The risks associated with exchange rate and money market indicators have drawn the attentions of econometricians, researchers, statisticians, and even investors in deposit money banks in Nigeria. The study targeted at modeling exchange rate and Nigerian deposit banks money market dynamics using trivariate form of multivariate GARCH model. Data for the period spanning from 1991 to 2017 on exchange rate (Naira/Dollar) and money market indicators (Maximum and prime lending rate) were sourced for from the central bank of Nigeria (CBN) online statistical database. The study specifically investigated; the dynamics of the variance and covariance of volatility returns between exchange rate and money market indicators in Nigeria were examine whether there exist a linkage in terms of returns and volatility transmission between exchange rate and money market indicators in Nigeria and compared the difference in Multivariate BEKK GARCH considering restrictive indefinite under the assumption of normality and that of student’s –t error distribution.  Preliminary time series checks were done on the data and the results revealed the present of volatility clustering. Results reveal the estimate of the maximum lag for exchange rate and money market indicators were 4 respectively. Also, the results confirmed that there were two co-integrating equations in the relationship between the returns on exchange rate and money market indicators.  The results of the diagonal MGARCH –BEKK estimation  confirmed  that diagonal MGARCH –BEKK in students’-t was  the best fitted and an appropriate model for modeling exchange rate and Nigerian deposit money market dynamics using trivariate form of multivariate GARCH model. Also, the study confirmed presence of two directional volatility spillovers between the two sets of variables.

Economic relationships are often modelled without consideration of a possible regime switch, the transmission from one regime to another and the duration of stay in a particular regime which are not captured by linear models. This study aimed to model and estimate the interdependence existing among Nigeria’s International Trade and Macroeconomic Stability. Specifically, this study sought to estimate and compare the estimated Models, select the best Model and determine the probabilities of stay, the expected duration of stay in a particular regime. The study adopted a quasi-experimental design. Time series data on the study variables from January 2000 to June 2019 were obtained from the Statistical Bulletin of the Central Bank of Nigeria. Models were specified accordingly, the statistical analyses were carried out using the Markov Switching Intercept Vector Autoregressive Models, the pre and post-diagnostic tests were also conducted. The unit root test results showed I (1). VAR lag length selection criteria choose lag 2. The MS-VAR analysis identified two regimes (expansion and contraction), the information criteria selected the Markov-Switching Intercept Autoregressive Heteroschedastic 2 Variance Auto-regression 2 [MSIARH (2) - VAR (2)]. The MS-VAR results in regime 1 showed that lags 1 and 2 of total export significantly affected total export and total import, Lags 1 and 2 of total import had significant effects on exchange rate while lags 1 of exchange rate and lags 1 and 2 of exchange rate had significant effects on inflation rate. In Regime 2, lag 1 of total export and lag 2 of exchange rate had significant effects on total export. Only lag 2 of inflation rate had significant effects on exchange rate while lag 2 of total export and lags 1 and 2 of exchange rate had significant effects on the inflation rate. The results also showed an 89% probability of staying in regime 1 for a duration of 8 months 8 days and 57% probability of staying in regime 2 for 2 months 10 days. It was concluded that the MSIARH (2) - VAR (2). It was recommended that the right-hand side variables should be tested for endogeneity before concluding on single or system equation. It was also recommended that the possibility of regimes should be verified before concluding on linear or nonlinear models.

The study applied Autoregressive Integrated Moving Average Intervention in modelling crude oil prices in Nigeria spanning the period from January 1986 to June 2017. The time plot of the series showed an abrupt increase in the series and this called for intervention modelling. The data was divided into three set (actual series, pre-intervention and post-intervention series). The Augmented Dickey Fuller (ADF) was used to test for unit root on each of the series and were all found to be non-stationary at levels, they (actual, pre and post- intervention series) were however non stationary at first difference. Eighteen models were estimated and the best model was the pre-intervention model that minimise the Akaike information criterion (AIC) (ARIMA (111)) with AIC of (4.4.578). The plot of the residual correlogram showed adequacy of the model. The model was adequate since there was no spike that cut the level of the correlogram and the histogram of the residual was normally distributed with probability values (0.0000).

Researcher every so often model economic relationship using linear models. Even when non-linear models are used, they hardly consider the possibility of regimes, the transmission from one regime to another and also the duration of stay in a particular regime. These are not captured by linear models. To address this, we applied the Markov-Switching Mean Vector Autoregressive  Model to model and estimate the interdependence between macroeconomic variables (International Trade and Macroeconomic Stability) within the context of the Nigerian economy. Specifically, the study analyzed the trends of total export, total import, exchange rate, and inflation rate in Nigeria within the study period, Estimated MSM-VAR models on the study variables and selected the best model using information criteria. The study empirically investigated the interdependence existing among the study variables, determined the probabilities of transition from one regime to another and the duration of stay in a regime. The study ascertained the relative significance of each random innovation in affecting the study variables. Monthly time series data 246 months spanning through January 2000 to June 2020 was collected form the Central Bank of Nigeria Statistical Bulletin. The study used the Markov-Switching Mean Vector Autoression in the Analysis. The results showed that all variables were stationary at first difference. The study chose 2 regimes and the model selection criteria selected [MS(2)-MVAR(2)]. The variables were largely self-explanatory and very strongly exogenous.

The interdependence among oil prices, exchange rates and inflation rates, and their response to shocks, was a cause of concern. Unrestricted Vector Autoregression (UVAR) was employed to analyse this interactions as well as to investigate the pattern of causality among the study variable. Annual data spanning from 1981 to 2017 was sourced from the Statistical Bulletin of the Central Bank of Nigeria. Pre-estimation analysis showed that all variables were integrated of order one 1(1), and there no cointegrating relationship. The inverse root of AR characteristic polynomial showed a stable VAR model. All lag length selection criteria chose a lag length of 1. The UVAR estimates and the test of significance particularly the granger causality test indicated significant influence and uni-directional effect from oil price to exchange rates. The Wald statistics, showed significant own shocks, and the impulse response showed that all variables were instantaneously affected by own shocks. Exchange rate was instantaneously affected by oil price; however, it ruled out the response in inflation rate to contemporaneous shocks in oil price. The variance decomposition further showed that at least 93.1%, 97.1% and 92.4% of the impulse response in oil price, exchange rate, and inflation rate respectively were from own shocks in the long run. The post estimation analysis showed that the VAR model was multivariate normal, the residual was homoscedastic, and there was no serial autocorrelation. It was recommended that the government should diversify the national income stream and consider policies that will control inflation.