Abstract
In this paper, we discuss impact of the Covid-19 pandemic on the North American financial markets and propose a framework for stress testing and financial scenario generation of market indicators. This framework includes the following main components:
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Epidemiological dynamic model describing evolution of the number of Susceptible, Infected, Recovered and Death cases with social distancing,
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Dynamical model describing dependence between financial indicators and growth of the pandemic in different geographical areas,
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Conditional stress scenario generation and financial portfolio analysis.
We apply an extended epidemiological model to analysis of Covid-19 pandemic spread and analyze its impact on some of the main financial indicators, including stock indices, credit spreads and FX rates, and characteristics of the pandemic process in different geographical areas. This analysis results in a model connecting the dynamics of the pandemic and that of the main financial indicators. The model allows one to generate pandemic scenarios under different assumptions on the parameters of the infectious disease and that of the social distancing policies. Once the pandemic scenarios are generated, one can transform them into a set of scenarios on macro-economic risk factors. Then, applying the conditional scenario technique we obtain a set of Monte Carlo scenarios on the risk factors driving the portfolio dynamics. The proposed dynamic models allow one to generate various financial stress scenarios on market indicators and compute the distribution of financial portfolio losses and their risk measures.
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Notes
- 1.
Similar shape is observed in the case of the European indices MIB and FTSE100.
- 2.
28 days since the beginning of the pandemic.
- 3.
In order to have a well defined time-dependent process we restrict time-dependent parameters \((\beta (t),\gamma (t),\eta (t)), \, t\in [0,T],\) be continuous functions except for a finite number of discontinuities (e.g., stepwise function).
- 4.
We use the repository available in https://github.com/pomber/covid19, which in turn cleans and processes the data from Johns Hopkins CSSE (see also, https://github.com/CSSEGISandData/COVID-19).
- 5.
Weak convergence of stochastic processes discussed in this section represents an independent interest. The rigorous presentation with the proof of (20) is deferred to a more specialized journal on stochastic processes.
- 6.
We hope that in the nearest future we will have more reliable data that would allow us to infer the statistical properties of the sickness time distribution.
- 7.
The measurement date is 29/03/2020.
- 8.
Recall that since we are not including the bias corrector \(\beta _0\), then there may be a small, but insignificant, mean in the distribution of \(\epsilon \).
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Acknowledgements
We are very grateful to Rustom Barua, Yijun Jiang, Alan Yang, Ken Wang, Rob Seidman, Andrew Marchenko, Yuliy Koshevnik, Ken Jackson and Vladimir Vinogradov for the interesting discussions and suggestions.
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Burmeister, C., Kreinin, A., Mendoza-Arriaga, R., Rasouli, H., Romanko, O. (2021). Analysis of Impact of Covid-19 Pandemic on Financial Markets. In: Agarwal, P., Nieto, J.J., Ruzhansky, M., Torres, D.F.M. (eds) Analysis of Infectious Disease Problems (Covid-19) and Their Global Impact. Infosys Science Foundation Series(). Springer, Singapore. https://doi.org/10.1007/978-981-16-2450-6_15
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