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Global Financial Crisis Volatility Impact and Contagion Effect on NAFTA Equity Markets Impacto de la volatilidad y efecto de contagio de la crisis global financiera en los mercados bursátiles del TLCAN Miriam Sosa* Edgar Ortiz** Fecha de recepción: 3 de agosto de 2016, Fecha de aceptación 16 de diciembre de 2016
RESUMEN
×
ϐ
ǡ
×±× ±
ȋ ͳǡͳȌ±
ȋͳǡͳȌǤϐ
À
ǣƬ ͷͲͲȋȌǡ ȋ±
ȌƬȋȌ× ǡʹͲͲ͵ʹ ʹͲͳͷǤ
ϐ
±
ǡÀ
ʹͲͲǤ ϐ
× ǣ Ͳͳǡ ͳͷǡ ͷǡͷͺ Palabras clave: ±
ǡ ǡǡǡ
ϐ
Ǥ
ABSTRACT The aim of this paper is to analyze the contagion effect and the impact of the ϔ
´ volatility, using rolling window correlation and a GARCH approach. Once the contagion effect is established through an increasing correlation during the crisis period, volatility changes and leverage effects are tested with symmetric and asymmetric GARCH models with a dummy variable in the variance equation. Canada, the United States and Mexico´s equity * **
Facultad de Economía, Universidad Nacional Autónoma de México, Ciudad de México, México,
[email protected]. Programa de Posgrado en Ciencias Políticas y Sociales, Universidad Nacional Autónoma de México, Ciudad de México, México,
[email protected].
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markets stock indexes daily yields, in US dollars, from January 2003 through February Ͷͷͻ Ǥ
ϔ
the whole period and an increasing volatility since the Global Financial Crisis.
ϔ
ǣ G01, G15, F65, C58 Key wordsǣ ǡ ǡǡϔ
ǡ
Ǥ
Introduction
T
ϐ
ȋ Ȍ ϐ
ϐ
ǡ
Ǥ ϐ
ϐ
ʹͲͲ ʹͲͳʹǤ
Ǧ
ʹͲͳͷǡ
ȋet alǤǡʹͲͳʹǡ ʹͲͳͷȌǤ ǡ
ǡǡǤ
ȋ Ȍǡ
ǡ ǡ ȋǤǤȌǡ
Ǥ ǤǤ
ǡ
ǡ ǡ
ϐ
Ǥ ǡϐ
Ǥ
ǡ
ǡ
ǡ Ǥ ǡ
Ǥ
ǡ
ǡ
ǡ
ǯ
Ǥ
68
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Global Financial Crisis Volatility Impact and Contagion Effect…
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ϐ
ǡ
ǡ
Ǥ
ȋǡ ǡ
Ȍǡ
Ǥ ǡ ϐ
ǡ
ǡ
Ǥ ǡ
ǡϐ
ǡǡ
ϐ
ϐ
Ǥ ǡ
Ǥ1 Ǥ
ͳ Ǥ
ʹ
Ǥ
Ǥ ǡ
Ǥ
1. Evidence from previous studies
ϐ
Ǥ ǡ ϐ
ǡ ǡ
Ǥ
ǡ
ϐ
Ǥ
ǡ
1
If the price of a security, commodity or asset is different in two different markets, then an arbitrageur will purchase the asset in the cheaper market and sell it the most expensive. Otherwise, the markets are integrated.
