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Impact of Anti-crisis Measures on the Volatility of the Stock Market Stress Index in the Euro Zone

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Market Microstructure and Nonlinear Dynamics

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Abstract

Several studies have attempted to measure the stress in the financial markets. But despite the diversity of their work no unanimous result seems founded. This highlights the importance of statistical measurement in the financial crises theory. Indeed, our aim work objective is to study the issues and challenges that rescue funds have to meet to enhance financial stability widely affected by financial institutions fragility. For this matter, by a study of the anti-crisis measures impact on the stock market stress volatility in the Euro Zone, we apply the ARCH/GARCH/EGARCH models to analyze linear and asymmetric volatility of financial stress. For this matter, we have developed, using some representative factors, a Stock Market Stress Index (SMSI) based on the standard portfolio theory. Our findings show that, SMSI is represented by an AR (3)-EGARCH (1, 3) which have shown its ability to capture the past and future events destabilizing the financial market. However, we suggest that only some measures have had a significant negative impact on the volatility of the Stress Index, leaving the way open, to one side, to a thorough study on the effectiveness and usefulness of the European stability fund created to save the Euro Zone, and on the other side, the fund ability to meet the long-sought challenge of a financial stability without reach. Thus, the chances of the European Financial Stability Facility (EFSF) to save the Euro Zone in the future seem modest in the current conditions.

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Notes

  1. 1.

    The FTSEurofirst are a series of European traded indices launched on 29 April 2003 by Euronext and FTSE Group, the leading global index provider. They combine high liquidity, wider coverage and a more accurate representation of the market, thus constituting the index range perfectly suited to trading in European equities. The FTSEurofirst 80 Index is a tradable index covering the Euro Area and as such occupies a prominent place in the FTSEurofirst series. It has been designed to reflect as accurately as possible the market performance of stock markets in the Euro Area, with an 80 sample values.

  2. 2.

    The CMAX calculated as a percentage of the current non-financial “Oil & Gas” stock market index, and its maximum level on a 2 years moving window \( \boldsymbol{CMA}{\boldsymbol{X}}_{\boldsymbol{t}}=\frac{x_t}{max\left[ x=\left({x}_{t- j}\Big|\boldsymbol{j}=\mathbf{0},\mathbf{1}\dots..\boldsymbol{T}\right)\right]} \).

  3. 3.

    The difference between the correlation coefficients of daily returns on the global index of Eurozone FTSEUROFIRST 80 Amsterdam market of NYSE Euronext and 10-year German Government benchmark bonds of a period of between 3 months and 2 years.

  4. 4.

    Reprocess the data by replacing the missing value with the previous in accordance with the predecessors method, limit the data series to week 5 working days from Monday to Friday, combine three data (indicators) in three new distributions, calculate volatility of each indicator to constitute the three independent variables which can capture the stress stock and finally rounding to five decimal when it comes to value, or three decimal in case of percentage according to the standards adopted by the Euro Zone.

  5. 5.

    Dummy 4: Allocation de 85 Billion € for Ireland

    Dummy 8: second envelop for Greece + increase of the intervention field of FESF/ESM

    Dummy 9: Increase of the envelop of the FESF with 1,000 billion €

    Dummy 10: Approbation of the envelop increase of the FESF

    Dummy 17: The Euro-group guaranteed assistance to the banking sector in Spain

    Dummy 18: MES inaugurated

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Author information

Authors and Affiliations

  1. University of Economic Sciences and Management of Tunis, Tunis, Tunisia

    Chaker Aloui

  2. University of Economic Sciences and Management of Tunis, 8 Rue Khartoum, cité Erriadh Borj Cedria Soliman, 8020, Tunis, Tunisia

    Abdelaziz Krim

Authors
  1. Chaker Aloui
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  2. Abdelaziz Krim
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Corresponding author

Correspondence to Abdelaziz Krim .

