Market risk agriculture

1. Market risk in agricultural commodity markets: a
wavelet-based copula modeling approach
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen
June 2012
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 1 / 16

2. Outline of the presentation
1 Introduction
2 Methodology
3 Results
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 2 / 16

3. Introduction
Motivations and research scope
Fluctuations of prices and comovements in agricultural commodities
markets are among the most important concerns for economic agents:
producers, consumers, investors, policymarkets,...especially in
agriculture-dominated countries
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 3 / 16

4. Introduction
Motivations and research scope
Fluctuations of prices and comovements in agricultural commodities
markets are among the most important concerns for economic agents:
producers, consumers, investors, policymarkets,...especially in
agriculture-dominated countries
A real matter over the last decade: prices of agricultural commodities
experienced signi…cant swings and extreme movements, leading to
market disruptions, higher costs for consumers and in‡ation threats
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 3 / 16

5. Introduction
Motivations and research scope
Fluctuations of prices and comovements in agricultural commodities
markets are among the most important concerns for economic agents:
producers, consumers, investors, policymarkets,...especially in
agriculture-dominated countries
A real matter over the last decade: prices of agricultural commodities
experienced signi…cant swings and extreme movements, leading to
market disruptions, higher costs for consumers and in‡ation threats
Complex patterns of price volatility: low short-term elasticity of
supply and demand, environmental risks (principally climatic hazards),
trade policies, monetary policies, biofuel, and “…nancialization” of
agricultural markets and the role of short-term investors on futures
markets
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 3 / 16

6. Introduction
Motivations and research scope
Fluctuations of prices and comovements in agricultural commodities
markets are among the most important concerns for economic agents:
producers, consumers, investors, policymarkets,...especially in
agriculture-dominated countries
A real matter over the last decade: prices of agricultural commodities
experienced signi…cant swings and extreme movements, leading to
market disruptions, higher costs for consumers and in‡ation threats
Complex patterns of price volatility: low short-term elasticity of
supply and demand, environmental risks (principally climatic hazards),
trade policies, monetary policies, biofuel, and “…nancialization” of
agricultural markets and the role of short-term investors on futures
markets
Research problem: we consider the problem of accurate market risk
modeling (VaR) for agricultural commodity products
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 3 / 16

7. Introduction
Motivations and research scope
Patterns in FFPI1: highest peak of 184.7 points (06/2008) or resp.
36% and 73% higher than one year and four years earlier, subject to
combined e¤ects of many factors (climatic conditions).
1A weighted average measure of the monthly change in international prices of a
basket of …ve food commodity groups: meat, dairy, cereals, oil and sugar
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 4 / 16

8. Introduction
Literature
Factors a¤ecting agriculture commodities: weather patterns,
seasonalities, market states, business cycles, and geopolitical
situations (Lu, 2002; Giot and Laurent, 2003)
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 5 / 16

9. Introduction
Literature
Factors a¤ecting agriculture commodities: weather patterns,
seasonalities, market states, business cycles, and geopolitical
situations (Lu, 2002; Giot and Laurent, 2003)
Market integration and volatility and risk models for agriculture
commodities markets: Cointegration (Zanias,1999; Natanelov et al.,
2011), ARCH-type models (Chatrath et al., 2002; Giot and Laurent,
2003)
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 5 / 16

10. Introduction
Literature
Factors a¤ecting agriculture commodities: weather patterns,
seasonalities, market states, business cycles, and geopolitical
situations (Lu, 2002; Giot and Laurent, 2003)
Market integration and volatility and risk models for agriculture
commodities markets: Cointegration (Zanias,1999; Natanelov et al.,
2011), ARCH-type models (Chatrath et al., 2002; Giot and Laurent,
2003)
Food-energy nexus and food-stock market nexus: Bayesian Markov
Chain Monte Carlo methods (Du et al. 2011), causality in variance
test and impulse response functions (Nazlioglu et al., 2013);
VAR-GARCH and DCC-GARCH (Mensi et al., 2013; Creti et al.,
2013)
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 5 / 16

