Financial Flows and The International Monetary System
Financial Flows and The International Monetary System Helene Rey (LBS, CEPR & NBER )
Sargan Lecture 2014
Rey
Royal Economic Society
Sargan Lecture 2014
1 / 52
What do we know about the costs and benefits of financial integration?
First, I will focus on the standard benefits of international financial flows Second, I will discuss some of the potential costs, due in particular to monetary policy spillovers Bottom line will be that we still do not have a unified framework to discuss all the relevant aspects and that the welfare benefits of financial flows cannot be taken for granted.
Rey
Royal Economic Society
Sargan Lecture 2014
2 / 52
Motivation Core question in international macroeconomics and finance
Where do gains from international financial integration come from? Conventional view I I
efficient allocation of capital: capital flows to emerging countries risk sharing: reduces volatility of aggregate consumption
Other possibilities (which I will not discuss here) I
effect on TFP (via financial markets development, institutional changes, macroeconomic policies...)
Rey
Royal Economic Society
Sargan Lecture 2014
3 / 52
A simple experiment
Stochastic neoclassical framework with two production economies An emerging (risky) country (5% volatility of productivity shocks) A relatively safer developed country (2.5% volatility) Emerging country starts with 50% of the capital of developed country. Questions What is the growth impact of financial integration? What is the dynamics of capital flows? How big are the gains from financial integration? Who benefits the most?
Rey
Royal Economic Society
Sargan Lecture 2014
4 / 52
Two classes of models to quantify welfare gains
Allocative efficiency of financial integration without aggregate risk I I
Gourinchas and Jeanne (2006). Small gains. Hoxha, Kalemli-Ozcan, Vollrath (2013). Large gains. Capital goods imperfect substitutes.
International risk sharing without production I I
I
I
Lucas (1982), Cole and Obstfeld (1991). Small gains. Van Wincoop (1999), Lewis (1999). Larger gains if high market price of risk. Kalemli-Ozcan, Sorensen, Yosha (2001). Potentially high gains (effect on specialization). Colacito and Croce (2010), Lewis and Liu (2013). Long run risk.
Rey
Royal Economic Society
Sargan Lecture 2014
5 / 52
Empirical evidence on gains from financial integration
Effect on growth and on consumption volatility I
Surveys: Eichengreen (2002); Kose et al. (2006); Henry (2007); Obstfeld (2009); Jeanne et al. (2012).
Mixed results: I I I I
depends on sample period there is a lot of country heterogeneity. endogeneity issues event studies, though useful, have a short time frame.
We cannot take the gains for granted.
Rey
Royal Economic Society
Sargan Lecture 2014
6 / 52
Modelling jointly the two types of gains in general equilibrium: Coeurdacier, Rey and Winant 2014
We need an integrated framework I I
Both types of gains are intertwined. Are they substitute or complement?
Convergence gains depend on distance from steady-state. But the steady-state itself is modified by financial integration in the presence of risk. We need a general equilibrium model. Emerging markets have integrated in waves.
Rey
Royal Economic Society
Sargan Lecture 2014
7 / 52
Two types of gains
Assess the growth dynamics and the welfare gains from financial integration in a neoclassical growth model I I I I
with aggregate uncertainty with heterogeneous countries with incomplete (or complete) markets in general equilibrium
Use a global approximation methods to study the transition path towards the long run world equilibrium Emphasize relation between risk, growth and capital accumulation
Rey
Royal Economic Society
Sargan Lecture 2014
8 / 52
Findings Growth and capital flows dynamics I
I
I
Tension between the buildup of precautionary assets by risky (emerging) country and the potential effect of capital scarcity in the short-run. Growth impact of financial integration for risky country depends on these two conflicting forces. Financial integration affects the degree of aggregate risk and hence precautionary savings motives. Terms of the tradeoff between efficiency and risk-sharing depends on the market price of risk.
Welfare gains I I
Remain small for emerging markets. More elusive than we think. Surprisingly, if anything, the safest (developed) countries are the main beneficiaries, particularly so if the price of risk is high.
Rey
Royal Economic Society
Sargan Lecture 2014
9 / 52
Baseline model of financial integration Technology
2 countries i = D, E with a stochastic neoclassical structure. One good perfectly tradable.
Production Cobb-Douglas technology: θ 1−θ yi,t = ai,t ki,t li,t
Productivity shocks: log (ai,t ) = (1 − ρ) log (ai,0 ) + ρ log (ai,t−1 ) + i,t Investment with convex adjustment costs ki,t+1 = (1 − δ) ki,t + kt ϕ Rey
Royal Economic Society
ii,t ki,t
Sargan Lecture 2014
10 / 52
Baseline model of financial integration Preferences
Epstein-Zin preferences Ui,t = (1 −
1−ψ β)ci,t
+β
1−γ Et Ui,t+1
1 1−ψ 1−ψ 1−γ
.
1/ψ = the elasticity of intertemporal substitution (EIS) γ the risk aversion coefficient Nests the CRRA case when 1/ψ = γ
Rey
Royal Economic Society
Sargan Lecture 2014
11 / 52
Baseline model of financial integration Asset market structure
Autarky Budget equation ci,t + ii,t = yi,t Stochastic discount factor mi,t+1 = β
ci,t+1 ci,t
−ψ
h
ψ−γ Ui,t+1 ψ−γ i 1−γ 1−γ Et Ui,t+1
Euler for investment h equation y
Et mi,t+1 θ k1,t+1 φ0i,t + 1,t+1
Rey
φ0i,t φ0i,t+1
(1 − δ) + φi,t+1 −
Royal Economic Society
ii,t+1 0 ki,t+1 φi,t+1
i
=1
Sargan Lecture 2014
12 / 52
Baseline model of financial integration Asset market structure
Financial Integration (riskfree bond only) Budget equation with pt =
1 rt =
price of the riskfree bond
ci,t = yi,t − ii,t − bi,t pt + bi,t−1 Investment Euler equation Optimal bond holdings pt = Et [mi,t+1 ]
Rey
Royal Economic Society
Sargan Lecture 2014
13 / 52
Baseline model of financial integration Definition of an equilibrium
Under autarky An equilibrium in a given country i is a sequence of consumption and capital stocks (ci,t ; ki,t+1 ) such that individual Euler equations for investment decisions are verified and goods market clears at all dates.
Financial Integration An equilibrium is a sequence of consumption, capital stocks and bond holdings in both countries (ci,t ; ki,t+1 ; bi,t )i={E ,D} and a sequence of bond prices pt such that Euler equations for investment decisions are verified in both countries, Euler equations for bonds are verified in both countries, bonds and goods market clear at all dates.
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Royal Economic Society
Sargan Lecture 2014
14 / 52
Structural parameters
Discount rate Capital share Depreciation rate Capital adjustment costs EIS Risk aversion
β θ δ ξ 1/ψ γ
0.96 0.3 0.1 0.2 1/4 4 to 50
Capital adjustment costs such that σ i = 3σ y Risk aversion γ = 4, CRRA case.
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Royal Economic Society
Sargan Lecture 2014
15 / 52
Volatility
Volatility matches the group of emerging markets E integrating to developed countries D since 1985. Emerging markets roughly twice as volatile.
E=Risky economy D=Safe economy
Autocorrelation 0.9 0.9
Standard deviation 5% 2.5%
Zero correlation of shocks in the baseline calibration (underestimation compared to the data, roughly 0.2)
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Royal Economic Society
Sargan Lecture 2014
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Capital scarcity
40 emerging markets liberalizing after 1985 (mostly 1988-1993). Roughly the same GDP size as developed countries at opening. → General Equilibrium effects cannot be neglected. On average, capital stocks (per efficiency units) of emerging countries E = 50% of developed countries D at time of integration. I
I
Compute capital stocks for emerging countries E integrating to developed countries D since 1985 (perpetual inventory method). Compare with capital stocks of already integrated countries.
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Royal Economic Society
Sargan Lecture 2014
17 / 52
Financial integration without aggregate risk
From autarky to a bond only economy: Gourinchas Jeanne (2006) in general equilibrium No shocks Capital starts 50% below steady-state in the emerging market E Rest of the world (developed) D starts at its autarky steady state
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Royal Economic Society
Sargan Lecture 2014
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Consumption : developed
1.14 1.12
2.95
2.85 2.80
6.0
1.08
2.75
5.5
1.06
2.70
5.0
1.04
2.65
4.5
1.020
50
100
150
200
Consumption : capital scarce
0.8
1.05 1.00 0.95 0.90 0.850
50
100
150
4.00
200
2.60 50
100
150
200 2.550
NFA/gdp : developed
3.0
0.7
2.8
0.6
2.6
0.5
2.4
0.4
2.2
0.3
2.0
0.2
1.8
0.1
1.6
0.00
Capital : developed
2.90
6.5
1.10
1.10
Interest rate %
7.5 7.0
50
100
150
200
1.40
50
100
150
200
Capital : capital scarce
50
100
150
200
Figure 1: The riskless case: dynamics along the deterministic path. Dotted lines (resp. solid lines) refer to autarky levels (resp. levels under integration).
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Financial integration without aggregate risk
Efficient reallocation of capital No precautionary savings in autarky. Only initial level of capital matters Capital goes where returns are higher (from developed to emerging) But... Gains from financial integration are transitory Integration speeds up transition towards unchanged steady-state level of capital. Interest rate increases in the ROW. Welfare gains are small!