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Ǥ ǯǤ
Ʋ
Ǥ
ȋʹͲͳ͵ȌǢ ϐ
Ǧ ǡ
Ǥ ǡ
ǡ
ϐǦǤ
ǡ ȋʹͲͳͷȌ
Ǥ
ϐ ϐ
ϐ
Ǧ
ǡ
ǤǤǡ Ǥ ƬȋʹͲͳȌǡ
Ʋ Ǥ Ǥ
Ǥ
ϐ
ǤǤ Ʋ Ǥ
et alǤǢ
ȋʹͲͳͷȌǢ ȋʹͲͳͷȌǢ et alǤ ȋʹͲͳͷȌǢ et alǤȋʹͲͳȌǤ
ǡ ȋʹͲͳȌ ƲǤǤǤ
Ǧ
Ǥ et alǤ ȋʹͲͳͷȌ
ȋ Ȍ
Ǥ
ǡ
Ǥ
ǣȋʹͲͳͷȌǢȋʹͲͳͷȌǢ ȋʹͲͳͷȌǢǡ et alǤȋʹͲͳͷȌǢet alǤȋʹͲͳȌǢȋʹͲͳȌǤ ǡǤ ǡ ȋʹͲͳͷȌ
Ǥ
ǡ
ǤǤǡ
Ǥ
ǡ ȋʹͲͳʹȌǤ
70
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Global Financial Crisis Volatility Impact and Contagion Effect…
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ǡǡǡ Ǥ
2. Data and methodology 2.1 Data
ȋ ȌǡȋȌǤǤȋƬͷͲͲȌ
ǡǡ
Ǥ
ͳǡ ʹͲͲ͵ ʹǡ ʹͲͳͷǤ Ǧ
ʹͲͲ͵ ͺǡ ʹͲͲ
ͻǡ ʹͲͲ ʹͲͳͷǡ
ϐ ȋʹͲͲͻȌ ȋʹͲͳʹȌǤ
2.2 Methodology
Ǥ
ǡ
ϐ
ȋƬ ǡʹͲͲ͵ȌǤϐǡ
Ǥ
Ͳ
Ǥ
ǡ
Ǧ Ǥ ͳϐ
Ͳǡ ͳͳǡʹʹǡǤ ǡ
ʹ͵ͻͷ
ϐ
ǡ
Ǥ ϐ
Ǥ
ǡ
Ǥ
ǡ
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Figure 1. Methodology to measure the rolling window correlation graphic representation.
Source: Prepared by author
ǡ
ǡ
Ǥ
2.3 GARCH model ȋ
Ȍǡ ϐ
ϐ
Ǥ
ϐǤ
ǣ
ǡ
ǡ
ǡ
Ǧ ǡ
Ǧ ȋet alǤǡͳͻͻͶȌǤ
ϐ
ǡ
ǡ
Ǥ
72
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Ǥ Ǥ ϐ
Ǣ
ܴ௧ ൌ ሺܥܲܫሻሺݐሻ െ ሺܥܲܫሻሺ ݐെ ͳሻ
(1)
ϐ
Ǥǡ
Ǥ ܪ ǣ ߜ ൌ Ͳ y ܪଵ ǣ ߜ ൏ ͳǤ
ǤǦ
ǡǡ
Ǥ
ȋǡͳͻͺƬǡͳͻͺȌ
Ǥ
ȋ Ƭ ǡ ʹͲͲͺȌǤ ǡ
ȋǡȌ
ϐǣ
݄௧ ൌ ߙ σୀଵ ߙ ߝ௧ି ଶ σୀଵ ߚ ݄௧ି
(2)
ߙ Ͳǡ ߙଵ ǡ ߙଶ ǡ ǥ ߙ Ͳ and ߚଵ ǡ ߚଶ ǡ ߚଷ ǡ ǥ ߚ Ͳ
ǡ ݄௧
Ǣ ߚ
ϐ
ǡ
ǡ ǡ
Ǣ ߙ is
ϐ
ǡ ǡ Ǥ ሺߙ ߚሻ value ǡ
t Ǣǡ ȋ ǡʹͲͳʹȌǤ
ǡ
Ǥ
ǣ
݄௧ ൌ ߙ σୀଵ ߙ ߝ௧ି ଶ σୀଵ ߚ ݄௧ି ߜ ܦ ISSN 2007-5383 versión digital,
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Ͳ ͺǡ ʹͲͲ Ǥ
ϐ
ϐ
ǡ
Ǥ ǡ
ǦǤ
ϐ
ϐ
ǡǤ
2.4 TARCH model
ǣ ȋͳͻͻͳȌǡ
Ǧ ȋ et alǤǡͳͻͻ͵ȌǡǦ ȋǡͳͻͻͶȌǡ ȋet alǤǡͳͻͻ͵ȌǡǦ ȋǡop citǤȌǦ ȋǡ ʹͲͲͶȌǤ
ϐ
:
ߪ ଶ ൌ ߙ σୀଵ ߚ ߪ௧ି ଶ σୀଵ ߙ ߝ௧ି ଶ σୀଵ ߛ ߝ௧ି ଶ ݀௧ି
(4)
݀௧ ൌ ͳ if ߝ௧ ൏0 ߝ௧ି Ͳ
Ǥ ߝ௧ି ൏ Ͳ ǡ
Ǥߙଵ
ߙଵ ߛଵ
Ǥ ߛଵ Ͳǡ
ǡ
ǡ ߛଵ ് Ͳǡ
ȋ ǡʹͲͳʹȌǤ
Ǥ
ߪ ଶ ൌ ߙ σୀଵ ߚ ߪ௧ି ଶ σୀଵ ߙ ߝ௧ି ଶ
(5)
σୀଵ ߛ ߝ௧ି ଶ ݀௧ି ߜ ݑܦ ͲͻǡʹͲͲǤ
ϐ
ǡ
ϐ
ǡ
Ǥ
74
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3. Empirical analysis ʹ ǡ ǤǤ
ǡ ϐ
Ǥ
ǡ ʹͲͲǡ
Ǥ
Ǥǡǡ ǡ
ȋ
Ȍ
Ǧ
Ǥǡ
Figure 2. Mexico, Canada and the U. S. Level Value and Stock Price Index Returns (January, 2003-February, 2015)
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Ǥ
Ǥ ͳ
Ǥ ȋ Ȍ
ϐ
ǡ ʹͲͲͺǤ
Ǥ Table 1. Stock Price Index Maximum Positive and Negative Variation and Highest and Lowest level index values Level