11. Introduction
Literature …ndings
Nonlinearity in agriculture commodities futures prices
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 6 / 16

12. Introduction
Literature …ndings
Nonlinearity in agriculture commodities futures prices
Increasing oil prices is the main driving factor of the recent upward
movements in agricultural markets
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 6 / 16

13. Introduction
Literature …ndings
Nonlinearity in agriculture commodities futures prices
Increasing oil prices is the main driving factor of the recent upward
movements in agricultural markets
Evidence of signi…cant food-energy/food-stock causality links and
short-term evolving comovements among agricultural commodities
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 6 / 16

14. Introduction
Some issues?
Existing researchs do not account for:
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 7 / 16

15. Introduction
Some issues?
Existing researchs do not account for:
1 Conditional dependence structure: linear or nonlinear?
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 7 / 16

16. Introduction
Some issues?
Existing researchs do not account for:
1 Conditional dependence structure: linear or nonlinear?
2 Tail dependence: left or right tail dependence or both?
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 7 / 16

17. Introduction
Some issues?
Existing researchs do not account for:
1 Conditional dependence structure: linear or nonlinear?
2 Tail dependence: left or right tail dependence or both?
3 Asymmetric dependence: the left tail dependence is lower than,
higher than or equal to the right tail dependence?
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 7 / 16

18. Introduction
Some issues?
Existing researchs do not account for:
1 Conditional dependence structure: linear or nonlinear?
2 Tail dependence: left or right tail dependence or both?
3 Asymmetric dependence: the left tail dependence is lower than,
higher than or equal to the right tail dependence?
4 Multiscale structure: the time horizons (short-, medium- and
long-term)?
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 7 / 16

19. Introduction
Questions raised in this paper?
Can we improve the accuracy of the VaR estimates and forecasts by
combining wavelet analysis and copula-GARCH approach?
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 8 / 16

20. Introduction
Questions raised in this paper?
Can we improve the accuracy of the VaR estimates and forecasts by
combining wavelet analysis and copula-GARCH approach?
1 Evaluate the market risk in agricultural commodity markets while
considering their comovement with the world stock market index,
which re‡ects the global …nancial market conditions.
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 8 / 16

21. Introduction
Questions raised in this paper?
Can we improve the accuracy of the VaR estimates and forecasts by
combining wavelet analysis and copula-GARCH approach?
1 Evaluate the market risk in agricultural commodity markets while
considering their comovement with the world stock market index,
which re‡ects the global …nancial market conditions.
2 Account for the potential of nonlinear, asymmetric, and time-scale
dependence
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 8 / 16

22. Introduction
Questions raised in this paper?
Can we improve the accuracy of the VaR estimates and forecasts by
combining wavelet analysis and copula-GARCH approach?
1 Evaluate the market risk in agricultural commodity markets while
considering their comovement with the world stock market index,
which re‡ects the global …nancial market conditions.
2 Account for the potential of nonlinear, asymmetric, and time-scale
dependence
Why this study is important?
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 8 / 16

23. Introduction
Questions raised in this paper?
Can we improve the accuracy of the VaR estimates and forecasts by
combining wavelet analysis and copula-GARCH approach?
1 Evaluate the market risk in agricultural commodity markets while
considering their comovement with the world stock market index,
which re‡ects the global …nancial market conditions.
2 Account for the potential of nonlinear, asymmetric, and time-scale
dependence
Why this study is important?
1 Risk management purpose
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 8 / 16

24. Introduction
Questions raised in this paper?
Can we improve the accuracy of the VaR estimates and forecasts by
combining wavelet analysis and copula-GARCH approach?
1 Evaluate the market risk in agricultural commodity markets while
considering their comovement with the world stock market index,
which re‡ects the global …nancial market conditions.
2 Account for the potential of nonlinear, asymmetric, and time-scale
dependence
Why this study is important?
1 Risk management purpose
2 VaR is established as the o¢ cial measure of market risk under the
current framework of the Basel II Accord
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 8 / 16

