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Erschienen in: Empirical Economics 4/2015

01.12.2015

Measuring spillover effects in Euro area financial markets: a disaggregate approach

verfasst von: Dimitrios P. Louzis

Erschienen in: Empirical Economics | Ausgabe 4/2015

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Abstract

This study examines the return (price) and volatility (uncertainty) spillovers among the money, stock, foreign exchange and bond markets in the Euro area. The analysis is conducted in a disaggregated manner with respect to the bond and stock indices and utilizes the generalized forecast error variance decomposition framework of a VAR model proposed by Diebold and Yilmaz (Int J Forecast 23:57–66, 2012). The asymptotic distribution of the generalized forecast error variance decomposition components and the corresponding standard errors are also derived. Our empirical results, based on a data set covering a twelve-year period (2000–2012), suggest a high level of total return and volatility spillover effects throughout the sample period. Stock markets across the Euro area countries are identified as the main transmitters of price spillovers, with the periphery countries transmitting the largest amount of spillovers during the crisis periods. Stock markets also play a key role in uncertainty transmission, but now, the propagation mechanism includes the core Euro area countries, which transmit volatility spillovers diachronically. The money, FX and bond markets are constant receivers of spillovers, with the exception of the Greek bonds, which transmitted spillovers during the peak of the Greek sovereign debt crisis in 2011–2012.

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Fußnoten
1
For a discussion on policy measures regarding financial contagion, see Dornbusch et al. (2001).
 
2
For an aggregate approach, see Ehrmann et al. (2011) and Louzis (2013).
 
3
Cholesky decomposition of \(\varvec{\varSigma }_\varepsilon \) is used to orthogonalize the errors, and the corresponding impulse responses (see Lutkepohl (1990), Lutkepohl (2005, Sect. 2.3.3) and references therein)
 
4
The spillover measures proposed in Diebold and Yilmaz (2009) use the Cholesky factorization of \(\varvec{\varSigma }_\varepsilon \) to orthogonalize the errors. However, their method suffers from variable reordering.
 
5
The elements of the decomposition matrix can also be normalized in terms of a column sum, as in Zhou et al. (2012).
 
6
We would like to thank two anonymous reviewers for this suggestion.
 
7
We would also like to thank an anonymous reviewer for proposing the incorporation of the US stock market into the analysis.
 
8
Total return bond indices assume that coupon payments are reinvested in the bonds.
 
9
Un-reported results show that grouping investment grade countries in a single index does not alter the main findings of the paper.
 
10
The authors explain that overnight rates have fallen well below repo rates after the collapse of Lehman Brothers, although the ECB could have prevented this sharp fall. Therefore, they interpret this decline as an expression of monetary policy and use overnight rates as a proxy for the ECB’s monetary policy stance.
 
11
The GFEV decompositions are defined as in Eq. (2), and the standard errors are derived from the results in Appendix A. The \(ij\mathrm{th}\) component of the table shows, in absolute terms, the portion of the forecast error variance of the variable \(i\) that is attributable to innovations of variable \(j\).
 
12
However, these results can be quite misleading if the true GFEV component is zero (see the discussion in the Appendix and Lutkepohl (1990, 2005)). As pointed out in Lutkepohl (2005, p. 125), the standard errors should be “regarded as rough indications of the sampling uncertainty,” which limits their value in terms of formal hypothesis testing.
 
13
For example, the element in column 5 (\(j=5\)) row 11 (\(i=11\)), i.e., \(\tilde{d}_{511,10}\), shows that 6.78 % (0.79 %) of Germany’s stock market return (volatility) forecast error variance is due to shocks to the Greek stock market. By contrast, the \(j=11, i=5\) element shows that 9.49 % (17.81 %) of the Greek stock market return (volatility) forecast error variance is attributable to the German stock market return (volatility) innovations.
Table 2
Returns spillover table for the Euro area financial markets
  