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Royal Economic Society
Sargan Lecture 2014
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Financial integration without aggregate risk
Welfare gains (% increase in permanent consumption) Partial General
Country E 1.03% 0.38%
Rest of the world D 0.29%
In partial equilibrium (small open economy), gains are small I
Transitory nature (Gourinchas and Jeanne (2006)).
In general equilibrium, welfare gains even SMALLER! I I
Must be shared between the two countries. Adverse General Equilibrium movements of world interest rate.
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Royal Economic Society
Sargan Lecture 2014
21 / 52
Financial integration in a risky world with capital scarcity
Integration with asymmetric aggregate risk and capital scarcity in the emerging market E is twice as volatile as D: σE = 2σD = 5%. Developed country D starts at autarky steady state. E starts with 50% of capital stock in D.
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Royal Economic Society
Sargan Lecture 2014
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Consumption : safe
1.12
7.0
1.10
6.5
1.09
6.0
1.08
5.5
1.07
5.0
1.06
4.5
1.05
4.0
1.040
50
100
150
Interest rate %
7.5
1.11
200
2.95 2.90 2.85 2.80 2.75 2.70
3.50
50
Consumption : risky
1.15
Capital : safe
3.00
100
150
200 2.650
NFA/gdp : safe
50
3.2
0.5
100
150
200
150
200
Capital : risky
3.0
1.10
2.8
1.05
0.0
2.6 2.4
1.00
2.2
0.5
0.95
2.0 1.8
0.90
1.6
1.0
0.850
50
100
150
200
0
50
100
150
200
1.40
50
100
Figure 2: Emerging market E volatile and capital scarce Dotted lines (resp. solid lines) refer to autarky levels (resp. levels under integration).
Rey
Royal Economic Society
Sargan Lecture 2014
23 / 52
Financial integration in a risky world with capital scarcity
Capital reallocation for precautionary motives vs efficiency reasons → Capital flows and growth reversals I
I
In the short-run, capital scarcity dominates: capital flows from D to E . Capital flows reversal in the medium-run as the precautionary motive dominates. Higher growth on impact in E compared to autarky initially, opposite in D. Reversal in the medium-run.
LOW welfare gains despite efficiency & risk-sharing gains. I I
Permanent increase in consumption is = 0.42% in D and 0.53% in E . Gains from faster convergence in E are reduced as financial integration makes E closer to its steady-state. Gains from risk-sharing and from efficiency are substitutes.
Rey
Royal Economic Society
Sargan Lecture 2014
24 / 52
Heterogenous dynamics and reversals: global imbalances come naturally
Capital reallocation for precautionary motives vs efficiency reasons → Global imbalances can be generated by the model very naturally Happens when the autarky interest rate in E is lower than the autarky interest rate in D (see Gourinchas and Rey (2014) Handbook of International Economics) Here this is due to the precautionary savings motive (see also Mendoza et al. and Angeletos and Panousi for the case of idiosyncratic risk)
Rey
Royal Economic Society
Sargan Lecture 2014
25 / 52
8 Arithmetic
GDP weighted
7
Output volatility (in %)
6
5
4
3
2
1
0 Africa
Asia
Latin America
Middle East
Southern Europe
Liberalizing emerging countries
Always opened developed countries
Figure 3: Volatility of real output growth per capita (in %, 1975-1995).
back
Source: PWT, Bekaert et al. (2005). 40 emerging markets liberalizing after 1985 (15 developed countries already integrated).
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Royal Economic Society
Sargan Lecture 2014
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Capital stock per efficiency unit (% of already intgrated developed countries)
90% Arithmetic
GDP weighted
80%
70%
60%
50%
40%
30%
20%
10%
0% Africa
Asia
Latin America
Middle East
Southern Europe
Liberalizing emerging countries
Figure 4: Capital stock at time of integration of emerging markets (ratio w.r.t developed countries). back Source: PWT, Bekaert et al. (2005). 40 emerging markets liberalizing after 1985 (15 developed countries already integrated).
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Royal Economic Society
Sargan Lecture 2014
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Consumption
1.09
NFA/gdp
0.0%
1.08
-10.0%
2.9
1.07
-20.0%
2.8
1.06
-30.0%
2.7
1.05
-40.0%
2.6
1.040
50
100
150
200
Consumption
1.3
-50.0%0
50
100
150
200
NFA/gdp
300.0%
Capital
3.0
2.50
south europe south europe (autarky) 50
100
150
200
Capital
3.5
250.0%
1.2
3.0
200.0% 1.1
150.0%
1.0
2.5
100.0% 2.0
50.0%
0.9
0.0% 0.8
1.5
middle east middle east (autarky)
-50.0%
0.70
50
100
150
200
-100.0%0
50
100
150
200
1.00
50
100
150
200
Figure 5: Dynamics along the risky path following integration the case of Early South Europe (top panel) and Late Middle-East (bottom panel). back South Europe = Greece-Portugal-Spain; Middle-East=Oman-Saudi Arabia. Rey
Royal Economic Society
Sargan Lecture 2014
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Southern Europe (early liberalizers) and the Middle East (late liberalizers): small gains
Early liberalizers (1986): Southern Europe has small gains due to (i) high correlation (0.6); (ii) small initial differences in capital stock (85%). Gains 0.08 %. Late liberalizers (1999): Middle-East has small gains despite being very capital scarce (35%) due to strong offsetting precautionary demand for safe assets. Volatile countries (8.1%). Gains of about 1%.
Rey
Royal Economic Society
Sargan Lecture 2014
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Robustness and bottom line
Asset market structure: incomplete vs complete markets Stochastic properties of the shocks (correlations) Market sizes (small open economy as a limit case) Long run risk (gives appropriate risk premia but not risk sharing) Empirical work likely to be misspecified (heterogeneity in dynamics and neglects general equilibrium effect) Conclusion: In the standard, frictionless benchmark model which has guided the intuition of economists and policy makers, gains from international financial integration (efficiency and risk sharing) are SMALL.
Rey
Royal Economic Society
Sargan Lecture 2014
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The importance of gross capital flows and external balance sheet of countries: a research agenda Gourinchas and Rey (2007): Analysis of the US external balance sheet at market value: ”From world banker to world venture capitalist”. First estimates of the ”exorbitant privilege” Gourinchas and Rey (JPE 2007): valuation effects are important for the external adjustment dynamics of countries (and they help predict exchange rates!) Gourinchas, Rey and Govillot (2010): The US has a very particular balance sheet: it earns excess returns on its net foreign asset position (exorbitant privilege) and provides insurance during global crises (exorbitant duty). Hence it is the world insurer. This is a different interpretation of the role of the centre country compared to the classic models (for example Krugman). Maggiori (2013) builds on our model. Gourinchas, Truempler and Rey (JIE 2012) provide estimates of the wealth transfers during the global crisis Rey
Royal Economic Society
Sargan Lecture 2014
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FACTS on gross capital flows
The size of the external balance sheet of countries have increased tremendously since the 1990s (Lane Milesi-Ferretti (2006), Gourinchas Rey (2007, 2014)) Gross capital flows are positively correlated with one another, across regions and across asset classes and negatively correlated with measures of uncertainty and risk aversion such as the VIX (Rey, Jackson Hole paper 2013, Passari and Rey (2014)) Gross capital flows follow a global financial cycle like leverage, credit creation and risky asset prices (Rey, Jackson Hole paper 2013, Miranda-Agrippino and Rey (2012))
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Royal Economic Society
Sargan Lecture 2014
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120
100
80
60
40
20
Other
Government Debt
Corporate Debt
Direct Investment
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
1972
1970
1968
1966
1964
1962
1960
1958
1956
1954
1952
0
Figure 1: Gross Liabilities of the United States, percent of GDP
140
Equity
Figure 6: External balance sheet of the United States, liabilities. Source Gourinchas and Rey (2014) Rey
