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Volume 10 Issue 1, January-March 2022 

ISSN: 2836-9416 

Impact Factor: 4.85 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

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1 | P a g e  

UNRAVELING THE IDIOSYNCRATIC VOLATILITY PUZZLE: 
INSIGHTS FROM THE SRI LANKAN MARKET 

 
 

Dr. Kasun S. Perera and Dr. Anusha R. Silva 
Department of Finance, Faculty of Management and Finance, University of Colombo, Sri Lanka 

 
Abstract: The Capital Asset Pricing Model (CAPM) has been a cornerstone in asset pricing literature, 
assuming that investors hold well-diversified portfolios, making idiosyncratic volatility irrelevant for 
pricing stock returns. However, Merton (1987) contends that information asymmetries prevent 
investors from achieving full diversification, making idiosyncratic volatility a critical factor in asset 
pricing. Supporting this argument, Goetzmann and Kumar (2008) provide empirical evidence that a 
significant portion of investor portfolios in the United States consists of undiversified holdings. 
This study reevaluates the traditional view by examining the role of idiosyncratic volatility in asset 
pricing, challenging the CAPM's assumption of perfect diversification. By considering the prevalence 
of undiversified portfolios, it explores how idiosyncratic volatility may indeed impact stock returns, 
shedding light on its relevance as a pricing factor. Through empirical analysis and theoretical 
insights, this research contributes to the ongoing discourse on asset pricing models and their 
applicability in real-world investment scenarios. 
Keywords: Capital Asset Pricing Model (CAPM), Idiosyncratic Volatility, Diversification, Asset 
Pricing, Information Asymmetry. 
 
  
1. Introduction  
The capital asset pricing model (CAPM), one of the major developments in the asset pricing literature, 
assumes investors hold the market portfolio in equilibrium (Fu, 2009). Hence, it denotes that only 
market risk should be priced in stock returns as the idiosyncratic volatilitycan be fully eliminated 
through diversification (PukthuanthongLe &Visaltanachoti, 2009). Therefore, all the empirical asset 
pricing models assume that the investors holdthe market portfolio in equilibrium so that they are not 
expecting a return for holding the idiosyncratic volatility as it can be fully eliminated through 
diversification. Hence, it is assumed only systematic risk should be priced in average stock returns and 
idiosyncratic volatility is irrelevant.  
However, Merton (1987) argues that due to existence of information asymmetries in the market, 
investors cannot fully diversify the idiosyncratic volatility as they unable to hold a well-diversified 
portfolio. Supporting Merton’s argument, Goetzmann and Kumar (2008) depict that out of a sample of 
more than 62,000 households in the United States during the period of 1991-1996, over 25 percent of 
the investor portfolios have only one stock whereas more than 50 percent of the investor portfolios have 
not more than three stocks. They further show that very smaller amount of investor portfolios (five 
percent to ten percent) have more than ten stocks. Hence, this shows that the idiosyncratic volatility is 
an important factor in asset pricing as the investors are holding undiversified investment portfolios.  

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American Research Journal of Economics, Finance and Management 