25. Introduction
Questions raised in this paper?
Can we improve the accuracy of the VaR estimates and forecasts by
combining wavelet analysis and copula-GARCH approach?
1 Evaluate the market risk in agricultural commodity markets while
considering their comovement with the world stock market index,
which re‡ects the global …nancial market conditions.
2 Account for the potential of nonlinear, asymmetric, and time-scale
dependence
Why this study is important?
1 Risk management purpose
2 VaR is established as the o¢ cial measure of market risk under the
current framework of the Basel II Accord
3 Price developments in agricultural markets may be very di¤erent from
the overall …nancial markets
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 8 / 16

26. Introduction
Questions raised in this paper?
Can we improve the accuracy of the VaR estimates and forecasts by
combining wavelet analysis and copula-GARCH approach?
1 Evaluate the market risk in agricultural commodity markets while
considering their comovement with the world stock market index,
which re‡ects the global …nancial market conditions.
2 Account for the potential of nonlinear, asymmetric, and time-scale
dependence
Why this study is important?
1 Risk management purpose
2 VaR is established as the o¢ cial measure of market risk under the
current framework of the Basel II Accord
3 Price developments in agricultural markets may be very di¤erent from
the overall …nancial markets
4 Economic agents simultaneously operate at di¤erent horizons (e.g.,
investors in commodity futures markets)
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 8 / 16

27. Introduction
Questions raised in this paper?
Can we improve the accuracy of the VaR estimates and forecasts by
combining wavelet analysis and copula-GARCH approach?
1 Evaluate the market risk in agricultural commodity markets while
considering their comovement with the world stock market index,
which re‡ects the global …nancial market conditions.
2 Account for the potential of nonlinear, asymmetric, and time-scale
dependence
Why this study is important?
1 Risk management purpose
2 VaR is established as the o¢ cial measure of market risk under the
current framework of the Basel II Accord
3 Price developments in agricultural markets may be very di¤erent from
the overall …nancial markets
4 Economic agents simultaneously operate at di¤erent horizons (e.g.,
investors in commodity futures markets)
5 Speci…c uncertainty experienced by agricultural markets due to
exogenous factors, such as climatic hazards
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 8 / 16

28. Introduction
Methodology
A Wavelet-GARCH-copula method:
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 9 / 16

29. Introduction
Methodology
A Wavelet-GARCH-copula method:
1 Decompose returns using wavelet analysis (Maximum Overlap
Discrete Wavelet Transform and multiresolution decomposition)
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 9 / 16

30. Introduction
Methodology
A Wavelet-GARCH-copula method:
1 Decompose returns using wavelet analysis (Maximum Overlap
Discrete Wavelet Transform and multiresolution decomposition)
2 Filter returns using ARCH-type model
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 9 / 16

31. Introduction
Methodology
A Wavelet-GARCH-copula method:
1 Decompose returns using wavelet analysis (Maximum Overlap
Discrete Wavelet Transform and multiresolution decomposition)
2 Filter returns using ARCH-type model
3 Fitting bivariate copula models (Normal, Frank, Clayton, Survival
Clayton, Gumbel, Survival Gumbel and Tawn)
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 9 / 16

32. Introduction
Methodology
A Wavelet-GARCH-copula method:
1 Decompose returns using wavelet analysis (Maximum Overlap
Discrete Wavelet Transform and multiresolution decomposition)
2 Filter returns using ARCH-type model
3 Fitting bivariate copula models (Normal, Frank, Clayton, Survival
Clayton, Gumbel, Survival Gumbel and Tawn)
4 Out-of-sample VaR forecast
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 9 / 16

33. Introduction
Methodology
Concept of wavelets:
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 10 / 16

34. Introduction
Methodology
Concept of wavelets:
Wavelet transform analysis is a mathematical tool that is used to
extract information from di¤erent kind of data and allows to analyze
the signals in both the time and frequency domains
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 10 / 16

35. Introduction
Methodology
Concept of wavelets:
Wavelet transform analysis is a mathematical tool that is used to
extract information from di¤erent kind of data and allows to analyze
the signals in both the time and frequency domains
Better than Fourier transform as the time information of a time series
is lost
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 10 / 16

