




































 American Finance & Banking Review; Vol. 2, No. 1; 2018 

                   ISSN 2576-1226  E-ISSN 2576-1234 

                        Published by Centre for Research on Islamic Banking & Finance and Business, USA 

 

 

64 
 

Monetary Policy and Return on Equity of Quoted Insurance Firms: 

A Time Series Study from Nigeria 
 

Macfubara, Minafuro Suzane1 , Norteh Dumbor1 & Gberesuu, Barida Barry1 

 

1Insurance and Risk Management Department, Ken Poly, Bori, Rivers State ,Nigeria 

Correspondence: Macfubara, Minafuro Suzane, Insurance and Risk Management Department, Ken Poly, Bori, 

Rivers State, Nigeria 
 

 

Received: May 5, 2018                             Accepted: May 26, 2018                        Online Published: June 6, 2018   

 

 

Abstract 

The financial system is the transmission channel of monetary policy. This study examines the effect of monetary 

policy on the performance of insurance firms in Nigeria from 1990 – 2017. The objective is to investigate the 

existing relationship between monetary policy instruments and the performance indicators of insurance companies. 

Secondary data were sourced from Stock Exchange factbook, Central Bank of Nigeria (CBN) Statistical Bulletin. 

Multiple linear regressions were formulated to examine the effect of the independent variables on the dependent 

variable. Return on equity was modeled as a function of treasury bill rate, monetary policy rate, interest rate, growth 
of money supply and exchange rate.  R2, T-Statistics, β Coefficient, F-Statistics and Durbin Watson were used to 

examine the extent to which the independent variables affect the dependent variables while augmented dickey fuller 

unit root test, granger causality test, cointgration test and error correction models was used to ascertain the dynamic 

relationship between monetary policy variables and return on equity of the insurance firms. Findings revealed that, 

all the explanatory variables have positive effect on return on equity except treasury bill rate.  The unit root test 

found that the variables are stationary at first difference, the cointgration test found the presence of long run 

relationship while the granger causality test found a uni-directional causality. The study concludes that monetary 

policy has moderate effect on the return on equity of the insurance firms. We recommend that management of 

insurance companies should devise measures of managing the negative effects of the monetary policy instruments to 

enhance the performance of the insurance companies. 

Keywords: Monetary Policy, Return on Equity, Quoted Insurance Firms, Monetary Policy Rate, Treasury Bill Rate. 

 

1. Introduction 

Monetary policy has long been acknowledged as instrument used to influence investment and other macroeconomic 

indicators. The opinion that the non-banks financial institutions matters in the transmission of monetary policy can 

be traced to the Radcliffe committee meeting of 1950s which suggested strongly that the non- bank financial 

institutions such as insurance companies can influenced and be influenced by monetary policy and thereby be 

brought under the control of monetary authorities. This led to the redefinition of money supply as Ms = C+ DD + 

SD + TD + NBFI (Onoh, 2002). 

Insurance companies provide unique financial services to the growth and development of every economy. Such 

specialized services ranges from underwriting of risk and mobilization of long-term fund for capital investment, 

hence monetary policy variables can affect the performance of the industry negatively or positively. The relationship 

between monetary policy and performance of private investment and financial institutions is  perennial issue in 
development economic judging from the hundreds of theoretical and empirical scholarly papers that have been 

written to capitalize how monetary variables such as interest rate, money supply, monetary policy rates and liquidity 

reserves, money supply, affect private investment or financial institutions. Morgan (1981) identified two casual 

relationships between monetary policy instruments and return on private investment which are finance-led 

hypothesis and growth led hypothesis. 

The effect of monetary policy variables such as interest rate, money supply, monetary policy rate and liquidity on 

the qualitative measures of insurance of insurance performance such as profit, investment, employment and cash 

flow is lacking in Literature. However, it is generally accepted in theory and principle that the financial sector which 



 
 

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includes the insurance industry is the transmission mechanism for the realization of government monetary and 

macroeconomic goals.  

Despite the growing literature on the relationship between monetary policy and the performance of the financial 
institutions, the effect of monetary policy on the Non-banks financial institutions such as the insurance companies is 

lacking in literature as significant proportion of literature is focused on the effect of money banks. This creates a 

knowledge gap in the effect of monetary policy on the performance of insurance companies in Nigeria.    Again, the 

existing literatures and findings on the effect of monetary policy and the performance of financial institution is 

inconclusive and controversial as some report positive while others report negative effect. This result to failures or 

report negative effect, this result to failures of monetary authorities in effect management of monetary policy to 

achieve prudential and financial sector stability. The failure of previous financial policies of government to achieve 

desirable economic growth was the concern that demands re-structuring of the system, especially in the era of an 

ailing economy (Dagogo and Okorie, 2014). This results to the difficulties of accessing the relative effect of 

monetary policy on the non-bank financial institutions. From the above, this study intends to examine the effect of 

monetary policy on the performance of insurance companies in Nigeria. The following null hypotheses are 

formulated from the variables: 

 There is no significant relationship between Treasury bill rate and return on equity of quoted insurance 

firms in Nigeria. 

 There is no significant relationship between monetary policy rate and return on equity of quoted insurance 

firms in Nigeria. 

 There is no significant relationship between interest rate and return on equity of quoted insurance firms in 

Nigeria. 

 There is no significant relationship between growth of money supply and return on equity of quoted 

insurance firms in Nigeria. 

 There is no significant relationship between exchange rate and return on equity of quoted insurance firms in 

Nigeria. 

