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The path through which the study step-by-step analysis procedures are obtained is found in the methodology. As such, this study attempts its methodology under the following sub-headings: research design, population and sample, sources of data, method of data analysis and model specification employed.
The research design to be adopted for this study is the quasi-experimental research design which is a type of behavioural sciences design. Specifically, it allows the usage of secondary data (pattern/trend/time – series), which are not in direct control of the researcher and suitable for evaluating the link between financial stress test and performance of Nigerian deposit money banks from the variables under review.
The population of this study will consists of all deposit money banks financial reports and indicators as made available by Central Bank of Nigeria.
This also constitutes the sampling to be used based on data provided by the apex financial regulatory body. For a research of this type it is best to use the entire deposit money bank in Nigeria since their data is ready available and to obtain the best result from the research being conduct.
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Therefore all the banks will be sampled.
The main source of data is secondary sourced from the Central Bank of Nigeria statistical bulletin of various issues. Other sources include the Nigerian Stock Exchange annual reports, National Bureau of Statistics; and Nigeria Deposit Insurance Corporation (NDIC).
The study will use two methods for analysing the variables in the study.
First, the descriptive analysis of skewness, kurtosis and Jarque-Bera statistics will be used to confirm the normality of the series, and the stylized facts (i.e. charts) of the series is carried out. Secondly, time-series - ordinary least square (OLS) regression analysis will also be used. Conventionally, prior estimation of time-series data, the study will perform the stationarity test of the series using Augmented Dickey Fuller (ADF) test to determine the stationarity status of the variables.
The order of stationarity involves series to be of either of order I(0), order one I(1), or order two I(2). A series of I(0) means the ordinary least square (OLS) is sufficient, a series of I(1) implies a test of cointegration is sufficient conducting either the vector error correction mechanism (VECM) where a long run relationship is present, or simply the vector autoregression (VAR) in the absence of no cointegration; while a combination of all I(0) and I(1) means autoregressive distributed lag (ARDL) is sufficient among others. Whichever the order of stationarity, it immediately tells us the next path of estimation. The study will use the E-views version 9 statistically software for its estimation analysis.
One method for calculating VaR is the variance/covariance which assumes the returns on risk factors are normally distributed, the correlations between risk factors are constant and the delta (or price sensitivity to changes in a risk factor) of each portfolio constituent is constant.
Using the correlation method, the volatility of each risk factor is extracted from the historical observation period. Historical data on investment returns is therefore required.
Correlation is a measure of degree to which the value of one variable is related to the value of another. The correlation coefficient is a single number that compares the strengths and directions of the movements in two instruments values. The direction of the coefficient determines the relative directions that the instruments move in, while its strength determines the strength of the relative movements. The value of the coefficient ranges from -1 to +1, depending on the nature of the relationship. So if, for example, the value of the correlation is 0.5, this means that one instrument moves in the same direction by half of the amount that the other instrument moves. A value of zero means that the instruments are uncorrelated, and their movements are independent of each other.
Correlation is a key element of many VaR models, including parametric models. It is particularly important in the measurement of the variance (hence volatility) of a portfolio. If we take the simplest example, a portfolio containing just two assets, equation (1) below gives the volatility of the portfolio based on the volatility of each instrument in the portfolio (x and y) and their correlation with one another.
V port= x2+ y2 +2xy⋅ ρ(xy) (1)
Where:
X is the volatility of asset x
Y is the volatility of asset y
ρ is the correlation between assets x and y.
The correlation coefficient between two assets uses the covariance between the assets in its calculation. The standard formula for covariance is shown at (2):
Cov =Σ (xi – x)(yi – y)
(n -1) (2)
Where the sum of the distance of each value x and y from the mean is divided by the number of observations minus one. The covariance calculation enables us to calculate the correlation coefficient, shown as (3):
r =Cov (1,2)_ (3)
s1 s2
Where:
S is the standard deviation of each asset.
Equation (1) may be modified to cover more than two instruments. In practice correlations are usually estimated on the basis of past historical observations. This is an important consideration in the construction and analysis of a portfolio, as the associated risks will depend to an extent on the correlation between its constituents. It should be apparent that from a portfolio perspective a positive correlation increases risk. If the returns on two or more instruments in a portfolio are positively correlated, strong movements in either direction are likely to occur at the same time. The overall distribution of returns will be wider and flatter, as there will be higher joint probabilities associated with extreme values (both gains and losses). A negative correlation indicates that the assets are likely to move in opposite directions, thus reducing risk.
It has been argued that in extreme situations, such as market crashes or large-scale market corrections, correlations cease to have any relevance, because all assets will be moving in the same direction. However under most market scenarios using correlations to reduce the risk of a portfolio is considered a satisfactory practice and the VaR number for diversified portfolio will be lower than that for an undiversified portfolio.
In order to ensure stationarity test as the examination of the equilibrium relationship between the variables, the Johansen multivariate co-integration technique will be adopted. This is based on two reasons. First, variables for analyses are 1(1) series which is a prediction for the adoption of the Johansen technique. Secondly, and as a consequence, there is a possibility of having more than one Co-integrating vector in the model. This is against the Engle-Granger technique which is only suitable for testing co-integration between two variables. Co-integration is use to test the long run relationship between each variable whether the data co-integration.
In line with the specific objective of this research work our model specification will examine the relationship between financial stress test and the performance of Nigeria deposit money banks from 1980 to 2018 which is 38years review. For our estimation the following variables will be taken into consideration, this include; monetary policy ratio, prime –lending - rate, loan to deposit rate, bank total assets and liquidity ratio.
The following function is therefore considered:
Financial Performance = F (MPR, PLR, LDR LR, BTA) (1)
The regression form of the model specification is thus:
FPt = α0 + α1 MPRt + α2PLRt+ α3LDRt + α4LRt + α5 BTAt + µt (2)
(α1, α2, α3, α4 ˃ 0, and α5 ˃ 0)
Where the dependent variable is Financial Performance and other variables on the right-hand side are independent variables.
Financial Performance = as a proxy for stress testing
MPR= monetary policy ratio
PLR= Prime lending ratio
LDR= Loan to deposit rate
LR = Liquidity ratio
BTA= Bank total asset
µt = Error term
α0 = Intercept of relationship in the model
α1 – α5 = Coefficient of each exogenous or explanatory variable.
To explore long-term equilibrium relationships among the variables, the Johansen co-integration test is applied. This multivariate technique is especially suited for time series data that are Integrated of Order 1, I(1), offering insights into the co-movement of variables over the long run.
This methodological framework sets the stage for a robust analysis of the nexus between financial stress tests and the performance of Nigerian deposit money banks. By integrating diverse analytical techniques and leveraging comprehensive secondary data, this study aims to contribute to the discourse on financial stability and regulatory efficacy within Nigeria's banking sector.
Methodological Approach to Analyzing Financial Stress Test and Bank Performance in Nigeria. (2024, Feb 22). Retrieved from https://studymoose.com/document/methodological-approach-to-analyzing-financial-stress-test-and-bank-performance-in-nigeria
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