36. Introduction
Methodology
Concept of wavelets:
Wavelet transform analysis is a mathematical tool that is used to
extract information from di¤erent kind of data and allows to analyze
the signals in both the time and frequency domains
Better than Fourier transform as the time information of a time series
is lost
Wavelets has been applied to economics and …nance, e.g., Fernandez
(2005) for ICAPM & VaR; Gallegati and Gallegati (2007) for output;
Rua and Nunes (2009) for market risk; Aguiar-Conraria and Soares
(2011) for oil and macroeconomy.
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 10 / 16

37. Introduction
Methodology
Concept of wavelets:
Wavelet transform analysis is a mathematical tool that is used to
extract information from di¤erent kind of data and allows to analyze
the signals in both the time and frequency domains
Better than Fourier transform as the time information of a time series
is lost
Wavelets has been applied to economics and …nance, e.g., Fernandez
(2005) for ICAPM & VaR; Gallegati and Gallegati (2007) for output;
Rua and Nunes (2009) for market risk; Aguiar-Conraria and Soares
(2011) for oil and macroeconomy.
Choice of wavelets: continuous vs. descrete wavelets
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 10 / 16

38. Introduction
Methodology
Concept of wavelets:
Wavelet transform analysis is a mathematical tool that is used to
extract information from di¤erent kind of data and allows to analyze
the signals in both the time and frequency domains
Better than Fourier transform as the time information of a time series
is lost
Wavelets has been applied to economics and …nance, e.g., Fernandez
(2005) for ICAPM & VaR; Gallegati and Gallegati (2007) for output;
Rua and Nunes (2009) for market risk; Aguiar-Conraria and Soares
(2011) for oil and macroeconomy.
Choice of wavelets: continuous vs. descrete wavelets
1 Continuous wavelet transform - CWT (e.g., Morlet and Gabor): maps
the original series, which is a function of just the time variable, into a
function of two variabes - time and frequency, but not e¢ cient in
multiscale analysis (rather denoising process)
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 10 / 16

39. Introduction
Methodology
Concept of wavelets:
Wavelet transform analysis is a mathematical tool that is used to
extract information from di¤erent kind of data and allows to analyze
the signals in both the time and frequency domains
Better than Fourier transform as the time information of a time series
is lost
Wavelets has been applied to economics and …nance, e.g., Fernandez
(2005) for ICAPM & VaR; Gallegati and Gallegati (2007) for output;
Rua and Nunes (2009) for market risk; Aguiar-Conraria and Soares
(2011) for oil and macroeconomy.
Choice of wavelets: continuous vs. descrete wavelets
1 Continuous wavelet transform - CWT (e.g., Morlet and Gabor): maps
the original series, which is a function of just the time variable, into a
function of two variabes - time and frequency, but not e¢ cient in
multiscale analysis (rather denoising process)
2 Discrete wavelet transform (DWT): enables to compute the transform
for a very special discrete choice of the parameter values for time andRiadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 10 / 16

40. Introduction
Methodology
Concept of wavelets:
Wavelet transform analysis is a mathematical tool that is used to
extract information from di¤erent kind of data and allows to analyze
the signals in both the time and frequency domains
Better than Fourier transform as the time information of a time series
is lost
Wavelets has been applied to economics and …nance, e.g., Fernandez
(2005) for ICAPM & VaR; Gallegati and Gallegati (2007) for output;
Rua and Nunes (2009) for market risk; Aguiar-Conraria and Soares
(2011) for oil and macroeconomy.
Choice of wavelets: continuous vs. descrete wavelets
1 Continuous wavelet transform - CWT (e.g., Morlet and Gabor): maps
the original series, which is a function of just the time variable, into a
function of two variabes - time and frequency, but not e¢ cient in
multiscale analysis (rather denoising process)
2 Discrete wavelet transform (DWT): enables to compute the transform
for a very special discrete choice of the parameter values for time andRiadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 10 / 16

41. Data
Our sample data consist of daily closing spot prices for eight
agricultural commodities widely traded in the Chicago Board of Trade
(CBOT) and New York Board of Trade (NYBOT)
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 11 / 16