2. Literature Review 
2.1 Insurance in Nigeria  

Insurance companies provide unique financial services to the growth and development of every economy. Such 

specialized service ranges from the underwriting of risks inherent in economic entities and the mobilization of large 

amount of funds through premium for long-term investment (Akotey et al, 2011). The risk absorption role of 

insurers promotes financial stability in the financial entities (Lowes, 2010). Categorically, insurance in Nigeria is 

classified into life, general, composite and reinsurance (Ezirim, 2003). Apart from the risk management functions, 

insurance policy such as life and whole life is an aspect that provide savings plans and in direct completion with the 

investment in other financial institutions such as the deposit money banks, savings associations, mutual funds and 

real estates and other real and financial investments (Ezirim and Muoghalu, 2002). Insurance plays financial 

intermediation function, a major source of long-term capital and facilitate the growth of the capital market (Catalan 

et al., 2000). Hence monetary and macroeconomic shocks can affect positively or negatively insurance investment.   
Nigerian government over the years has embarked on monetary and macroeconomic reforms to enhance real and 

portfolio investment in the financial sector. For instance the deregulation of interest rate and the  financial sector in 

the last quarter of 1986, the reforms in the foreign exchange market to attract foreign real and portfolio investment, 

the establishment of National Insurance Commission (NAICON) in 1997. The enactment of the insurance Act 2003 

which increase the capital base of the categorized insurance businesses to N15m for life insurance, N200m for 

general insurance and N350m for reinsurance and the recapitalization policy in 2005 which further increase the 

capital base to N2billion life insurance, N3billion non life and N10billion reinsurance which reduce number of 

insurance companies from 104 to 49, reinsurance from 4 to 2 (Fatula,  2007) with the objective of consolidating in 

the sector maximize investment returns and to attract foreign capital infusion (Ayeleso, 2010).  However, the extents 

to which these reforms have affected investment in the insurance industry remain a matter of research interest as 

investment in the industry continues to decline. Record revealed that only 10% of Nigerian have insurance policy of 

any type (Mordi, 1990, Wilson, 2004). A close examination of CBN Report (2012) indicate that total investment of 
Nigerian insurance industry total N336,247.9 in 2008, N343,894.2 in 2009, N351,459.9 in 2010 and N 359,192.0 in 

2011 representing a marginal annual increase of 2.89% compared with the commercial banks of 43.78%. 

2.2 Conceptual of Monetary Policy 

Monetary policy is defined by the Central Bank of Nigeria (CBN) as combination of measures designed to regulate 

value supply and cost of money in an economy, in consonance with the level of economic activities. Odufalu, (1994) 

defined monetary policy as the combination of measures taken by monetary authorities (the CBN and the ministry of 



 
 

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finance) to influence directly or indirectly both the supply of money and credit to the economy and the structure of 

interest rate for economic growth, price stability and balance of payment equilibrium. He added that the CBN is 

empowered by decree 25 of 1991 Act, to formulate and implement monetary policy in Nigeria, in consultation with 
the ministry of finance subject to the approval of the President. Onyido (1993) sums it up when he said that 

monetary policy is therefore applied to influence the availability and cost of credit in order to control the money 

supply policy. He generally describe the action taking by the Central Bank as using tools / instrument at its disposal 

to influence monetary conditions in particular, the quantity and supply of money in the macro-economic goals.  

2.3 Agency Theory  

Most of the hypotheses formulated in the following are based on the economic principal-agent theory, where a 

positive effect stems from the amelioration of the shareholder-management conflict, by disciplining the 

management. Analogously, an aggravation of the conflict results in a negative effect. The principal-agent theory is 

part of the new institutional economics, which developed as extension of the neoclassicism. It abandons the 

assumption of a complete market by allowing informational asymmetries and transaction costs to cause incomplete 

contracts. This leads to a methodological individualism, which does no longer consider institutions as profit 

maximizing collectives, but as a “nexus for a complex set of explicit and implicit contracts of individuals. 
Consequently, the economic focus on markets is shifted to man-made institutions, incorporating the individual into 

economic theory. 

2.4 Empirical Review 

Mazviona, Dube and Sakahuhwa (2017) examined factors affecting the performance of insurance companies in 

Zimbabwe. We utilized secondary data from twenty short-term insurance companies. The data was for the period 

from 2010 to 2014. We used factor analysis and multiple linear regression models to determine the factors affecting 

performance and identifying their impact. The findings revealed that expense ratio, claims ratio and the size of a 

company significantly affect insurance companies’ performance negatively, whilst leverage and liquidity affect 

performance positively.   Gonga and Sasaka (2017) investigated the determinants of financial performance of 

selected insurance firms in Nairobi County. The target population was 55 licensed insurance firms (42 locally owned 

insurance firms and 13 Foreign owned insurance firms). The study used two respondents in each insurance firm who 
were Finance Managers and Corporate Affairs Managers and all these had total of 96 respondents. The study used 

both primary and secondary data. The main primary data source was semi structured questionnaire. The data from 

the study was analyzed qualitatively and quantitatively using percentages, means and frequency distribution with the 

aid of Statistical Package for Social Sciences (SPSS) version17. Since data was descriptive, variants such as means, 

frequencies and percentages were used to describe the findings of the study. Bivariate ANOVA statistical data 

analytical technique was used to find the determinants of financial performance of selected insurance firms in 

Nairobi County. The study concluded that insurance firms had liquid investments which helped them to settle claims 

especially if their underwriting income cannot cover claims. The firms would sell off their investments if they 

lacked money to settle claims. Majority of insurance firms relied on cash flow from operations in liquidity 

management. This implied that all firms had certain source of funds for liquidity management. The study 

recommended that insurance firms should establish a well matched portfolio of their assets and liability in terms of 

cash flows or rather they should ensure that they create additional reserve so that it can assist them to cover the 
interest rate since low interest may create a discrepancy on the earnings. 

Yuvaraj and Abate (2013) examined on factors affecting profitability of insurance companies for nine years (2003-

2011) in Ethiopia using 7 firm specific factor (i.e. age of company, size of company, volume of capital, leverage 

ratio, liquidity ratio, growth and tangibility of assets) on profitability. According to their regression results they 

found that size is most important factor and positively related with profitability.  Daniel and Tilahun (2013) also 

studied on Firm specific factors that determine insurance companies’ performance in Ethiopia using 7 firm specific 

factors (i.e. size, leverage, tangibility, Loss ratio (risk), growth in writing premium, liquidity and age) on 9 insurance 

companies for six consecutive year staring from 2005-2010 and they found that insurers’ size is statistically 

significant and positively related with return on total asset.   

Abate (2012) studied factors affecting insurers profitability in Ethiopia sampling nine of insurance companies for 

nine years (2003-2011) and found out that assize is the most important determinant factors of profitability and 
positively related with it. Mistere (2015) also found out the same result. The effect of size on profitability in this 

study also was found to be significant and positively related with profitability.   Nino (2016) examined the 

association between Insurer-specific indicators and macroeconomics on profitability in Philippine non-life insurance 

market utilizing the panel data over the period of 2008 through 2012. Return on assets (ROA) and operating ratio 

were used for profitability. The study found out that that firm size significantly affects profitability both in ROA and 

operating ratio.  



