Dynamic dependence and extreme risk comovement: The case of oil prices and exchange rates

Abstract

This article aims at investigating the dynamic dependence and extreme risk comovement of oil price and exchange rates in seven oil‐importing and seven oil‐exporting countries. For this purpose, we use six representative time‐varying copula models and four kinds of tail dependences to assess the downside and upside conditional value‐at‐risk measures (CoVaRs). Our findings indicate that the dependence of crude oil returns and exchange rates is negative for most pairs, that is, the rise (fall) in oil prices was accompanied by the appreciation (depreciation) of foreign currency against the US dollar. The oil price–exchange rate dependences in oil exporters is slightly larger than in oil importers, even though the dependence is weak in general. More interestingly, we find strong evidence of significant risk comovement between crude oil returns and exchange rates through the analysis of lower–upper and upper–lower tail dependence as well as downside and upside CoVaRs. This comovement particularly showed asymmetric effects. Finally, we sum up some implications for investors, policymakers and exchange rate regulators from a holistic perspective of empirical findings.

Conditional Dynamic Dependence and Risk Spillover between Crude Oil Prices and Foreign Exchange Rates: New Evidence from a Dynamic Factor Copula Model

“…Furthermore, the corresponding Kendall dependence is weak for all oil-exchange-rate pairs; nevertheless, the oil exporters' exchange rates have relatively stronger links with crude oil prices than the oil importers. This finding is almost consistent with the overall sample results of Liu et al [43]. The higher oil prices can transfer the wealth from oil importers to oil exporters, leading to the devaluation of the currencies of oil importers; however, the currencies of oil exporters will be appreciated instead.…”

“…We find that the oil exporters' conditional dependence characteristics for the oilexchange-rate pairs are almost the same as in the Copula-GARCH model shown in Liu et al [43]; nevertheless, we have identified a much larger dependence magnitude between the financial markets in consideration of respective common factors. The factor copula model can capture the relatively greater variation in the market dependence after the 2008 global financial crisis and in the COVID-19 era.…”

“…Only when there is the extreme fluctuation during the economic crashes can the idiosyncratic factors make a substantial contribution to the calculation of the %∆CoVaR. However, in contrast to the results based on the Copula-GARCH model [43], there is almost no conditional risk spillover from the exchange rate market to the crude oil market in the long run.…”

“…China has become the world's top crude oil importer with continuously increasing oil demand [39]. Compared with the results based on the Copula-GARCH model in Liu et al [43], the conditional dependence magnitude of the CNY-OIL pair becomes larger, especially after year 2015 when China overtook the United States and became the largest oil importer. We infer that with the rapid development of the economy, China's demand for crude oil has a progressively significant impact on the volatility of the global oil market price.…”

The oil price plummeted in 2014–2015: Is there an effect on Chinese firms' labour investment?

“…In an early study, Hamilton (1983) shows that rising oil price is a driving force of the U.S. economic recession. Since then, numerous researchers have discovered that oil price can significantly impact macroeconomic activity and the financial market, including economic growth (Känzig, 2021; Kilian, 2009), inflation (Gong et al, 2020; Wen et al, 2019), exchange rate (Liu et al, 2021; Živkov et al, 2019), and stock return (Bouri et al, 2017; Kwon, 2022; Wang & Wang, 2019). Besides, it is generally known that many firms utilize oil as a significant production factor.…”