42. Data
Our sample data consist of daily closing spot prices for eight
agricultural commodities widely traded in the Chicago Board of Trade
(CBOT) and New York Board of Trade (NYBOT)
The sample period runs from 3 October 2003 to 31 August 2010,
yielding 1789 observations. Returns series are computed by using the
di¤erence in the logarithm of the two consecutive prices and
expressed in percentage.
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 11 / 16

43. Results
Time-scale behavior of commodity returns
We use the Daubechies least asymmetric wavelet …lter of level 8
(LA8) to decompose each of the nine return series (eight commodities
and world stock market index) into three di¤erent periodicities
ranging from short to long-term periodicity: D1, D2 and D3
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 12 / 16

44. Results
Time-scale behavior of commodity returns
We use the Daubechies least asymmetric wavelet …lter of level 8
(LA8) to decompose each of the nine return series (eight commodities
and world stock market index) into three di¤erent periodicities
ranging from short to long-term periodicity: D1, D2 and D3
These wavelet …lter coe¢ cients correspond respectively to 2-4, 4-8
and 8-16 days period since we use daily data and set the number of
scales J to be three
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 12 / 16

45. Results
Wavelet variance and correlation analyses
Investors in agricultural commodity markets with very short-term
investment horizons are thus confronted with high risks. Commodities
returns also exhibit signi…cant di¤erences in volatility over di¤erent
scales.
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 13 / 16

46. Results
Wavelet variance and correlation analyses
Investors in agricultural commodity markets with very short-term
investment horizons are thus confronted with high risks. Commodities
returns also exhibit signi…cant di¤erences in volatility over di¤erent
scales.
Investors in agricultural commodity markets with very short-term
investment horizons are confronted with high risks
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 13 / 16

47. Results
Tail dependence
Whenever the extreme comovement exists, the dependence structure
is asymmetric, except for Soybean oil-MSCI pair.
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 14 / 16

48. Results
Investors in agricultural commodity markets with very short-term
investment horizons are confronted with high risks
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 15 / 16

49. Results
Investors in agricultural commodity markets with very short-term
investment horizons are confronted with high risks
Commodities returns have higher correlations with the USD/EUR
exchange rate and the MSCI world market index than with the FFE
rate
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 15 / 16

50. Results
Investors in agricultural commodity markets with very short-term
investment horizons are confronted with high risks
Commodities returns have higher correlations with the USD/EUR
exchange rate and the MSCI world market index than with the FFE
rate
The returns linkages are essentially stronger at the smallest scale
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 15 / 16

51. Results
Investors in agricultural commodity markets with very short-term
investment horizons are confronted with high risks
Commodities returns have higher correlations with the USD/EUR
exchange rate and the MSCI world market index than with the FFE
rate
The returns linkages are essentially stronger at the smallest scale
The degree and the structure of dependence are not constant across
time scales
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 15 / 16

52. Results
Investors in agricultural commodity markets with very short-term
investment horizons are confronted with high risks
Commodities returns have higher correlations with the USD/EUR
exchange rate and the MSCI world market index than with the FFE
rate
The returns linkages are essentially stronger at the smallest scale
The degree and the structure of dependence are not constant across
time scales
The interdependence during market extreme (positive or negative)
movements is asymmetric and scale-dependent
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 15 / 16

53. Results
Investors in agricultural commodity markets with very short-term
investment horizons are confronted with high risks
Commodities returns have higher correlations with the USD/EUR
exchange rate and the MSCI world market index than with the FFE
rate
The returns linkages are essentially stronger at the smallest scale
The degree and the structure of dependence are not constant across
time scales
The interdependence during market extreme (positive or negative)
movements is asymmetric and scale-dependent
The wavelet-copula model leads to more accurate VaR forecasts than
the traditional VaR approaches
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 15 / 16

54. Thank you for your attention!
Riadh Aloui, Mohamed Safouane Ben Aïssa and Duc Khuong Nguyen () 06/12 16 / 16