 
 

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Hamdan (2008) examined determinants of insurance company’s profitability in UAE, the study revealed that there is 

significantly positive relationship between profitability and size. Bilal Javaria et al. (2013) similarly, investigated on 

the determinants of profitability in insurance sector of Pakistan with a panel data set of 31 insurance firms, and they 
found that size, earnings volatility and age of the firm are significant determinants of profitability.  Bilal Javaria et 

al. (2013) investigated on the determinants of profitability in insurance sector of Pakistan with a panel data set of 31 

insurance firms (life insurance sector and no-life Insurance) of Pakistan from 2006-2011 the study suggests an 

opposite and significant relationship between leverage ratio as   independent variables and profitability. Shami and 

Ahmed (2008) explore on determinants of Insurance Companies' Profitability in UAE using 5 firm specific variables 

and they found that an opposite and significant relationship between leverage ratio as independent variables and 

profitability. Hen-Ying Lee (2014) also estimated the effects of firm specific factors and macroeconomic factors on 

profitability (measured by operating ratio and ROA) of property liability insurance industry in Taiwan and he found 

that financial leverage is significantly and negatively correlated with profitability. 

AnilaÇekrezi (2015) investigated factors that affect financial performance of Albanian Insurance Companies the 

study population consisted of 5 insurance companies with private capital, for the period 2008-2013 with a total of 30 

data. The results showed that leverage has negative impact on the financial performance (ROA) of these companies.  
Therefore, in this study too it was found out that leverage has a significant effect and statistically negative 

relationship with profitability. Curak et al. (2012) examined the determinants of the profitability of the Croatian 

composite insurers’ between 2004 and 2009. The determinants of profitability, selected as explanatory variables 

include both internal factors specific to insurance companies and external factors specific to the economic 

environment. By applying panel data technique, the authors show that underwriting risk (loss ratio) had a significant 

influence on insurers’ profitability.  

Umotho (2013) examined the relationship between firm specific and macroeconomic variables with financial 

performance of insurance companies. In his study he investigated the relationship of interest rate; inflation rate 

(CPI), currency exchange rate fluctuations, money supply, GDP, as macro-economic factor, and Claim ratio (CR) 

and he found that Claim ratio has positive effect on ROA.  Therefore, this study is also consistent with most of the 

previous studies and found out that loss ratio is negatively related with profitability and its effect is significant. 
Chen-Ying Lee (2014) also estimated the effects of firm specific factors and macroeconomic factors on profitability 

(measured by operating ratio and ROA) of property-liability insurance industry in Taiwan and he found that 

reinsurance is significantly and positively correlated with operating ratio. However, in case of profitability measured 

by ROA, he found that underwriting reinsurance is positively correlated with ROA.  Ornella and Anderloni (2014) 

tested the impact of several firm characteristics, such as dimension, capital structure and investment policies on 

economic performance for a panel of non-life insurance firms operating in the main European markets spanning 

from 2004 to 2012.The findings suggest that various factors contribute to the performance measured by return on 

equity and return on asset. According to the study, the three main areas that constitute the core insurance activity 

(insurance in its narrower sense, financial and reinsurance activities) strongly influence profitability, but reinsurance 

does not seem to contribute either positively or negatively to performance.  

 Muhaizam Ismail (2013) investigates the determinants of financial performance of general Islamic and conventional 

insurance companies in Malaysia using panel data over the period of 2004 to 2007, using investment yield as the 
performance measure. This measure is related to a number of economic and firm specific variables, which are the 

profit/interest rate levels, equity returns, size of company, retakaful/reinsurance dependence, solvency margin, 

liquidity, and contribution/premium growth, chosen based on relevant theory and literature. Based on the empirical 

results, the study found that retakaful dependence is statistically significant determinants of the investment 

performance of the general Islamic insurance companies in Malaysia.  

Kozak (2011) examined determinants of profitability of non-life insurance companies in Poland during integration 

with the European financial system for the period of 2002–2009 and suggested that Companies improve profitability 

and cost efficiency with an increase of their gross premiums and decrease of total operating expenses. Additionally 

increases of the GDP growth and the market share of foreign owned companies positively impact profitability of 

nonlife insurance companies during the integration period.  Cassandra et al (2015) in their multivariate analysis, they 

find evidence that market concentration and insurers’ underwriting profits are positively related. More specifically, 
insurers in states with greater market concentration are more profitable than insurers in states with lower levels of 

market concentration  

Öner Kaya (2015) investigated the determinants of profitability in the Bosnia and Herzegovina insurance industry 

between the years of 2005– 2010. According to their results, age of company, market share, and past performance 

are positively and significantly related with current profitability they have also found that foreign owned companies 

perform better than domestically owned companies; and there is no significant relationship between diversification 



 
 

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and profitability.  In this study also, it was found that market share correlates positively wit profitability and had 

insignificant effect, which is consistent to the above studies.  Michael Doumpos et al (2012) Using a sample of over 

2000 nonlife insurance firms operating in 91 countries  between 2005 and 2009,they found that macroeconomic 
indicators such as real GDP growth, inflation, and income inequality influence the overall performance of firms and 

a statistically significant effect on the overall performance of insurers.  

Kozak (2011) examined determinants of profitability of non-life insurance companies in Poland and according to the 

study of the GDP growth and the market share of foreign owned companies positively impact profitability of non-

life insurance companies.  Suheyli Reshid (2015) found that economic growth rate has significant influence on 

profitability. Mister (2015) found that economic growth is not significant determinants of profitability. Hadush 

(2015) found that GDP is negatively but significantly related with profitability. GDP growth shows positive but 

insignificant relationship with insurers’ profitability.  Hana Mariam (2015) found that out that GDP growth shows 

positive but insignificant relationship with insurers’ profitability.  Nino Datu (2016) examined the association 

between Insurer-specific indicators and macroeconomics on profitability in Philippine non-life insurance market 

utilizing the panel data over the period of 2008 through 2012. Return on assets (ROA) and operating ratio were used 

for profitability. According to the study there was no evidence found in the Gross Domestic Product (GDP) on 
profitability in both ROA and operating ratio.  In this study, GDP was found to be negatively related with 

profitability and it has insignificant effect on profitability. Therefore the studies were found to be contradictory.   

