Contagion of the Subprime Financial Crisis on Frontier Stock Markets: A Copula Analysis
Abstract
:1. Introduction
2. Materials and Methods
- America and Europe—Argentina, Croatia, Estonia, Romania, Slovenia;
- Africa—Kenya, Mauritius, Morocco, Nigeria and Tunisia;
- Middle East—Bahrain, Jordan, Kuwait, Lebanon, Oman;
- Asia—Pakistan,2 Sri Lanka, Vietnam.
- Calm period: from 4 January 2005 to 31 July 2007 (671 data points);
- Crisis period: from 1 August 2007 to 7 December 2009 (614 data points).
- (1)
- We calculated the returns of each series, and removed autocorrelation and heteroscedasticity applying an ARMA-GARCH model. The standardized residuals were extracted and labeled as filtered returns.5
- (2)
- The filtered returns were divided into two periods—one of calm and one of crisis. The maximum likelihood value was used to evaluate the parametric distribution functions (Gaussian, t-Student, logistic and Gumbel) estimated for both calm and crisis periods. We used the Akaike information criterion (AIC) to select the most appropriate parametric distribution function for each series.
- (3)
- Five pure and three mixed copulas were considered for each series and the most appropriate one was selected. The pure copulas were: Clayton, t-Student, Gumbel, Frank, and Gaussian. Mixed copulas included: Clayton-Gumbel (CG), Gumbel-Survival-Gumbel (GSG) and Clayton-Gumbel-Frank (CGF). The marginal distributions selected in the second step were used to estimate the copulas and maximum likelihood and AIC values were the basis for the selection of the most appropriate model.
- (4)
- After the copulas’ estimation, the , τ and coefficients were used to assess the degree of dependence between the variables.
- (5)
- Lastly, we employed the bootstrap method proposed by Trivedi and Zimmer (2005) to estimate the copulas’ variance-covariance matrix V of parameters and other indicators.
- (a)
- We used the IFM method to obtain the marginal distributions of both the vector of parameters ( and ) and the vector of copulas’ dependence parameters . The global vector of the parameters was .
- (b)
- From the original data, we drew a random sample with replacement.
- (c)
- On the random sample, we again used the IFM method to re-evaluate , and θ and stored their values.
- (d)
- We repeated steps (b) and (c) R times and used the estimated parameters, , and for the Rth re-estimation. The global vector of parameters was .
- (e)
- We obtained the standard errors of the parameters by taking the squared roots of the elements in the main matrix V. .
3. Results
4. Discussion
Author Contributions
Funding
Conflicts of Interest
References
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1 | The main providers of frontier markets’ indices are MSCI, FTSE Russell, a unit of the London Stock Exchange Group, and Standard & Poor’s. The list of criteria used by the first two (mainly related to restrictions on foreign ownership of listed stocks and minimum liquidity requirements), and thus the list of markets they consider as frontier are very similar. In the Standard and Poor’s classification, macroeconomic indicators play a more prominent role and their list is usually larger than those of MSCI’s and FTSE’s. We followed the choice of most financial researchers investigating frontier markets and chose MSCI as the source of our data. Details on their classification can be found at https://www.msci.com/market-classification. |