Index
Maximum variation
High Level/Change IPC
TSE
Positive
Negative
3680.127
722.170
0.162
-0.115
Date
11/04/2013
24/11/2003
29/10/2008
06/10/2008
Level/Change
15323.764
5849.301
0.095
-0.105
Date
31/10/2007
18/05/2004
29/10/2008
01/12/2008
2115.480
676.530
0.104
-0.095
24/02/2015
09/03/2009
14/10/2008
15/10/2008
Level/Change S&P 500
Low
Date
Source: Prepared by author using Bloomberg and Economatica data.
3.1 Rolling window correlation analysis ͵
Ǥ
ǤǤ
Ǥ
ǤǤ Ǥ
Ǥ
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ǤǤ
ǡǡ
ǤǤ
Ǥ Figure 3. Rolling window correlation results
Source: Prepared by author based on results obtained.
͵ǡϐǡǡ
ϐ
ǡ
ͲǤͷ͵Ǣ
ϐ
ǡ
ͲǤͺǤ ǡ
ǡ
ȋʹͲͲǦ
ʹͲͳʹȌͲǤǤ ǡ ϐ
ͲǤ
Ǥ
ǡ ͵
Ǥ
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3.2 GARCH model results
ʹ͵Ǥǡǡǡǡ
Ǧ
Ǥʹ
Ǧ
͵
ϐ
Ǥ Table 2. Descriptive Statistics before Crisis Period- January 1, 2001 to August 8, 2007 &RXQWU\
6'
0HDQ
.XUWRVLV
6NHZQHVV -DUTXH%HUD$5&+ F-statistics Probability
86
0.0084
0.0005
4.3735
-0.0342
73.99635*
5.81 (2)*
0.00
&DQDGi
0.0098
0.0012
4.2862
-0.3532
84.2478*
3.48 (3)**
0.02
México
0.0145
0.0016
5.9237
-0.2688
345.745*
9.78 (1)*
0.00
Reported values are statistically representative at *1%,** 5% and ***10% significance levels. Note: the statistic test ARCH LM corresponds to the Lagrange multiplier used to detect the ARCH effect; the null hypothesis represents the absence of heteroscedasticity, distribution of that parameter is ߣʹ ሺ݇ሻ.
Table 3. Descriptive Statistics Crisis Period- August 8, 2007 to February 27, 2015 &RXQWU\
6'
0HDQ
86
0.015
0.0002380
10.371
-0.454
3483.70*
172.03*
0.00
&DQDGD
0.016 -0.0000319
9.703
-0.484
2897.44*
191.95*
0.00
Mexico
0.020
11.086
0.256
4146.74*
103.35*
0.00
0.0000574
.XUWRVLV 6NHZQHVV -DUTXH%HUD$5&+ F-statistics Probability
Reported values are statistically representative at * 1%,** 5% and ***10% significance levels. Note: As in Table 2, the statistic test ARCH LM corresponds to the Lagrange multiplier used to detect the ARCH effect; the null hypothesis represents the absence of heteroscedasticity, distribution of that parameter is ߣʹ ሺ݇ሻ.
