# On Identifying the Systemically Important Tunisian Banks: An Empirical Approach Based on the △CoVaR Measures

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Material and Methods

_{q}

^{sys/i}) as well as its exposure to aggregate shocks (ΔCoVaR

_{q}

^{i/system}). Second, based on CoVaR’s estimates we construct systemic risk cartography that allowed for putting forward the Tunisian banks systemic risk involvement.

#### 3.1. Overview of Tunisian Banking Sector

#### 3.2. Data Description

#### 3.3. VaR Estimation

_{95%}and VaR

_{50%}correspond respectively to the worst 112 days and the worst 1117 days over the course of the sample. The VaR of bank i at quantile q is

_{t}is the expected returns and h

_{t}refers to the standardized variances.

^{i}

_{q}. The average VaR

^{i}

_{q,t}estimates are presented in Table 3.

#### 3.4. CoVaR Estimation

_{q}

^{i}and β

_{q}

^{i}using the following equation:

_{q}

^{i,sys}= α

_{q}

^{sys}+ β

_{q}

^{sys}X

^{sys}

_{q}

^{i,sys}: the return of the bank I at quantile q conditional to the return of the banking system

^{i}: the return of the banking sector.

^{i}: VaR of institution i at q%.

^{sys/Xi=VaRq}is shown in Table 4.

_{q}

^{sys/i}= CoVaR

_{qsys}

^{/Xi=VaR q}− CoVaR

_{qsys}

^{/Xi= Mdian}

_{q}

^{i/system}= CoVaR

_{q}

^{i/Xsystem=VaRq}− CoVaR

_{q}

^{i/Xsystem=Med}

^{sys/i}. Hence, it can be claimed that this bank has a significant influence on the banking system. Moreover, BTE is the most vulnerable bank to sector’s risk. It is closely followed by the BH, ATB, BNA and the STB. Thus, it can be asserted that public banks are the most vulnerable to the banking sector’s financial distress. Also, it is important to note that the least exposed entity is the one among those presenting the lowest contribution (the second lowest contribution) to the sector’s systemic risk, namely the BIAT.

#### 3.5. Back Testing

## 4. Discussion: The Positioning of Tunisian Banks Based on Their Systemic Risks’ Implication

^{i/sys}) and the second (vertical axis) points to the contribution of banks to the system risk.

**Zone****I:**- The lowest contributor and the highest exposed;
**Zone****II:**- The lowest contributor, the lowest exposed;
**Zone****III:**- The highest contributor and the highest exposed;
**Zone****IV:**- The highest contributor and the lowest exposed;

**and ΔCoVaR**

^{sys/i}_{q}

^{i/system}measures.

## 5. Conclusions

_{q}

^{sys/i}) as well as its exposure to aggregate shocks (ΔCoVaR

_{q}

^{i/system}). Based on CoVaR estimates, we set a perceptual map that allows us to explain and revise banks systemic risk in the Tunisian context.

**and ΔCoVaR**

^{sys/i}_{q}

^{i/system}measures. On the other hand, BT and BIAT exhibit the smallest contribution and exposure measures, and hence they are the less concerned by systemic risk.

## Author Contributions

## Funding

## Conflicts of Interest

## References

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1 | TUNBANK (Tunis Bank) is the stock market index exclusively for the Tunisian banking sector which contains the 11 banks listed on the stock market. |

**Figure 1.**Evolution of the TUNINDEX and TUNBANK during the period 31 December 2012–September 2018. Source: Periodic Conjuncture Report N°121–October 2018, Tunisian Central Bank (Banque Centrale de Tunisie 2018).

Authors | Context | Systemic Risk Measures | Results |
---|---|---|---|

Chan-Lau (2009) | Financial institutions in Europe, Japan, and the United States. | △CoVaR | The results indicate that risk codependence is stronger during distress periods. |

Gauthier et al. (2010) | Canadian banks | A network-based framework and a Merton model | The authors conclude that financial stability can be substantially enhanced by implementing a system perspective on bank regulation. |

Acharya et al. (2010) | European and American contexts | SRISK and stress tests | They show that regulatory capital shortfalls measured relative to total assets provide similar rankings to SRISK for U.S. stress tests. On the contrary, rankings are substantially different when the regulatory capital shortfalls are measured relative to risk–weighted assets. Greater differences are observed in the European stress tests. |

Reboredo and Ugolini (2015) | European sovereign debt markets | CoVaR | The systemic risks are similar in all countries before the crisis and the decoupling of debt market and systematic risk were globally reduced in the European market after the onset of the Greek debt crisis. |

Grieb (2015) | Asian and Russian context | The model of nonlinear factors, and Logistic regression model | The authors show that the systemic risk of hedge fund is increasing. |

Kupiec and Güntay (2016) | Different countries | MES and CoVar | They conclude that CoVaR and MES are not reliable measures of systemic risk. |

Lin et al. (2016) | Taiwan financial institutions | Different risk measures like SRISK, MES, CoVaR | The main results indicate that although these three measures differ in their definition of the contributions to systemic risk, all are quite similar in identifying systemically important financial institutions (SIFIs). |

Karimalis and Nomikos (2017) | European large banks | Copula and CoVaR | They highlight the importance of liquidity risk at the outset of the financial crisis in summer 2007 and find that changes in major macroeconomic variables can contribute significantly to systemic risk. |

Brownlees and Engle (2017) | Top international financial firms | SRISK | They offered a ranking of institutions in the different crisis stages. |

Hmissi et al. (2017) | Tunisian context | CES measure | They find that Tunisian public banks (STB, BNA and BH) are the riskiest systemically banking sector. |