2 | Pakistan was reclassified as an emerging market in May 2017. We considered it as a frontier market because it was how the country was classified at the time of data collection (before May 2017). |
3 | Two random variables x and y are concordant, if the large and small values of x are associated with the large and small values of y (Nelsen 2007). |
4 | |
5 | Henceforth, the word “returns” means filtered returns. |
6 | On 12 October 2008, European leaders met in Paris and announced the recapitalization of European banks and the implementation of plans to guarantee bank deposits for five years. They also announced the funding of rescue plans and an increase in short-term credits. On 13 October 2008, stock markets worldwide improved. On 28 October 2008, in response to an anticipated cut on central banks’ reference rates of interest, the Dow Jones industrial index increased 11%, according to the New York Times. In October, the International Monetary Fund’s announcement of a release of emergency aid loans (including to countries of the Western Europe) reverberated back to the USA, as many affected countries were USA trading partners. We also found news of unexpected good performance for the Dow Jones industrial index on 13 November 2008. |
7 | The financial crisis had a strong negative impact on Argentinean pension funds, prompting the government to declare their nationalisation and transference to the National Social Security Administration. Guarantee and sustainability funds were established to manage the values involved (see e.g., Arza 2009). |
8 | We thank an anonymous referee for having suggested this line of reasoning. |
America and Europe | Argentina | Croatia | Estonia | Romania | Slovenia |
Calm Period | t-Student | t-Student | t-Student | t-Student | t-Student |
AIC | −873.574 | −909.431 | −834.21 | −942.442 | −738.872 |
Crisis Period | logistic | t-Student | t-Student | logistic | logistic |
AIC | −881.197 | −880.042 | −852.455 | −912.316 | −900.513 |
Africa | Kenya | Mauritius | Morocco | Nigeria | Tunisia |
Calm Period | Logistic | t-Student | t-Student | Logistic | logistic |
AIC | −910.287 | −882.319 | −939.81 | −863.441 | −882.354 |
Crisis Period | Logistic | Logistic | Logistic | Logistic | t-Student |
AIC | −906.751 | −892.378 | −888.617 | −888.698 | −868.285 |
Middle East | Bahrain | Jordan | Kuwait | Lebanon | Oman |
Calm Period | t-Student | t-Student | Logistic | t-Student | t-Student |
AIC | −910.429 | −915.494 | −933.674 | −773.791 | −874.433 |
Crisis Period | t-Student | Logistic | t-Student | t-Student | Logistic |
AIC | −874.598 | −888.34 | −852.198 | −706.412 | −876.142 |
Asia | Pakistan | Sri Lanka | Vietnam | - | - |
Calm Period | Logistic | t-Student | logistic | - | - |
AIC | −971.585 | −922.345 | −908.606 | − | − |
Crisis Period | Logistic | t-Student | Gaussian | - | - |
AIC | −879.494 | −849.888 | −885.919 | − | − |
Index | USA/Argentina | USA/Croatia | USA/Estonia | USA/Romania | USA/Slovenia |
---|---|---|---|---|---|
Calm period | |||||
Selected Copula | Gumbel-Survival- Gumbel | Gaussian | Clayton | Gumbel | Frank |
AIC | −139.762 | 1.7522 | −6.3143 | 0.7817 | 0.0339 |
Dependence Parameters (θ1) | 1.1328 (9.1147) | −0.0193 (0.0559) | 0.1131 (0.0438) | 1.0141 (0.0153) | 0.3254 (0.2402) |