Money market
FX market
Stock markets
EONIA (\(j=1\))
Eur/Usd (\(j=2\))
Ireland (\(j=3\))
Portugal (\(j=4\))
Greece (\(j=5\))
Spain (\(j=6\))
Italy (\(j=7\))
France (\(j=8\))
Belgium (\(j=9\))
Austria (\(j=10\))
Money market
EONIA (\(i=1\))
0.00
0.02
8.03
13.65
6.00
8.23
10.43
10.15
7.65
7.49
FX market
Eur/Usd (\(i=2\))
0.00
0.24
7.55
12.32
5.96
8.68
10.79
10.37
7.78
7.51
Stock markets
Ireland (\(i=3\))
0.00
0.03
11.51
11.92
6.56
8.18
10.24
9.64
7.59
8.12
Portugal (\(i=4\))
0.00
0.06
6.88
33.36
4.14
5.39
7.66
7.10
5.44
7.26
Greece (\(i=5\))
0.00
0.08
6.60
10.46
16.78
8.02
9.76
8.95
6.95
7.85
Spain (\(i=6\))
0.00
0.05
7.63
14.08
5.69
9.02
10.76
9.91
7.53
7.88
Italy (\(i=7\))
0.00
0.04
7.29
13.55
7.51
8.59
11.66
9.86
7.18
7.46
France (\(i=8\))
0.00
0.04
7.69
11.23
7.04
8.62
10.76
10.82
7.81
7.78
Belgium (\(i=9\))
0.00
0.05
7.39
13.67
6.41
8.10
10.37
9.60
9.52
7.97
Austria (\(i=10\))
0.00
0.12
7.69
11.14
8.06
8.24
10.02
9.08
7.55
13.62
Germany (\(i=11\))
0.00
0.05
7.21
10.55
6.78
9.10
11.03
10.43
7.62
7.50
Neth (\(i=12\))
0.00
0.03
7.64
14.13
7.09
7.91
10.12
9.84
7.63
7.57
USA (\(i=13\))
0.00
0.04
8.35
11.61
7.69
8.37
10.40
9.96
7.77
8.30
Bond markets
Ireland (\(i=14\))
0.00
0.08
7.27
13.02
8.45
8.56
10.52
9.68
7.26
8.20
Portugal (\(i=15\))
0.00
0.05
7.80
12.05
6.69
8.32
10.52
9.81
7.87
8.39
Greece (\(i=16\))
0.00
0.14
6.70
11.78
10.52
8.11
9.75
8.81
6.96
8.58
Spain (\(i=17\))
0.00
0.05
7.88
13.45
6.42
8.36
10.48
9.87
7.68
8.45
Italy (\(i=18\))
0.00
0.05
7.45
16.35
7.42
8.05
10.05
9.41
6.94
7.50
Inv.Grade (\(i=19\))
0.00
0.07
7.40
13.00
7.94
8.79
10.67
9.72
7.37
8.38
 
To
0.00
1.06
134.45
227.96
126.39
147.63
184.34
172.20
132.59
142.19
Net
\(-\)100.00
\(-\)98.69
45.96
161.32
43.17
56.65
96.01
83.02
42.10
55.81
  
Stock markets
Bond markets
From
Germany (\(j=11\))
Neth (\(j\)=12)
USA (\(j=13\))
Ireland (\(j=14\))
Portugal (\(j=15\))
Greece (\(j=16\))
Spain (\(j=17\))
Italy (\(j=18\))
Inv.Grade (\(j=19\))
Money market
EONIA (\(i=1\))
11.05
10.65
6.30
0.01
0.03
0.04
0.03
0.05
0.19
100.00
FX market
Eur/Usd (\(i=2\))
11.22
10.96
6.17
0.03
0.03
0.07
0.04
0.06
0.20
99.76
Stock markets
Ireland (\(i=3\))
10.12
9.99
5.65
0.03
0.02
0.24
0.01
0.01
0.13
88.49
Portugal (\(i=4\))
7.77
7.50
7.21
0.01
0.01
0.03
0.03
0.04
0.10
66.64
Greece (\(i=5\))
9.49
9.12
5.12
0.06
0.06
0.53
0.01
0.01
0.14
83.22
Spain (\(i=6\))
10.56
10.17
6.33
0.03
0.02
0.21
0.01
0.01
0.12
90.98
Italy (\(i=7\))
10.39
9.95
6.00
0.04
0.03
0.24
0.02
0.02
0.16
88.34
France (\(i=8\))
10.94
10.68
5.81
0.06
0.05
0.45
0.03
0.03
0.14
89.18
Belgium (\(i=9\))
10.04
10.34
6.02
0.05
0.04
0.31
0.02
0.02
0.11
90.48
Austria (\(i=10\))
9.37
9.43
5.16
0.03
0.02
0.28
0.02
0.01
0.16
86.38
Germany (\(i=11\))
12.23
10.68
5.92
0.11
0.11
0.44
0.07
0.06
0.13
87.77
Neth (\(i=12\))
10.69
10.81
6.24
0.01
0.01
0.07
0.02
0.03
0.17
89.19
USA (\(i=13\))
10.66
10.43
6.05
0.01
0.01
0.16
0.01
0.02
0.16
93.95
Bond markets
Ireland (\(i=14\))
10.35
9.82
6.01
0.12
0.06
0.41
0.03
0.02
0.15
99.88
Portugal (\(i=15\))
10.50
10.32
5.74
0.28
0.72
0.50
0.14
0.13
0.17
99.28
Greece (\(i=16\))
9.19
8.81
5.31
0.44
0.43
3.85
0.25
0.21
0.15
96.15
Spain (\(i=17\))
10.52
10.30
6.15
0.03
0.02
0.13
0.04
0.03
0.14
99.96
Italy (\(i=18\))
10.33
9.63
6.44
0.01
0.01
0.11
0.03
0.04
0.17
99.96
Inv.Grade (\(i=19\))
10.34
9.89
5.94
0.03
0.02
0.24
0.02
0.02
0.16
99.84
 
