Copula-Based Assessment of Co-Movement and Tail Dependence Structure Among Major Trading Foreign Currencies in Ghana

Abstract

This paper examines the joint movement and tail dependence structure between the pair of foreign exchange rates (EUR, USD and GBP) against the GHS, using daily exchange rates data expressed in GHS per unit of foreign currencies (EUR, USD and GBP) between the time range of 24 February 2009 and 19 December 2019. We use different sets of both static (time-invariant) and time-varying copulas with different levels of dependence and tail dependence measures, and the study results reveal positive dependence between all exchange rates pairs, though the dependencies for EUR-USD and GBP-USD pairs are not as strong as the EUR-GBP pair. The findings also reveal symmetric tail dependence, and dependence evolves over time. Notwithstanding this, the asymmetric tail dependence copulas provide evidence of upper tail dependence. We compare the copula results to DCC(1,1)-GARCH(1,1) model result and find the copula to be more sensitive to extreme co-movement between the currency pairs. The afore-mentioned findings, therefore, offer forex market players the opportunity to relax in hoarding a particular foreign currency in anticipation of domestic currency depreciation.

Copula Modelling to Analyse Financial Data

“…The dependence of financial markets during a period of extreme fluctuations has received considerable attention within the literature, see Mensah and Adam (2020). The copula co-movements capture how shocks in a specific market may transcend to other currency markets.…”

“…The copula co-movements capture how shocks in a specific market may transcend to other currency markets. Therefore, measuring the co-movements and tail dependence structures and determining the volatility spill-over and evolution over time is essential in risk management, diversification and pricing, see Mensah and Adam (2020).…”

“…Appropriate models for serial correlation dependence are necessary Emura et al (2017). Financial institutions and investors are concerned about volatility and global currency markets, which can dominate the stock and bond markets, see Mensah and Adam (2020). Copula models that contain time-series have been employed extensively to study the co-movement and tail dependence structure of financial data.…”

“…Financial copula modelling is about identifying the time-series within the data, and there are standard families of multivariate models for financial time series. These models are the GARCH-type and/or stochastic volatility type models Fermanian (2017), and there is a dominant use of GARCH models Mensah and Adam (2020). Econometric literature contains a preponderance of evidence that the conditional volatility of economic time-series changes through time, see Patton (2012).…”

“…Literature for copula time-series modelling using ARIMA-type models can be sparse, whereas, the literature for copula time-series modelling using ARMA-GARCH models is far more abundant Mensah and Adam (2020), as the ARMA or ARMA-GARCH process models the volatility within the dataset, making them suitable for financial data. Testing whether an ARMA or ARMA-GARCH model is appropriate should be undertaken.…”