Dependence Parameters (θ2) | 1.5781 (0.2037) | - | - | - | - |
Weight Parameters (w1) | 0.3830 (0.1560) | - | - | - | - |
Weight Parameters (w2) | 0.617 (0.1571) | - | - | - | - |
Kendal’s τ | 0.2709 (0.0271) | −0.0123 (0.0356) | 0.0535 (0.0196) | 0.0139 (0.0146) | 0.0361 (0.0265) |
Spearman’s | 0.3874 (0.0371) | −0.0184 (0.0533) | 0.0804 (0.0293) | 0.021 (0.0219) | 0.0542 (0.0397) |
Tail λU | 0.2767 (0.0509) | - | - | 0.0192 (0.0200) | - |
Tail λL | 0.0598 (0.0502) | - | 0.0022 (0.0090) | - | - |
Crisis period | |||||
Selected Copula | Clayton-Gumbel-Frank | Gaussian | Clayton | Gaussian | Frank |
AIC | −246.486 | −29.7359 | −4.4351 | −30.4806 | −4.26 |
Dependence Parameters (θ1) | 0.4927 (5.9462) | 0.2244 (0.0361) | 0.1028 (0.0420) | 0.2258 (0.0384) | 0.6145 (0.2411) |
Dependence Parameters (θ2) | 1.6875 (1.0905) | - | - | - | - |
Dependence Parameters (θ3) | 12.1250 (17.193) | - | - | - | - |
Weight Parameters (w1) | 0.4379 (0.1493) | - | - | - | - |
Weight Parameters (w2) | 0.2865 (0.1703) | - | - | - | - |
Weight Parameters (w3) | 0.2756 (0.1892) | - | - | - | - |
Kendal’s τ | 0 | 0.1441 (0.023) | 0.0489 (0.0189) | 0.145 (0.0251) | 0.068 (0.0264) |
Spearman’s | 0.4003 (0.0295) | 0.2147 (0.0347) | 0.0734 (0.0283) | 0.216 (0.0369) | 0.1019 (0.0395) |
Tail λU | 0.141 (0.0955) | - | - | - | - |
Tail λL | 0.1072 (0.0640) | - | 0.0012 (0.0072) | - | - |
Index | USA/Kenya | USA/Mauritius | USA/Morocco | USA/Nigeria | USA/Tunisia |
---|---|---|---|---|---|
Calm period | |||||
Selected Copula | Gumbel | Frank | Frank | Gaussian | Gaussian |
AIC | −0.8706 | −4.6634 | 1.9684 | 1.9947 | 1.7778 |
Dependence Parameters (θ1) | 1.0365 (0.0222) | 0.6027 (0.2321) | −0.041 (0.2357) | −0.0028 (0.0364) | −0.0183 (0.0558) |
Kendal’s τ | 0.0352 (0.0205) | 0.0667 (0.0254) | −0.0046 (0.0261) | −0.0018 (0.0234) | −0.0116 (0.0355) |
Spearman’s | 0.0529 (0.0307) | 0.1 (0.0380) | −0.0068 (0.0392) | −0.0027 (0.0350) | −0.0174 (0.0533) |
Tail λU | 0.0482 (0.0278) | - | - | - | - |
Tail λL | - | - | - | - | - |
Crisis period | |||||
Selected Copula | Clayton | Gumbel | Gumbel | Gaussian | Gaussian |
AIC | 1.3172 | −1.3033 | 0.9576 | 0.8488 | 1.1614 |
Dependence Parameters (θ1) | −0.1158 (0.0278) | 1.028 (0.0193) | 1.014 (0.0161) | −0.0431 (0.0958) | 0.0369 (0.0422) |
Kendal’s τ | −0.0129 (0.0133) | 0.0272 (0.0180) | 0.0138 (0.0154) | −0.0274 (0.0611) | 0.0235 (0.0269) |
Spearman’s | −0.0193 (0.0200) | 0.041 (0.0270) | 0.0209 (0.0231) | −0.0411 (0.0915) | 0.0353 (0.0403) |
Tail λU | - | 0.0374 (0.0245) | 0.0191 (0.0210) | - | - |
Tail λL | 0 (0.000) | - | - | - | - |
Index | USA/Bahrain | USA/Jordan | USA/Kuwait | USA/Lebanon | USA/Oman |
---|---|---|---|---|---|
Clam period | |||||
Selected Copula | Gumbel | Frank | Frank | Gumbel | Gaussian |
AIC | −2.3309 | 1.9928 | 1.7384 | 0.9466 | −0.9323 |
Dependence Parameters (θ1) | 1.0424 (0.0234) | 0.0195 (0.2135) | 0.1233 (0.2507) | 1.0208 (0.0191) | 0.0664 (0.0356) |
Kendal τ | 0.0407 (0.0214) | 0.0022 (0.0237) | 0.0137 (0.0277) | 0.0204 (0.0180) | 0.0423 (0.0227) |
Spearman | 0.0611 (0.0320) | 0.0033 (0.0355) | 0.0205 (0.0416) | 0.0307 (0.0271) | 0.0634 (0.0340) |
Tail λU | 0.0556 (0.0288) | - | - | 0.0281 (0.0246) | - |
Tail λL | - | - | - | - | - |
Crisis period | |||||
Selected Copula | Frank | Clayton | Frank | Clayton | Clayton |
AIC | 1.4757 | 1.2378 | 1.7977 | −0.6647 | 0.9685 |
