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Linking Covid-19 Epidemic and Emerging Market OAS: Evidence Using Dynamic Copulas and Pareto Distributions

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Behavioral Finance and Asset Prices

Part of the book series: Contributions to Finance and Accounting ((CFA))

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Abstract

This chapter investigates the dependence of the option-adjusted spread (OAS) for several ICE BofA Emerging Markets Corporate Plus Indexes on the Covid-19 pandemic outbreaks between March 1, 2020, and April 30, 2021. We investigate whether the number of new cases, reproduction rate, death rate, and stringency policies have resulted in an increase/decrease in the spreads. We study the bivariate distributions of epidemiological indicators and spreads to investigate their concordance using dynamic copula analysis and estimate the Kendall rank correlation coefficient. We also investigate the effects of the epidemiological variables on the extreme values of the spreads by fitting a tail index derived from a Pareto type I distribution. We highlight the existence of correlations, robust to the type of copulas used (Clayton or Gumbel). Moreover, we show that the epidemiological variables explain well the extreme values of the spreads.

This work was supported by French National Research Agency Grant ANR-17-EURE-0020 and by a Grant from the Institut Louis Bachelier (Institut Europlace de Finance).

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Notes

  1. 1.

    See Kate Duguid (https://www.reuters.com/article/us-usa-credit-idUKKBN27P26W).

  2. 2.

    See the article by Tom Arnold (https://www.reuters.com/article/us-markets-emerging-corpbonds-graphic-idUSKBN2830JX).

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Author information

Authors and Affiliations

  1. Faculty of Economics and Management, Aix-Marseille University, Marseille, France

    Imdade Chitou & Julien Esposito

  2. Institut Louis Bachelier, Aix-Marseille University, CNRS, AMSE, Marseille, France

    Gilles Dufrénot

Authors
  1. Imdade Chitou
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  2. Gilles Dufrénot
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  3. Julien Esposito
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Corresponding author

Correspondence to Gilles Dufrénot .

Editor information

Editors and Affiliations

  1. IAE Lille University School of Management, University of Lille, Lille, France

    David Bourghelle

  2. IAE Lille University School of Management, University of Lille, Lille, France

    Pascal Grandin

  3. IAE Lille University School of Management, University of Lille, Lille, France

    Fredj Jawadi

  4. IAE Lille University School of Management, University of Lille, Lille, France

    Philippe Rozin

Appendices

Appendix 1: Fitting Weibull, Log-Normal, and Gamma Distributions to OAS

Fig. 9
Four scatterplots. 1. A graph of density versus data plots a histogram and Weibull, log-normal, and gamma distribution curves with decreasing trends. 2. A graph of C D F versus data plots Weibull, log-normal, and gamma distribution curves with increasing trends. 3. A graph between empirical versus theoretical quantities plots Weibull, log-normal, and gamma distribution curves with increasing trends. 4. A graph between empirical versus theoretical probabilities plots Weibull, log-normal, and gamma distribution curves with increasing trends.

Asia

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Fig. 10
Four scatterplots. 1. A graph of density versus data plots a histogram and Weibull, log-normal, and gamma distribution curves with decreasing trends. 2. A graph of C D F versus data plots Weibull, log-normal, and gamma distribution curves with increasing trends. 3. A graph between empirical versus theoretical quantities plots Weibull, log-normal, and gamma distribution curves with increasing trends. 4. A graph between empirical versus theoretical probabilities plots Weibull, log-normal, and gamma distribution curves with increasing trends.

Latin America

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Appendix 2: Graphs of Time-Varying Copulas

Fig. 11
Four scatterplots of theta versus observations for new confirmed cases, and confirmed deaths. 1. A horizontal line is plotted at 6 on the y-axis. 2. A horizontal line is plotted at zero on the y-axis. 3 and 4 are scatter plots of reproduction rate, and government stringency.

Time-varying θ with Clayton copula: Latin America. X-axis: observations, y-axis: θ

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Fig. 12
Four scatterplots of theta versus observations for new confirmed cases, confirmed deaths, reproduction rate, and government stringency in Latin America. 1. A horizontal line is plotted at 4 on the y-axis. 2. A horizontal line is plotted at 1 on the y-axis. 3, and 4 have fluctuating trends.

Time-varying θ with Gumbel copula: Latin America. X-axis: observations, y-axis: θ

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Fig. 13
Four scatterplots of theta versus observations for new confirmed cases, confirmed deaths, reproduction rate, and government stringency in Africa and Mena. 1. The graph has increasing trend. 2. A horizontal line is plotted at 0 on the y-axis. 3. The graph has fluctuating trend. 4. The trend fluctuate about 4 on y-axis.

Time-varying θ with Clayton copula: Africa and MENA. X-axis: observations, y-axis: θ

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Fig. 14
Four scatterplots of theta versus observations for new confirmed cases, confirmed deaths, reproduction rate, and government stringency in Africa and Mena. 1. A horizontal line is plotted at 4 on y-axis. 2, and 3 plot horizontal lines at 1 on the y-axis. 4. The trend fluctuate about 2.92 on y-axis.

Time-varying θ with Gumbel copula: Africa and MENA. X-axis: observations, y-axis: θ

Full size image
Fig. 15
Four scatterplots of theta versus observations for new confirmed cases, confirmed deaths, reproduction rate, and government stringency in Asia. 1. It has a rising trend. 2, and 3 plot horizontal lines at 0 on the y-axis. 4. It has a fluctuating trend.

Time-varying θ with Clayton copula: Asia. X-axis: observations, y-axis: θ

Full size image
Fig. 16
Four scatterplots of theta versus observations for new confirmed cases, confirmed deaths, reproduction rate, and government stringency in Asia. 1. It has a fluctuating trend. 2, and 3 plot horizontal lines at 1 on the y-axis. 4. It has a fluctuating trend.

Time-varying θ with Gumbel copula: Asia. X-axis: observations, y-axis: θ

Full size image

Appendix 3: Evidence of Nonlinear Relationships

Fig. 17
Four scatterplots of spreads in Latin Africa and Mena versus epidemic indicators, f 1, f 2, f 3, and f 4, respectively plot curves. 1. An inverted U-shaped curve. 2. Curve with a decreasing trend. 3. A rising curve. 4 plot curve with a fluctuating trend.

Lowess smoother spreads versus epidemic indicators (Africa and MENA)

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Fig. 18
Four scatterplots of spreads in Latin America versus epidemic indicators, f 1, f 2, f 3, and f 4, respectively plot curves. 1. An inverted U-shaped curve. 2. Curve with a decreasing trend. 3, and 4 plot curves with fluctuating trends.

Lowess smoother spreads versus epidemic indicators (Latin America)

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Fig. 19
Four scatterplots of spreads in Asia and Mena versus epidemic indicators, f 1, f 2, f 3, and f 4, respectively plot curves.

Lowess smoother spreads versus epidemic indicators (Asia)

Full size image

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Chitou, I., Dufrénot, G., Esposito, J. (2023). Linking Covid-19 Epidemic and Emerging Market OAS: Evidence Using Dynamic Copulas and Pareto Distributions. In: Bourghelle, D., Grandin, P., Jawadi, F., Rozin, P. (eds) Behavioral Finance and Asset Prices. Contributions to Finance and Accounting. Springer, Cham. https://doi.org/10.1007/978-3-031-24486-5_3

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