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Liability Equity Equity Equity Equity Equity Equity Equity FDI FDI FDI FDI FDI FDI FDI Debt Debt Debt Debt Debt Debt Debt Credit Credit Credit CreditCredit Flows N. Am. LatAm CE. EU W. EU Em.As Asia Africa N. Am LatAm CE. EU W. EU Em.As Asia Africa N. Am LatAm CE. EU W. EU Em.As Asia Africa N. Am LatAm CE. EU W. EU Em.As Equity N. Am 1.00 Equity LatAm 0.39 1.00 Equity CE. EU 0.52 0.49 1.00 Equity W. EU 0.63 0.35 0.50 1.00 Equity Em. As 0.37 0.24 0.28 0.47 1.00 Equity Asia 0.24 0.31 0.28 0.40 0.31 1.00 Equity Africa 0.41 0.22 0.26 0.55 0.34 0.26 1.00 FDI N. Am 0.54 0.06 0.07 0.45 0.52 ‐0.07 0.22 1.00 FDI LatAm 0.41 0.10 0.08 0.29 0.32 ‐0.07 0.04 0.68 1.00 FDI CE. EU 0.46 0.11 0.08 0.18 0.23 ‐0.12 0.09 0.61 0.65 1.00 FDI W. EU 0.57 0.21 0.19 0.38 0.35 0.01 0.16 0.61 0.59 0.75 1.00 FDI Em. As 0.47 0.24 0.16 0.34 0.36 ‐0.04 0.04 0.65 0.77 0.69 0.64 1.00 FDI Asia 0.36 0.16 0.03 0.29 0.30 ‐0.17 0.05 0.60 0.70 0.57 0.51 0.69 1.00 FDI Africa 0.33 0.01 0.10 0.18 0.03 ‐0.16 ‐0.19 0.31 0.36 0.35 0.35 0.34 0.27 1.00 Debt N. Am 0.42 0.17 0.32 0.51 0.29 0.21 0.31 0.40 0.39 0.55 0.51 0.48 0.37 0.08 1.00 Debt LatAm 0.20 0.40 0.33 0.16 0.13 0.00 ‐0.05 0.16 0.35 0.13 0.05 0.31 0.26 0.06 0.10 1.00 Debt CE. EU 0.37 0.42 0.50 0.43 0.13 0.17 0.19 0.14 0.35 0.14 0.12 0.47 0.21 0.04 0.37 0.52 1.00 Debt W. EU 0.49 0.05 0.33 0.50 0.23 0.27 0.47 0.29 0.10 0.44 0.27 0.25 0.02 0.10 0.58 ‐0.13 0.28 1.00 Debt Em. As 0.40 0.58 0.65 0.35 0.20 0.23 0.20 0.13 0.24 0.25 0.37 0.35 0.15 0.02 0.32 0.38 0.53 0.14 1.00 Debt Asia 0.16 0.18 0.24 0.22 0.16 ‐0.04 0.16 0.35 0.31 0.30 0.30 0.45 0.26 0.14 0.45 0.27 0.42 0.19 0.39 1.00 Debt Africa 0.26 0.27 0.39 0.18 0.07 0.14 0.09 0.12 0.21 0.10 0.01 0.41 0.21 0.07 0.21 0.46 0.61 0.15 0.44 0.32 1.00 Credit N. Am. 0.29 ‐0.02 0.21 0.38 0.15 ‐0.01 0.32 0.20 0.02 0.19 0.20 0.12 0.09 0.04 0.37 0.14 0.23 0.25 0.23 0.25 0.03 1.00 Credit LatAm 0.41 0.34 0.21 0.26 0.12 0.04 0.22 0.38 0.35 0.42 0.27 0.48 0.35 0.24 0.35 0.25 0.41 0.30 0.29 0.46 0.28 0.22 1.00 Credit CE. EU 0.42 0.25 0.27 0.28 0.32 0.15 0.21 0.54 0.38 0.72 0.55 0.47 0.36 0.28 0.54 0.14 0.13 0.56 0.25 0.48 0.12 0.17 0.55 1.00 Credit W. EU 0.19 ‐0.03 0.24 0.31 0.19 ‐0.16 0.26 0.27 0.08 0.20 0.30 0.19 0.13 0.15 0.45 0.20 0.25 0.33 0.26 0.45 0.16 0.63 0.30 0.34 1.00 Credit Em. As 0.25 0.54 0.39 0.21 0.10 0.16 0.05 0.22 0.16 0.30 0.29 0.38 0.24 0.00 0.40 0.31 0.33 0.15 0.56 0.51 0.27 0.24 0.45 0.48 0.28 1.00 Credit Asia 0.08 ‐0.03 0.02 ‐0.01 0.00 ‐0.40 ‐0.12 0.23 0.23 0.32 0.24 0.31 0.23 0.25 0.32 0.18 0.17 ‐0.01 0.13 0.37 0.08 0.43 0.35 0.23 0.52 0.37 Credit Africa 0.11 0.06 0.01 0.15 0.01 ‐0.20 0.12 0.40 0.30 0.35 0.33 0.24 0.37 0.18 0.32 0.11 0.00 0.13 0.03 0.34 ‐0.02 0.24 0.30 0.40 0.36 0.30
Credit Credi Asia Africa
1.00 0.31 1.00
Figure 7: Gross inflows, all asset classes (FDI, debt, equity, bank credit), by geographical areas (North America, Western Europe, Latin America, Central and Eastern Europe, Asia, Emerging Asia, Africa). Green colour means positive correlations. Red colour means negative correlations.
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AUSTRALIAAUSTRIA AUSTRALIA AUSTRIA CANADA CHILE FINLAND FRANCE GERMANY IRELAND ITALY JAPAN MEXICO NETHERLANDS NEW_ZEALAND PORTUGAL SPAIN SWEDEN UNITED_KINGDOM UNITED_STATES BRAZIL HUNGARY ICELAND INDONESIA KOREA SOUTH_AFRICA THAILAND TURKEY ARGENTINA BOLIVIA DENMARK BULGARIA
-0.03919 -0.0783 0.364054 0.100472 0.190713 0.005077 0.172136 -0.0129 -0.20074 -0.10831 -0.02821 -0.01299 -0.06376 0.154012 0.25975 -0.05647 -0.02192 0.047299 0.372528 0.338919 0.019426 0.095806 0.164466 0.067353 0.398686 0.116753 -0.00606 -0.0934 0.553896
1 0.067584 0.29706 -0.10728 0.287145 0.364336 0.339794 0.485044 -0.07153 -0.08906 0.489775 0.103868 0.459259 0.23794 0.085319 0.273989 0.32381 0.245288 0.05932 0.202173 -0.25196 0.479603 0.131883 0.079829 0.39218 0.165977 -0.04934 0.272219 0.228968
CANADA
1 0.121492 -0.20695 0.260391 -0.00254 0.067105 0.051584 0.148568 0.408351 0.103542 0.17618 -0.06874 0.002846 0.016785 0.20269 0.351381 -0.01257 0.207625 0.156361 0.055582 0.278784 0.407864 0.031594 0.094947 0.068755 -0.21408 0.087744 0.005534
CHILE
1 0.05688 0.195877 0.214196 0.000212 0.157937 -0.09421 0.153052 0.328249 0.074398 0.199103 0.232048 -0.01075 0.067834 0.087048 0.272613 0.164218 0.292669 0.142922 0.444456 0.302301 0.13894 0.479531 0.292711 0.113475 0.191762 0.419518
FINLAND FRANCE
1 -0.2255 0.273523 -0.23447 0.195965 -0.03117 0.049611 0.026484 0.132465 0.069607 0.264049 0.235438 0.019154 -0.05837 0.123847 -0.14993 -0.02744 0.039648 0.026408 0.048513 0.074368 0.18951 0.089206 -0.04634 -0.25613 -0.06609
1 0.338801 0.345899 0.224629 0.102579 0.183267 0.493335 0.052823 0.130124 0.143297 0.230897 0.58315 0.531937 0.181591 0.188959 0.437046 -0.06924 0.371338 0.346471 0.155929 0.434547 0.114934 -0.42719 0.355034 0.302221
GERMANY IRELAND
1 0.21873 0.326936 0.262973 0.145611 0.668607 0.357321 0.467637 0.19019 0.357402 0.602274 0.352123 0.261831 -0.0535 0.134206 -0.17055 0.333072 0.405936 0.129609 0.4329 0.137146 -0.18726 0.22795 -0.01933
1 0.25476 0.19343 -0.10153 0.234938 0.19417 0.110902 0.340718 0.24294 0.181967 0.120313 0.145499 0.613377 0.44218 -0.197 0.129954 0.286568 -0.20812 0.323702 0.005837 -0.13705 0.515129 0.406331
ITALY
1 -0.06614 -0.22191 0.475112 -0.01398 0.052289 0.255213 0.033907 0.213288 0.202874 0.39888 -0.02975 0.250595 -0.10087 0.341104 0.16244 0.001764 0.375367 0.180605 -0.24828 0.317972 0.103423
JAPAN
1 0.159234 0.238301 0.046652 0.009973 -0.05793 0.183161 0.335044 0.344271 0.135462 0.01985 0.001629 0.045922 0.063662 0.124653 0.135792 -0.06506 0.13466 -0.05866 0.101776 -0.24724
MEXICO
1 0.006497 0.108238 0.150952 0.029603 0.155903 0.200393 0.148369 0.139834 0.03095 0.007042 0.051728 0.287044 0.224681 0.154524 0.16111 0.023164 -0.18278 -0.11829 0.029811
NETHERLANDS NEW_ZEALAND PORTUGALSPAIN
1 0.270466 0.390095 0.123084 0.388029 0.708758 0.547633 0.333743 -0.10298 0.34903 -0.21799 0.601859 0.302293 0.109657 0.370165 0.111102 -0.25584 0.442472 0.028431
1 0.235531 0.084087 0.184722 0.268579 0.140554 0.138307 0.109635 0.160696 0.000543 0.108412 0.425234 0.085309 0.101776 -0.03712 -0.10656 0.142632 0.099002
1 0.180748 0.29936 0.457877 0.329652 0.213888 -0.09413 0.190961 -0.18597 0.279877 0.085374 0.080449 0.202492 0.073611 -0.01032 0.108962 -0.08067
1 0.147261 0.079752 -0.13748 0.213097 0.301012 0.385438 0.009624 0.041975 0.199305 -0.08612 0.469351 0.160795 -0.06809 0.130126 0.364018
SWEDEN UNITED_KINGDOM UNITED_STATES BRAZIL
1 0.408673 0.303454 -0.07787 0.066677 0.288864 -0.23098 0.26525 0.240269 -0.03345 0.209643 -0.04791 -0.2837 0.185383 0.12315
1 0.755091 0.259281 -0.17567 0.295907 -0.18887 0.49277 0.269579 0.133924 0.192439 0.0264 -0.31801 0.198069 -0.14574
1 0.176764 -0.13407 0.235227 -0.10979 0.41201 0.215307 0.146537 0.112757 0.059029 -0.29058 0.043795 -0.17249
1 0.035018 0.172176 0.129328 0.375652 0.050682 0.24028 0.423942 0.208558 -0.16396 0.084885 0.100103
HUNGARY ICELAND
1 0.485081 0.047538 0.016215 0.253556 -0.15156 0.373183 0.028335 -0.09647 0.184791 0.584315
1 -0.03615 0.316394 0.235918 0.086512 0.532554 0.103663 -0.21105 0.393783 0.63702
INDONESIAKOREA__REPUBLIC_OF SOUTH_AFRICA THAILAND TURKEY
1 -0.04667 0.093512 0.35821 -0.00088 0.03084 0.133945 -0.10793 0.018221
1 0.295303 0.372182 0.360258 0.117845 -0.32987 0.229327 0.209706
ARGENTINABOLIVIA
DENMARK BULGARIA
1 0.134048 1 0.353566 0.203871 1 0.172182 -0.06023 0.241317 1 -0.16796 -0.03544 -0.14298 -0.01039 1 0.164987 -0.06768 0.199252 0.09416 -0.22527 1 0.233736 0.065649 0.562799 0.076346 -0.1701 0.302194
1
Figure 8: Gross bank credit inflows by countries and exchange rate regimes (countries in light grey are floaters, countries in darker grey are peggers). Green colour means positive correlations. Red colour means negative correlations. Source: Passari and Rey (2014)
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Royal Economic Society
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space constraints. Table 1 (b): Conditional correlations of liability flows with the VIX, quarterly, 1990Q1‐2012Q4.. Correlations inflows / VIX Equity FDI Debt Credit
North Latin America America ‐0.06
‐0.31
Central Eastern Europe
Western Europe
‐0.32
‐0.38
Emerging Asia Asia ‐0.08
‐0.34