Dependence Parameters (θ1) | 0.1775 (0.2554) | 0.0316 (0.0327) | −0.1158 (0.3465) | 0.0695 (0.0417) | 0.0303 (0.0270) |
Kendal τ | 0.0197 (0.0282) | 0.0156 (0.0155) | −0.0129 (0.0384) | 0.0336 (0.0193) | 0.0149 (0.0129) |
Spearman | 0.0296 (0.0424) | 0.0234 (0.0234) | −0.0193 (0.0575) | 0.0505 (0.0289) | 0.0224 (0.0194) |
Tail λU | - | - | - | - | - |
Tail λL | - | 0.000 (0.001) | - | 0.000 (0.004) | 0.000 (0.000) |
Index | USA/Pakistan | USA/Sri Lanka | USA/Vietnam |
---|---|---|---|
Calm period | |||
Selected Copula | Gumbel | Frank | Clayton-Gumbel |
AIC | −0.8617 | 1.9415 | 1.8099 |
Dependence Parameters (θ1) | 1.0387 (0.0237) | −0.0563 (0.2582) | 0.0000 (0.1679) |
Dependence Parameters (θ2) | - | - | 19.5076 (15.524) |
Weight Parameters (w1) | - | - | 0.9789 (0.1108) |
Weight Parameters (w2) | - | - | 0.0211 (0.1108) |
Kendal τ | 0.0373 (0.0218) | −0.0063 (0.0286) | 0.02 (0.0162) |
Spearman | 0.056 (0.0326) | −0.0094 (0.0429) | 0.021 (0.0215) |
Tail λU | 0.051 (0.0294) | - | 0.0204 (0.0138) |
Tail λL | - | 0 (0.0048) | |
Crisis period | |||
Selected Copula | Gaussian | Gumbel | Frank |
AIC | 1.1473 | 1.2567 | 1.6064 |
Dependence Parameters (θ1) | 0.0363 (0.0343) | 1.0153 (0.0162) | −0.1499 (0.2747) |
Kendal τ | 0.0231 (0.0219) | 0.015 (0.0154) | −0.0167 (0.0303) |
Spearman | 0.0347 (0.0328) | 0.0227 (0.0232) | −0.025 (0.0455) |
Tail λU | - | 0.0207 (0.0211) | - |
Country | ∆τ | p Value | p Value | Country | ∆τ | p Value | p Value | ||
---|---|---|---|---|---|---|---|---|---|
USA and Europe | Africa | ||||||||
Argentina | 0 | − | 0.0067 | 0.447 | Kenya | −0.0197 | 0.775 | −0.0296 | 0.775 |
Croatia | 0.1555 | 0.000 *** | 0.1924 | 0.000 *** | Mauritius | −0.03819 | 0.889 | −0.0570 | 0.888 |
Estonia | −0.0047 | 0.572 | −0.0071 | 0.572 | Morocco | 0.0189 | 0.279 | 0.0284 | 0.279 |
Romania | 0.1313 | 0.000 *** | 0.1953 | 0.000 *** | Nigeria | −0.0261 | 0.784 | −0.0392 | 0.145 |
Slovenia | 0.0305 | 0.21 | 0.0457 | 0.21 | Tunisia | 0.0352 | 0.165 | 0.0528 | 0.165 |
Middle East | Asia | ||||||||
Bahrain | −0.0208 | 0.722 | −0.0313 | 0.722 | Pakistan | −0.0161 | 0.703 | −0.0241 | 0.703 |
Jordan | 0.0138 | 0.329 | 0.0208 | 0.329 | Sri Lanka | 0.0241 | 0.21 | 0.0164 | 0.209 |
Kuwait | −0.000 | 0.755 | −0.000 | 0.755 | Vietnam | 0.000 | 0.515 | 0.008 | 0.441 |
Lebanon | 0.0133 | 0.299 | 0.0200 | 0.299 | - | - | - | - | - |
Oman | −0.0257 | 0.837 | −0.0385 | 0.652 | - | - | - | - | - |
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Mohti, W.; Dionísio, A.; Ferreira, P.; Vieira, I. Contagion of the Subprime Financial Crisis on Frontier Stock Markets: A Copula Analysis. Economies 2019, 7, 15. https://doi.org/10.3390/economies7010015
Mohti W, Dionísio A, Ferreira P, Vieira I. Contagion of the Subprime Financial Crisis on Frontier Stock Markets: A Copula Analysis. Economies. 2019; 7(1):15. https://doi.org/10.3390/economies7010015
Chicago/Turabian StyleMohti, Wahbeeah, Andreia Dionísio, Paulo Ferreira, and Isabel Vieira. 2019. "Contagion of the Subprime Financial Crisis on Frontier Stock Markets: A Copula Analysis" Economies 7, no. 1: 15. https://doi.org/10.3390/economies7010015
APA StyleMohti, W., Dionísio, A., Ferreira, P., & Vieira, I. (2019). Contagion of the Subprime Financial Crisis on Frontier Stock Markets: A Copula Analysis. Economies, 7(1), 15. https://doi.org/10.3390/economies7010015