To
183.51
178.67
107.52
1.26
0.98
4.46
0.76
0.77
2.68
Total
Net
95.74
89.48
13.58
\(-\)98.62
\(-\)98.30
\(-\)91.69
\(-\)99.20
\(-\)99.18
\(-\)97.15
92.08
The ijth element of the table is computed as in Eq. (3) and shows the proportion of a 10-step forecast error variance of variable \(i\) (rows), which is accounted for by innovations in variable \(j\) (columns). Table entries are normalized with respect to their row sum, i.e., the sum of row elements adds to 100. The diagonal elements (\(j=i\)) are the own variance shares estimates, which show the fraction of the forecast error variance of market \(i\) which is due to its own shocks. The column “From” shows the total spillovers received by a particular market from all other markets, while the row “To” shows the spillover effects directed by a particular market to all other markets. The measure “Total” shows the level of total spillovers in the Euro area markets
Table 3
Volatilities spillover table for the Euro area financial markets
  
Money market
FX market
Stock markets
EONIA (\(j=1\))
Eur/Usd (\(j=2\))
Ireland (\(j=3\))
Portugal (\(j=4\))
Greece (\(j=5\))
Spain (\(j=6\))
Italy (\(j=7\))
France (\(j=8\))
Belgium (\(j=9\))
Austria (\(j=10\))
Money market
EONIA (\(i=1\))
0.00
0.02
4.17
2.95
0.90
1.84
15.84
5.73
17.47
13.90
FX market
Eur/Usd (\(i=2\))
0.00
0.03
4.65
2.21
1.39
2.08
15.51
5.62
17.35
15.44
Stock markets
Ireland (\(i=3\))
0.00
0.02
5.15
3.17
0.98
1.86
15.29
5.56
17.41
15.22
Portugal (\(i=4\))
0.00
0.02
4.56
3.85
1.08
1.93
15.70
5.58
16.95
14.58
Greece (\(i=5\))
0.00
0.04
4.37
3.36
1.67
2.09
16.01
5.70
16.12
14.44
Spain (\(i=6\))
0.00
0.01
4.12
2.60
0.85
1.74
15.75
5.62
17.51
15.30
Italy (\(i=7\))
0.00
0.02
3.75
2.97
0.99
1.94
19.71
5.63
16.50
12.00
France (\(i=8\))
0.00
0.01
3.12
0.87
0.55
1.69
15.50
6.43
18.51
12.05
Belgium (\(i=9\))
0.00
0.01
3.78
2.28
0.79
1.60
14.06
5.35
23.55
13.60
Austria (\(i=10\))
0.00
0.01
4.16
2.12
0.93
1.76
14.02
5.38
16.17
22.05
Germany (\(i=11\))
0.00
0.01
3.33
1.73
0.79
1.65
14.70
5.81
14.45
10.52
Neth (\(i=12\))
0.00
0.01
3.86
2.24
0.94
1.83
15.20
5.90
17.52
12.84
USA (\(i=13\))
0.00
0.02
3.95
2.56
0.89
1.82
15.84
5.73
17.18
14.29
Bond markets
Ireland (\(i=14\))
0.00
0.02
4.19
2.44
0.93
1.99
16.71
5.82
16.45
14.02
Portugal (\(i=15\))
0.00
0.06
3.44
1.62
1.80
2.21
15.60
5.46
14.97
14.70
Greece (\(i=16\))
0.00
0.18
2.01
1.01
2.43
1.73
6.57
2.76
5.88
12.03
Spain (\(i=17\))
0.00
0.07
4.92
3.34
1.69
2.32
16.10
5.34
14.90
18.04
Italy (\(i=18\))
0.00
0.02
3.92
2.62
1.06
1.84
15.01
5.81
16.56
13.69
Inv.Grade (\(i=19\))
0.00
0.03
4.13
2.85
1.22
2.01
16.08
5.78
16.62
14.23
 
To
0.02
0.57
70.43
42.96
20.21
34.22
269.46
98.58
288.53
250.90
Net
\(-\)99.98
\(-\)99.40
\(-\)24.42
\(-\)53.19
\(-\)78.12
\(-\)64.04
189.17
5.02
212.08
172.96
  