Africa ‐0.25
0.10
0.35
0.07
0.06
0.08
0.16
0.07
‐0.30
‐0.15
‐0.36
‐0.23
‐0.28
‐0.06
‐0.22
‐0.29
‐0.15
‐0.16
‐0.24
‐0.26
0.09
‐0.14
Figure 9: Conditional correlations of liability flows with the VIX (conditioning In Table 1(c), I investigate whether fluctuations in the VIX are also associated with changes in credit variables are the world short term real rate and the world growth rate) creation and leverage using various measures. We report the conditional correlations controlling again for the classic push factors (world growth rate and short term real rate). Following Forbes (2012), I measure leverage as the ratio of private credit by deposit money banks and other financial institutions to bank deposits, including demand, time and saving deposits in nonbanks. The precise definitions of leverage and domestic credit can be found in Appendix B. Table 1(c) offers this striking finding: in all areas of the world, credit growth is negatively linked to Rey Royal Economic Society Sargan Lecture 2014
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Correlations credit / VIX
North Latin America America
Central Eastern Europe
Western Emerging Europe Asia
Asia
Africa
Domestic credit growth Leverage
‐0.26
‐0.14
‐0.14
‐0.11
‐0.01
‐0.30
0.01
‐0.17
0.05
0.30
‐0.09
‐0.12
‐0.25
0.03
‐0.32
0.06
0.07
‐0.21
‐0.06
‐0.31
0.01
Leverage growth
Figure 10: Conditional correlations of domestic credit growth, leverage and To sum up, the data show (i) commonality in capital inflows – and outflows – across regions leverage growth with the VIX (conditioning variables are the world short term real rateand types of assets (except for FDI flows and a subset of Asian and African flows). The and the world growth rate) commonality is particularly strong for credit and portfolio debt inflows (see Figures 1a,b) but is absent for net capital flows (Figure 1c); (ii) surges in gross capital flows in period of low volatility and decline in flows when the VIX goes up (with the exception of FDI flows); a large volatility and pro‐cyclicality of credit flows (see Figure 2 and Tables 1a,b); (iii) increases in credit growth around the world in parallel with falls of the VIX (see Table 1c); (iv) increases in Rey Royal Economic Society Sargan Lecture 2014
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Dynamic Factor Model for risky asset prices and monetary policy of the US: Miranda Agrippino and Rey (2012) We estimate global, regional and asset-specific factors from a collection of world risky asset prices. price series (i,t) = common component (t) + idiosyncratic (i,t) Using a set of restrictions on the coefficient matrices of the Dynamic Factor Model we further decompose the common component in two: common component (t) = global factor (t) + regional factors (t) Each price series is then the sum of three components I I
I
a global factor that is a common to all price series in the set a region (or market) specific component that captures aggregate shocks that affect all assets traded on the same market or belonging to the same category (i.e. commodities) an idiosyncratic asset-specific component
Formally: xi,t = µi + λi,G ftG + λi,M ftM + ξi,t Rey
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Sargan Lecture 2014
(1) 38 / 52
Dynamic Factor Model for asset prices: Data
the global set that we consider is a collection of monthly asset prices (logs) [N=428] spanning the twenty-years-period from 1990 to 2010 that combines information from I
US market [S&P 500, n=165]
I
European market: Euro Area + United Kingdom [S&P Euro + FTSE100, n=144]
I
Asian market: Japan + Singapore + Hong Kong + South Korea + Taiwan [Topix Core 30 + S&P Asia, n=48]
I
Commodities and Corporate bonds markets [n=71]
Rey
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Sargan Lecture 2014
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Dynamic Factor Model for risky asset prices: Data
data are taken at monthly frequency (EOM value) to reduce the noise in daily figures while preserving long term characteristics of the series the global set is split into six subsamples or blocks each of which will load a specific factor together with the global one. The blocks are: 2
US Europe, further decomposed into Euro area and UK
3
Asia
4
Commodities
5
Corporate
1
Rey
Royal Economic Society
Sargan Lecture 2014
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Dynamic Factor Model for risky asset prices: Data
the DFM is cast in state space form and estimated on the stationary return series using Maximum Likelihood (Doz, Giannone, Reichlin (2006), Watson, Reis (2007)) Factors for the price series are then obtained via cumulation (Bai, Ng (2004)) The number of factors [1 global & 1 per each block] and lag length of factors VAR [1] are selected using standard criteria and tests)
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Figure 11: Global factor in risky asset prices and VIX. Source: Rey (2013)
To sum up, we have now established in flow data (across most types of flows and regions, but with Rey
.
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Figure 12: Decomposition of the global factor in a volatility component and a risk aversion component Rey
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Facts: The Global Financial Cycle (Rey, Jackson hole (2013)) Strong commonality in gross capital inflows and outflows around the world Negative co-movements of these gross flows with the VIX, index of market risk aversion and uncertainty. Credit flows are especially pro-cyclical An important part of the variance of a large cross section of 858 risky asset prices (stocks, corporate bonds, commodities) distributed on five continents is explained by one single global factor. This global factor is closely related to the VIX (negatively) Credit growth and, for most areas, leverage and leverage growth co-move negatively with the VIX
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Figure 13: G-sifi bank leverage. Quarterly growth of total assets over quarterly growth of leverage ratio, all available history Rey
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Sargan Lecture 2014
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Figure 14: Capital market banks leverage. Quarterly growth of total assets over
of leverage ratio, availableover history erlyquarterly growthgrowth of aggregated totalallassets quarterly growth of aggreg Rey
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Sargan Lecture 2014
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Figure 15: Commercial banks leverage. Quarterly growth of total assets over quarterly growth of leverage ratio, all available history
terly growth of aggregated total assets over quarterly growth of aggregated age ratio, 2005-Q1 to 2010-Q10. Source: . Bloomberg, authors calculations
da Agrippino & Rey (LBS) Rey
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Shanghai, May 27, 2013 Sargan Lecture 2014
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47 / 52
Global Financial Cycle and excess credit growth
Global banks raising funds in particular in the US (dollar is the main currency of global banking). Surges in credit flows associated with increases in leverage worldwide Procyclicality: Credit creation when measured risks are low, asset prices pushed up further, spreads compressed, healthy looking balance sheets etc, measured risks lower etc... The global financial cycle is not aligned with countries specific macroeconomic conditions. Symptoms can go from benign to large asset price bubbles and excess credit creation, which are among the best predictors of financial crises.
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Monetary policy spillovers of the centre country of the International Monetary System (US) (Rey (2013 Jackson )
A VAR analysis suggests monetary policy in the centre country is an important determinant of the global financial cycle (Miranda Agrippino and Rey (2012) When the Federal Funds rate goes down, the VIX falls, banks leverage rises, as do gross credit flows. A fall in the VIX leads to an increase in global domestic credit. Estimates suggest that between 9 and 30 percent of the variance of the VIX is explained by shocks to fed funds rate (1990-2007)
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Figure 16: VAR Analysis Rey
.