Chen-Ying Lee (2014) found that inflation rates exhibit negative correlation with ROA, but is not significantly 

different from zero.  Curak et al. (2012) examined the determinants of the profitability of the Croatian composite 

insurers’ between 2004 and 2009.The finding showed that inflation and return on equity have a significant influence 

on insurers’ profitability. Michael Doumpos et al (2012) Using a sample of over 2000 nonlife insurance firms 

operating in 91 countries between 2005 and 2009,they found that macroeconomic inflation influence the overall 

performance of firms and a statistically significant effect on the overall performance of insurers.  ViktoriaNikolaus 

(2015) examines determinants of firm performance of Indonesian and Dutch firms over the period of 2009-2013. 

The study found that Inflation, which is high in Indonesia, has a negative influence. The more moderate inflation 

rate of the Netherlands leads to a positive, although not significant effect.  
Nino Datu (2016) examined the association between Insurer-specific indicators and macroeconomics on profitability 

in Philippine non-life insurance market utilizing the panel data over the period of 2008 through 2012. Return on 

assets (ROA) and operating ratio were used for profitability. According to the study there was no evidence found 

that inflation has effect on profitability in both ROA and operating ratio. In this study, inflation has negative 

relationship with profitability and it affects profitability insignificantly.  Chukwulozie (2006) explained that low 

level of income, low level of education, lack of insurance awareness, high inflation rate, lack of reliable Actuarial 

data for research and underdeveloped financial market had affected savings for life insurance consumption. 

Although, his work was based on life insurance as a source of long term savings, there is no empirical evidence to 

justify his work.  Zhu (2007) explained that life insurance and stock purchases are independent of each other; life 

insurance purchases influence by individual’s income, bequest intensity, risk attitude, survival probability, and the 

insurance risk premium and stock purchases are affected by individuals’ income, risk attitude, the risk free rate of 

return, stock return, stock volatility. Life insurance and stock purchases are positively related with each other and 
affected by all factors. Chui and Kwok (2008) examined that national culture affects the consumption pattern of life 

insurance across countries. Research hypothesis were tested empirically by using Hofstede’s cultural dimensions 

and data from 1976-2001 across 41 countries and found that individualism indeed has a significant, positive on the 

life insurance consumption, whereas power distance and masculinity/ femininity have significant, negative effects. 

Chen and Mau (2009) focused on ethical and non-ethical sales behavior of salesperson’s regarding customer trust in 

the salespersons’ and in the company which affects customer loyalty in the life insurance industry and found that the 

salesperson’s ethical sales behavior does play a crucial role in customer loyalty through customer trust.  

Malik (2011) investigated that determinant of profitability in insurance industry in Pakistan, the effect of factors 

such as age of company, size of company, volume of capital, loss ratio and leverage ratio on profitability. The 

sample of 35 life and non-life insurance cover the period of 2005-2009 and concluded that there is positive 

association between size of company and profitability and there is no relationship between profitability and age of 
company and also showed that volume of capital is significantly and positively related to profitability. Leverage 

ratio and loss ratio showed negative but significant relationship with profitability. 

Tang (2001) revealed that some sources of customer value such as relational quality price and corporate image were 

differentiated significantly across psychographic segments while service and product qualities were not significantly 

affected by psychographics, service quality was found to be the core factors to all customers. By applying factor 

analysis and K-means clustering methods were used to develop psychographic segments and concluded that 



 
 

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demographic and psychographic characteristics found to have significant effect on sources from which customers 

derived value. Ayaliew (2013) examine the determinants of life insurance for a time series data for the period 1991-

2000. There is a casual relationship between life insurance sector development and economic growth in the 
developing country. This study showed that life insurance is determined by per capita income, life expectancy, real 

interest rate and inflation.  

Oke et al (2010) examined the determinants of life insurance consumption in Nigeria during the period 1970-2005 

within an error correction framework. Co-integration technique revealed that real gross domestic product and 

structural adjustment facility positively and significantly influence life insurance consumption in Nigeria while 

indigenization policy and domestic interest rate are statistically significant but inversely related to life insurance 

consumption. On the other hand return on investment, inflation rate, openness of the economy, political instability 

are insignificant predictors of life insurance consumption in Nigeria. Moullee et al., (2013) showed that insurance 

consumption decision is influenced by monetary considerations such as consumer’s evaluation of a service in 

monetary terms and the search for the possibility to reduce the amount premiums payable for insurance and 

indicated that demographical and socio-economical characteristics of consumers influence their behavior. Factor 

analysis and multiple regression analysis were used to determine how the factors are formed and their relative’s 
weights. Five factors had been identified the acceptability of insurance condition, insurance service providers 

competence, consumers monetary attitudes towards insurance, the positively of consumers insurance experience, 

and the possibility to reduce the amount of premiums payable for insurance. 

Lee (2001) found that without aging process, the purchase rate in 1990 and 1995 was lover. The baby boomers 

purchase less life insurance than their earlier counterparts and this phenomenon consequently led to the decline of 

recent life insurance purchases in the U.S. Men show a strong age effect and strong negative cohort effects while 

women have strong positive cohort effects. Wee et al (2007) examined the determinants of life insurance 

consumption in OCED countries and found that there is significant positive income elasticity of life insurance 

demand. Demand increases with the no of dependents and level of education and decrease with the expectancy and 

social security expenditure whereas high inflation and real interest rates tend to decrease consumption. Life 

insurance demand was better explained when the product market and socioeconomic factors were jointly considered. 
Omar and Frimpong (2007) found that increased level of consumer consciousness and lack of welfare benefits were 

increasing growth factors for the life insurance market in Nigeria and the purchase behavior towards life insurance 

was determined by normative factors, the suggestion was that the initial point of contact for marketing 

communication regarding the purchase of life insurance should have family and friends. 

Beck and Webb (2002) highlighted the issues of finding the reasons behind the variations in life insurance 

consumption by using unbalanced panel data of 68 countries
 

from 1961 to 2000. They employed four various 

proxies of consumption, economic, demographic
 

and institutional
 

factors. Results cleared that countries with large 

income per capita, stable banking sector and lower inflation tend to use huge quantity of life insurance. In addition 

to its life insurance consumption was observed to be directly affected by private savings and real interest rate. 

Demographic elements such as education, urbanization, life expectancy, young and old dependency ratio had not 

any robust effect on the life insurance consumption.  Hwang and Gao (2003), examined the elements for life 

insurance demand in China by explaining the huge growth in this industry after the economic reforms of 1978. 
Study found that the basic element that have effected people to buy insurance policies are positively related to upper 

stages of economic security, the rise in the education level and the modification in social structure. However, this 

study had not found an inverse influence of inflation on life insurance consumption; even China faced large inflation 

in the mid-1990s.  