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ǡ
ǡ
Ǥ Ǧ
ǡ
ǡ
Ǥ
ǡ
Ǥǡ
Ǥ
ǡ
Ǥ ǡ ȋͳǡͳȌ ʹ͵
ǡ
ȋǡͳͻͺȌǤ ǡ Ǧʹ ͵ǡ
ͳΨ ϐ
Ǥ
ǤͶ Table 4. Unit Root Testing of Daily Returns of Stock markets ADF
3UHFULVLV
86.
&DQDGD
Mexico
Phillips- Perron &ULVLV
3UHFULVLV
&ULVLV
Levels
FD
Levels
FD
Levels
FD
Levels
FD
-32.46
-16.59
-43.26
-21.41
-32.72
-307.81
-43.26
-376.45
(0.00)*
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
-29.98
-14.69
-35.25
-17.16
-29.99
-322.64
-35.21
-369.68
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
-29.48
-18.26
-35.19
-18.75
-29.34
-313.92
-38.02
-332.14
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
Critical MacKinnon criteria at a significance level of 1% is -3.44. Null hypothesis, series has unit root *Values within parentheses indicate probabilities
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Ǣ
ͳΨ ϐ
Ǥǡ
ϐ ȋȌϐ
Ǥ
ϐǡ
Ǧ Ǥ
Ǥ
ͲǤͲͷǡ
Ǥ
3.3 Application of the GARCH (1,1) model ǡ
Ǥ ȋͳǡͳȌ
ǡ
ϐ
Ƚ1Ⱦ1 βͲǡ ϐ
ͻͲΨ
ϐ
Ǥ ȋͳǡͳȌϐ
ǡͷǤ Ǧ
ǡ
ǡ
ϐ
ǡ ͲǤͲͷǤ ǡͷ ȋͳǡͳȌ
ϐ
ǡ ͲǤͲͷǤ ǡ
Ƚ1ǡǡȽ1ΪȾ1
ǡ
ǡǤǤ
ϐǤ
ϐ
ϐ
Ƚ1
ǡǡ
Ǥ
ϐ
ȋ Ȍǡ
Ǣ
ϐ
ǤǤ
ϐ
ϐ
ͳͲΨϐ
Ǣ
Ǥ Ǥ
Ǥ ǡ
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Table 5. GARCH models coefficients -January 1, 2001 to February 27, 2015
&RHI¿FLHQWV
0H[LFR
86
&DQDGD
Į
7.47E-06
2.52E-06
1.93E-06
(0.00)*
(0.00)
(0.00)
0.0946
0.1027
0.0668
(0.00)
(0.00)
(0.00)
0.8782
0.8746
0.9236
(0.00)
(0.00)
(0.00)
0.9728
0.9701
0.9905
Dummy
1.24E-06
7.20E-07
-3.17E-07
0.2198
0.0732
0.3007
$5&+/0
0.0077
3.3911
0.1118
(0.9297)
(0.0657)
(0.7381)
Į
ȕ
Įȕ *$5&+
*Values within parentheses indicate probabilities.
ϐ
Ǥ ϐ
ǦǤ
ǡ
ϐ
ͲǤͲͷǤ
3.4 Crisis and volatility changes applying a TARCH model ϐ
ǡ
ǡǢǡ Ǥ
ȋͳǡͳȌ
ǤǤ
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ǡȋɀȌ δͲȋͳȌ
ϐ
ǡ
Ǥ
Ƚ1ǡƲ
Ƚ1Ϊɀǡǡ
ϐ
Ǥ ǡ
ǡ
ǤǤǤǡ
ϐ
Ǥ
ϐ
ϐ
ǡ
ϐ
ǡ
ǡ
ϐ
Ǥǡ Ǧ
ϐ
ǡͲǤͲͷǤ Table 6. TARCH models coefficients -January 1, 2001 to February 27, 2015
&RHI¿FLHQWV
0H[LFR
86
&DQDGD
Į
6.70E-06
1.17E-05
2.29E-06
(0.00)*
(0.00)
(0.00)
0.003568
0.17777
0.035707
0.5261
(0.00)
(0.00)
0.130937
0.199725
0.052788
(0.00)
(0.00)
(0.00)
0.90793
0.703279
0.924504
(0.00)
(0.00)
(0.00)
ĮȖ
0.911498
0.881049
0.960211
Dummy in variance
-1.09E-06
5.01E-07
-6.24E-07
(0.1346)
(0.7707)
(0.0381)
0.4542
0.7475
0.2126
(0.5004)
(0.4548)
(0.6447)
Į
ȕ
5(6,'$5&+ Ȗ
$5&+/0
*Values within parentheses indicate probabilities.
