finally, regression analysis on those models is conducted for different countries’ samples. Both OLS and panel data analysis is undergone. Results suggest important and consistent geographical differences in relations between shadow banking and key determinant variables’ effects. Thus, this essay provides financial authorities with a valuable benchmark to which they should pay attention before designing optimal policies seeking to reduce the financial risk that shadow banking entails.

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

2. STRUCTURE
• INTRODUCTION
 RELEVANCE
 DEFINITION
 DISTRIBUTION
• RESEARCH
 AIM OF THE STUDY
 METHODOLOGY
 MAIN CONTRIBUTIONS
• RESULTS & CONCLUSIONS
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3. INTRODUCTION
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4. 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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5. 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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6. Source: International Monetary Fund (2014).
Traditional and Shadow Banking credit intermediation.
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DEFINITION

7. 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

8. RESEARCH
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9. 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

10. 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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11. 11
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.

12. 12
• 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

13. 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

14. 14
MEASURE

15. 15
MEASURE

16. 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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17. RESULTS & CONCLUSIONS
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18. 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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19. 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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20. 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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21. 21
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?”

22. THANK YOU FOR YOUR ATTENTION!
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