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The International Monetary System and the Trilemma (Passari and Rey (2014)) Trilemma: with free capital mobility, independent monetary policies are feasible if and only if exchange rates are floating (dominant paradigm in international finance). Instead, whenever capital is freely mobile, cross-border flows and leverage of global financial institutions transmit monetary conditions globally, even under floating exchange-rate regimes Gross credit and debt flows are key for the international transmisison of monetary spillovers. The global financial cycle transforms the trilemma into a dilemma or an irreconcilable duo: independent monetary policies are possible if and only if the capital account is managed, directly or indirectly.
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Conclusions
A very rich research agenda in international macroeconomics and finance as important questions are still unanswered. A lot more details at http://www.helenerey.eu/ Thank you!
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Sargan Lecture 2014
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Sargan Lecture 2014
Rey
Royal Economic Society
Sargan Lecture 2014
1 / 52
What do we know about the costs and benefits of financial integration?
First, I will focus on the standard benefits of international financial flows Second, I will discuss some of the potential costs, due in particular to monetary policy spillovers Bottom line will be that we still do not have a unified framework to discuss all the relevant aspects and that the welfare benefits of financial flows cannot be taken for granted.
Rey
Royal Economic Society
Sargan Lecture 2014
2 / 52
Motivation Core question in international macroeconomics and finance
Where do gains from international financial integration come from? Conventional view I I
efficient allocation of capital: capital flows to emerging countries risk sharing: reduces volatility of aggregate consumption
Other possibilities (which I will not discuss here) I
effect on TFP (via financial markets development, institutional changes, macroeconomic policies...)
Rey
Royal Economic Society
Sargan Lecture 2014
3 / 52
A simple experiment
Stochastic neoclassical framework with two production economies An emerging (risky) country (5% volatility of productivity shocks) A relatively safer developed country (2.5% volatility) Emerging country starts with 50% of the capital of developed country. Questions What is the growth impact of financial integration? What is the dynamics of capital flows? How big are the gains from financial integration? Who benefits the most?
Rey
Royal Economic Society
Sargan Lecture 2014
4 / 52
Two classes of models to quantify welfare gains
Allocative efficiency of financial integration without aggregate risk I I
Gourinchas and Jeanne (2006). Small gains. Hoxha, Kalemli-Ozcan, Vollrath (2013). Large gains. Capital goods imperfect substitutes.
International risk sharing without production I I
I
I
Lucas (1982), Cole and Obstfeld (1991). Small gains. Van Wincoop (1999), Lewis (1999). Larger gains if high market price of risk. Kalemli-Ozcan, Sorensen, Yosha (2001). Potentially high gains (effect on specialization). Colacito and Croce (2010), Lewis and Liu (2013). Long run risk.
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Sargan Lecture 2014
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Empirical evidence on gains from financial integration
Effect on growth and on consumption volatility I
Surveys: Eichengreen (2002); Kose et al. (2006); Henry (2007); Obstfeld (2009); Jeanne et al. (2012).
Mixed results: I I I I
depends on sample period there is a lot of country heterogeneity. endogeneity issues event studies, though useful, have a short time frame.
We cannot take the gains for granted.
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Royal Economic Society
Sargan Lecture 2014
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Modelling jointly the two types of gains in general equilibrium: Coeurdacier, Rey and Winant 2014
We need an integrated framework I I
Both types of gains are intertwined. Are they substitute or complement?
Convergence gains depend on distance from steady-state. But the steady-state itself is modified by financial integration in the presence of risk. We need a general equilibrium model. Emerging markets have integrated in waves.
Rey
Royal Economic Society
Sargan Lecture 2014
7 / 52
Two types of gains
Assess the growth dynamics and the welfare gains from financial integration in a neoclassical growth model I I I I
with aggregate uncertainty with heterogeneous countries with incomplete (or complete) markets in general equilibrium
Use a global approximation methods to study the transition path towards the long run world equilibrium Emphasize relation between risk, growth and capital accumulation
Rey
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Sargan Lecture 2014
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Findings Growth and capital flows dynamics I
I
I
Tension between the buildup of precautionary assets by risky (emerging) country and the potential effect of capital scarcity in the short-run. Growth impact of financial integration for risky country depends on these two conflicting forces. Financial integration affects the degree of aggregate risk and hence precautionary savings motives. Terms of the tradeoff between efficiency and risk-sharing depends on the market price of risk.
Welfare gains I I
Remain small for emerging markets. More elusive than we think. Surprisingly, if anything, the safest (developed) countries are the main beneficiaries, particularly so if the price of risk is high.
Rey
Royal Economic Society
Sargan Lecture 2014
9 / 52
Baseline model of financial integration Technology
2 countries i = D, E with a stochastic neoclassical structure. One good perfectly tradable.
Production Cobb-Douglas technology: θ 1−θ yi,t = ai,t ki,t li,t
Productivity shocks: log (ai,t ) = (1 − ρ) log (ai,0 ) + ρ log (ai,t−1 ) + i,t Investment with convex adjustment costs ki,t+1 = (1 − δ) ki,t + kt ϕ Rey
Royal Economic Society
ii,t ki,t
Sargan Lecture 2014
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Baseline model of financial integration Preferences
Epstein-Zin preferences Ui,t = (1 −
1−ψ β)ci,t
+β
1−γ Et Ui,t+1
1 1−ψ 1−ψ 1−γ
.
1/ψ = the elasticity of intertemporal substitution (EIS) γ the risk aversion coefficient Nests the CRRA case when 1/ψ = γ
Rey
Royal Economic Society
Sargan Lecture 2014
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Baseline model of financial integration Asset market structure
Autarky Budget equation ci,t + ii,t = yi,t Stochastic discount factor mi,t+1 = β
ci,t+1 ci,t
−ψ
h
ψ−γ Ui,t+1 ψ−γ i 1−γ 1−γ Et Ui,t+1
Euler for investment h equation y
Et mi,t+1 θ k1,t+1 φ0i,t + 1,t+1
Rey
φ0i,t φ0i,t+1
(1 − δ) + φi,t+1 −
Royal Economic Society
ii,t+1 0 ki,t+1 φi,t+1
i
=1
Sargan Lecture 2014
12 / 52
Baseline model of financial integration Asset market structure
Financial Integration (riskfree bond only) Budget equation with pt =
1 rt =
price of the riskfree bond
ci,t = yi,t − ii,t − bi,t pt + bi,t−1 Investment Euler equation Optimal bond holdings pt = Et [mi,t+1 ]
Rey
Royal Economic Society
Sargan Lecture 2014
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Baseline model of financial integration Definition of an equilibrium
Under autarky An equilibrium in a given country i is a sequence of consumption and capital stocks (ci,t ; ki,t+1 ) such that individual Euler equations for investment decisions are verified and goods market clears at all dates.
Financial Integration An equilibrium is a sequence of consumption, capital stocks and bond holdings in both countries (ci,t ; ki,t+1 ; bi,t )i={E ,D} and a sequence of bond prices pt such that Euler equations for investment decisions are verified in both countries, Euler equations for bonds are verified in both countries, bonds and goods market clear at all dates.
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Royal Economic Society
Sargan Lecture 2014
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Structural parameters
Discount rate Capital share Depreciation rate Capital adjustment costs EIS Risk aversion
β θ δ ξ 1/ψ γ
0.96 0.3 0.1 0.2 1/4 4 to 50
Capital adjustment costs such that σ i = 3σ y Risk aversion γ = 4, CRRA case.
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Royal Economic Society
Sargan Lecture 2014
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Volatility
Volatility matches the group of emerging markets E integrating to developed countries D since 1985. Emerging markets roughly twice as volatile.
E=Risky economy D=Safe economy
Autocorrelation 0.9 0.9
Standard deviation 5% 2.5%
Zero correlation of shocks in the baseline calibration (underestimation compared to the data, roughly 0.2)
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Royal Economic Society
Sargan Lecture 2014
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Capital scarcity
40 emerging markets liberalizing after 1985 (mostly 1988-1993). Roughly the same GDP size as developed countries at opening. → General Equilibrium effects cannot be neglected. On average, capital stocks (per efficiency units) of emerging countries E = 50% of developed countries D at time of integration. I
I
Compute capital stocks for emerging countries E integrating to developed countries D since 1985 (perpetual inventory method). Compare with capital stocks of already integrated countries.
Rey
Royal Economic Society
Sargan Lecture 2014
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Financial integration without aggregate risk
From autarky to a bond only economy: Gourinchas Jeanne (2006) in general equilibrium No shocks Capital starts 50% below steady-state in the emerging market E Rest of the world (developed) D starts at its autarky steady state
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Consumption : developed
1.14 1.12
2.95
2.85 2.80
6.0
1.08
2.75
5.5
1.06
2.70
5.0
1.04
2.65
4.5
1.020
50
100
150
200
Consumption : capital scarce
0.8
1.05 1.00 0.95 0.90 0.850
50
100
150
4.00
200
2.60 50
100
150
200 2.550
NFA/gdp : developed
3.0
0.7
2.8
0.6
2.6
0.5
2.4
0.4
2.2
0.3
2.0
0.2
1.8
0.1
1.6
0.00
Capital : developed
2.90
6.5
1.10
1.10
Interest rate %
7.5 7.0
50
100
150
200
1.40
50
100
150
200
Capital : capital scarce
50
100
150
200
Figure 1: The riskless case: dynamics along the deterministic path. Dotted lines (resp. solid lines) refer to autarky levels (resp. levels under integration).
