







































 
 

 

40 
© 2025 by the authors; licensee Asian Online Journal Publishing Group 
 

Economy 
Vol. 12, No. 2, 40-50, 2025 

ISSN(E) 2313-8181: / ISSN(P) 2518-0118: 
DOI: 10.20448/economy.v12i2.6779 

© 2025 by the authors; licensee Asian Online Journal Publishing Group 

 
 

 
 
 
Modelling asymetric response of stock market volatility to monetary policy: 
Empirical evidence from Nigeria 

 
Zainab Said Suwaid1 
Lawal Wasiu Omotayo2 

 

 
( Corresponding Author) 

 
1,2Department of Economics Bayero University Kano and Al-Hikmah University Ilorin, Nigeria. 
Email: zzsuwaid.sce@buk.edu.ng  
Email: suki4wisdom@yahoo.com  

 
Abstract 

This paper analyses how stock market volatility responds to monetary policy during bull and bear 
market phases in the period from the first quarter of 1990Q1 to the fourth quarter of 2023Q4 
using MS-VAR model. It investigated stock market fluctuation in both the bull and bear periods 
using the composite index of the Nigerian Stock Exchange (NSE), the All Share Index and the 
most appropriate monetary policy indicator, the interest rates. The monetary policy shocks were 
found to positively respond to the stock market volatility with relatively small volatility in the 
first regime. For the second regime, the graph shows that an increase in monetary policy shock 
positively affects volatility at the onset before afterwards turning the move into the negative side 
of volatility. This policy advice is that the Central Bank of Nigeria (CBN) should be extra cautious 
when setting and enforcing fiscal measures. Furthermore, due to the erratic performance by 
Nigeria stock market, the government and the relevant authorities should avoid interfering with 
the market during these situations as such interferences may trigger further instabilities to the 
market, because such measures merely slow the causes down, and do not bring lasting solutions. 

 
Keywords: Exchange rate, Interest rate, Money supply Markov switching. 

 
Citation | Suwaid, Z. S., & Omotayo, L. W. (2025). Modelling 
Asymetric response of stock market volatility to monetary policy: 
Empirical evidence from Nigeria. Economy, 12(2), 40-50. 
10.20448/economy.v12i2.6779 
History:   
Received: 26 April 2025 
Revised: 19 May 2025 
Accepted: 10 June 2025 
Published: 16 June 2025 
Licensed: This work is licensed under a Creative Commons 

Attribution 4.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Funding:   This study received no specific financial support. 
Institutional Review Board Statement: Not Applicable 
Transparency: The   authors   confirm   that   the   manuscript   is   an   
honest, accurate, and transparent account of the study; that no vital features of 
the study have been omitted; and that any discrepancies from the study as 
planned have been explained. This study followed all ethical practices during 
writing. 
Data Availability Statement: The  corresponding  author  may  provide  
study data upon reasonable request 
Competing Interests: The author declares that there are no conflicts of 
interests regarding the publication of this paper. 
Authors’ Contributions: Both authors contributed equally to the conception 
and design of the study. Both authors have read and agreed to the published 
version of the manuscript.  

 
Contents 
1. Introduction ...................................................................................................................................................................................... 41 
2. Literature Review ............................................................................................................................................................................ 42 
3. Methodology ..................................................................................................................................................................................... 44 
4. Results and Findings ....................................................................................................................................................................... 45 
5. Discussion of Findings .................................................................................................................................................................... 48 
6. Conclusion and Recommendation ................................................................................................................................................ 48 
References .............................................................................................................................................................................................. 49 
Appendix ................................................................................................................................................................................................ 50 
 

 
 

 

 

 

https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
https://www.doi.org/10.20448/economy.v12i2.6779


Economy, 2025, 12(2): 40-50 

41 
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Contribution of this paper to the literature 
A major weakness observed in the previous studies is that annual data were employed However, this 
study has used monthly and quarterly data. Consequently, the bulk of the studies utilized the stock 
market as the representation of the overall financial market neglecting the rest of the market segments. 
Among all but one or two of the literatures scrutinized in this section, only two incorporate the usage of 
MS VAR.  

 
1. Introduction 

CBN’s major responsibility as stated expressly in the CBN Act of 2007 was to achieve price stability and a 
sound monetary system. In achieving these, updated information and tendencies related to the functioning of the 
economy must be well established. As a result, research towards a better perspective of the Nigerian economy will 
assist the CBN in attaining its monetary policy goals. Stock market fluctuation has been a worrisome factor in the 
financial markets among policy makers, analysts, corporate executives and economists. During the 1980’s there 
was explosive development of new financial markets in the international level as futures and options contracts on 
interest rates, stock indices, and foreign exchange rates. These markets experienced spectacular expansion until the 
world stock market was damped in October 1987, resulting into serious consideration on shocks and effects of 
these new financial products. Hence, a number of reforms concerning the financial markets were introduced and 
instituted. As the economies experienced high growth and high fluctuations in asset prices, there arise the need to 
question the efficient markets hypothesis, which asserts that financial markets must provide the correct value for 
securities.  