Editor information

Editors and Affiliations

  1. GREQAM/DEFI, Aix-Marseille University, Les Milles, France

    Gilles Dufrénot

  2. University of Evry, Evry, France

    Fredj Jawadi

  3. ESSCA School of Management, Boulogne-Billancourt, France

    Waël Louhichi

Appendix: Modeling AR (3)-EGARCH (1, 3) with Dummies

Appendix: Modeling AR (3)-EGARCH (1, 3) with Dummies

Dependent variable: LOG(GARCH01)

Method: least squares

Date: 21/02/13 Time: 12:11

Sample (adjusted): 30/03/2007 21/12/2012

Included observations: 300 after adjustments

LOG(GARCH01) = C(5) + C(6)*ABS(RESID01(−1)/@SQRT(GARCH01(−1))) + C(7)*RESID01(−1)/@SQRT(GARCH01(−1)) + C(8)*LOG(GARCH01(−1)) + C(9)*LOG(GARCH01(−2)) + C(10)*LOG(GARCH01(−3)) + C(11)*DUMMY_01 + C(12)*DUMMY_02 + C(13)*DUMMY_03 + C(14)*DUMMY_04 + C(15)*DUMMY_05 + C(16)*DUMMY_06 + C(17)*DUMMY_07 + C(18)*DUMMY_08 + C(19)*DUMMY_09 + C(20)*DUMMY_10 + C(21)*DUMMY_11 + C(22)*DUMMY_12 + C(23)*DUMMY_13 + C(24)*DUMMY_14 + C(25)*DUMMY_15 + C(26)*DUMMY_16 + C(27)*DUMMY_17 + C(28)*DUMMY_18

 

Coefficient

Std. error

t-Statistic

Prob.

C(5)

−0.500306

2.70E−14

−1.85E+13

0.0000

C(6)

0.230145

2.96E−15

7.78E+13

0.0000

C(7)

0.104172

1.90E−15

5.49E+13

0.0000

C(8)

−0.738203

6.74E−15

−1.09E+14

0.0000

C(9)

0.843386

3.09E−15

2.73E+14

0.0000

C(10)

0.847934

6.71E−15

1.26E+14

0.0000

C(11)

−9.09E−14

1.62E−14

−5.600118

0.0000

C(12)

1.45E−13

2.26E−14

6.411759

0.0000

C(13)

−5.83E−14

1.73E−14

−3.380413

0.0008

C(14)

−1.27E−14

1.24E−14

−1.021210

0.3080

C(15)

2.79E−14

1.33E−14

2.101775

0.0365

C(16)

−4.56E−14

1.78E−14

−2.565545

0.0108

C(17)

4.17E−14

2.14E−14

1.948705

0.0523

C(18)

−1.72E−14

1.68E−14

−1.023086

0.3072

C(19)

−4.63E−15

1.58E−14

−0.292981

0.7698

C(20)

−4.75E−14

3.46E−14

−1.372428

0.1710

C(21)

1.17E−13

4.52E−14

2.577687

0.0105

C(22)

−5.77E−14

3.32E−14

−1.738225

0.0833

C(23)

5.83E−14

2.44E−14

2.386890

0.0177

C(24)

−8.26E−14

2.78E−14

−2.973726

0.0032

C(25)

7.75E−15

2.00E−14

0.387827

0.6984

C(26)

4.31E−14

1.86E−14

2.314832

0.0214

C(27)

−2.65E−14

1.72E−14

−1.539506

0.1248

C(28)

−2.48E−15

1.33E−14

−0.186766

0.8520

R-squared

1.000000

Mean dependent var

−6.598519

Adjusted R-squared

1.000000

S.D. dependent var

0.620863

S.E. of regression

3.18E−14

Sum squared resid

2.79E−25

Durbin-Watson stat

3.023873

 

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Aloui, C., Krim, A. (2014). Impact of Anti-crisis Measures on the Volatility of the Stock Market Stress Index in the Euro Zone. In: Dufrénot, G., Jawadi, F., Louhichi, W. (eds) Market Microstructure and Nonlinear Dynamics. Springer, Cham. https://doi.org/10.1007/978-3-319-05212-0_9

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