Can We Beat the Random-Walk Model for the South African Rand–U.S. Dollar and South African Rand–UK Pound Exchange Rates? Evidence from Dynamic Model Averaging

Abstract

Traditionally, the literature on forecasting exchange rates with many potential predictors have primarily only accounted for parameter uncertainty using Bayesian Model Averaging (BMA). Though BMA-based models of exchange rates tend to outperform the random walk model, we show that when accounting for model uncertainty over and above parameter uncertainty through the use of Dynamic model Averaging (DMA), the gains relative to the random walk model are even bigger. That is, DMA models outperform not only the random walk model, but also the BMA model of exchange rates. We obtain these results based on fifteen potential predictors used to forecast two South African Rand-based exchange rates. In the process, we also unveil variables, which tends to vary over time, that are good predictors of the Rand-Dollar and Rand-Pound exchange rates at different forecasting horizons. JEL Classification codes: C11, C53, F37, F47

The Impact of Oil Shocks in a Small Open Economy New-Keynesian Dynamic Stochastic General Equilibrium Model for an Oil-Importing Country: The Case of South Africa

“…Following the early works of Rasche and Tatom (1977), Mork and Hall (1980), Hamilton (1983), and Hickman et al (1987), which investigated the effects of oil shocks on the business cycles in the United States, a large international literature exists that has analyzed the impact of oil price shocks on macroeconomic variables for both developing and developed economies (see for example Perez de Gracia, 2003, 2005;Jiménez-Rodríguez and Sánchez, 2005;Manera, 2008, 2009;Baumeister et al, 2010;Sánchez, 2011;Gupta and Wohar, forthcoming, for Dagut, 1978;Kantor and Barr, 1986;McDonald and van Schoor, 2005;Bellamy, 2006;Kohler, 2006;Nkomo, 2006;Swanepoel, 2006;Wakeford, 2006Wakeford, , 2012Fofana et al, 2009;Gupta and Hartley, 2013;Aye et al, 2014, forthcoming;Balcilar et al, 2014, forthcoming;Kin and Courage, 2014;Ajmi et al, 2015;de Bruyn et al, 2015;Gupta and Kanda, 2015;Tshepo, 2015;Chisadza et al, forthcoming;Gupta and Kotze, forthcoming). In general, these studies tend to agree to the fact that oil shocks are inflationary for the South African economy.…”

Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R

“…Finally, it is worth to notice that DMA was already applied to several markets. Except the already mentioned oil market, this method was used in forecasting gold price [149][150][151], cooper price [152], carbon market [153], inflation [154][155][156][157][158][159], GDP [158,159], real estate markets [160][161][162], exchange rates [163,164] and stock markets [151,[165][166][167][168]. It is also clear that this method has gained an increasing interest in economics since 2015.…”

An Overview of Dynamic Model Averaging Techniques in Time‐series Econometrics

“…For instance, going back to the generalized Phillips curve mentioned above, there are a myriad of theories that suggest a link between variables, such as the unemployment rate, T-bill rates, level of economic activity, house prices, and the rate of inflation. Therefore, the practitioner is faced with the situation, where he (she) the purpose of our study is not to list every single publication (or working paper) that applies DMA in one way or another, we can list the following interesting applications: Dangl and Halling (2012), Liu et al (2015), and Naser and Alaali (2018) with regard to predicting aggregate equity returns; Koop and Tole (2013) in the context of forecasting the spot price of carbon permits; Buncic and Moretto (2015), Drachal (2016), and Naser (2016) with regard to predicting commodity prices; Bruyn et al (2015), Beckmann and Schüssler (2016), Byrne et al (2018), and Beckmann et al (2020) in the context of forecasting exchange rates; Gupta et al (2014) with regard to forecasting foreign exchange reserves; Bork and Møller (2015), Risse and Kern (2016), and Wei and Cao (2017) in the context of forecasting house price changes; Aye et al (2015) and Baur et al (2016) with regard to predicting the rate of return on the price of gold; Koop and Korobilis (2011) and Filippo (2015) with regard to forecasting non-U.S. rate of inflation; Byrne et al (2017) with respect to forecasting the term structure of government bond yields; and Wang et al (2016), Liu et al (2017), Nonejad (2017b), and Ma et al (2018) with respect to forecasting equity return and commodity price volatility.…”