Di Clemente (2018) | European banking system | Extreme value theory (EVT) | They showed the connection between a single financial institution and the financial system. |

Khiari and Nachnouchi (2018) | Tunisian context | CoES and MDS methodologies | They show that public banks respectively along with the two most important private banks hold the leading positions in the systemic risk rankings |

Duan (2019) | Chinese context | CoVar | Authors find that the risk spillover value of China Pacific Insurance Company is the largest, followed by China Life Insurance Company, Ping’an Insurance Company of China is the last. |

N | Mean | Standard Deviation | Skewness | Kurtosis | |
---|---|---|---|---|---|

AB | 2235 | −0.029 | 1.895 | −21.775 | 797.500 |

ATB | 2235 | −0.035 | 1.254 | −0.238 | 4.565 |

Attijari | 2235 | 0.028 | 1.188 | −0.098 | 7.265 |

BH | 2235 | −0.034 | 1.789 | −5.047 | 105.419 |

BT | 2235 | −0.112 | 5.024 | −41.939 | 1895.974 |

BTE | 2235 | −0.045 | 1.402 | 0.176 | 13.287 |

BNA | 2235 | 0.009 | 1.551 | 0.452 | 4.082 |

UBCI | 2235 | −0.036 | 1.810 | −6.527 | 158.577 |

BIAT | 2235 | 0.028 | 1.249 | 0.199 | 4.339 |

STB | 2235 | −0.062 | 1.781 | 0.300 | 2132 |

UIB | 2235 | 0.011 | 1.170 | −0.948 | 22.974 |

TUNBANK | 2235 | 0.022 | 0.645 | −0.368 | 10.108 |

Banks | BT | BIAT | UBCI | TIJARI | BH | UIB | AB | STB | ATB | BNA | BTE |
---|---|---|---|---|---|---|---|---|---|---|---|

Average VaRiq,t | −0.017454 | −0.020229 | −0.039537 | −0.015918 | −0.026186 | −0.015632 | −0.022018 | −0.029432 | −0.020185 | −0.023813 | −0.022569 |

Banks | BT | BIAT | UBCI | TIJARI | BH | UIB | AB | STB | ATB | BNA | BTE |
---|---|---|---|---|---|---|---|---|---|---|---|

COVaR i/sys | −0.039081 | −0.042360 | −0.067702 | −0.040310 | −0.050530 | −0.037090 | −0.037834 | −0.052711 | −0.042089 | −0.053175 | −0.056501 |

Expoure ΔCoVaR_{q}^{i/sys} | Contribution ΔCoVaR^{sys/i} | |
---|---|---|

AB | −0.032594 | −0.021378 |

ATB | −0.036435 | −0.02148 |

ATTIJARI | −0.028878 | −0.018332 |

BH | −0.045772 | −0.020545 |

BIAT | −0.021259 | −0.018508 |

BNA | −0.036213 | −0.020529 |

BT | −0.022813 | −0.015852 |

BTE | −0.056501 | −0.021164 |

STB | −0.035057 | −0.021463 |

UIB | −0.029841 | −0.019827 |

UBCI | −0.028356 | −0.022987 |

Short Positions | ||||

Quantile | Success Rate | Kupiec LRS ^{1} | p-Value | ESF ^{2} |

0.9500 | 0.95302 | 0.43762 | 0.50827 | 0.035825 |

0.9750 | 0.97002 | 2.1380 | 0.14369 | 0.039317 |

0.9900 | 0.98613 | 3.0178 | 0.082353 | 0.045175 |

0.9950 | 0.99060 | 6.8889 | 0.0086733 | 0.046838 |

0.9975 | 0.99284 | 12.890 | 0.00033043 | 0.048614 |

Long Positions | ||||

Quantile | Failure Rate | Kupiec LRS | p-Value | ESF |

0.0500 | 0.033110 | 15.162 | 9.8682 × 10^{−5} | −0.040975 |

0.0250 | 0.019239 | 3.3011 | 0.069235 | −0.049888 |

0.0100 | 0.009396 | 0.084060 | 0.77187 | −0.068311 |

0.0050 | 0.0062640 | 0.66418 | 0.41509 | −0.085305 |

0.0025 | 0.0044743 | 2.8248 | 0.092818 | −0.10279 |

^{1}LR refers to the likelihood ratio statistic.

^{2}ESF refers to the expected shortfall.

First Dimension Horizontal Axis (Bank’s Exposure) | Second Dimension Vertical Axis (Bank’s Contribution) | |
---|---|---|

Axis | ΔCoVaR^{sys/i} | ΔCoVaRq^{i/system} |

Exposure ΔCoVaR _{q}^{i/system} | Contribution ΔCoVaR ^{sys/i} | |
---|---|---|

(min ΔCOVaR + max ΔCOVaR)/2 | −0.0194195 | −0.03888 |

∑ ΔCOVaR/11 | −0.02018773 | −0.03397445 |

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**MDPI and ACS Style**

Khiari, W.; Ben Sassi, S.
On Identifying the Systemically Important Tunisian Banks: An Empirical Approach Based on the △CoVaR Measures. *Risks* **2019**, *7*, 122.
https://doi.org/10.3390/risks7040122

**AMA Style**

Khiari W, Ben Sassi S.
On Identifying the Systemically Important Tunisian Banks: An Empirical Approach Based on the △CoVaR Measures. *Risks*. 2019; 7(4):122.
https://doi.org/10.3390/risks7040122

**Chicago/Turabian Style**

Khiari, Wided, and Salim Ben Sassi.
2019. "On Identifying the Systemically Important Tunisian Banks: An Empirical Approach Based on the △CoVaR Measures" *Risks* 7, no. 4: 122.
https://doi.org/10.3390/risks7040122