Sen and Madheswaran (2007) investigated the role of economic and political variables in the life insurance 

consumption pattern of 4 SAARC, 6 Asian and 2 greater China region economies from 1994 to 2004. Insurance 

penetration and density were the dependents element in cross country analysis and the estimates of fixed and random 

effects model proved that incomes, savings and inflation were main variables in describing insurance consumption. 

Study also done the time series analysis of life insurance demand for India from 1965 to 2004 and findings cleared 

that income (GDP per capita), financial depth, per policy price of insurance products and real interest rates were 

significant factors.  Nesterova (2008) explored the modifications in life insurance demand for 14 countries of former 
Soviet Union and Central and Eastern Europe including Ukraine from 1996-2006. Panel results cleared that 

economies with greater life expectancy at birth, income and education level, old dependency ratio had larger life 

insurance consumption while, financial development, inflation and real interest rate decreased the life insurance 

demand across countries, whereas, young dependency ratio, urbanization level and institutional factors did not had 

any significant relation to life insurance demand.  



 
 

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Ade et al, (2010) explored the elements of life insurance consumption in Nigeria by using Co integration and Error 

Correction Model from 1970-2005. Study found the presence of a long run link and a short run dynamics among the 

factors. Co-integration results cleared that real gross domestic product and SAP directly and significantly affect Life 
Insurance demand in Nigeria while indigenization policy and interest rate are statistically significant but negatively 

linked to life insurance demand. On the other side study found that return on investment, inflation, openness and 

political instability are insignificant determinants of life insurance demand.  

Chen and Wong (2004) revealed that size, investment and liquidity are significant determinants of the profitability 

of insurers. However, Ahmed et al., (2011) in a similar study of the Pakistani life insurance industry, claimed that 

liquidity is not a significant determinant of insurers’ profitability. They posited that, whereas size and risk (loss 

ratio) are significant and positively related to the profitability of insurance firms, leverage is negative and hence 

decreases the profitability of insurers significantly.  

Malik (2011) delved into the determinants of the financial performance of 35 listed life and non-life companies 

covering the period of 2005 to 2009. Although his study covers both sectors of the insurance business, much of his 

findings seem to confirm that of Ahmed et al (2011). Specifically, Malik found that whereas size and capital have 

strong positive association with insurers’ profitability, loss ratio and leverage have strong inverse relationship with 
profitability.  Hrechaniuk et al. (2007) examined the financial performance of insurance companies in Spain, 

Lithuania and Ukraine. Their results showed a strong correlation between insurers’ financial performance and the 

growth of the written insurance premiums.  Pervan and Pavic (2010) and Curak et al (2011) investigated into the 

impacts of firm-specific, industry-specific and macroeconomic variables on the financial performance of the 

Croatian non-life and composite insurance companies respectively. The results of Pervan and Pavic revealed an 

inverse and significant influence of ownership, expense ratio and inflation on profitability. In lending support to the 

findings of Pervan and Pevic (2010), Curak et al (2011) indicated that size, underwriting risk, inflation and equity 

returns have significant association with composite insurers’ financial performance. 

There has not been any known study on the effect of monetary policy on the performance of the insurance industry 

in Nigeria. Similar study such as Aburieme (2008) examined the effect of monetary policy on the performance of 

Nigerian banking industry. The neglect in empirical research can be traced to the indirect effect of monetary policy 
on the insurance industry. 

3. Research Methodology 

Data  collections  for  this  study  were  from  secondary  sources  of  information.  The sources include the Central 

Bank of Nigeria (CBN), financial statement of the quoted insurance firms, textbooks, and journals, write ups and 

various publications such as CBN statistical bulletin and periodical Bulletin. A regression method of Ordinary 

Least Square (OLS), granger causality test, error correction estimate, Augmented Dickey Fuller unit root test and 

Johansen co-integration test of research design were adopted to ascertain the effect of monetary policy on return 

on equity of the quoted insurance firms within the periods covered in this study.  

3.1 Analytical Framework  

The econometric model to consider in this study takes monetary policy and return as the explanatory variables and 

return on equity as dependent variable respectively. These variables are used at constant  prices  to  obtain  a  

reliable  parameter  estimates  in  the  time  series  regression.  This study used the multiple regression models.  The test 
is basically   modeled    based   on   an   estimated   regression model, by Ordinary Least Square (OLS) estimator 

(Pesaranet al., 2001).  An F-test of the joint significance of the coefficients of the model, t h e  variables was 

used to test the hypothesis of no co-integration among the variables against the presence of co- integration among 

the variables .  The null  hypothesis  of no co-integration bet ween  the dependent  and the independent  

var iables as specified below:  

ROE = f (EXR, INTR, TBR, MPR, G-M2)                                                                                                           1 

It is empirically stated as  

ROE = 0 EXR1 INTR2 TBR3 MPR4  25 MG               2 

 Where  

ROE = Return on Equity of the Quoted Insurance Firms 

EXR = Naira Exchange Rate per US Dollar 

INTR = Interest Rate 

TBR = Treasury Bill Rate 

MPR = Monetary Policy Rate 

G-M2 = Growth of Money Supply 

A-Priori Expectation: 00,,, 35421    



 
 

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Formulating model for hypotheses testing  

Hypothesis I 

ROE = 0 +  EXR2         3 

ROE = 0 +  INTR2         4 

ROE = 0 +  TBR2         5 

ROE = 0 +  MPR2         6 

ROA = 0 +   22 MG         7 

3.2 Estimation Procedure 

3.2.1 Unit Root Test 

Most of time series have unit root as demonstrated by many studies including Nelson and Plosser (1982), Stock and 

Watson (1988) and Campbell and Peron (1991). Therefore, their means of variance of such time series are not 

independent of time. Conventional regression technique based on non-stationary time series produce spurious 
regression and statistic may simply indicate only correlated trends rather true relationship Granger and Newbold 

(1974). Spurious regression can be detected in regression model by low Durbin Watson and relatively moderate R2. 

Therefore, to distinguish between correlation that arises from share trend and one associated with an underlying 

causal relationship; we use both the Augmented Dickey fuller (Dickey and Fuller, 1979, 1981)  

ttt XX   1           8 

The null hypotheses for the ADFstatistic test are H0. 