Monetary policy and its impact on stock market volatility have also received considerable attention in the 
literature with focused on developed countries (Bomfim, 2003; Chatziantoniou, Duffy, & Filis, 2013; Chen & 
Clements, 2007; Farka, 2009; Konrad, 2009; Lobo, 2002; Vähämaa & Äijö, 2011). The synthesis of various previous 
studies reveals that central bank monetary policy is an important determinant of stock market volatility. The 
evidence indicates that the responses of stock returns and volatility to policy interest rate changes are symmetric. 
While analyzing stock returns, Lobo (2000); Bernanke and Kuttner (2005); Chuliá, Martens, and Van Dijk (2010) 
and Zare, Azali, and Habibullah (2013) investigated the asymmetries associated with the direction of the monetary 
policy shocks. Guo (2004); Andersen, Bollerslev, Diebold, and Vega (2007) and Basistha and Kurov (2008) worked 
on business cycles asymmetry. Besides, Jiang (2018) focused on the asymmetrical effects of the positive and 
negative monetary policy shocks on stock market volatility. However, existing literature testing the asymmetry 
effect of stock market volatility to monetary policy during the bull and bear phases remains scanty.  

In the last four decades, co-movement between stock markets and monetary variables has attracted 
considerable emphasis from economists and financiers of the developed and emerging economies (Hassan, 2017). 
Theoretically this linkage is perhaps obvious since monetary variables are known to have a pivotal role in the 
course of determining a firm’s cash flows and the systematic risk of the firm. Because stock prices equal the present 
value of expected dividend tutorials, any prospective or contingent alteration in information related to inflation, 
interest rates, exchange rates and other monetary variables might disturb stock prices through alliterating either 
expectations of dividends or discount rates, or perhaps both (Arnold & Vrugt, 2006). Hence, it is theoretically 
plausible to claim that the conditional variance of current stock returns is associated with the conditional variance 
of expected future cash flows and discount rates, together with their conditional covariance. Since both expected 
cash flow and discount rate depends on monetary variables, it is not surprising to find that higher variability of 
these variables cause a similar response in the volatility of current returns on stock (Poon & Granger, 2003). A 
cross-section of African countries have operated in the context of continuing macroeconomic troubles accentuated 
by constant shifts in monetary and macroeconomics. As a result, the over-arching question arise; to what extent are 
these policies yielding the desired economic impacts with regards to the financial market? Currently, people have 
differing views in relation to the impacts of monetary policy. For instance, while asserting their tenets, monetarist 
vehemently support that monetary policy plays a very important role in impacting economic activities, they argue 
that unexpected changes in stock of money cause movements in output and growth. They further strengthen that 
only if the money supply rises unexpectedly can the central bank stimulate economic growth (Adeolu, Kehinde, & 
Bolarinwa, 2012).  

The analysis of the available literature also shows that decisions on monetary policy affect the real part of the 
economy (Ioannidis & Kontonikas, 2008). From their study, found out that monetary policy affects stock prices and 
that there is a pass-through effect on consumption and investment spending in the real sector. This is in 
congruency with Modigliani’s life cycle hypothesis where there is a positive relationship between consumer’s and 
investment spending as well as Tobin’s q hypothesis, investment expenditure and prices for stocks (Ewing, 2001). 
Further, Chami, Cosimano, and Fullenkamp (1999) established that the main policy channel through which the 
effects of monetary policy are transmitted is significantly different from the money and credit channels, but rather 
it involves change in asset prices. This is supportive of the idea that the monetary policy might be transmitted 
through the stock market as an additional mechanism. Moreover, Laopodis (2013) established that monetary policy 
affects the real economy through financial markets implying that these markets are essential component in 
transmitting effects of monetary policy actions on the real economy.  

Several studies  on the monetary policy and the financial market  can also be explained with reasonable body of 
literature such as Ajie and Nenbee (2010) using co-integration and Error Correction Modeling found that money 
supply and interest rate (the proxy for monetary policy) had short-run significant effects on stock prices in Nigeria, 
in the same year (Okpara, 2010) carried out same study and found that monetary policy is significant in 
determining stock market returns only in the long run using Two Stage Least Squared method. This study is in 
line with the work undertaken by Adeniji, Ben, and E. (2018) who applied bi-variate and multivariate VAR and 
partly in line with the study undertaken by Osakwe and Chukwunulu (2019) who employed the Ordinary Least 
Square regression techniques. Different from this results is the work of Abaenewe and Ndugbu (2012) this research 
proved monetary policy does not explain changes in equity prices in Nigeria. Although the literature suggests an 
interaction between monetary policy and the stock market, a major gap exists, However, as Pitelis (2013) has noted 



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and as Laopodis (2013) has observed and included in our previous discussion, there is no unique model that 
organizes the empirical findings of this relationship across several economies or different types of monetary 
systems. Of interest therefore is to ask whether or not bull and bear market response asymmetry to monetary 
policy reforms exist? This may be the missing link on the variant of the monetary policy inability to exact its main 
targets.  After the introductory section, this study is organized into four additional sections. Section 2.0 reviews the 
existing conceptual, theoretical, and empirical literature, while Section 3.0 outlines the methodological framework 
for the empirical investigation. In Section 4.0, the empirical model is estimated, and the findings are discussed. 
Finally, Section 5.0 presents the conclusions and offers policy recommendations. 
 