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Financial integration without aggregate risk
Efficient reallocation of capital No precautionary savings in autarky. Only initial level of capital matters Capital goes where returns are higher (from developed to emerging) But... Gains from financial integration are transitory Integration speeds up transition towards unchanged steady-state level of capital. Interest rate increases in the ROW. Welfare gains are small!
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Financial integration without aggregate risk
Welfare gains (% increase in permanent consumption) Partial General
Country E 1.03% 0.38%
Rest of the world D 0.29%
In partial equilibrium (small open economy), gains are small I
Transitory nature (Gourinchas and Jeanne (2006)).
In general equilibrium, welfare gains even SMALLER! I I
Must be shared between the two countries. Adverse General Equilibrium movements of world interest rate.
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Financial integration in a risky world with capital scarcity
Integration with asymmetric aggregate risk and capital scarcity in the emerging market E is twice as volatile as D: σE = 2σD = 5%. Developed country D starts at autarky steady state. E starts with 50% of capital stock in D.
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Consumption : safe
1.12
7.0
1.10
6.5
1.09
6.0
1.08
5.5
1.07
5.0
1.06
4.5
1.05
4.0
1.040
50
100
150
Interest rate %
7.5
1.11
200
2.95 2.90 2.85 2.80 2.75 2.70
3.50
50
Consumption : risky
1.15
Capital : safe
3.00
100
150
200 2.650
NFA/gdp : safe
50
3.2
0.5
100
150
200
150
200
Capital : risky
3.0
1.10
2.8
1.05
0.0
2.6 2.4
1.00
2.2
0.5
0.95
2.0 1.8
0.90
1.6
1.0
0.850
50
100
150
200
0
50
100
150
200
1.40
50
100
Figure 2: Emerging market E volatile and capital scarce Dotted lines (resp. solid lines) refer to autarky levels (resp. levels under integration).
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Financial integration in a risky world with capital scarcity
Capital reallocation for precautionary motives vs efficiency reasons → Capital flows and growth reversals I
I
In the short-run, capital scarcity dominates: capital flows from D to E . Capital flows reversal in the medium-run as the precautionary motive dominates. Higher growth on impact in E compared to autarky initially, opposite in D. Reversal in the medium-run.
LOW welfare gains despite efficiency & risk-sharing gains. I I
Permanent increase in consumption is = 0.42% in D and 0.53% in E . Gains from faster convergence in E are reduced as financial integration makes E closer to its steady-state. Gains from risk-sharing and from efficiency are substitutes.
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Heterogenous dynamics and reversals: global imbalances come naturally
Capital reallocation for precautionary motives vs efficiency reasons → Global imbalances can be generated by the model very naturally Happens when the autarky interest rate in E is lower than the autarky interest rate in D (see Gourinchas and Rey (2014) Handbook of International Economics) Here this is due to the precautionary savings motive (see also Mendoza et al. and Angeletos and Panousi for the case of idiosyncratic risk)
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8 Arithmetic
GDP weighted
7
Output volatility (in %)
6
5
4
3
2
1
0 Africa
Asia
Latin America
Middle East
Southern Europe
Liberalizing emerging countries
Always opened developed countries
Figure 3: Volatility of real output growth per capita (in %, 1975-1995).
back
Source: PWT, Bekaert et al. (2005). 40 emerging markets liberalizing after 1985 (15 developed countries already integrated).
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Capital stock per efficiency unit (% of already intgrated developed countries)
90% Arithmetic
GDP weighted
80%
70%
60%
50%
40%
30%
20%
10%
0% Africa
Asia
Latin America
Middle East
Southern Europe
Liberalizing emerging countries
Figure 4: Capital stock at time of integration of emerging markets (ratio w.r.t developed countries). back Source: PWT, Bekaert et al. (2005). 40 emerging markets liberalizing after 1985 (15 developed countries already integrated).
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Consumption
1.09
NFA/gdp
0.0%
1.08
-10.0%
2.9
1.07
-20.0%
2.8
1.06
-30.0%
2.7
1.05
-40.0%
2.6
1.040
50
100
150
200
Consumption
1.3
-50.0%0
50
100
150
200
NFA/gdp
300.0%
Capital
3.0
2.50
south europe south europe (autarky) 50
100
150
200
Capital
3.5
250.0%
1.2
3.0
200.0% 1.1
150.0%
1.0
2.5
100.0% 2.0
50.0%
0.9
0.0% 0.8
1.5
middle east middle east (autarky)
-50.0%
0.70
50
100
150
200
-100.0%0
50
100
150
200
1.00
50
100
150
200
Figure 5: Dynamics along the risky path following integration the case of Early South Europe (top panel) and Late Middle-East (bottom panel). back South Europe = Greece-Portugal-Spain; Middle-East=Oman-Saudi Arabia. Rey
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Southern Europe (early liberalizers) and the Middle East (late liberalizers): small gains
Early liberalizers (1986): Southern Europe has small gains due to (i) high correlation (0.6); (ii) small initial differences in capital stock (85%). Gains 0.08 %. Late liberalizers (1999): Middle-East has small gains despite being very capital scarce (35%) due to strong offsetting precautionary demand for safe assets. Volatile countries (8.1%). Gains of about 1%.
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Robustness and bottom line
Asset market structure: incomplete vs complete markets Stochastic properties of the shocks (correlations) Market sizes (small open economy as a limit case) Long run risk (gives appropriate risk premia but not risk sharing) Empirical work likely to be misspecified (heterogeneity in dynamics and neglects general equilibrium effect) Conclusion: In the standard, frictionless benchmark model which has guided the intuition of economists and policy makers, gains from international financial integration (efficiency and risk sharing) are SMALL.
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The importance of gross capital flows and external balance sheet of countries: a research agenda Gourinchas and Rey (2007): Analysis of the US external balance sheet at market value: ”From world banker to world venture capitalist”. First estimates of the ”exorbitant privilege” Gourinchas and Rey (JPE 2007): valuation effects are important for the external adjustment dynamics of countries (and they help predict exchange rates!) Gourinchas, Rey and Govillot (2010): The US has a very particular balance sheet: it earns excess returns on its net foreign asset position (exorbitant privilege) and provides insurance during global crises (exorbitant duty). Hence it is the world insurer. This is a different interpretation of the role of the centre country compared to the classic models (for example Krugman). Maggiori (2013) builds on our model. Gourinchas, Truempler and Rey (JIE 2012) provide estimates of the wealth transfers during the global crisis Rey
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FACTS on gross capital flows
The size of the external balance sheet of countries have increased tremendously since the 1990s (Lane Milesi-Ferretti (2006), Gourinchas Rey (2007, 2014)) Gross capital flows are positively correlated with one another, across regions and across asset classes and negatively correlated with measures of uncertainty and risk aversion such as the VIX (Rey, Jackson Hole paper 2013, Passari and Rey (2014)) Gross capital flows follow a global financial cycle like leverage, credit creation and risky asset prices (Rey, Jackson Hole paper 2013, Miranda-Agrippino and Rey (2012))
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120
100
80
60
40
20
Other
Government Debt
Corporate Debt
Direct Investment
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
1972
1970
1968
1966
1964
1962
1960
1958
1956
1954
1952
0
Figure 1: Gross Liabilities of the United States, percent of GDP
140
Equity
Figure 6: External balance sheet of the United States, liabilities. Source Gourinchas and Rey (2014) Rey