Non stationary (unit root) and H0: Stationary respectively  

3.2.2 Cointegration 

To search for possible long run relationship amongst the variables, we employ the Johansen and Juselius (1990) 

approach. Thus, the study constructed a p-dimensional (4x1) vector auto regression model with Gaussian errors that 

can be expressed by its first differenced error correction form as 

ttktkttt YYYYY    1112211 .....
    

9 

Where Yt are the data series studied, t  is i. i. d, N(0,∑) i + -1 + A1 + A1  + A2 + A3 + ……. + Ai for i = 

1,2,3……..,k-1, П = I – A1 – A2 - ……-Ak. The П matrix conveys information about the long term relationship 

among the Yt variables studied. Hence, testing the cointegration entails testing for the rank r of matrix П by examine 

whether the eigenvalues of П are significantly different from zero. Johansen and Juselius (1990) proposed two tests 

statistics to determine the number of cointegrating vectors (or the rank of П), namely the trace and the maximum 

eigen-value (-trace) is computed as; 

)1(
1 


n

rj jInTtrace                                                                                                                       10 

The trace tests the null hypothesis that “at most” r cointegration vector, with “more than” r vectors being the 

alternative hypothesis. The maximum eigenvalue test is given as: 

)1( 1max  rTIn            11 

It tests the null hypothesis of r cointegrating vectors against the alternative hypothesis of r + 1 cointegration vectors. 

In the equation (3) and (4), is the sample size and  is the largest canonical correlation. 
3.2.3 Granger Causality  

In case we do not find any evidence for cointegration among the variables, the specification of the Granger causality 
will be a vector auto regression (VAR) in the first difference form. However, if will find evidence of cointegration, 

there is the need to augment the Granger-type causality test model with a one period lagged error term. This is a 

crucial step because as noted by Engel and Granger (1987). 

 XXYY
n

i

at

n

i

y

ot 







1

11

1

1

                                                                                          

12 

and 

t

n

i

t

y
n

i

ot XYXYX 







1

111

1

                                                                                   13 



 
 

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3.2.4 Error Correction Model (ECM) 

Co-integration is a prerequisite for the error correction mechanism. Since co-integration has been established, it is 

pertinent to proceed to the error correction model. 

4. Analysis and Discussion of Findings 

To contribute on the existing body of knowledge on the effect of monetary policy on the performance of insurance 

companies in Nigeria, this study used data sourced from Central Bank of Nigeria Statistical Bulletin, comprising 

Growth rate of Money Supply, Interest rate, Monetary Policy rate, Exchange Rate and Treasury Bill Rate   variable 

as independent variables while insurance performance is the dependent variables which is proxy by return on capital 

employed of the industry. The multiple regression models formulated in section three of this study was used to 

examine if there are any significant affect between monetary policy variables and the performance indicator of 

insurance companies. 

 

Table 1:  Presentation of Results 

Variable Coefficient Std Errs. T-Statistics Prob. 

TBR -3.128729 4.405528 -0.710182 0.4854 

MPR 0.558755 5.215878 0.107126 0.9157 

INTR 1.697928 3.954184 0.429400 0.6720 

G_M2 1.438339 1.280460 1.123299 0.2740 

EXR 0.901562 0.335393 2.688077 0.0138 

C 235.5590 105.8539 2.225322 0.0371 

R-squared 0.434751    

Adjusted R-squared 0.300168    

F-statistic 3.230356    

Prob(F-statistic) 0.025598    

Durbin-Watson stat 2.082706    

Source: Extracts from E-view 

4.1 Interpretation of Results 

From the regression results, the coefficient of determination (R2) shows that 43.4 % and 30. 0% variance in the 

return on equity of insurance companies can be traced to the independent variables in the model. The Durbin Watson 

statistics of 2.08 indicate the presence of negative serial autocorrelation between the variables. The F-statistics of 

3.2303356 at the probability of 0.0025598 signify goodness of fit of the model and conclude that there is significant 

relationship between the independent variables and the dependent variable. 

The regression intercept shows the positive effect of the dependent variable at constant. The independent variables 

are positively related to the dependent variable except Treasury bill rate. The positive coefficient of 1.69 as 
parameter for interest rate, 0.558 as parameter for monetary policy rate, 3.559, 1.438 as Growth of money supply 

and 0.901 for exchange rate indicates that an increase of 1% will lead to increase return on equity of the quoted 

insurance firms 1.6%, 0.5%, 0.9%, 3.5%, while the negative coefficient of 3.128 will reduce return on investment by 

3.1%.  

 

Table 2: Unit Root Test Summary Results at Level  

Variable ADF Statistics Mackinnon Prob. Order Of Intr. 

1% 5% 10% 

ROE -3.353256 -3.711457 -2.981038 -2.629906 0.0225 1(0) 

TBR -2.934911 -3.711457 -2.981038 -2.629906 0.0550 1(0) 

MPR -2.807462 -3.711457 -2.981038 -2.629906 0.0710 1(0) 

INTR -3.152949 -3.711457 -2.981038 -2.629906 0.0349 1(0) 

G_M2 -3.998872 -3.752946 -2.998064 -2.629906 0.2163 1(0) 

EXR -2.184019 -3.711457 -2.981038 -2.622989 0.0000 1(0) 

 

Unit Root Test Summary Results at First Difference 

ROE -5.409135 -3.769597 -3.004861 -2.642242 0.0003 1(1) 

TBR -4.455081 -3.808546 -3.020686 -2.650413 0.0025 1(1) 



 
 

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MPR -6.952318 -3.724070 -2.986225 -2.632604 0.0000 1(1) 

INTR -8.903711 -3.752946 -2.998064 -2.638752 0.0000 1(1) 

G_M2 -4.464667 -3.788030 -3.012363 -2.646119 0.0023 1(1) 

EXR -4.422308 -3.724070 -2.986225 -2.632604 0.0019 1(1) 

Source: Extracts from E-view 

 

The stationarity of the variables were examined using the Augmented Dickey Fuller tests. The results of the 

stationarity test of the variables are presented in Table II. The results reveal the order of integration and the 

significance level of the variables of the model. After the application of the ADF test on the level  and first 

difference series, the computed variables of the ADF statistics are more negative than the MacKinnon critical 

values; we therefore reject the null hypothesis that the time series data variables are non-stationary (have a unit 

root). The time series exhibit difference stationarity (i.e. stationary at first difference).  