2. Literature Review  
Monetary policy is deliberate manipulation of quantity, price and availability of money and credit by the 

monetary authorities to attain certain macroeconomic objectives relating to internal and external balance. This is 
achieved through controlling money supply and or interest rates so as to control the general money supply in the 
economy. Okigbo (2008) stated that monetary policy literally made reference to all the steps which were taken to 
regulate the supply of money and credit in an economy. On the other hand, the financial market is a small platform 
that brings together surplus units and deficit units for various income generating activities. This includes financial 
institutions of savers who mobilize funds in the form of savings and lenders who need these funds. While financial 
intermediation is the payments made from one center having excess funds to another center in need.  This role of 
transferring financial resources from the surplus units to the deficit units is what is referred to as financial 
intermediation. Reszat (2008) classified financial markets into internal financial markets and external financial 
markets.  

In Nigeria like every other countries, the central bank of Nigeria has other mandates that include; to attain full 
employment, to have maximum sustainable growth, and stable interest and exchange rates. To achieve these 
objectives they regulate and interfere with the financial markets (Central Bank of Nigeria, 2018).  

Monetary policy influences the real economy mainly through the financial market, this appears to be which the 
missing connecting link through which the monetary policy and the real economic segment of the country. There 
are several different ways through which monetary policy impacts the financial markets. Nevertheless, all possible 
transmission mechanisms starting from the monetary policy on the financial markets going down to the real sector 
of the economy stem from the monetary policy instrument. More specifically, the paper shows that in the case of 
emerging markets (EMs) the conditions influencing financial development have mainly depended on long-term 
international interest rates which in turn are connected with the monetary policies of developed countries (Sobrun 
& Turner, 2015). Typically, the monetary policy instrument a price in any financial market that is not directly set 
directly by or closely monitored by the monetary authority. For the majority of central banks with flexible foreign 
exchange regimes today, this instrument is a short term interest rate. In fixed a fixed exchange rate regime, a 
particular exchange rate serves as the instrument whereas, in monetary targeting regime, the amount of central 
bank money in the banking system is usually as the instrument.  

This research is based on the present value or discounted cash flow theory which gives much understanding in 
a situation where changes in monetary policies affect stock markets and their prices. The above model depicts that 
St = D (t+j) / R, t is specifically postulating the constant discount rate. For its simplified form it posits that the 
investors are free to choose between two deliberate investment options: either a stock with an expected gross 

return Et ⌊S(t+1)+D(t+1) ⌋/St or invest in a risk-less bond with constant nominal gross rate of return 1+R within a 
given period of time (one period). According to the theory of arbitrage concept, if investors are to pay lesser 
attention to the one to pick from the two choices possible, then the return on investing in the stock or bond must 

be anticipate i.e., 𝐸𝑡[𝑆𝑡+1 + 𝐷𝑡+1]/𝑆𝑡 = 1 + 𝑅. When taken further, this gives rise to anticipated difference 
equation presented in Equation 1 below;  

 

𝑆𝑡 = 𝐸𝑡 [∑ (
1

1+𝑅
)𝑗𝐷𝑡+𝑗

𝑘
𝑗=1 ] + 𝐸𝑡 [(

1

1+𝑅
) 𝑆𝑡+𝑘]      (1) 

In this context, 𝐸𝑡represents the conditional expectations operator based on information available to market 
participants at time t, R is the return rate used by these participants to discount future dividends, and K refers to 
the investor’s time horizon (or stock holding period). The standard transversality condition indicates that as the 
horizon K increases, the second term on the right side of Equation 1 approaches zero, implying that there are no 
rational stock price bubbles, as demonstrated in Equation 2 below: 

 

𝑙𝑖𝑚
𝑘→∞

𝐸𝑡 [(
1

1+𝑅
)𝑘𝑆𝑡+𝑘] = 0     (2) 

Thus, based on the discussion by Campbell and MacKinlay (1996) regarding models of rational bubbles that 
relax the transversality condition, along with their development of the present value model using variable discount 
rates, we arrive at the familiar form of the present value model presented in Equation 3. 

 

𝑆𝑡 = 𝐸𝑡 [(∑
1

1+𝑅
)𝑗𝐷𝑡+𝑗

𝑘
𝑗−1 ]      (3) 

 
From Equation 3, it can be inferred that a variation in monetary policy can move stock prices in two folds.  
The empirical studies of monetary policy interactions and financial market fluctuations can be divided into 

three. These will involve examining studies on developed countries, the second will be on emerging economies and 
the last on developing economies.  

Andrés, Mestre, and Vallés (1999) explained how prices, output and the exchange rate responded to a monetary 
policy shock. The evidence based on the SVAR model confirms that the Spanish economy is characterized by the 
efficiency of the asset price adjustment, nominal rigidities and long run monetary non-neutrality. The main source 
of the shock is the interest rate, while the exchange rate has a tendency to over-reverse the long-term value. Also, 
the estimates are not characterized by a liquidity puzzle, price puzzle, or exchange rate puzzle. Again, Mumtaz and 



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Zanetti (2013) analyzed the impact of monetary policy uncertainty by employing structural vector autoregression 
(SVAR), model enriched in two ways. First, it allows for time-varying variance of monetary policy shocks with a 
stochastic volatility model. Second, it allowed exhibiting the dynamics of the interactions between the levels of the 
endogenous variables of the VAR and the time varying volatility. The results shows that nominal interest rate, 
output growth, and inflation fall in reaction to an increase in the volatility of monetary policy in the US. It also 
builds a Dynamic stochastic general equilibrium model with stochastic volatility in monetary policy which gives 
similar results.  

