Common Shocks in a T wo-country Business Cycle Model I. Jaccard - PowerPoint PPT Presentation

Capital Flows and the Adjustment to Common Shocks in a T wo-country Business Cycle Model I. Jaccard and F. Smets, ECB Workshop on the Economics of Cross-Border Banking Paris, 13-14 December 2013 NOTE: The views expressed are those of the authors and do not necessarily reflect those of the ECB 0

The views expressed in this study are those of the authors and do not necessarily reflect those of the ECB. 1

1. Introduction High degree of business cycle synchronization in the euro area. Aggregate consumption is more volatile in countries whose trade balance co-move negatively with output. Real short-term interest rates are on average lower and bank lending rates higher in countries where aggregate consumption is more volatile. 2

1. Stylized facts 1.6 1.4 Consumption volatility 1.2 1 0.8 0.6 0.4 0.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 Corr(trade balance,output) 3

1. Stylized facts 4 3.5 3 Bank lending rates 2.5 2 1.5 1 0.5 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Consumption Volatility 4

1. Stylized facts 1 Real interest rates 0.5 0 -0.5 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Consumption volatility 5

1. Questions Is it possible to reproduce these stylized facts in a model in which common shocks are the only source of business cycle fluctuations? What determines the direction of capital flows? And how does the cyclicality of capital flows affect the welfare cost of business cycle fluctuations? 6

1. What we do Two-country model with incomplete markets. Identify the sources of cross-country heterogeneity using SMM. Simplest possible model. 7

2. Literature Two-country model with complete markets: Backus, Kehoe and Kydland (1992, 1995). Frictions in international asset markets: Cole (1988), Baxter and Crucini (1995), Kollmann (1996), Arvanitis and Mikkola (1996), Heathcote and Perri (2002). Financial intermediaries: Olivero (2010), Kollmann, Enders and Muller (2011). Asset pricing in production economies: Jermann (1998), Jaccard (2013). 8

2. Literature Heterogeneity in the euro area: Cecchetti (1999), LaPorta, Lopez-de-Silanes, Shleifer and Vishny (1997, 1998), Danthine, Giavazzi, Vives and von Thadden (1999). 9

2. Potential sources of heterogeneity 1.6 1.4 Consumption volatility 1.2 1 0.8 0.6 0.4 0.2 0.5 1 1.5 2 Governance index 10

2. Stylized facts by country Table 1 Table 2  y  c /  y  N /  y  x /  y   tb , y  E  r L  E  r D    ims , y  E  r F  E  WGI  (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Austria 1.43 0.28 0.59 1.84 0.17 Austria 1.62 0.12 -0.66 0.6 1.77 Belgium 0.9 -0.32 -0.65 0.4 1.43 Belgium 1.13 0.56 0.83 2.97 -0.25 Finland 2.35 0.60 0.51 1.82 0.30 Finland 1.71 -0.15 -0.59 0.56 2.06 France 1.14 0.52 0.68 2.74 -0.54 France 1.81 0.38 -0.28 0.76 1.39 Germany 1.74 0.36 0.55 2.31 0.52 Germany 2.8 0.33 -0.52 0.93 1.64 Greece 2.89 0.31 -0.05 -0.42 0.58 Greece 1.82 1.38 n.a. 5.66 -0.60 Ireland 2.45 0.96 1.14 3.70 -0.40 Ireland 2.48 -0.11 0.05 0.23 1.62 Italy 1.45 0.69 0.63 1.90 -0.26 Italy 2.03 -0.13 -0.54 0.24 0.54 Netherlands 1.40 0.61 0.74 3.13 0.32 Netherlands 1.62 -0.22 -0.68 0.3 1.88 Portugal 3.87 0.08 -0.64 0.06 1.06 Portugal 1.24 1.11 0.94 2.82 -0.60 Spain 1.18 1.13 1.20 3.49 -0.83 Spain 1.72 -0.22 -0.16 -0.24 1.18 11

2. Aggregate statistics Table 3  y  c /  y  x /  y  N /  y   nco , y  E  nco / y  (1) (2) (4) (3) (4) (5) Periphery 1.29 0.81 2.53 0.81 -0.74 -2.27 Core 1.43 0.35 2.4 0.57 0.20 3.02 E  r L  E  r D  E  rr  E  WGI  (6) (7) (9) (10) Periphery 2.60 -0.01 -0.03 1.0 Core 1.74 0.02 0.59 1.70 12

3. The environment Each economy is composed of a representative agent, financial intermediary and firm. The financial intermediary allocates capital to the final goods producing sector. The production of bank loans/financial services is subject to a technological constraint. Lending and borrowing between the two financial sectors is the only source of international trade. 13

3. Market structure Households Households Financial Financial sector sector Firms Firms Firms 14

3. The final-goods producing sector Maximize profits: max N Ft , y Lt , k t  Ft  y t  w t N Ft  r Lt y Lt subject to:  N Ft 1   y t  A t y Lt 15