 
Table 3: Johansen Co-Integration Test Results: Maximum Eigen 

Hypothesized  

No. of CE(s) 

Eigen value Trace Statistics 0.05  

Critical Value 

Prob.** Decision 

None *  0.842539  127.8286  95.75366  0.0001 Reject H0 

At most 1 *  0.752612  81.61417  69.81889  0.0043 Reject H0 

At m+ost 2  0.551278  46.69427  47.85613  0.0640 Reject H0 

At most 3  0.437992  26.66048  29.79707  0.1102 Accept H0 

At most 4  0.274238  12.25448  15.49471  0.1451 Accept H0 

At most 5 *  0.156036  4.241139  3.841466  0.0394 Accept H0 

Trace Statistics 

None *  0.842539  46.21446  40.07757  0.0090 Reject H0 

At most 1 *  0.752612  34.91990  33.87687  0.0374 reject H0 

At most 2  0.551278  20.03379  27.58434  0.3388 Accept H0 

At most 3  0.437992  14.40600  21.13162  0.3326 Accept  H0 

At most 4  0.274238  8.013342  14.26460  0.3774 Accept H0 

At most 5 *  0.156036  4.241139  3.841466  0.0394 Reject H0 

Source: Extracts from E-view 

Having  established  the  presence  of  unit  root  in  most  of  our  variables,  we  conducted multivariate  co-

integration  tests  using  Johansen  Maximum  Likelihood  tests  to  determine whether  a  long  run  relationship  

exist  between  the  variables  of  the  model.  The results of   the cointegration test presented in the table   III above.  

The results of the Johansen’s co-integration test reflect the two statistics test namely, the trace statistic and the 

maximum eigen-value proposed by Johansen and Juselius (1990). From the Tables, the trace statistic is small 

when the values of the characteristic roots are closer to zero (and its value will be large in relation to the values of 

the characteristic roots which are further from zero).  
The  other  test,  the  maximum  eigen-value  is  an  alternative  test  statistic  which  tests  the  null  hypothesis that 

the number of r co-integrated vectors is r against the alternative of (r+1) co- integrated vectors. (i.e. the null 

hypothesis r = 0 is tested against the alternative that r = 1; r = 1 against the alternative r = 2). If the estimated 

value of the characteristic root is found to be close to zero, then the maximum eigen-value will be small.  

The co-integration results  suggest the existence of two co-integrating vectors as the trace statistics rejects the 

null hypothesis of no co-integrating vector at  5%  significant  level  and  accept  the  alternate  hypothesis  two  

co-integrating  vectors. Similarly, the maximum eigen-value rejects the null hypothesis of r = 0 co-integrating 

vector at 5% significant level and accepts the alternate hypothesis of two co-integrating vectors. Therefore,  since  

both  test  statistics  suggest  the  presence  of  two  co-integrating vector, we can conclude that the variables are 

co-integrated and follow long-run equilibrium relationship.  

 
 

Table 4: Normalized Conintegrating Equation 

ROE TBR MPR INTR G_M2 EXR  

 1.000000 -16.68808  3.265243  9.267604  0.185339 -1.814423  



 
 

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  (4.11521)  (5.30918)  (2.68680)  (0.87176)  (0.22047)  

Source: Extracts from E-view 

Test for normalized cointegration is necessary to ascertain the direction of long run relationship between the 

dependent variable and independent variables. From the table above, treasury bills and exchange rate have negative 

long run while monetary policy, interest rate and growth of money supply have positive long run effect on return on 
equity of the quoted insurance firms. 

 

Table 5: Error Correction Estimate 

Error Correction: D(ROE) D(TBR) D(MPR) D(INTR) D(G_M2) 

CointEq1 -0.291610 -0.015426  0.009619  0.026969  0.045262 

  (0.24888)  (0.00982)  (0.00513)  (0.00643)  (0.03422) 

 [-1.17167] [-1.57109] [ 1.87602] [ 4.19338] [ 1.32251] 

      

CointEq2 -7.608238 -1.290766  0.921757  1.211761  4.573301 

  (16.4824)  (0.65025)  (0.33957)  (0.42591)  (2.26649) 

 [-0.46160] [-1.98502] [ 2.71448] [ 2.84510] [ 2.01779] 

      

C  14.43325 -0.518821  0.105338  1.028680  1.885706 

  (25.6970)  (1.01378)  (0.52941)  (0.66402)  (3.53359) 

 [ 0.56167] [-0.51177] [ 0.19897] [ 1.54917] [ 0.53365] 

 R-squared  0.556701  0.551684  0.850064  0.863455  0.627888 

 Adj. R-squared  0.073103  0.062613  0.686497  0.714496  0.221947 

 Sum sq. resids  152532.0  237.4014  64.74072  101.8491  2884.225 

 S.E. equation  117.7563  4.645638  2.426009  3.042862  16.19266 

 F-statistic  1.151164  1.128023  5.197053  5.796615  1.546748 

 Log likelihood -139.1394 -61.55491 -45.96256 -51.39978 -91.52202 

 Akaike AIC  12.67829  6.212909  4.913547  5.366649  8.710168 

 Schwarz SC  13.31640  6.851022  5.551659  6.004761  9.348281 

 Mean dependent  7.036250 -0.173333 -0.031250  0.437917 -0.312083 

 S.D. dependent  122.3118  4.798282  4.332833  5.694772  18.35751 

 Determinant resid covariance (dof adj.)  2.69E+08    

 Determinant resid covariance  5443935.    

 Log likelihood -356.3928    

 Akaike information criterion  35.94940    

 Schwarz criterion  39.63082    

Source: Extracts from E-view 

Results from error correction estimates proved that return on equity and treasury bills have negative coefficient 

while other variables in the model have positive coefficient in equation I this also confirm the results in equation II.  

The R2 and adjusted R2  shows the  explained variation of the variables.  
 

Table 6: Parsimonious Error Correction Results 

Variable Coefficient Std. Error t-Statistic Prob.   