Furthermore, Ma, Wang, and He (2022) analyse the within and cross-sectional mechanisms and interaction 
between economic policy uncertainty (EPU) and stock market realized volatility in G7 countries. The directional 
spillover indicator was then computed utilizing the monthly economic policy uncertainty index and the realized 
volatility series for each country. The analysis showed that the degree of the spillover effect of economic policy 
uncertainty on volatility of the stock markets is higher in the USA, Japan, and Canada and reveals regional 
resemblance. Meanwhile, we find that economic and policy uncertainty has stronger and more persistent responses 
to the stock markets of France, Germany as well as Italy and its effects last for 3 to 18 months. More recently, 
Yang, Zhou, Du, Du, and Zhou (2023) analyzed the link between the global oil price, stock prices, and policy 
interactions for China and US. The study applied time-varying parameter stochastic volatility vector 
autoregression model to examine the transmission of the global oil price shocks, Chinese and US stock market 
volatility and economic policy uncertainty indices in China and the US for the period 2003-2020. The analysis 
established positive and significant relationship between fluctuations of the stock markets in the two countries and 
shifts in the international prices of oil. It was identified that the US stock market had a higher effect on the global 
oil market than the Chinese stock market. Besides, the research showed that the increase of the economic policy 
uncertainty steepens the oil price volatility in the global markets while the increase in oil price volatility enhances 
the volatility of the stock markets and economic policy uncertainty in the two countries. 

Yoshino, Taghizadeh-Hesary, Hassanzadeh, and Prasetyo (2014) in their study on emerging economies 
employed VECM on Tehran stock prices with respect to exogenous monetary policy shocks for the period 
1998Q1to2013Q2. They found that monetary policy affects stock market prices through three channels: money 
supply, rates of exchange, and inflation. The evidence are in favour of their hypothesis that stock prices invariably 
increase following a change in monetary policy regime. The variance decomposition analysis further shows that 
after ten periods, more than half of the forecast error variance in Tehran Stock Exchange Price Index originates 
from the US dollar–Iranian rial exchange rate shocks while a less than one fifth fraction of this variance owes its 
source to the Iranian real GDP shocks. They argue that this evidence is explained by an endogenous adjustment of 

stock price to monetary policy shocks. Interestingly, Atiş and Erer (2018) examined the asymmetric responses of 
stock market returns and volatility to monetary policy during bull and bear markets in Turkey from 2002 to 2016. 
The study uses markov switching model with the policy rate as the monetary policy measure. The empirical results 
revealed that monetary policy is more potent during a bull market environment. Again, Si, Zhao, Li, and Ding 
(2021) investigated the fluctuating volatility interconnectedness between different forms of policy uncertainty and 
sectoral markets in China in time and frequency variations. Base on the time frequency connectedness index 
method, the empirical results illustrated a highly connected network between policy uncertainty and Chinese 
sectoral stock market especially under mid and long run horizons. Most significantly, policy makers were least 
concerned with the monetary policy contributing the least to spillovers of the four classification types of policy 
uncertainty. 

In Nigeria, Babajide, Isola, and Olukayode (2016) who uses autoregressive Distributed Lag bound testing 
techniques with the view of analyzing the relationship between monetary policy instruments and the stock market 
in Nigeria. The accumulation of evidence suggested that monetary policy tools affected the character of stock 
market in the country. Partially, this study is relevant to Shehu UR (2021) work, which applied GARCH and 
EGARCH methodologies to test the effect of monetary policy innovations on stock returns prior to the Nigeria 
stock exchange during the Global Financial Crisis. The empirical analysis showed that the unsystematic 
(unexpected) parts of policy innovations concerning money supply and monetary policy rate are negatively 
associated with Nigeria stock exchange returns while systematic (anticipated) parts are not. In the same vein, 
Adeniji et al. (2018) applied an empirical analysis on the impact of monetary policy shocks on stock market price 
volatility in Nigeria using time series data from June 1999 to December 2016. Two quantitative methods were used 
in the analysis, the autoregressive distributed lag (ARDL) model and the exponential generalized conditional 
heteroscedasticity (EGARCH) model. The study discovered that monetary policy shocks affect stock market price 
thereafter experiencing increased volatility in Nigeria. However, among the set of monetary policy indicators, 
interest rate on its own was significant at both the short and long run in trying to explain the variability of the 
stock market prices, while M1 was only significant in the short run only. Osakwe and Chukwunulu (2019) analyzed 
the relationship between monetary policy and stock market performance in Nigeria for the period of 1986 to 2015 
using OLS regression analysis. Consequently, the findings revealed that money supply and exchange rate 
positively influenced stock price movements and were respectively highly significant, but interest rate was 
insignificantly negative. In all, it emerges that variables of monetary policy explain 94 percent of the fluctuations of 
stock market performance in Nigeria. In particular, Aliyu (2021) noted that monetary policy can be used to control 
stock market operations, his present work proved that it has a significant positive impact on stock market 
performance.  

