Abstract
This paper analyzes the volatility dynamics in the financial markets of the (three) most powerful countries from a military perspective, namely, the U.S., Russia, and China, during the period 2015–2018 that corresponds to their intervention in the Syrian war. As far as we know, there is no literature studying this topic during such an important distress period, which has had very serious economic, social, and humanitarian consequences. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH (1, 1)) model yielded the best volatility results for the in-sample period. The weighted historical simulation produced an accurate value at risk (VaR) for a period of one month at the three considered confidence levels. For the out-of-sample period, the Monte Carlo simulation method, based on student t-copula and peaks-over-threshold (POT) extreme value theory (EVT) under the Gaussian kernel and the generalized Pareto (GP) distribution, overstated the risk for the three countries. The comparison of the POT-EVT VaR of the three countries to a portfolio of stock indices pertaining to non-military countries, namely Finland, Sweden, and Ecuador, for the same out-of-sample period, revealed that the intervention in the Syrian war may be one of the pertinent reasons that significantly affected the volatility of the stock markets of the three most powerful military countries. This paper is of great interest for policy makers, central bank leaders, participants involved in these markets, and all practitioners given the economic and financial consequences derived from such dynamics.
Highlights
Political uncertainty occurs due to many factors like elections and changes in the government or parliament, changes in policies, strikes, minority disdain, foreign intervention in national affairs, and others
An EGARCH (p, q) is defined as: log σ2t j=1 β j log σ2t− j θi g et− j j=1 where g(et ) = δet + α(|et | − E|et |) are variables i.i.d. with zero mean and constant variance. It is through this function that depends on both the sign and magnitude of et, that the EGARCH model captures the asymmetric response of the volatility to innovations of different sign, allowing the modeling of a stylized fact of the financial series: negative returns provoke a greater increase in volatility than positive returns do
This paper revealed original common points among the most powerful military countries in the world regarding the behavior of their financial markets during the period 2015–2018, which corresponds to their intervention in the Syrian war
Summary
Political uncertainty occurs due to many factors like elections and changes in the government or parliament, changes in policies, strikes, minority disdain, foreign intervention in national affairs, and others. The importance of the U.S, China, and Russia among military countries is highly reinforced For this reason, we opted to study the dynamics of their financial markets to comprehend the risks and opportunities they might face, which would affect their worldwide exposure. Another comparative study, conducted by Hou and Li [13], investigated the transmission of information between the U.S and China’s index futures markets using an asymmetric dynamic conditional correlation GARCH (DCC GARCH) approach.
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