3. Households Budget constraint:  Tt  w t N Bt  w t N Ft  r Dt d t  c t  x t Time allocation constraint: L t  1  N Bt  N Ft Adjustment costs: 1     2 d t  1 x t  d t  1  d t  1   d t 16

3. Households Habit accumulation:    h t  1  mh t   1  m  c t    L t Habits in the composite good (Jaccard 2013):   t log  c t    L t max c t , N Bt , N Ft , x t , d t  1 , h t  1 E 0  t  0    h t  Needed to match the very low mean risk-free rate observed in the data (Weil 1989, Jermann 1998) 17

3. The financial intermediary Technological constraint:  y Lt  1   1   L  y Lt  Z t  d t  b t     1   1   b t N Bt Profits:   Bt  r Lt y Lt  w t N Bt  r Dt d t  r Bt b t   r Bt b t Profit maximization:   t  t b t , k t , N Bt E 0  t  0  0  Bt max y Lt  1 , d t , b t ,  18

3. Market equilibrium A competitive equilibrium in the economy is a sequence of prices: r D ,  ,  w , w , r L ,  r L , r B ,  r B , r D ,   , q ,  q , p L ,  p L ,  ,  And quantities c , h ,  x , d ,  d , b ,  y ,  y , c ,  h , x ,  b , N B , N B , N F , N F that satisfy households and firms efficiency conditions as well as the two resource  constraints: 1    r Bt b t  c t  x t    N Ft A t y Lt r Bt b t  1     A t  b t   c t    N Ft x t  r Bt b t y Lt r Bt 19

3. Additional implications Risk-free rates:  t  1 1 1  r Ft   E t  t r Ft     t  1 1  E t 1     t 20

3. Calibration and results To simplify the quantitative analysis, we focus on three sources of cross-country heterogeneity and calibrate the remaining parameters. In the data, average deposit rates across country blocks are almost similar. Differences in subjective discount factors cannot be a major source of heterogeneity (calibrated to match the investment share). 21

3. SMM estimation Table 4       m m 3.77 4.48 0.70 0.62 0.81 0.64 Table 5 Data Model E  r F  0.59 0.58 E  r F  -0.03 0.0 E  r L  1.74 1.73 E  r L  2.60 2.61 std  x  / std  y  2.40 2.40 std  x  / std  y  2.53 2.52 22

3. Calibration and results Table 6 Output Consumption Hours volatility volatility volatility Data Model Data Model Data Model Periphery 1.29 1.28 0.81 0.76 0.81 0.78 Core 1.43 1.44 0.35 0.64 0.57 0.63 Cyclicality Mean trade balance trade balance Data Model Data Model Periphery -0.74 -0.99 -2.27 -3.5 Core 0.20 0.99 3.02 2.87 23

4. The direction of capital flows Output -3 Capital outflow 11x 10 -3 7x 10 10 6 Periphery 9 Core 5 8 4 7 3 6 2 5 1 4 0 Core 3 -1 Periphery 2 -2 1 -3 20 40 60 80 100 120 20 40 60 80 100 120 Trade balance -3 Loans 2x 10 0.012 1.5 Periphery 0.01 Core 1 0.008 0.5 0 0.006 -0.5 0.004 -1 Core 0.002 -1.5 Periphery -2 0 20 40 60 80 100 120 20 40 60 80 100 120 24

4. Identifying the sources of heterogeneity Use the symmetric model as a benchmark:   3.77,         0.7, m  m  0.81 And identify the effects of: (i) Differences in adjustment costs, (ii) Differences in financial structures, (iii) Differences in attitudes towards risk. 25

4. Identifying the sources of heterogeneity Table 7 Data Symmetric Model 1 Model 2 Model 3        m  m  case (1) (2) (3) (5) (6)   nco , y  0.20 0 0.99 -0.99 -0.99   nco ,  y  -0.74 0 -0.99 0.99 0.99 E  nco / y  - 0.06 3.03 0 3.16 -0.2 E  nco /  y  -2.25 0 -3.90 0.06 0.2 26

5. A financial accelerator mechanism Technological constraint:  y Lt  1   1   L  y Lt  Z t  d t  b t     N Bt 1   1   b t Asset pricing condition:  t  1 p Lt   E t  t  1   L  p Lt  1  r Lt  1  27

5. A financial accelerator mechanism Factor demand and supply curves: d t  b t ,  i Lt i Lt r Bt   p Lt r Bt   1     p Lt b t ,  i Lt i Lt w Bt   1    p Lt N Bt , r Dt   p Lt d t  b t , 28

5. A financial accelerator mechanism Asset price, core countries Asset price, periphery countries 0.045 0.04 0.04 Benchmark model 0.035 No habits No habits 0.035 0.03 Benchmark 0.03 0.025 0.025 0.02 0.02 0.015 0.015 0.01 0.01 0.005 0.005 0 0 20 40 60 80 100 120 20 40 60 80 100 120 29