Volume 10 Issue 1, January-March 2022 

ISSN: 2836-9416 

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2 | P a g e  

Accordingly, Merton (1987) anticipates a positive relationship between average stock returns and 
idiosyncratic risk. He argues that investors are expecting a premium for bearing the idiosyncratic 
volatility. However, some empirical findings have created a substantive puzzle in the asset pricing 
literature in relation to the aforementioned relationship.   
For instance, Ang, Hodrick, Xingand Zhang(2006) demonstrate that the portfolios with the highest 
idiosyncratic volatility yield significantly lower returns where they conclude that it has created a 
puzzling surprise in the asset pricing literature. However, Bali and Cakici (2008) note that this relation 
mainly depends on several factors such as choices of data frequency, portfolio weighting schemes, break 
point calculations and choice of screens in sample selection. This is clearly in line with Fama (1998) 
who reports that changes in the long term returns of the stocks are highly sensitive to the methodology 
and statistical approaches that are used to measure them in different studies. 
In addition to that, it is surprising to observe the existence of idiosyncratic volatility in the United 
States, as it is considered to be one of the highly transparent markets in the world (Pukthuanthong-Le 
&Visaltanachoti, 2009). Nevertheless, the existence of idiosyncratic volatility becomes further 
complicated in the context of other markets. For instance, Kumari, MahakudandHiremath(2017) note 
the existence of idiosyncratic volatility becomes complicated in the context of emerging markets as 
these markets characterize with features such as higher transaction costs, multiple tax regimes, lack of 
transparency, illiquidity which are unique to such markets.Therefore, this clearly challenges the 
standpoint of empirical asset pricing models such as CAPM on the relation between risk and returns of 
an asset.Hence, it is questionable whether the systematic risk is the only risk that should be priced in 
stock returns (Pukthuanthong-Le &Visaltanachoti, 2009).  
Since, a considerable body of extant literature on idiosyncratic volatility is focused on developed stock 
markets such as the United States, it is important to investigate the existence of idiosyncratic volatility 
from another market context’s point of view. Accordingly, the present study focuses on the idiosyncratic 
puzzle from the Sri Lankan context as there is a dearth of research on idiosyncratic volatility in Sri 
Lanka and particularly in the frontier market context. Though, Pukthuanthong-Le and Visaltanachoti 
(2009) examine the pricing of idiosyncratic volatility by using the CAPM, Sri Lankan stock market has 
been given only a cursory attention in that study. Hence, there is a need of an in-depth study focusing 
only on the Sri Lankan stock market. Thus, this study revisits the relationship between average stock 
returns and idiosyncratic volatility with an updated data while using thefive-factor asset pricing model 
ofFama and French (2015). Moreover, the current study employs the Exponential Generalized 
Autoregressive Conditional Heteroscedasticity (EGARCH) model to estimate the idiosyncratic volatility 
of stocks.  
Therefore, the contribution of the present study to the existing literature is two-fold. Firstly, it sheds 
light on idiosyncratic volatility puzzle from a frontier market point of view and thereby it explains the 
influence of idiosyncratic volatility on average stock returns. Secondly and more importantly, this study 
provides novel striking evidence on the characteristics of idiosyncratic volatility particularly in terms of 
profitability and investment factors with the use of a five-factor asset pricing model of Fama and French 
(2015). The remainder of the paper consists as follows; section 2 discusses the existing literature in the 
light of idiosyncratic volatility while section 3 elaborates the data and methodology employed in the 

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3 | P a g e  

current study. Section 4 provides a comprehensive analysis of data whereas section 5 provides the 
conclusion of the study.  
2. Review of Related Literature  
Based on the foundation laid by the portfolio selection problem of Markowitz (1952), Modern Portfolio 
Theory (MPT) notes that the investment portfolios are constructed based on the performance of 
different assets and risk appetite of the investors. However, being a normative theory, portfolio 
selection explains how investors should behave while as a positive theory, asset pricing attempts to 
predict investment decisions based on mean-variance analysis (Fabozzi, Gupta&Markowitz, 2002). 
Hence, the asset pricing theory builds a nexus between risk and return of an asset.     
Even though the asset pricing theory emerges with the CAPM of Sharpe (1964), Famaand French 
(2004) note that simplified assumptions of CAPM made it empirically less successful; many extensions 
have been made to the CAPM in order to examine the relation between risk and return of an asset. For 
instance, arbitrage pricing model (Ross, 1976), three-factor asset pricing model (Fama& French, 1993), 
four-factor asset pricing model (Carhart, 1997) and five-factor asset pricing model (Fama& French, 
2015) are some of the popular factor models that develop to determine the price of an asset.  
Nevertheless, all factor models expect investors to act upon the changes in the market as quickly as they 
observe them.This is so because the financial models presume that markets are frictionless and 
investors are equipped with all information (Merton, 1987). On contrary, the empirical evidence shows 
various trading frictions in the market that prevent investors from making accurate investment 
decisions (Hou&Moskowitz, 2005; Miller & Scholes, 1982;Amihud&Mendelson, 1986; Amihud, 2002; 
Pastor &Stambaugh, 2003). 
Moreover, the information asymmetries in the market prevent the investors from holding diversified 
portfolios. In the context of a stock market, there are low priced securities with high idiosyncratic 
volatility where Kumar (2009) identifies them as ‘lottery-like’ securities. Confirming the findings of 
Kumar (2009), Bali, Cakiciand Whitelaw (2011) highlight that investors tend to choose ‘lottery-like’ 
securities to overcome the imperfect diversification problem. Hence, it is questionable to what extent 
the role of idiosyncratic volatility can be ignored in asset pricing decisions.   
Moreover, in the presence of information asymmetries in the market, factor models poorly perform in 
capturing the diversification decisions of investors (Merton, 1987). Therefore, Ang et al. (2009) argue 
that there is a possibility of generating a nexus between average stock returns and idiosyncratic 
volatility since the factor models fail to specify the role of idiosyncratic volatility in asset pricing 
decisions. This clearly highlights that idiosyncratic volatility plays a critical rolein investment decisions.  
Despite its relative significance in investment decisions, scholars have used different methods to 
estimate the idiosyncraticvolatility of stocks. For instance, in the path breaking seminal work of Ang et 
al. (2006) on idiosyncratic volatility, the authors use one month lagged idiosyncratic volatility as a 
proxy for idiosyncratic volatility of stocks while Bali andCakici, (2008) also adopt the same technique 
in their study. In contrast, while highlighting the estimation errors of the previous techniques, Fu 
(2009) suggeststhe EGARCH technique of Nelson (1991) to estimate the idiosyncratic volatility of 
stocks. Similarly, Pukthuanthong-Le and Visaltanachoti(2009)and Kumariet al.(2017) also follow Fu’s 
approach in order to estimate the idiosyncratic volatility of stocks.  

