11.Empirical Modeling of Nigerian Exchange Rate Volatility (original) (raw)
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Empirical Modeling of Nigerian Exchange Rate Volatility
In this study, we examined the volatility of Naira/US Dollar and Naira/UK Pound Sterling exchange rates in Nigeria using GARCH model.The data on the monthly exchange rates were collected from Central Bank of Nigeria which spanned through the period 2007-2010, and the analysis of the series was carried out using Econometric software (E-view 7.0) Investigation conducted on the exchange rates showed that volatility on the returns is persistent. The result of normality test indicated that the series residuals are asymmetric The plots on the original series and unit root test on the return series established the nonstationarity status of Nigerian foreign exchange series.The paper therefore recommends that the impact of policies of government on foreign exchange rates should be investigated.
Asian Journal of Probability and Statistics, 2019
This paper examines the exchange rate volatility with GARCH-type model of the daily exchange rate return series from) models. The result from all models indict presence of volatility in the five currencies and equally indicate that most of the asymmetric models rejected the existence of a leverage effect except for models with volatility break. For GARCH (1, 1), GJR-GARCH (1, 1,) EGARCH (1,1) and TGARCH (1, 1), it was observed that India have the best exchange rate with the highest log-likelihood (Log L) and the lowest AIC and BIC followed by USA, China, Spain and United Kingdom respectively. The four models was later compared for the exchange rates of the five countries under consideration i.e. China, India, Spain, UK and USA to select the best fitted model for each country and it was discovered that GJR-GARCH (1,1) is the best fitted model for all the countries followed by GARCH (1,1), TGARCH (1,1) and EGARCH (1,1) in that order.
Modeling Volatility in Nigeria Foreign Exchange Market Using GARCH-type Models
Journal of Mathematical Theory and Modelling, 2017
In this study, the performance of GARCH-type model is considered in modelling Nigeria foreign exchange returns. The datasets consists of the foreign exchange of Nigeria naira for the periods before recession and during recession. It is observed that volatility is higher during recession than when there was no recession. Model selection criteria based on Hannan-Quinn Information Criterion (HQIC) shows that Gaussian process is least considered model to capture the variability in foreign exchange rate returns in Nigeria, but student's t and Generalized Error distribution are more suitable, therefore forecast performance was used to access each of the Asymmetric models. The empirical analysis shows that GARCH (1, 1) and gjrGARCH (1, 1) with Student's t error distribution and iGARCH(1, 1), sGARCH(1,1), and csGARCH (1,1) are the best fitted models. Fifty days out-of-sample forecast shows that csGARCH (1, 1) based on Generalized Error distribution is the best predictive model based on Mean Square Error (MSE), and sGARCH based on Mean Absolute Error (MAE) and Directional Absolute Error (DAE). The study recommends that future study should consider alternative error distributions with a view to realizing a more robust volatility forecasting model that could guarantee sound policy choices. 1. Introduction Financial time series data often consist of periods of calm behaviour alternating with periods of very wild fluctuations. The study on the volatility of exchange rate is closely linked to the risk of assets, as volatility measures exposure to risk. Higher volatility leads to large variations of return, hence higher risk. Volatility of exchange rate provide useful information in measuring risk, and a number models are applied in forecasting exchange rate movement and evaluating the performance of the local currencies in international market. Statement made by (Hamadu and Adeleke 2009) which cannot be ignored is that forecasting currency exchange rate rates is an important financial problem that has recorded a great deal of attention particularly because of intrinsic difficulty and practical applications. The issue of modelling exchange rate volatility has gained considerable importance in the research studies since 1973, when many countries shifted towards floating exchange rate from fixed exchange rate regime. Part of the studies were conducted to understand the behaviour of exchange rate and to explain the sources of its movements and fluctuations. There has been excessive volatility of the Nigeria Naira against major foreign currencies in the exchange market since the adoption of flexible exchange–rate regimes in 1986. Therefore, continuous exchange rate volatility was thought to have led to currency crises, distortion of production patterns as well as sharp fluctuations in external reserve (Bala and Asemota 2013). Exchange rate volatility is a major challenge facing development of an economy, making planning more problematic and investment more risky. Nigeria being a developing nation highly dependent on foreign trade, and these trades relies on exchange rate. This show that the impact of exchange rate variability on economies especially developing ones is not only in one direction. Many studies have adopted diverse techniques in modelling exchange rate volatility. Hamadu and Adeleke (2009) modelled and compared Multilayer Perception Back Propagation Neural Network (MLPBPNN) model with several models, along with ARIMA generated by Expert Modeler System (EMS) to model Nigerian foreign exchange while Adeleke et. al., (2015) modelled daily exchange rate using extreme value theory, among others. In modelling volatility, popular and frequently applied models to estimate exchange rate volatility are the autoregressive conditional heteroscedastic (ARCH) model advanced by Engle (1982) and generalized ARCH (GARCH) model developed independently by Bollerslev (1986) and Taylor (1987) considered to be symmetric. Extension of the symmetric GARCH is the like of EGARCH, IGARCH, TGARCH, fGARCH, GJRGARCH, CSGARCH, TGARCH, among others. The GARCH-type model is a popular type of model being used to model stock and exchange rate volatility. Lim and Sek (2013) used both GARCH-types to model and identify the superior model in capturing the characteristics of stock market at different type. In the recent times, the GARCH-type of models has been adopted in various capacities. Hu and Tsay (2014) consider a sample estimate of generalized kurtosis matrix and proposed test statistics for detecting linear combinations that do not have conditional heteroscedascity, they applied the test to weekly log returns of seven exchange rates against US dollars. Kalli and Griffin (2015) proposed stochastic Volatility (SV) model drawing strength from auto-regressive SV models, aggregation of auto-regressive process, and Bayesian non-parametric
Comparative Analysis of Naira/US Dollar Exchange Rate Volatility using GARCH Variant Modeling
2021
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 ne...
