Capital flows and exchange rate volatility: experience of emerging economies

The Dynamics of Foreign Portfolio Investment and Exchange Rates: An Interconnection Approach in ASEAN

“…The data used is quarterly from 2002Q1-2018Q4. The dependent variable is FPI inflows for ASEAN represented by the data on net foreign portfolio purchases in terms of liabilities divided by the nominal gross domestic product (refer to Baek (2006), Rafi & Ramachandran (2018), Rai & Bhanumurthy (2004), Singhania & Saini (2018). I use FPI in terms of liabilities to determine the ownership of foreign assets that enter into or exist in a country.…”

“…Ouedraogo (2017) uses a one-year change from the Spot exchange rate (percent) to capture exchange rate change. Also, I use exchange rate volatility to capture the risk of the exchange rate (as used in Rafi & Ramachandran (2018), Baek (2006), Ndou et al (2017), Diebold (1988), Yu et al (2007)). Following Ndou et al (2017), this study examines the exchange rate risk implications from two main assumptions, specifically looking at the impact of expected and unexpected exchange rate risk on foreign portfolio investment in the ASEAN Region.…”

“…The neoclassical theory advocates that capital flows respond to differences in interest rates between countries. Following Rafi & Ramachandran (2018), Singhania & Saini (2018, Bhasin & Khandelwal (2019), Garg & Dua (2014), for instance, I use the difference between the interest rate on the 10-year country-i government bond and the interest rate on the 10-year US government bond to represent the interest rate differential.…”

“…To the best of our knowledge, existing literature has not adequately addressed the spatial inter-relation of FPI between regions and between one country and another. First, existing literature uses conventional methodologies, which hold geographical interdependence factors as exogenous when the FPI behavior is investigated empirically (Ahmed and Zlate, 2014;Garg and Dua, 2014;Haider et al, 2016;Rafi and Ramachandran, 2018;Singhania and Saini, 2018;and Srinivasan and Kalaivani, 2015). As those methods cannot capture the effects of third countries in examining the portfolio flow determinants (particularly in exchange rate dynamics), a spatial panel data model can overcome this problem.…”

Comparison of the GARCH and stochastic models: An application to the Mexican peso-us dollar exchange rate

“…To compare the differences, the literature has mainly focused on their forecasting performance (Rossi, 2013;Clark and Ravazzolo, 2015;Chan and Grant, 2016). Knowing the best model for a financial series is a fundamental issue for making decisions, especially in cases of emerging and free-floating economies where volatility tends to be recurring (Neumeyer and Perri, 2005;Rafi and Ramachandran, 2018). In this study, a comparison of some GARCH and SV models was made.…”