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Volume 10 Issue 1, January-March 2022 

ISSN: 2836-9416 

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Although, Ang et al. (2006) assume that idiosyncratic volatility follows a random walk, Fu (2009) 
denies the assumption of time varying property of idiosyncratic volatility can be approximated by a 
random walk process. Supporting Fu’s argument, based on a cross country analysis with a sample of 36 
countries, Pukthuanthong-Le and Visaltanachoti (2009) state that adoption of one month lagged 
idiosyncratic volatility estimation method leads to severe estimation errors. Therefore, based on the 
empirical evidence, Fu (2009) and Pukthuanthong-Le and Visaltanachoti (2009) negate the use lagged 
idiosyncratic volatility of stocks to derive at the inferences between average stock returns and 
idiosyncratic volatility.   
In spite of the strengths and weaknesses of each estimation method, the empirical findings on 
idiosyncratic volatility have created a substantive puzzle in the asset pricing literature. However, as per 
Bali and Cakici (2008), the existence of methodological differences among previous studies leads to 
conflicting arguments. Therefore, Fu (2009) emphasises that idiosyncratic volatility warrants not only 
a special attention but also a quality estimation process in deriving at the inferences between average 
returns and idiosyncratic volatility.  
3. Data and Methodology  
3.1 Data 
The data includes monthly stock returns and other accounting details pertinent to 214 non-financial 
firms listed on the Colombo Stock Exchange (CSE) over a period of 163 months from September 2004 
to March 2018. All required data is obtained from CSE data library, annual reports of listed companies 
and annual reports of Central Bank of Sri Lanka. Further, following Sriyalatha (2008), monthly stock 
returns are adjusted for bonus issues and rights issues. As in Fama and French (1992), Samarakoon 
(1997), and Abeysekera and Nimal (2016), this study excludes the firms with negative book-to-market 
ratio and firms listed under the banks, finance and insurance sector since such firms are heavily geared 
and higher level of gearing indicates distress risk for non-financial firms (Fama& French, 1992). The 
data includes with respect to the following variables; all share total return index (ASTRI) is used as the 
proxy for market return (Rm) while three-month government Treasury-Bill rate is used as a proxy for 
risk free rate of return (Rf).   
The market capitalization is used as a proxy for size (Size) while the book-to-marketequity (B/M) ratio 
is used as the proxy for value. Moreover, net profit as a fraction of book equity is used as a proxy for 
profitability (Prof) while the annual growth rate of the assets is used as the proxy for investment (Inv). 
 