Modelling Naira/Dollar Exchange Rate Volatility: Application Of Garch And Assymetric Models
2009
properties are investigated for the Nigerian foreign exchange. The impact of the deregulation of Foreign exchange market on volatility was investigated by presenting results separately for the period before deregulation, Fixed exchange rate period (January 1970-August 2006) and managed float regime (September 2006-December 2007). The results from all the models show that volatility is persistent. The result is the same for the fixed exchange rate period and managed float rate regime. The results from all the asymmetry models rejected the hypothesis of leverage effect. This is in contrast to the work of Nelson (1991). The APARCH model and GJR-GARCH model for the managed floating rate regime show the existence of statistically significant asymmetry effect. The TS-GARCH and APARCH models are found to be the best models.
Empirical Analysis of Exchange Rate Volatility and Nigeria Stock Market Performance
2015
Since 1993 Nigeria exchange rate started running beyond digit level as against United-state dollar amidst pursuance of growth of macroeconomic indicators. While available data show that growth in key macroeconomic indicators specifically exchange rate and inflation rate has impacted negatively on the growth of Nigerian stock market through GARCH process and ECM estimation techniques, interest rate was found to have impacted positively. This paper examined betweenexchange rate volatility and stock market performance using Generalised Autoregressive Conditional Heteroskedascity (1.1) (GARCH) model in establishing the relationship. A Vector Error Correction Model of stock market performance was estimated to examine the impact of exchange rate volatility. It was found from the available data spanning 1986 to 2013 that long run volatility in exchange rate has strong negative impact on the change in the performance of the Nigerian stock exchange market having proved the uni-directional re...
Forecasting of Exchange Rate Volatility between Naira and US Dollar Using GARCH Models
International Journal of Academic Research in Business and Social Sciences, 2014
Exchange rates are important financial problem that is receiving attention globally. This study investigated the volatility modeling of daily Dollar/Naira exchange rate using GARCH, GJR-GARCH, TGRACH and TS-GARCH models by using daily data over the period June 2000 to July 2011. The aim of the study is to determine volatility modeling of daily exchange rate between US (Dollar) and Nigeria (Naira). The results show that the GJR-GARCH and TGARCH models show the existence of statistically significant asymmetry effect. The forecasting ability is subsequently assessed using the symmetric lost functions which are the Mean Absolute Error (MAE), Root Mean Absolute Error (RMAE), Mean Absolute Percentage Error (MAPE) and Theil inequality Coefficient. The results show that TGARCH model provide the most accurate forecasts. This model will captured all the necessary stylize facts (common features) of financial data, such as persistent, volatility clustering and asymmetric effects.
On the use of ARIMA and GARCH in Modelling Nigeria’s Naira: Us Dollar Monthly Exchange Rates
Asian Journal of Probability and Statistics
This paper aimed at modelling the volatility of monthly average official exchange rate (Naira/USD) using the Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) for the period January, 1981 to December, 2021. The data for the study was obtained from Central Bank of Nigeria 2021 Statistical Bulletin. The time plot, Augmented Dickey Fuller (ADF) and Phillip’s Perron (PP) were used to check for the Stationarity of the Series. It was discovered that the series is not stationary, thus the need for differencing to make it stationary. Based on the findings of the study, it was concluded that the ARIMA (0, 2,2) and GARCH (1,1) with Student’s t-distribution are the optimal models for modeling monthly average official exchange rates return (Naira/USD) in Nigeria.
Exchange Rate Volatility and Macroeconomic Performance in Nigeria
IntechOpen, 2022
The study examined the asymmetric relationship between exchange rate volatility and macroeconomic performance in Nigeria covering the period between 1986Q1 and 2019Q4. The Non-linear Generalised Autoregressive Distributive Conditional Heteroscedasticity (GARCH) model was employed. The study was motivated as a result of periodic increase in exchange rate of naira to a dollar and instability of macroeconomic variables in the economy. The presence of Autoregressive Distributive Conditional Heteroscedasticity (ARCH) effect established the use of non-linear GARCH models which showed that volatility was persistent over the period of study. Consequently, the result revealed that exchange rate volatility exhibited a positive relationship with trade balance, industrial output and inflation in the study period. Thus, good news prevailed more over bad news in the foreign exchange market. The study therefore recommended that monetary authorities in Nigeria should regulate exchange rate and macroeconomic variables in order to control the general price level in the economy.