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ǡ
ǡ
Ǥ
ʹͲͳͳ ϐ
ϐ ʹͲͳͶǤ
ǡ
ȋ Ȍ Ǥ
Ǥ
4. Conclusion
ǣ
ǡ Ǥ Ǥ
ϐ
ǡ
ʹͲͲ Ǥ Ǥ Ǥ
ͳǡ ʹͲͲ͵ ʹǡ ʹͲͳͷǤ
ǡ
ǡ ƬͷͲͲ ǡ ȋͳǡͳȌ ȋͳǡͳȌǤ
Ǥ
Ƭ ƬǤ
ϐ
Ǧ Ǥ
ϐ
Ǧ
ǡ
Ǥ
ǡǡ
ϐ ϐǤ ǡ
ȋͳǡͳȌ Ǥ
Ǥ
ǡ
Ǥ Ǥǡ
ϐ
ǡ
ϐǤ
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ǡȋͳǡͳȌ
ǡ ǡ
ϐ
Ǥ
Ǣǡ
Ǥǡ
ǤǤǡ
Ǥ
ϐ
ǡ
ϐ
Ǥ
ǡǤǤ
Ǥ
ǡ
ǡ
ǡǡ
Ǥ
ǡ
ǡ ϐ
Ǥ
Referencias bibliográficas ǡ Ǥǡ ǡǤǡǡǤǡƬǡǤȋʹͲͳʹȌǤDzǯ
ǣ ȋǤͳͺͲʹͺȌǤNational Bureau of Economic ResearchǤ ǡǤǤǡ ǡǤǤǡƬǡǤ ǤȋʹͲͳȌǤDzǡ
ǡǦǣ
ǤdzAvailable at SSRN 2718841Ǥ Ǥ Ǥ Ǥ Ǥ ȋͳͻͻͶȌǤ Dz ǡ
dzǤ
ϔ
Ǧsin Finance Journalǡ ʹǡǤͶͲͷǦͶ͵ͺǤ ǡǤȋͳͻͺȌǤDz
dz. Journal of Econometrics ͵ͳǡǤ͵ͲǦ͵ʹǤ ǡǤǡǡǤ ǤǡƬǡǤǤȋͳͻͻͶȌǤDzdzǤHandbook of econometricsǡͶǡǤʹͻͷͻǦ͵Ͳ͵ͺǤ
84
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Global Financial Crisis Volatility Impact and Contagion Effect…
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ǡǤǡǤȋʹͲͲͺȌǤDz
ǡ
ǣ
dzǤCuadernos de Economía, Ǥ ǡïǤͶͺǡ ʹͲͲͺǡǤʹͺǦ͵ͳͻǤ ǡ Ǥ Ǥ ȋʹͲͳͷȌǤ Dz
ǣǮȂ ǯdzǤGlobal Business ReviewǡͳȋͳȌǡǤͷͲǦͲǤ Ǥǡ Ǥ Ǥ Ǥ Ǥ Ǥ ȋͳͻͻ͵ȌǤ Dz
dzǤJournal of Empirical Finance.ͳȋͳȌǡ Ǥͺ͵ǦͳͲǤ ǡ Ǥ Ǥǡ Ƭ ǡ Ǥ ȋͳͻͺȌǤ Dz
dzǤEconometric reviewsǡͷȋͳȌǡǤͳǦͷͲǤ
ǤǤǡǤ ǤǤȋͳͻͻ͵ȌǤDz
dzǤThe Journal of Finance.ͶͺȋͷȌǡǤͳͻǦͳͺͲͳ Ǥ Ǥ ȋʹͲͲͶȌǤ Dz
dzǤ International Review of Financial Analysisǡͳ͵ȋͶȌǡǤͶ͵͵ǦͶʹǤ
ǡ Ǥǡ
ǡǤǡƬǡǤ ǤȋʹͲͳͷȌǤDz
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dzǤJournal of Financial MarketsǡʹǡǤͶǦͺͶǤ
ǡǤǡƬǡǤȋʹͲͳȌǤDz
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dzǤResearch in International Business and Financeǡ͵ǡǤͳͻǦͳͻͷǤ
ǡǤȋʹͲͳʹȌǤDz
dzǤQuest-Journal of Management and ResearchǡʹȋʹȌǡǤ͵ͷǦͶͶǤ ǡ Ǥǡ Ƭ ǡ Ǥ ȋʹͲͳȌǤ Dz
dzǤ FOCUS. Journal of International Businessǡ ʹȋʹȌǤ ǡǤǡƬǡǤȋʹͲͳͷȌǤDz