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Liability Equity Equity Equity Equity Equity Equity Equity FDI FDI FDI FDI FDI FDI FDI Debt Debt Debt Debt Debt Debt Debt Credit Credit Credit CreditCredit Flows N. Am. LatAm CE. EU W. EU Em.As Asia Africa N. Am LatAm CE. EU W. EU Em.As Asia Africa N. Am LatAm CE. EU W. EU Em.As Asia Africa N. Am LatAm CE. EU W. EU Em.As Equity N. Am 1.00 Equity LatAm 0.39 1.00 Equity CE. EU 0.52 0.49 1.00 Equity W. EU 0.63 0.35 0.50 1.00 Equity Em. As 0.37 0.24 0.28 0.47 1.00 Equity Asia 0.24 0.31 0.28 0.40 0.31 1.00 Equity Africa 0.41 0.22 0.26 0.55 0.34 0.26 1.00 FDI N. Am 0.54 0.06 0.07 0.45 0.52 ‐0.07 0.22 1.00 FDI LatAm 0.41 0.10 0.08 0.29 0.32 ‐0.07 0.04 0.68 1.00 FDI CE. EU 0.46 0.11 0.08 0.18 0.23 ‐0.12 0.09 0.61 0.65 1.00 FDI W. EU 0.57 0.21 0.19 0.38 0.35 0.01 0.16 0.61 0.59 0.75 1.00 FDI Em. As 0.47 0.24 0.16 0.34 0.36 ‐0.04 0.04 0.65 0.77 0.69 0.64 1.00 FDI Asia 0.36 0.16 0.03 0.29 0.30 ‐0.17 0.05 0.60 0.70 0.57 0.51 0.69 1.00 FDI Africa 0.33 0.01 0.10 0.18 0.03 ‐0.16 ‐0.19 0.31 0.36 0.35 0.35 0.34 0.27 1.00 Debt N. Am 0.42 0.17 0.32 0.51 0.29 0.21 0.31 0.40 0.39 0.55 0.51 0.48 0.37 0.08 1.00 Debt LatAm 0.20 0.40 0.33 0.16 0.13 0.00 ‐0.05 0.16 0.35 0.13 0.05 0.31 0.26 0.06 0.10 1.00 Debt CE. EU 0.37 0.42 0.50 0.43 0.13 0.17 0.19 0.14 0.35 0.14 0.12 0.47 0.21 0.04 0.37 0.52 1.00 Debt W. EU 0.49 0.05 0.33 0.50 0.23 0.27 0.47 0.29 0.10 0.44 0.27 0.25 0.02 0.10 0.58 ‐0.13 0.28 1.00 Debt Em. As 0.40 0.58 0.65 0.35 0.20 0.23 0.20 0.13 0.24 0.25 0.37 0.35 0.15 0.02 0.32 0.38 0.53 0.14 1.00 Debt Asia 0.16 0.18 0.24 0.22 0.16 ‐0.04 0.16 0.35 0.31 0.30 0.30 0.45 0.26 0.14 0.45 0.27 0.42 0.19 0.39 1.00 Debt Africa 0.26 0.27 0.39 0.18 0.07 0.14 0.09 0.12 0.21 0.10 0.01 0.41 0.21 0.07 0.21 0.46 0.61 0.15 0.44 0.32 1.00 Credit N. Am. 0.29 ‐0.02 0.21 0.38 0.15 ‐0.01 0.32 0.20 0.02 0.19 0.20 0.12 0.09 0.04 0.37 0.14 0.23 0.25 0.23 0.25 0.03 1.00 Credit LatAm 0.41 0.34 0.21 0.26 0.12 0.04 0.22 0.38 0.35 0.42 0.27 0.48 0.35 0.24 0.35 0.25 0.41 0.30 0.29 0.46 0.28 0.22 1.00 Credit CE. EU 0.42 0.25 0.27 0.28 0.32 0.15 0.21 0.54 0.38 0.72 0.55 0.47 0.36 0.28 0.54 0.14 0.13 0.56 0.25 0.48 0.12 0.17 0.55 1.00 Credit W. EU 0.19 ‐0.03 0.24 0.31 0.19 ‐0.16 0.26 0.27 0.08 0.20 0.30 0.19 0.13 0.15 0.45 0.20 0.25 0.33 0.26 0.45 0.16 0.63 0.30 0.34 1.00 Credit Em. As 0.25 0.54 0.39 0.21 0.10 0.16 0.05 0.22 0.16 0.30 0.29 0.38 0.24 0.00 0.40 0.31 0.33 0.15 0.56 0.51 0.27 0.24 0.45 0.48 0.28 1.00 Credit Asia 0.08 ‐0.03 0.02 ‐0.01 0.00 ‐0.40 ‐0.12 0.23 0.23 0.32 0.24 0.31 0.23 0.25 0.32 0.18 0.17 ‐0.01 0.13 0.37 0.08 0.43 0.35 0.23 0.52 0.37 Credit Africa 0.11 0.06 0.01 0.15 0.01 ‐0.20 0.12 0.40 0.30 0.35 0.33 0.24 0.37 0.18 0.32 0.11 0.00 0.13 0.03 0.34 ‐0.02 0.24 0.30 0.40 0.36 0.30
Credit Credi Asia Africa
1.00 0.31 1.00
Figure 7: Gross inflows, all asset classes (FDI, debt, equity, bank credit), by geographical areas (North America, Western Europe, Latin America, Central and Eastern Europe, Asia, Emerging Asia, Africa). Green colour means positive correlations. Red colour means negative correlations.
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AUSTRALIAAUSTRIA AUSTRALIA AUSTRIA CANADA CHILE FINLAND FRANCE GERMANY IRELAND ITALY JAPAN MEXICO NETHERLANDS NEW_ZEALAND PORTUGAL SPAIN SWEDEN UNITED_KINGDOM UNITED_STATES BRAZIL HUNGARY ICELAND INDONESIA KOREA SOUTH_AFRICA THAILAND TURKEY ARGENTINA BOLIVIA DENMARK BULGARIA
-0.03919 -0.0783 0.364054 0.100472 0.190713 0.005077 0.172136 -0.0129 -0.20074 -0.10831 -0.02821 -0.01299 -0.06376 0.154012 0.25975 -0.05647 -0.02192 0.047299 0.372528 0.338919 0.019426 0.095806 0.164466 0.067353 0.398686 0.116753 -0.00606 -0.0934 0.553896
1 0.067584 0.29706 -0.10728 0.287145 0.364336 0.339794 0.485044 -0.07153 -0.08906 0.489775 0.103868 0.459259 0.23794 0.085319 0.273989 0.32381 0.245288 0.05932 0.202173 -0.25196 0.479603 0.131883 0.079829 0.39218 0.165977 -0.04934 0.272219 0.228968
CANADA
1 0.121492 -0.20695 0.260391 -0.00254 0.067105 0.051584 0.148568 0.408351 0.103542 0.17618 -0.06874 0.002846 0.016785 0.20269 0.351381 -0.01257 0.207625 0.156361 0.055582 0.278784 0.407864 0.031594 0.094947 0.068755 -0.21408 0.087744 0.005534
CHILE
1 0.05688 0.195877 0.214196 0.000212 0.157937 -0.09421 0.153052 0.328249 0.074398 0.199103 0.232048 -0.01075 0.067834 0.087048 0.272613 0.164218 0.292669 0.142922 0.444456 0.302301 0.13894 0.479531 0.292711 0.113475 0.191762 0.419518
FINLAND FRANCE
1 -0.2255 0.273523 -0.23447 0.195965 -0.03117 0.049611 0.026484 0.132465 0.069607 0.264049 0.235438 0.019154 -0.05837 0.123847 -0.14993 -0.02744 0.039648 0.026408 0.048513 0.074368 0.18951 0.089206 -0.04634 -0.25613 -0.06609
1 0.338801 0.345899 0.224629 0.102579 0.183267 0.493335 0.052823 0.130124 0.143297 0.230897 0.58315 0.531937 0.181591 0.188959 0.437046 -0.06924 0.371338 0.346471 0.155929 0.434547 0.114934 -0.42719 0.355034 0.302221
GERMANY IRELAND
1 0.21873 0.326936 0.262973 0.145611 0.668607 0.357321 0.467637 0.19019 0.357402 0.602274 0.352123 0.261831 -0.0535 0.134206 -0.17055 0.333072 0.405936 0.129609 0.4329 0.137146 -0.18726 0.22795 -0.01933
1 0.25476 0.19343 -0.10153 0.234938 0.19417 0.110902 0.340718 0.24294 0.181967 0.120313 0.145499 0.613377 0.44218 -0.197 0.129954 0.286568 -0.20812 0.323702 0.005837 -0.13705 0.515129 0.406331
ITALY
1 -0.06614 -0.22191 0.475112 -0.01398 0.052289 0.255213 0.033907 0.213288 0.202874 0.39888 -0.02975 0.250595 -0.10087 0.341104 0.16244 0.001764 0.375367 0.180605 -0.24828 0.317972 0.103423
JAPAN
1 0.159234 0.238301 0.046652 0.009973 -0.05793 0.183161 0.335044 0.344271 0.135462 0.01985 0.001629 0.045922 0.063662 0.124653 0.135792 -0.06506 0.13466 -0.05866 0.101776 -0.24724
MEXICO
1 0.006497 0.108238 0.150952 0.029603 0.155903 0.200393 0.148369 0.139834 0.03095 0.007042 0.051728 0.287044 0.224681 0.154524 0.16111 0.023164 -0.18278 -0.11829 0.029811
NETHERLANDS NEW_ZEALAND PORTUGALSPAIN
1 0.270466 0.390095 0.123084 0.388029 0.708758 0.547633 0.333743 -0.10298 0.34903 -0.21799 0.601859 0.302293 0.109657 0.370165 0.111102 -0.25584 0.442472 0.028431
1 0.235531 0.084087 0.184722 0.268579 0.140554 0.138307 0.109635 0.160696 0.000543 0.108412 0.425234 0.085309 0.101776 -0.03712 -0.10656 0.142632 0.099002
1 0.180748 0.29936 0.457877 0.329652 0.213888 -0.09413 0.190961 -0.18597 0.279877 0.085374 0.080449 0.202492 0.073611 -0.01032 0.108962 -0.08067
1 0.147261 0.079752 -0.13748 0.213097 0.301012 0.385438 0.009624 0.041975 0.199305 -0.08612 0.469351 0.160795 -0.06809 0.130126 0.364018
SWEDEN UNITED_KINGDOM UNITED_STATES BRAZIL
1 0.408673 0.303454 -0.07787 0.066677 0.288864 -0.23098 0.26525 0.240269 -0.03345 0.209643 -0.04791 -0.2837 0.185383 0.12315
1 0.755091 0.259281 -0.17567 0.295907 -0.18887 0.49277 0.269579 0.133924 0.192439 0.0264 -0.31801 0.198069 -0.14574
1 0.176764 -0.13407 0.235227 -0.10979 0.41201 0.215307 0.146537 0.112757 0.059029 -0.29058 0.043795 -0.17249
1 0.035018 0.172176 0.129328 0.375652 0.050682 0.24028 0.423942 0.208558 -0.16396 0.084885 0.100103
HUNGARY ICELAND
1 0.485081 0.047538 0.016215 0.253556 -0.15156 0.373183 0.028335 -0.09647 0.184791 0.584315
1 -0.03615 0.316394 0.235918 0.086512 0.532554 0.103663 -0.21105 0.393783 0.63702
INDONESIAKOREA__REPUBLIC_OF SOUTH_AFRICA THAILAND TURKEY
1 -0.04667 0.093512 0.35821 -0.00088 0.03084 0.133945 -0.10793 0.018221
1 0.295303 0.372182 0.360258 0.117845 -0.32987 0.229327 0.209706
ARGENTINABOLIVIA
DENMARK BULGARIA
1 0.134048 1 0.353566 0.203871 1 0.172182 -0.06023 0.241317 1 -0.16796 -0.03544 -0.14298 -0.01039 1 0.164987 -0.06768 0.199252 0.09416 -0.22527 1 0.233736 0.065649 0.562799 0.076346 -0.1701 0.302194
1
Figure 8: Gross bank credit inflows by countries and exchange rate regimes (countries in light grey are floaters, countries in darker grey are peggers). Green colour means positive correlations. Red colour means negative correlations. Source: Passari and Rey (2014)
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space constraints. Table 1 (b): Conditional correlations of liability flows with the VIX, quarterly, 1990Q1‐2012Q4.. Correlations inflows / VIX Equity FDI Debt Credit
North Latin America America ‐0.06
‐0.31
Central Eastern Europe
Western Europe
‐0.32
‐0.38
Emerging Asia Asia ‐0.08
‐0.34
Africa ‐0.25
0.10
0.35
0.07
0.06
0.08
0.16
0.07
‐0.30
‐0.15
‐0.36
‐0.23
‐0.28
‐0.06
‐0.22
‐0.29
‐0.15
‐0.16
‐0.24
‐0.26
0.09
‐0.14