C 22.20226 23.58401 0.941412 0.3637 

D(ROE(-1)) 0.390055 0.475306 0.820639 0.4266 

D(ROE(-2)) -0.070217 0.361520 -0.194228 0.8490 

D(ROE(-3)) -0.133370 0.230699 -0.578110 0.5731 

D(TBR(-1)) 0.211941 4.788389 0.044262 0.9654 

D(MPR(-2)) -2.197283 5.266743 -0.417200 0.6833 

D(INTR(-1)) -0.629065 4.381392 -0.143576 0.8880 

D(G_M2) 2.897508 1.350514 2.145486 0.0514 



 
 

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D(EXR(-1)) -1.616601 1.341164 -1.205371 0.2495 

ECM(-1) -1.682663 0.617255 -2.726040 0.0173 

R-squared 0.679901     Mean dependent var 6.596522 

Adjusted R-squared 0.458293     S.D. dependent var 125.0413 

S.E. of regression 92.03131     Akaike info criterion 12.18116 

Sum squared resid 110106.9     Schwarz criterion 12.67485 

Log likelihood -130.0833     Hannan-Quinn criter. 12.30532 

F-statistic 3.068043     Durbin-Watson stat 2.270015 

Prob(F-statistic) 0.032935    

Source: Extracts from E-view 

Information from the unit root tests, and the estimated co-integrating relationship were used to specify the 

short-run Error Correction dynamic Model. The test was conducted to reconcile the short-run and long run 

dynamism. The result obtained for the model is explained in Table V above.  The  coefficient  of  the  error  

correction  model  for  the  estimated  ROE  equations  is statistically significant and negative. Specifically, if the 

actual equilibrium value is too high, the error correction term will bring it down, while if it is too low, the error 

correction term will raise it. The value of the coefficient however implies that when ROE is out of its long run 

trend, 116% of the error is corrected at each level to restore equilibrium but with a stronger and significant effect.  

Statistically, the fit is good for ROE with R2 indicating 67.9% of the total variation as explained by the included 

variables.  The remaining 32.1 percent of the total variation in ROE is unaccounted for by the regression line and is 

attributed to the factors included in the disturbance term (μ). The presence of unit root in the residual series usually 

drive Durbin-Watson test towards zero, but the value of this statistic (2.27), which is approximately 2, is 

within the acceptable limit for zero autocorrelation and it is considered interesting because it reinforces the 

acceptance of the null hypothesis of no serial correlation in the residual of the model.  
Table 7: Pairwise Granger Causality Tests 

 Null Hypothesis: Obs F-Statistic Prob.  

 TBR does not Granger Cause ROE  25  0.51402 0.6058 

 ROE does not Granger Cause TBR  4.30244 0.0279 

 MPR does not Granger Cause ROE  25  0.45448 0.6412 

 ROE does not Granger Cause MPR  2.54165 0.1039 

 INTR does not Granger Cause ROE  25  0.56170 0.5790 

 ROE does not Granger Cause INTR  0.18347 0.8338 

 G_M2 does not Granger Cause ROE  25  0.28828 0.7526 

 ROE does not Granger Cause G_M2  0.84385 0.4448 

 EXR does not Granger Cause ROE  25  2.32850 0.1233 

 ROE does not Granger Cause EXR  0.66066 0.5274 

Source: Extracts from E-view 

The results above show causality between private return on equity and monetary policy rate as well as their 

independent variables as used in this study. The null hypothesis in case of return on equity and monetary policy rate 

is not accepted. As stated in the methodology, null hypothesis is rejected if Fcal>Ftab; accept otherwise, at 5% level 
of significance. From result presented we say likewise, all other variables exhibit no causal relationship, therefore 

we accept the null hypothesis. 

4.2 Test of Hypotheses 

Treasury bill rate and return on equity: T-cal  -0.710 < 2.080 T-critical, Probability value 0.4854 > 0.05, 

accept null hypothesis 

Monetary policy rate and return on equity: T-cal  0.107 < 2.080 T-critical, Probability value 0.9157 > 0.05, 

accept null hypothesis 

Interest rate and return on equity: T-cal  0.429 < 2.080 T-critical, Probability value 0.6720 > 0.05, accept null 

hypothesis 

Growth of money supply and return on equity: T-cal  1.123 < 2.080 T-critical, Probability value 0.2740 > 0.05, 

accept null hypothesis. 



 
 

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Exchange rate and return on equity: T-cal  2.688 > 2.080 T-critical, Probability value 0.0138 > 0.05, accept 

alternate hypothesis. 

4.3 Discussion of Findings 
Apart from the monetary policy objectives of achieving economic grows, full employment, price stability and 

external balance, one of the objective of monetary policy is to ensure financial market stability. According to the 

IMF report 1990 on financial stability index, profitability is a measure of financial stability. A profitable company is 

able to withstand monetary and macro economic shocks in the operating environment and maximize shareholders 

wealth. Again apart from the internal factors that determines profitability of the insurance firms, the monetary policy 

have been found to significantly impact on the performance of the financial institutions, this is because the financial 

market is a transmission mechanisms of the monetary policy. 

The findings of this study revealed that monetary policy examined in this study have positive relationship with the 

performance of the insurance companies in Nigeria. This finding confirms the objective of monetary policy in 

ensuring financial system stability. The findings confirm the findings of Okoye (2014) on the positive impact of the 

monetary policy in investment of insurance companies in Nigeria, the findings in contrary with the finding of Jiwan 

(2012) on the negative relation between monetary variable and the performance of commercial bank in Nigeria. The 
positive relationship of the monetary policy variables can be trace to the fact that insurance company does not transit 

much of the monetary policies compared to the commercial banks.    

5. Conclusion 

The objective of this study was to establish the relationship between monetary policy and performance of insurance 

companies in Nigeria. Time series data were source from Central Bank of Nigeria statistical bulletin. The study 

modeled return on capital employed as the function of interest rate, monetary policy rate, Treasury bill rate, growth 

of money supply and exchange rate. From the findings 43.4% and 30.0% variation on return on equity of the 

insurance firms can be traced to the monetary policy variable examined in the study. From the above the study 

concludes that there is significant relationship between monetary policy and the performance of insurance 

companies in Nigeria.     

6. Recommendations 
The management of the insurance companies should device measures of managing the negative effect of monetary 

policy instrument on the performance of the insurance companies and the monetary authorities should 

harmonize the profitability objectives of the insurance companies with that of monetary policy to avert the 

negative effect. 

 Interest policies should be revisited or fully deregulated to allow market forces of demand and supply to 

avert the negative effect on the performance of the insurance firms and there should be expansionary 

monetary policy that enhances the investment of the insurance companies for better performance. 

 The operating environment of the insurance companies should be overhauled to enhance effective 

management of the monetary environment and there should also be measures to effectively manage the 

monetary policy shocks that occur in the process of administering monetary policy. 

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