From the literatures reviewed, it can be deduced that most studies on financial market in Nigeria were confined 
mainly to the stock market segment of the financial market and the methodologies which were employed were 
primarily VAR, OLS and VECM. A major weakness observed concerning the previous studies is that annual data 
were employed except for some of them. However, this study has used monthly and quarterly data. Again, the bulk 
of the studies utilized the stock market as the representation of the overall financial market neglecting the rest of 
the market segments. Among all but one or two of the literatures scrutinized in this section, only two incorporate 
the usage of MS VAR.  
 



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3. Methodology  
Hamilton (1989) Markov-Switching (MS) autoregressive models have become widely recognized as an effective 

alternative for representing key features of the business cycle. Consequently, a growing number of empirical 
studies have adopted regime-switching models to capture the nonlinearities and asymmetries observed in various 
macroeconomic variables (Boldin, 1996; Garcia & Perron, 1996; Kim, Nelson, & Startz, 1998; Krolzig, 1996; 
Krolzig & Toro, 2001).The basic specification of the Markov Switching model assumes that deviations of 
dependent variable from its mean follow a p-th order autoregressive process: 

∆𝑦𝑡   −   (𝑠𝑡)  =  1 (∆𝑦𝑡−1  −   (𝑠𝑡−1 ))+. . . ,2(∆𝑦𝑡−𝑝  −   (𝑠𝑡−𝑝 )) +  𝜀𝑡     (4) 

The errors, et are assumed to be independently and identically distributed (ii) with a mean of zero and a 

constant variance of σ2 while the process mean, μ is influenced by a latent variable, st. This relationship suggests 
that different regimes correspond to distinct conditional distributions of yt the latent variable strepresents the state 
of the business cycle, distinguishing between two regimes: "expansion" and "contraction." The autoregressive 
parameters in model (3.1) can vary depending on the state st within the Markov chain. 

∆𝑦𝑡   −  𝑐 (𝑠𝑡)  =  1(𝑠𝑡 )∆𝑦𝑡−1 +. . . +𝑝(𝑠𝑡)   +   𝜀𝑡     (5) 

If st can assume one of M distinct values represented by the integers 1 through M, Equation 5 illustrates a 
mixture of M autoregressive models. In the case of two regimes, model 3.1 characterizes a "falling" state when st = 
1 and a "rising" state when st = 2 for the yt variable. Regime 1 is categorized as an economy in recession and can 
be represented as follows: 

∆𝑦𝑡   − 𝑐1 (𝑠𝑡)  =  11∆𝑦𝑡−1 +. . . +𝑝1∆𝑦𝑡−𝑝   +  𝜀𝑡   (6) 

Regime 2 is classified as an economy in expansion and can be represented as: 

∆𝑦𝑡   − 𝑐2 (𝑠𝑡)  =  12∆𝑦𝑡−1 +. . . +𝑝2∆𝑦𝑡−𝑝   +  𝜀𝑡   (7) 

The latter process is an ergodic Markov chain with a finite number of states, characterized by its transition 
probabilities. 


𝑖𝑗

 =  
𝑟

(𝑠𝑡 = 𝑗 /𝑠𝑡−1  =  𝑖),     ∑ 𝑀
𝑖=𝑗 𝑖𝑗

=  1    (8) 

For ∀ij =1. . , M, it is specifically assumed that st follows an ergodic Markov process with M states, 
characterized by an irreducible transition matrix. 

However, fixed or constant transition probabilities are too restrictive to fully capture stock market dynamics. 
As a result, an extension of Hamilton (1989) model allows for time-varying transition probabilities, as explored by 
Filardo (1994) and Diebold, Hahn, and Tay (1999). The Markov Switching model with time-varying transition 
probabilities provides more flexibility than models with fixed probabilities. For example, it can identify systematic 
changes in transition probabilities around turning points, better capturing complex patterns of temporal 
persistence and enabling the modeling of varying expected durations over time. 

The applied Markov model enables volatility to vary across regimes, allowing it to capture the time-varying 
volatility characteristic of stock markets, which is an important stylized fact in financial markets. In this regard, the 
model incorporates Filardo (1994) framework. 

 

𝑃𝑖𝑗,𝑡
= 𝑃𝑟[𝑅𝑡 = 𝑗/𝑅𝑡 = 𝑖, 𝑍𝑡−1 =

𝑒𝑥𝑝(𝜆𝑖,𝑗,0+𝑍′𝑡−1𝜆𝑖,𝑗,1)

1+𝑒𝑥𝑝(𝜆𝑖,𝑗,0+𝑍′𝑡−1𝜆𝑖,𝑗,1)
       (9) 

Where: 

1...,2,1;...,2,1 −== MjMi  and 
−

=− =−====
1

1 ,,,1,,, ;...,2,11],/[
m

j tjitttrtmji MiPZiRMRPP  

M represents the number of regimes, and Rt is a first-order Markov variable. Zt is a vector of economic 
variables that drive the transition between regimes. The study applies Filardo (1994) time-varying probabilities to 
analyze the effect of oil price shocks on stock market performance, modeled under two distinct regimes. Transitions 
between these regimes are determined by a probability process. 

