Detecting exchange rate contagion using copula functions

Abstract

We study exchange rate dependencies between seven countries from four different regions of the world. Our sample includes two developed countries, the United Kingdom and Germany (representing the Euro Area), two large emerging Asian economies, South Korea and Indonesia, two Latin American countries, Brazil and Chile, and South Africa. The currencies of all of these countries are actively traded in global forex markets and all of them are important for large international portfolio composition and rebalancing. We construct multivariate copula functions using a regular vine copula approach, allowing for very flexible dependency structures. We find evidence of exchange rate contagion for our set of countries. However, important asymmetries are worth noting. First, contagion occurs only during periods of exchange rate appreciation of the different currencies with respect to the United States Dollar. We do not find evidence of contagion for any pair of exchange rates during periods of currency depreciations. Second, contagion is more frequent in pairs of countries that include either the United Kingdom or Germany. In fact, the largest tail dependence coefficient corresponds to the pair composed by these two countries' exchange rates. Third, contagion occurs more within countries of a same region, for instance, between Brazil and Chile, and between Korea and Indonesia. This result shows that, in episodes of large currency appreciation, hedging strategies for global investors taking positions in large markets require regional diversification.

Financial contagion and contagion channels in the forex market: A new approach via the dynamic mixture copula-extreme value theory

“…It allows one to describe any multivariate distribution with its marginal distributions and a copula that describes the dependence structure between the random variables. It is a flexible and effective tool to describe various patterns of dependence structures and has been widely used to measure financial contagion (e.g., Jayech, 2016 ; Cubillos-Rocha et al, 2019 ; Fenech and Vosgha, 2019 ). According to Sklar’s (1959) theorem, let Z 1 and Z 2 denote two random variables with bivariate joint distribution function and two continuous marginal distribution functions F 1 and F 2 , then there is a unique copula C : [0, 1] 2 → [0, 1] such that …”

“…However, the lower and upper tail dependence often coexist between two financial markets, and asymmetrical behavior is usually observed. To accommodate this, four static mixture copulas, Clayton–Gumbel (CG), Clayton–survival Clayton (CSC), Gumbel–survival Gumbel (GSG), and Symmetric–Joe Clayton (SJC), have been constructed to measure tail dependence (e.g., Jayech, 2016 ; Wang et al, 2018 ; Cubillos-Rocha et al, 2019 ). They can capture both the upper- and lower-tail dependence and allow them to be asymmetric.…”

“…To our knowledge, the literature on financial contagion in the forex market is relatively scarce. Celık (2012) , Yang et al (2016) , Kilic (2017) and Cubillos-Rocha et al (2019) use different approaches to confirm the existence of financial contagion in the forex market. By developing an econometric methodology, Gravelle et al (2006) find that fundamental-induced contagion in the forex market exists, especially in European countries.…”

“…With a copula model, any multivariate distribution can be estimated by separately estimating the marginal distributions and the copula function. Copula models have also been used to study financial contagion (e.g., Hoesli and Reka, 2015 ; Jayech, 2016 ; Zhang and Liu, 2018 ; Cubillos-Rocha et al, 2019 ; Niţoi and Pochea, 2020 ). However, most copula models neglect some aspects of the complex dependence between markets, such as nonlinearity, asymmetry, time-varying patterns, and upper- and lower-tail dependence.…”

COVID-19 and currency dependences: Empirical evidence from BRICS

“…Currencies quoted in the USD tend to co-move with each other because of the USD flows ( Cubillos-Rocha et al., 2019 ). Meanwhile, about two months after the onset of the pandemic in China, Saudi authorities offered discounts in oil prices which caused the largest fall of 20% to 30% in crude oil price since the Gulf war.…”