Stock markets
Bond markets
From
Germany (\(j=11\))
Neth (\(j=12\))
USA (\(j=13\))
Ireland (\(j=14\))
Portugal (\(j=15\))
Greece (\(j=16\))
Spain (\(j=17\))
Italy (\(j=18\))
Inv.Grade (\(j=19\))
Money market
EONIA (\(i=1\))
18.72
12.87
4.92
0.05
0.02
0.37
0.04
0.18
0.01
100.00
FX market
Eur/Usd (\(i=2\))
16.21
11.72
3.53
0.33
0.30
3.11
0.12
0.36
0.03
99.97
Stock markets
Ireland (\(i=3\))
17.59
12.36
4.78
0.06
0.03
0.26
0.04
0.20
0.01
94.85
Portugal (\(i=4\))
17.53
12.20
5.13
0.10
0.07
0.41
0.07
0.23
0.02
96.15
Greece (\(i=5\))
17.81
11.97
4.79
0.15
0.12
0.97
0.09
0.26
0.03
98.33
Spain (\(i=6\))
18.48
12.72
4.66
0.07
0.03
0.28
0.05
0.20
0.01
98.26
Italy (\(i=7\))
18.10
12.46
4.72
0.14
0.09
0.68
0.06
0.21
0.02
80.29
France (\(i=8\))
22.55
14.24
3.62
0.09
0.06
0.43
0.05
0.23
0.01
93.57
Belgium (\(i=9\))
17.34
12.78
4.43
0.04
0.02
0.17
0.03
0.15
0.01
76.45
Austria (\(i=10\))
16.71
11.91
3.97
0.06
0.04
0.44
0.04
0.22
0.01
77.95
Germany (\(i=11\))
29.26
12.55
4.52
0.05
0.04
0.35
0.05
0.17
0.02
70.74
Neth (\(i=12\))
20.45
14.16
4.36
0.06
0.03
0.31
0.05
0.20
0.02
85.84
USA (\(i=13\))
19.07
12.84
5.04
0.08
0.05
0.37
0.05
0.21
0.02
94.96
Bond markets
Ireland (\(i=14\))
18.88
12.42
4.29
0.36
0.16
0.89
0.09
0.29
0.03
99.64
Portugal (\(i=15\))
15.30
10.70
3.06
0.89
1.27
8.02
0.24
0.59
0.06
98.73
Greece (\(i=16\))
4.06
3.74
1.11
2.21
2.37
50.07
0.58
1.12
0.15
49.93
Spain (\(i=17\))
12.39
10.63
3.76
0.71
0.90
3.94
0.28
0.61
0.07
99.72
Italy (\(i=18\))
21.19
12.86
4.91
0.04
0.03
0.17
0.05
0.21
0.02
99.79
Inv.Grade (\(i=19\))
18.35
12.42
4.67
0.16
0.14
0.93
0.08
0.27
0.03
99.97
 
To
310.71
213.39
75.22
5.31
4.51
22.09
1.78
5.69
0.55
Total
Net
239.97
127.56
\(-\) 19.75
\(-\) 94.33
\(-\) 94.22
\(-\) 27.84
\(-\) 97.94
\(-\) 94.11
\(-\) -99.43
90.27
The ijth element of the table is computed as in Eq. (3) and shows the proportion of a 10-step forecast error variance of variable \(i\) (rows), which is accounted for by innovations in variable \(j\) (columns). Table entries are normalized with respect to their row sum, i.e., the sum of row elements adds to 100. The diagonal elements (\(j=i\)) are the own variance shares estimates, which show the fraction of the forecast error variance of market \(i\) which is due to its own shocks. The column “From” shows the total spillovers received by a particular market from all other markets, while the row “To” shows the spillover effects directed by a particular market to all other markets. The measure “Total” shows the level of total spillovers in the Euro area markets
 
14
For example, the USA and the Spanish stock market capitalization as a percentage of GDP for 2012 is 119 and 74 %, respectively, whereas the German and Italian stock market capitalization is only 44 and 24 %, respectively. The data for the stock market capitalization as % of GDP are obtained from the World Bank database: http://​data.​worldbank.​org/​indicator/​CM.​MKT.​LCAP.​GD.​ZS.
 
15
The average total return (volatility) spillover index for Germany, France, Italy, Spain and the Netherlands is 50.50 % (71.57 %), whereas for Belgium, Austria, Greece, Portugal and Ireland, it is 44.65 % (63.62 %).
 
16
Lutkepohl (1990) provides analytical expressions for the asymptotic distribution of the orthogonalized forecast error variance decomposition measures (see also Lutkepohl (2005, Section 3.7)
 
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Metadaten
Titel
Measuring spillover effects in Euro area financial markets: a disaggregate approach
verfasst von
Dimitrios P. Louzis
Publikationsdatum
01.12.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Empirical Economics / Ausgabe 4/2015
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI
https://doi.org/10.1007/s00181-014-0911-x

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