Regime 1: .......................................................
)'exp(1

)'exp(
]1/1[

11 110

1
1110

1

j

n

j t

j
t

n

j

ttr

Z

Z
RRP









= −

−=

−

++

+
=== (10) 

 

Regime 2: ................................................
)''exp(1

)'exp(
]2/2[

21 120

21 120

1

j

n

j t

j

n

j t

ttr

Z

Z
RRP









= −

= −

−

++

+
===  (11) 

 
The probability of staying in a low stock performance regime is determined by the previous regime, which 

could either be low or high stock performance, while the probability of transitioning to a high stock performance 
regime is similarly based on whether the prior regime was low or high stock performance. Zt  is a vector of j 
macroeconomic and policy-related variables used to forecast the future trajectory of stock performance. This 
approach aligns with the arbitrage pricing theory, which posits that mispriced securities may create short-term 
risk-free profit opportunities. By allowing transition probabilities to fluctuate over time, we can better understand 
the factors driving shifts between the low stock performance regime (R t -1, R t-1) and the high stock performance 
regime (Rt=2), as well as the reverse transitions. 

The key parameters that influence the transition probabilities between regimes are the coefficients λ. 

evaluating the sign of these coefficients is crucial. For example, if the coefficient 11λis positive, the associated 
economic factor Z significantly increases the probability of staying in the high stock performance regime (regime 
1). On the other hand, if the coefficient is negative, the corresponding macroeconomic variable Z reduces the 
likelihood of remaining in the low stock performance regime and raises the probability of transitioning between 

high and low stock performance regimes (regime 2). Similarly, the coefficient 12λ reflects the effect of the 



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exogenous variable Z on the probability of remaining in the high stock performance regime (regime 2) or shifting 
to the low stock performance regime (regime 1), depending on whether this coefficient is positive or negative, 
respectively. The period of investigation spanned 1990Q1 to 2023Q4 Published time series data which give 
numerical values about the variables under study will be collected from relevant secondary sources which include; 
National Bureau of Statistics (NBS) publications, Nigeria Stock Exchange (NSE). Publications, World Bank and 
International Financial Statistics (IFS), and Central Bank of Nigeria (CBN) Annual Reports and Statistical Bulletin 
(various issues). 
 

4. Results and Findings  
4.1. Statistical Properties of Variable  

The result of the descriptive statistics presented in Appendix 1 shows evidence of significant variations as 
shown by the huge difference between the minimum and maximum values for all variables except for M2 which has 
lesser variation. The study utilised skewness, kurtosis and Jarque-Bera statistics to test for normality of the data 
series. The skewness test indicates that some of the variables are negatively skewed while some are positively 
skewed. The EXRATE is the least skewed among the variables which implies that information in the variables has 
a long tail in the negative direction. From the kurtosis statistics, the study observed all variables are leptokurtic 
which portrays the asymmetry of the distribution. Jarque-Bera statistic is the most encompassing normality test 
which further reinforced the earlier tests by showing that the study can reject the null hypothesis of normality for 
all the variables because as seen the test statistic for all variables is not close to zero. The average for the variables 
is greater than the standard deviation except for TBILLS and REALGDP. This indicates that all observations are 
closer to the mean with the exception of TBILLS and REALGDP. It is important to mention that the descriptive 
statistics only provides a historical background for the behaviour of the time series data as such information obtain 
cannot be used to make a general inference. 

The Markov Switching Vector Autoregressive model in regime 1 presented in the Table 1 shows that the lag 
of interest rate has significant effects on interest rate, the lag of IPI is also significant in explaining IPI. Also, the 
lag of M2 significantly affected M2, also the lag of ALLSHR has significant effects on ALLSHR. Unlike regime 1, 
in regime 2 the lag of EXR has significant effects in EXR whereas the lag of INTR is has no significant effects in 
INTR. The lag of IPI has significant effects on IPI. However, the lag of M2 and ALLSHR has no significant effects 
on M2 and ALLSHR. 
 
Table 1. VAR estimation. 

 EXR INTR IPI M2 ALLSHR 

Regime 1 

EXR 
 0.120 
 0.001 

 0.003 
 0.005 

 0.008 
 0.011 

-0.002 
-0.005 

        0.245*** 
-0.103 

INTR 
 0.006 
 0.018 

 0.330*** 
-0.068 

-0.018 
-0.006 

 0.011 
 0.006 

-0.030 
 0.157 

IPI 
-0.221 
 0.074 

 0.020 
-0.049 

        0.213*** 
       0.317*** 

-0.017 
 0.008 

 0.281 
 0.151 

M2 
 0.537 
 0.578 

-0.147 
 0.258 

-0.094 
 0.061 

      0.328*** 
      0.337*** 

0.252 
   -1.750** 

ALLSHR 
-0.04 

-0.082 
-0.034 
 0.062 

 0.012 
 0.012 

 0.001 
    -0.009** 

       0.254*** 
 0.076 

Regime 2 

EXR 
-0.084 

  3.132*** 
 0.063 
-0.003 

-0.009 
-0.124 

        0.089*** 
-0.016 

 0.427 
-0.913 

INTR 
 2.076 

 11.466 
-0.085 
-1.897 

-1.038* 
-0.304 

 0.076 
-0.121 

 0.640 
-7.418 

IPI 
 29.213 
-11.446 

-2.533 
 1.684 

 5.351** 
-0.419 

-0.750 
 0.044 

-16.200 
 8.474 

M2 
 6.320 
-0.962 

 10.487 
-10.605 

 3.407 
-1.520 

 1.772 
 1.297 

 17.017 
  -20.234 

ALLSHR 
 7.368 
 0.115 

-0.449 
-0.008 

 0.035 
-0.045 

-0.154 
 0.0248 

-2.360 
 0.206 

Note: *** indicates significant at 1% (2.58), ** at 5% (1.96), * at 10% (1.65). 

