Modelling foreign exchange rate co-movement and its spatial dependence in emerging markets: a spatial econometrics approach

Empirical Economics https://doi.org/10.1007/s00181-023-02482-y Modelling foreign exchange rate co-movement and its spatial dependence in emerging markets: a spatial econometrics approach Charles Raoul Tchuinkam Djemo1 · Joel Hinaunye Eita1 Received: 26 May 2022 / Accepted: 31 July 2023 © The Author(s) 2023 Abstract This paper studies the impact of macroeconomic factors on co-movement in the foreign exchange rate markets. Data of foreign exchange rates from 24 merging markets is used to this end along with a dynamic spatial Durbin model as to examine spatial dependencies among markets. Our empirical findings show no evidence that cultural ties exert a role in spreading macroeconomic shocks in the exchange rate of a country to the exchange rates of other countries. Moreover, we show that economic closeness through foreign direct investment (FDI) and international bilateral trade is the most prominent channel in spreading macroeconomic shocks and spatial effects in emerging markets through the foreign exchange rates. In addition, geographical proximity reinforces the interdependence relationship of emerging markets. Our findings show that the co-movement of foreign exchange rate markets across the selected emerging markets is positively influenced by their gross domestic product (GDP) and interest rate differential and negatively affected by the terms of trade and remittance. In addition, we reveal that terms of trade, the inflation differential, and remittance are the most prominent fundamental factors affecting foreign exchange rate movements. Keywords Spatial dependence · Emerging markets · Spatial panel · Foreign exchange rates · Co-movement · Macroeconomic fundamentals JEL Classification C23 · C33 · F31 · E44 B Joel Hinaunye Eita Charles Raoul Tchuinkam Djemo ; 1 School of Economics, University of Johannesburg, Johannesburg, South Africa 123 C. R. T. Djemo, J. H. Eita 1 Introduction Examining the co-movement between exchange rates is of great importance for both investors and the central banks as it reveals discernible patterns of how local currency jointly appreciates or depreciates against a foreign currency. The central bank also benefits from knowing how the exchange rates co-move when drafting policy interventions. Moreover, large swings in the exchange rate affect the real economy, financial markets, international competitiveness, real income, and inflation, as exchange rate movement affects the price of imported goods and the competitiveness of export firms. Therefore, understanding the behaviour pattern of foreign exchange rate and how local currency jointly moves against foreign currency becomes crucial given its countless implications for economic policies and portfolio allocation since a negative shock from one market can be quickly transmitted to other markets in the network through contagion effect. The issue of co-movement had received considerable thought in the impressive literature devoted to examining the behaviour of financial markets. An appropriate analysis of the co-movement of foreign exchange rates reduces the hedging costs of exposure to it. Although there is limited research on the co-movement analysis of foreign exchange rates focusing on emerging markets, assessing the co-movement pattern of foreign exchange rates in emerging markets has become an exciting topic as emerging markets are the largest market worldwide and characterised by high volatility and uncertainty. According to International Monetary Fund (IMF) data, Emerging markets (EMs) represent one of the largest economies around the world. Its total population represents 57% of the world population, with the GPD estimated at 25% of the global GDP, making these markets more attractive for investors seeking a riskreturns opportunity. Studies addressing the co-movement between foreign exchange rates and emphasising cross-markets or cross-regional analysis have more attraction and interest in the emerging market context. Emerging markets represent more than 48% of the daily turnover of global financial markets, with China the largest EMs. It is, therefore, essential to understanding not only how a shock to one country is transmitted to other countries through cross-country linkages but also to estimate the degree of spatial autocorrelation between foreign exchange rates and macroeconomic factors that may influence the co-movement in emerging markets. Assessing the co-movement among currencies helps explore cross-border linkages, which transmit market-specific shocks to other markets while constructing international portfolio diversification and explaining how one market responds to currency fluctuations in other markets. Mensah and Adam (2020) argued that currency markets are not only influenced by idiosyncratic factors such as macroeconomic factors and monetary policy direction but also by other external drivers. These external drivers may be geographically related phenomena (Asgharian et al. 2013; Amidi and Majidi 2020; Shikimi and Yamada 2019), crossmarket spatial effects, cross-market trade linkages (Frankel and Rose 1998; Jiang et al. 2022), cultural ties (Falck et al. 2012) and financial integration linkages. It is, therefore, imperative to assess the co-movement between foreign exchange rates through these external channels. 123 Modelling foreign exchange rate co-movement and its spatial … The dependence structure of foreign exchange rates, also known as the bilateral linkages between currency pairs, can be defined as the co-movement between the appreciation and depreciation of currency pairs. In contrast, spatial dependence refers to the degree of spatial autocorrelation between independently measured values observed in geographical spaces (Crawford 2009). Legendre and Legendre (1998) argue that spatial dependence is a property of a spatial stochastic process in which the outcomes at a different location may be different. Further to the spatial dependence definition that clearly emphasises the existence of spatial autocorrelation, Overmars et al. (2003) define then the spatial correlation as the property of random variables to take values over a distance that are more similar or less similar than expected for randomly associated pairs of observations, due to geographic proximity. In recent years, scholars and practitioners have begun to understand financial markets’ dynamic relationship and co-movement. The extensive literature dealing with the issues of co-movement and dependencies of financial markets has been more directed into stock markets and commodities markets, while little attention has been placed on foreign exchange rate markets and, more precisely, in the emerging markets context. A considerable number of studies on the co-movement and dependencies have been devoted to examining the co-movement between foreign exchange rate and stock market or commodities markets (Carvalho and Gupta 2018; Embrechts et al.2002; Ghosh et al. 2021; Yeap et al. 2020) w (...truncated)