Modelling short-run and long-run predictors of foreign portfolio investment volatility in low-income Southern African Development Community countries

Abstract

Orientation: This study examined the main predictors of net foreign portfolio investment volatility in low-income Southern African Development Community (SADC) countries. Based on the World Bank data (July 2014), the selected countries are Zimbabwe, Zambia, Malawi, Lesotho, Madagascar, Mozambique, DRC, Swaziland and Tanzania.Research purpose: The purpose of this study is to establish the main drivers of net foreign portfolio investment volatility in low-income SADC countries.Motivation for the study: This study is also motivated by mixed findings in foreign portfolio investment debate on why capital flows are more volatile and difficult to manage in developing than in advanced economies. Although it is acknowledge that developing markets are characterised by the poor quality of financial institutions in economies with weak macroeconomic fundamentals, which ultimately pose a greater risk of sudden stops or reversal of foreign portfolio flows, findings remain inconclusive on what actually drives net foreign portfolio investment volatility in low-income countries.Research approach/design and method: The Panel Autoregressive Distributed Lag (P-ARDL) model is employed to determine the short- and long-run drivers of such investment volatility in these countries. The study uses quarterly data for the period spanning 2000 to 2015.Main findings: The findings reveal that all the variables in the model, namely money supply, world output, general prices, real gross domestic product, domestic interest rates and international interest rates, are significant predictors of net foreign portfolio investment volatility. However, positive long-run effects are observed from world gross domestic product, real gross domestic product, prices and money supply, whilst domestic interest rates and international interest rates displayed a negative association in the long run. These findings are consistent with both the economic literature and the empirical literature, which suggest that an increase in interest rates or higher interest rates affects foreign portfolio investment. Similarly, in the short run, all the variables employed in the model are the main predictors of net foreign portfolio investment volatility in low-income SADC countries.Practical/managerial implications: Policy-makers should embark on policies and programs that promote economic performance in order to attract stable foreign portfolio flows that will lead to stable markets and reduce volatility in the economy. Policy consistency is thus, recommended to attract investors to the region and ensure that stock and bond markets are viable and stable.Contribution/value-add: Unlike other existing studies, the measure of volatility employed in this is considered superior as it is based on net portfolio flows which reflect changes in an economy’s overall current account position. The study informs and advances the current discourse on the causes of capital flow volatility in the field of investment theory and practice.

What are the drivers of female labour market participation in North Africa?

“…The research adopted the Panel Auto Regressive Distributed Lag (PARDL) model, developed by Pesaran and Shin (1999) because it helped to determine the factors affecting FLP in North Africa. The PARDL is applicable when the variables are integrated in the order of 1 and 0 (Giles 2013;Mamvura & Sibanda 2020). The model was selected because it enables the estimation of short-run and long-run parameters and is regarded as a useful model in panel analysis (Mamvura & Sibanda 2020;Shin, Yu & Greenwood 2014).…”

“…The PARDL is applicable when the variables are integrated in the order of 1 and 0 (Giles 2013;Mamvura & Sibanda 2020). The model was selected because it enables the estimation of short-run and long-run parameters and is regarded as a useful model in panel analysis (Mamvura & Sibanda 2020;Shin, Yu & Greenwood 2014). Panel Auto Regressive Distributed Lag was used because it helps reduce the chances of spurious regression (Gholami, Sang-Vong Tom & Heshmati 2005).…”