Figure 9: Conditional correlations of liability flows with the VIX (conditioning In Table 1(c), I investigate whether fluctuations in the VIX are also associated with changes in credit variables are the world short term real rate and the world growth rate) creation and leverage using various measures. We report the conditional correlations controlling again for the classic push factors (world growth rate and short term real rate). Following Forbes (2012), I measure leverage as the ratio of private credit by deposit money banks and other financial institutions to bank deposits, including demand, time and saving deposits in nonbanks. The precise definitions of leverage and domestic credit can be found in Appendix B. Table 1(c) offers this striking finding: in all areas of the world, credit growth is negatively linked to Rey Royal Economic Society Sargan Lecture 2014
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Correlations credit / VIX
North Latin America America
Central Eastern Europe
Western Emerging Europe Asia
Asia
Africa
Domestic credit growth Leverage
‐0.26
‐0.14
‐0.14
‐0.11
‐0.01
‐0.30
0.01
‐0.17
0.05
0.30
‐0.09
‐0.12
‐0.25
0.03
‐0.32
0.06
0.07
‐0.21
‐0.06
‐0.31
0.01
Leverage growth
Figure 10: Conditional correlations of domestic credit growth, leverage and To sum up, the data show (i) commonality in capital inflows – and outflows – across regions leverage growth with the VIX (conditioning variables are the world short term real rateand types of assets (except for FDI flows and a subset of Asian and African flows). The and the world growth rate) commonality is particularly strong for credit and portfolio debt inflows (see Figures 1a,b) but is absent for net capital flows (Figure 1c); (ii) surges in gross capital flows in period of low volatility and decline in flows when the VIX goes up (with the exception of FDI flows); a large volatility and pro‐cyclicality of credit flows (see Figure 2 and Tables 1a,b); (iii) increases in credit growth around the world in parallel with falls of the VIX (see Table 1c); (iv) increases in Rey Royal Economic Society Sargan Lecture 2014
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Dynamic Factor Model for risky asset prices and monetary policy of the US: Miranda Agrippino and Rey (2012) We estimate global, regional and asset-specific factors from a collection of world risky asset prices. price series (i,t) = common component (t) + idiosyncratic (i,t) Using a set of restrictions on the coefficient matrices of the Dynamic Factor Model we further decompose the common component in two: common component (t) = global factor (t) + regional factors (t) Each price series is then the sum of three components I I
I
a global factor that is a common to all price series in the set a region (or market) specific component that captures aggregate shocks that affect all assets traded on the same market or belonging to the same category (i.e. commodities) an idiosyncratic asset-specific component
Formally: xi,t = µi + λi,G ftG + λi,M ftM + ξi,t Rey
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Dynamic Factor Model for asset prices: Data
the global set that we consider is a collection of monthly asset prices (logs) [N=428] spanning the twenty-years-period from 1990 to 2010 that combines information from I
US market [S&P 500, n=165]
I
European market: Euro Area + United Kingdom [S&P Euro + FTSE100, n=144]
I
Asian market: Japan + Singapore + Hong Kong + South Korea + Taiwan [Topix Core 30 + S&P Asia, n=48]
I
Commodities and Corporate bonds markets [n=71]
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Dynamic Factor Model for risky asset prices: Data
data are taken at monthly frequency (EOM value) to reduce the noise in daily figures while preserving long term characteristics of the series the global set is split into six subsamples or blocks each of which will load a specific factor together with the global one. The blocks are: 2
US Europe, further decomposed into Euro area and UK
3
Asia
4
Commodities
5
Corporate
1
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Dynamic Factor Model for risky asset prices: Data
the DFM is cast in state space form and estimated on the stationary return series using Maximum Likelihood (Doz, Giannone, Reichlin (2006), Watson, Reis (2007)) Factors for the price series are then obtained via cumulation (Bai, Ng (2004)) The number of factors [1 global & 1 per each block] and lag length of factors VAR [1] are selected using standard criteria and tests)
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Figure 11: Global factor in risky asset prices and VIX. Source: Rey (2013)
To sum up, we have now established in flow data (across most types of flows and regions, but with Rey
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Figure 12: Decomposition of the global factor in a volatility component and a risk aversion component Rey
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Facts: The Global Financial Cycle (Rey, Jackson hole (2013)) Strong commonality in gross capital inflows and outflows around the world Negative co-movements of these gross flows with the VIX, index of market risk aversion and uncertainty. Credit flows are especially pro-cyclical An important part of the variance of a large cross section of 858 risky asset prices (stocks, corporate bonds, commodities) distributed on five continents is explained by one single global factor. This global factor is closely related to the VIX (negatively) Credit growth and, for most areas, leverage and leverage growth co-move negatively with the VIX
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Figure 13: G-sifi bank leverage. Quarterly growth of total assets over quarterly growth of leverage ratio, all available history Rey
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Figure 14: Capital market banks leverage. Quarterly growth of total assets over
of leverage ratio, availableover history erlyquarterly growthgrowth of aggregated totalallassets quarterly growth of aggreg Rey
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Figure 15: Commercial banks leverage. Quarterly growth of total assets over quarterly growth of leverage ratio, all available history
terly growth of aggregated total assets over quarterly growth of aggregated age ratio, 2005-Q1 to 2010-Q10. Source: . Bloomberg, authors calculations
da Agrippino & Rey (LBS) Rey
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Global Financial Cycle and excess credit growth
Global banks raising funds in particular in the US (dollar is the main currency of global banking). Surges in credit flows associated with increases in leverage worldwide Procyclicality: Credit creation when measured risks are low, asset prices pushed up further, spreads compressed, healthy looking balance sheets etc, measured risks lower etc... The global financial cycle is not aligned with countries specific macroeconomic conditions. Symptoms can go from benign to large asset price bubbles and excess credit creation, which are among the best predictors of financial crises.
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Monetary policy spillovers of the centre country of the International Monetary System (US) (Rey (2013 Jackson )
A VAR analysis suggests monetary policy in the centre country is an important determinant of the global financial cycle (Miranda Agrippino and Rey (2012) When the Federal Funds rate goes down, the VIX falls, banks leverage rises, as do gross credit flows. A fall in the VIX leads to an increase in global domestic credit. Estimates suggest that between 9 and 30 percent of the variance of the VIX is explained by shocks to fed funds rate (1990-2007)
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Figure 16: VAR Analysis Rey
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The International Monetary System and the Trilemma (Passari and Rey (2014)) Trilemma: with free capital mobility, independent monetary policies are feasible if and only if exchange rates are floating (dominant paradigm in international finance). Instead, whenever capital is freely mobile, cross-border flows and leverage of global financial institutions transmit monetary conditions globally, even under floating exchange-rate regimes Gross credit and debt flows are key for the international transmisison of monetary spillovers. The global financial cycle transforms the trilemma into a dilemma or an irreconcilable duo: independent monetary policies are possible if and only if the capital account is managed, directly or indirectly.
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Conclusions
A very rich research agenda in international macroeconomics and finance as important questions are still unanswered. A lot more details at http://www.helenerey.eu/ Thank you!
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