ǡ
dzǤJournal of Accounting and Financeǡͳͷȋ͵ȌǡǤͳͶͳǤ ǡ ǤǡƬǡǤȋʹͲͳͷȌǤDz
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ǫdz Economics Bulletinǡ͵ͷȋͶȌǡǤʹͳͳͲǦʹͳʹͷǤ ǡǤǡǡǤǡƬǡǤȋʹͲͳͷȌǤDz
dzǤJournal of Applied Business Researchǡ͵ͳȋʹȌǡǤ͵ͷǤ
ISSN 2007-5383 versión digital,
ISSN 2007-5375 versión impresa
85
Estocástica:
Miriam Sosa y Edgar Ortiz
FINANZAS Y RIESGO
ǡǤǡƬǡǤȋʹͲͳͷȌǤDz
ǡdzǤEconometricsǡ͵ȋʹȌǡǤʹͳͷǦʹ͵ʹǤ ǡǤǡǡǤǤǡƬϐǡ ǤȋʹͲͳȌǤDz
ǣ
dzǤJournal of International Financial Markets, Ǥ ǡǤǤȋͳͻͻͳȌǤDz
ǣ
dzǤ
ǣ
ǡǤ͵ͶǦ͵ͲǤ ǡ Ǥ Ǥǡ Ƭ ǡ Ǥ Ǥ ȋʹͲͳȌǤ Dz ǡ
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ϐ
ǡ
ǯ ǣ dzǤApplied EconomicsǡͶͺȋͺȌǡǤͻǦͺ͵Ǥ ǡ Ǥ Ǥǡ ǡ Ǥ Ǥǡ ǡ Ǥǡ Ƭ
ǡ Ǥ ȋʹͲͳͷȌǤ Dz
ǣ
dzǤJournal of Financial StabilityǡͳͺǡǤͳʹǦͳ͵ͺǤ ǡ Ǥ ǤǤǡƬǡǤȋʹͲͳ͵ȌǤDzǦ ×
dzǤRevista Mexicana de Economía y Finanzas. Nueva Época/Mexican Journal of Economics and FinanceǡͺȋʹȌǡǤͳʹͻǦͳͷͷǤ ǡ Ǥǡ Ƭ ǡ Ǥ ȋʹͲͲ͵ȌǤ Dz ǣ
dzǤJournal of International Financial Markets, Institutions and Moneyǡͳ͵ȋʹȌǡǤͳͳǦͳͺǤ ǡǤȋʹͲͳͷȌǤDz
ǣ
dzǤEnergy ProcediaǡͷǡǤʹͷͷǦʹͷʹǤ ǡ Ǥǡ ǡ Ǥǡ Ƭ ǡ Ǥ ȋʹͲͳͷȌǤ Dz
ǣ
dzǤInternational Review of Financial Analysisǡ͵ͻǡǤǦͳͺǤ ǡǤȋͳͻͺȌǤModeling Financial Time SeriesǤ ƬǡǤ ǡ ǤǤǡƬǡ ǤǤȋʹͲͲͻȌǤDz
dzǤAmer
ǣ
ǡͳȋͳȌǡǤͷͺǦͺ͵Ǥ ǤǤȋͳͻͻͶȌǤDz
dzǤJournal of Economic
ǡͳͷǡǤͻ͵ͳǦͻͷͷǤ ǡ ǤǤȋʹͲͳͷȌǤ
ǣ ǤǤ
86
Volumen 7, número 1, enero - junio 2017, pp. 67-88
Global Financial Crisis Volatility Impact and Contagion Effect…
Estocástica:
FINANZAS Y RIESGO
APPENDIX 1 Rolling window correlation, pre-crisis period (Jan 1, 2003- Ago 8, 2007)
Rolling window correlation, crisis period (Ago 9, 2007- Feb 27, 2015).
ISSN 2007-5383 versión digital,
ISSN 2007-5375 versión impresa
87