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This study analyses the extent of volatility in exchange rate in Nigeria covering the sustainable democratic transitions between 1999 and 2011 using daily returns.. The empirical evidence indicates that the behaviour of exchange rate tends to change over short periods of time with inconsistent leverage effects and persistence of shocks. Thus, apply...
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... Our methodology begins with the univariate GARCH model of Bollerslev (1986), which is an extension of the Engle (1982) ARCH model. Following Salisu's (2011) approach, our standard GARCH (p, q) model is as represented in Equation (1), where is the lagged terms of the conditional variance, while is the squared error term. ...
Motivated by the increasing evidence of oil price-related transition risk from climate change, we employ the classic GARCH (1,1) and its extended variant (GARCH-X) to identify the degree of oil market volatility that is due to climate risk. We find that climate risk increases the persistence of volatility in the oil markets.
... On the other hand, excessive exchange rate volatility can harm a country's economy. According to Salisu [28] , this may be related to the fact that frequent changes in exchange rates can result in massive losses for foreign exchange traders and investors. There is also concern that frequent changes in exchange rates harm a country's balance of payments, which not only distorts international comparisons but also poses a threat to the SDG goal of mitigating global imbalances and exchange rate conflicts. ...
... Following Salisu [28] 's procedure, the preliminary analysis as demonstrated herein is performed in two phases. Starting with the first phase of the preliminary analysis, presented in Table 1 , is the summary statistics for pre-GFC (non-crisis period) and the crisis periods of GFC and COVID-19. ...
... However, where there is evidence of volatility, then the implication, as shown in Eq. (8) , is that the variance is larger. Following Salisu's [28] procedure, we further extended Eq. (7) to accommodate the lag of the conditional variance and subsequently arrived at the GARCH model of Bollerslev (1982), which is an extension of the Engle [9] ARCH model. ...
The oil price has been increasingly identified as a key fundamental in the dynamics of exchange rates. As a result, we investigate how changes in oil prices affect the dynamics of exchange rates during crisis periods. We hypothesised that the potential of oil prices as an amplifier of exchange rate volatility during crises varies for economic and non-economic crises with divergent origins. Consequently, we classified the crisis sample into two sub-samples: the great recession caused by the 2007 global financial crisis (GFC) is defined as a crisis sample with an economic origin, while the great lockdown caused by the recent COVID-19 outbreaks defined our crisis period with a non-economic origin. We used the GARCH model and its many extensions and noted three findings that strengthened our contributions to the empirical analysis of exchange rate volatility. First, we show that the divergent origins of economic and non-economic crises matter in terms of the extent to which they heighten exchange rate volatility. Second, the persistence of exchange rate volatility during COVID-19 is exacerbated by changes in international oil prices. Finally, our finding of varying dynamics of persistent (transient) exchange rate volatility across different samples of turbulent periods provides investors with evidence-based insights not to generalise their portfolio selection strategy amidst economic crises of different origins.
... Applying the Vector Autoregressive (VAR) model with a quarterly data for the period 2002-2011, the outcome of the study indicates that the consumer price increases responded to foreign exchange appreciation against the Algerian Dinar. Salisu (2011) carried out a study to analyze exchange rate volatility in Nigeria over a period of 1999 and 2011. ...
... A lot of work has been done on exchange rate volatility under fixed and floating regimes in developed and developing countries that had adopted the two regimes (for instance Dallah 2011;Afees 2011;Chipili, 2009a;Olowe 2009;Bangake 2006;Yang, 2006;Yoon and Lee, 2008a;Stancik 2006;Kočenda and Valachy 2006;Longmore and Robinson 2004;Canales-Kriljenko and Karl 2004;Esquivel and Felipe 2002;Singh, 2002;Arize, et al, 2000;Flood and Rose, 1998;Christian, 1998;McKenzie 1997;Hasan and Wallace 1996;Lothian and Taylor 1996;Arize 1995;West and Chow 1995;Papell, 1988;Meese and Rose 1991;Savvides 1990;Mussa 1986;Stockman 1983;Chowdhury, 1993). The consensus view in these studies is that exchange rate is more volatile under a flexible regime than under a fixed arrangement. ...
The paper examines volatility of RMB exchange rate return of onshore and offshore markets. The onshore rate covered 4/01/2008–5/09/2016 while offshore spanned 31/12/2008-22/09/2016, the returns were not normally distributed and were integrated of order zero I(0). The Ljung-Box Q statistics depicts the presence of autocorrelation in return series and Ljung-Box Q2 statistics of power transformed for conditional heteroscedasticity for lags of 6, 12 and 20 all indicated the presence of conditional heteroscedascity. The exchange rates volatility was persistent in both markets. However, offshore return was more persistent while leverage effects exist in both markets. Asymmetry power Autoregressive conditional Heteroscedastic (APARCH) model was the best model for forecasting purposes in both markets while Glosten, Jogannathan and Rankle, Generalized Autoregressive conditional Heteroscedastic (GJR-GARCH) model and Integrated Generalized Autoregressive conditional Heteroscedastic (I-GARCH) were the worst models in onshore and offshore return markets respectively. APARCH model should be adopted for future studies.
... For example, Salisu and Mobolaji (2013) find robust structural breaks that coincide with the period of global financial crisis for the Nigerian naira/USD exchange rate. In addition, Salisu (2011) provides some stylized facts about exchange rate management in Nigeria and also finds that the behavior of the exchange rate in Nigeria tends to change over short periods of time. ...
This study tests for MDH in two prominent foreign exchange (FX) markets in Africa, Nigeria and South Africa using three benchmark currencies (euro, dollar and pound sterling). Data utilized cover time series closing rate data set of five-day weekly frequency spanning December 14, 2001 to September 26, 2014. The study considers both the linear and nonlinear measures for MDH with better size and power properties. We also capture structural break endogenously from the data stream using Perron (2006) unit root test with structural break. Three striking findings are discernible from our analyses. First, on average, the South African FX market appears to be more efficient than the Nigerian FX market. Thus, the latter may be more susceptible to speculations than the former. Second, ignoring significant structural breaks may render statistical inferences invalid. Third, the choice of methodology does matter when testing for MDH of foreign exchanges in Africa.
This paper employed variant GARCH models to examined official, interbank and Bureau de change returns volatilities. Using monthly exchange rate of Naira/USD from January 2004 to September 2020 (2004:1-2020:9), the returns were not normally distributed and stationary at level. Ljung-Box Q statistic and Ljung-Box Q2 statistics of power transformed using power 0.25, 0.5 and 0.75 for conditional heteroscedasticity for lags of 6, 12 and 20 indicated present of conditional heteroscedascity in all returns. The study found exchange rate volatility in Official, interbank and Bureau de change exchange rate returns were persistent. However, Bureau de change return was more persistent while official exchange rate return was the least persistent. Also, leverage effect exist in all the three exchange rate returns and asymmetric model were the best model for estimating exchange rate return while IGARCH was the worst model to estimate exchange rate return in Nigeria. There is need to incorporate news impact when developing exchange rate policy by monetary authority in Nigeria.