 

4.2. Transition Probabilities 
The constant Markov transition probabilities and expected durations for sample 1990Q1 to 2023Q4 is 

presented below. 

Note that P(i,k) = 𝑃(𝑠(𝑡)) = 𝑘|𝑠(𝑡−1 = 𝑖) 
Where row = i and column = k 

𝑃𝑖𝑗 = [
𝑃11 𝑃12

𝑃21 𝑃22
] = [

0.923162 0.076838
1.000000 4.91𝐸 − 11

] 

Hence 𝑃11 = 0.923162, 𝑎𝑛𝑑 𝑃22 = 4.91𝐸 − 11. 𝑊ℎ𝑒𝑟𝑒; 𝑃11 + 𝑃12 = 1, 𝑃21 + 𝑃22 = 1 
Constant expected durations:  

    
   1  2 

  13.01439 1.000000 
    

The transition probabilities output shows the probability of the stock market being in state 1 or state 2 at time 
t given its state at time t-1. The constant transition probabilities indicate that when the stock market is in state 1, 
there is a 92.32% probability it will remain in that state in the next period and a 7.68% probability it will transition 



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to state 2. On the other hand, when the stock market is in a state 2, there is a very low probability of 4.91E-11 
(essentially zero) that it will remain in that state in the next period, and a 100% probability it will transition to 
state 1. The expected durations provide the average length of time the stock market is expected to remain in each 
state before transitioning to the other state. The expected duration for state 1 is 13.01 quarters, while the expected 
duration for state 2 is 1 quarter. These transition probabilities and expected durations suggest that the stock 
market is more likely to remain in state 1 for an extended period, and if it transitions to state 2, it is likely to return 
to a state 1 relatively quickly. This asymmetric response of the stock market to both states may be due to the 
influence of monetary policy, as the VAR model suggests. 

The probability of being in State 1 (recession economy that is the bear market period) as indicated in Figure 1 
is relatively high during the period of 2009 to 2013, but it gradually decreases afterwards. Also, the probability of 
being in State 2 (expansion economy that is the bull market period) is relatively low during the period of 2009 to 
2013, but it gradually increases afterwards. Consequently, from around 2016 onwards, the probability of being in 
State 2 becomes higher than the probability of being in State 1, indicating that the economy is more likely to be in 
an expansion state than a recession state during this period. In all, the smoothed regime probabilities suggests that 
the economy went through a period of recession during the aftermath of the 2008 financial crisis, but it gradually 
recovered and shifted towards an expansion state in the following years. 

 

 
Figure 1. Markov switching probabilities.  

 
As indicated in the Figure 2a, the response of NASI to INTR (monetary policy) shocks is mostly positive, 

except for the second period where it is negative. This suggests that during state 1, monetary policy has a positive 
impact on stock market volatility, except for the immediate response. However, we also see that the magnitudes of 
the responses are relatively small. The largest response occurs in period 3, where the increase in stock market 
volatility due to monetary policy shock is around 0.009. In contrast, the smallest response occurs in period 10, 
where the increase in stock market volatility due to monetary policy shock is only 0.000394. In all, the regime 1 
IRF suggests that there is a slightly positive reaction of stock market volatility to changes in monetary policy, but 
the magnitudes of the responses are relatively small. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 



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  Regime Dependent IRF  

 
Figure 2a. Regime Dependent IRF for regime 1. 

 
Again, as indicated in Figure 2b  in regime 2, an increase in monetary policy shock (INTR) initially results in a 

positive impact on stock market volatility (NASI). However, this effect is short-lived, as volatility quickly declines 
into negative territory in the following period. The negative response of stock market volatility to monetary policy 
in this regime suggests that contractionary monetary policy can negatively affect the stock market during bear 
markets. Conversely, an increase in monetary policy shock also leads to an initial positive impact on stock market 
volatility in the first period, but this effect strengthens in subsequent periods and lasts longer. The positive 
response of stock market volatility to monetary policy in this regime indicates that expansionary monetary policy 
can have a stimulating effect on the stock market during bull markets. 

This supports one view of how monetary policy affects the stock market, which suggests that an increase in the 
money supply leads to higher stock prices, thereby stimulating both the stock market and the wider economy. Since 
stock prices are influenced by expected dividends and interest rates, any unexpected changes in monetary policy 
are likely to impact stock prices either directly through the interest rate channel or indirectly by altering the 
factors that affect dividends. The findings of this study align with those of Zare et al. (2013). 

 

 
Figure 2b. Regime dependent IRF for regime 2. 

 

4.3. Diagnostic Checks 
The reliability of any estimated econometric model is based on passing some diagnostic check. The most 

known diagnostic check in VAR framework are the serial correlation and normality test. This study employs the 
Breusch-Godfrey test for serial correlation and the AR roots test for normality. The result for the model estimated 
for Nigeria is presented with Table 2 for stability test as well as Figure 3 and Table 3 for serial correlation test of 
the model. The result of the test indicates no serial correlation and also passed the normality test. 
 
Table 2. Stability test result.  

Root Modulus 

0.714 0.714 
0.242 0.242 
0.206 0.206 
0.046 0.046 



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No root lies outside the unit circle. VAR satisfies the stability condition. 
 

 
Figure 3. Inverse root polynomial.  

 
The result of the inverse root as illustrated in Figure 3 indicated that no root lies outside the unit circle as such 

the VAR satisfies the stability condition. 
 
Table 3. Serial correlation. 

Lags Q-stat Prob. Adj Q-stat Prob. DF 

1 1.553 ---- 1.565 ---- ---- 
2 12.243 0.727 12.428 0.714 16 

Note: *test is valid only for lags larger than the VAR lag order. DF is degrees of freedom for (approximate) chi-square distribution. 

 
From the Table 3, probability is greater than 0.5 and the rule states that if probability is less than 0.5 there is 

serial correlation and if probability is greater than 0.5 there is no serial correlation. Therefore, in this case since 
probability is greater than 0.5 there is no case of serial correlation. 
 

5. Discussion of Findings  
In alignment with the study's objective to explore the asymmetric response of stock market volatility to 

monetary policy during bull and bear market periods using the Markov Switching Vector Autoregressive 
framework, the study analyzed stock market volatility during both bullish and bearish phases by utilizing the 
aggregate stock market indices from the Nigeria Stock Exchange (NSE) specifically, the All-Share Index—and the 
interest rate as a relevant monetary policy indicator. To enhance the empirical findings, additional control variables 
were included in the model, such as money supply (M2), the spot exchange rate (EXRATE), and the Industrial 
Production Index (IPI). The results indicate a slightly positive response of stock market volatility to monetary 
policy shocks, though the magnitude of these responses is relatively small in regime 1. In contrast, regime 2 
suggests that an increase in monetary policy shock initially has a positive impact on stock market volatility. 
However, the effect is brief, with volatility quickly shifting to negative territory in the second period. The empirical 
results from the Markov-switching analysis also suggest that monetary policy is more effective during bear market 
periods (regime 1) than during bull markets (regime 2), thus reinforcing the estimates of asymmetry. These 
findings align with previous studies by Chen and Clements (2007); Kurov (2010); Jansen and Tsai (2010) and 
Konrad (2009) which demonstrated that monetary policy tends to be more effective in bear markets. The negative 
response of stock market volatility to monetary policy in this regime indicates that contractionary monetary policy 
may adversely impact the stock market during bear phases. This aligns with the research of Adeniji et al. (2018) 
and Laopodis (2013). Furthermore, these findings support the expectations of the discounted cash flow model 
theory, which posits that changes in monetary policy can influence stock returns. Specifically, there is a direct effect 
on stock returns due to changes in the discount rate used by market participants. A more restrictive monetary 
policy raises the capitalization rate for firms' future cash flows, which causes a decrease in stock prices. 
 

6. Conclusion and Recommendation  
This study examined the relationship between monetary policy dynamics and financial market volatility in 

Nigeria. This relationship is a 2-way relationship as one affects the other and also the latter has effects on the 
former. Information from the financial market is crucial for both market participants and the central bank. The 



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central bank seeks to understand how its monetary policy actions influence financial markets, while market 
participants use this information to evaluate stock prices and manage their portfolios. In theory, stock prices reflect 
the expected present value of future net cash flows. As a result, an expansionary monetary policy typically boosts 
future cash flows or lowers the discount rates applied to them, leading to a positive relationship with stock prices. 
This study explored the dynamics of monetary policy and financial market volatility in Nigeria. Using Markov 
Switching Vector Autoregressive model from 1990q1 to 2023q4. It was discovered that there is slightly positive 
response of stock market volatility to monetary policy shocks, but the magnitudes of the responses are relatively 
small during bear market periods. Whereas bull market period suggests that an increase in monetary policy shock 
leads to an initial positive impact on stock market volatility which is short lived and then later becomes negative. 
Consequent upon this findings, it is well established that financial markets are highly responsive to economic 
policies, so the government and the Central Bank of Nigeria (CBN) must exercise caution when formulating and 
implementing these policies. To mitigate the impact of economic policies on the stock market and lessen the risk of 
volatility, it is recommended that greater emphasis be placed on the formulation of monetary policy. 
 
List Abbreviation 
(NBS) National Bureau of Statistics, (NSE) Nigeria Stock Exchange, World Bank and International Financial 
Statistics (IFS) and (CBN) Central Bank of Nigeria Annual Reports and Statistical Bulletin. 
 

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 Appendix 
 
Appendix 1. Descriptive statistics. 

Variables INTR EXCHR IPI M2 ALLSHR 

Mean 15.56 95.8 789 12.4 356 

Median 14.27 99.8 809 12.6 347 

Maximum 30.23 206 217 16.1 777 

Minimum 5.91 7.60 145 8.20 313 

Std deviation 5.23 57.60 535 2.26 258 

Skewness 0.69 0.001 0.61 0.02 0.15 

Kurtosis 3.27 2.09 2.72 1.70 1.44 

Jaque-Bera 10.60 4.50 8.28 8.98 13.5 

Probability 0.00 0.11 0.02 0.01 0.001 

Sum 199 123 1.02 203 455 

Sum sq develop 350 421 3.63 130 8.43 

Observation 128 128 128 128 128 

 

 

  

 

 

 

 

 

 

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