3.2Factor Construction  
At the end of September each year t, the factor return portfolios are constructed and reformed at the 
end of September year t+1 (Samarakoon, 1997;Abeysekera&Nimal, 2017). According to Abeysekera and 
Nimal (2016), this enables to overcome the look-ahead biasness problem. In the current study, the 
factor return portfolios are constructed based on independent 2 x 3 sorts on Size-B/M, Size-Prof, and 
Size-Inv. The stocks are sorted as big and small stocks based on the market caiptalisationwhere the 
stocks in the top 50 percent of the market capitalization is categorized as Big (B) stocks while bottom 
50 percent is categorized as the Small (S) stocks (Fama& French, 1993).  

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Moreover, based on B/M, the stocks are categorised as growth (G), neutral (N) and value (V) stocks 
(bottom 30 percent, middle 40 percent, top 30 percent) and the intersection of independent 2 x 3 sorts 
produce six portfolios:  
SG, SN, SV, BG, BN, BV(Fama& French, 1993). Similarly, the stocks are categorised as weak (W), 
neutral (N), robust (R) based on Prof and as aggressive (A), neutral (N), conservative (C) based on Inv 
which leads to generate 2 x 3 sorts of Size-Prof (SW, SN, SR, BW, BN, BR) and Size-Inv (SA, SN. SC, 
BA, BN,BC) (Fama& French 2015).  
In addition to conventional size factor based on 2 x 3 sort of Size-B/M(SMBB/M), the use of 2x3 sorts on 
SizeProf and Size-Invproduce two additional size factors namely, SMBPorfandSMBInv. Therefore, size 
factor (SMB) from the three 2x3 sorts is defined as the average of SMBB/M, SMBPorfandSMBInv. Table 1 
shows a summary of factor construction process in the current study.  
Table 1: Construction of size, value, profitability and investment factors  

 
Sort   Breakpoints    Factors and their components   
2x3 sorts on Size  
and B/M, or Size  
and Prof, or Size 
and Inv   

Size:  CSE 
median 

   SMBB/M  = (SG + SN + SV)/3 – (BG + BN +BV)/3   
SMBPorf= (SR + SN + SW)/3 – (BR + BN +BL)/3   
SMBInv= (SA + SN + SC)/3 – (BA + BN + BC)/3   
SMB = (SMBB/M + SMBProf+ SMBInv)/3   

 B/M:  30th   
percentiles   

and  70th   
HML= (SV + BV)/2 – (SG + BG)/2   

 Prof:  30th  
percentiles   

and  70th   
RMW= (SR + BR)/2 – (SW + BW)/2   

 Inv:  30th  
percentiles   

and  70th   
CMA= (SC + BC)/2 – (SA + BA)/2   

Note: Researchers’ construction based on Fama and French (2015). Size, B/M, Prof and Inv are market 
capitalisation, book-to-market ratio, profitability and investment respectively. In the 2x3 sorts, the Size 
group, small (S), neutral (N) and big (B), the B/M group, growth (G), neutral (N) andvalue (V), the 
Profgroup, robust (R), neutral (N) and weak (W), the Inv group, conservative (C), neutral (N) and 
aggressive (A). The factors are SMB (small minus big), HML (value minus growth), RMW (robust minus 
weak), CMA (conservative minus aggressive).  
3.3 Estimation of Idiosyncratic Volatility   
As in Fu (2009), in the current study the authors have employed the EGARCH (p,q) model of Nelson 
(1991) to estimate the idiosyncratic volatility of stocks and generated nine different EGARCH models 
for each stock using the permutation of1 p 3, 1 q 3 order. Akaike Information Criterion (AIC) has 
been used in order to determine the best model for each stock.The mean and variance equations of the 
EGARCH (p,q) model are specified in the Equation (1) and Equation (2).   
Rit – Rft = αi + bi(Rmt – Rft) + siSMBt + hi HMLt + riRMWt + ci CMAt + εit  
where εit~N (0, σit2)                 (1)    

  

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ISSN: 2836-9416 

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6 | P a g e  

 whereRit - Rftis excess return of stock i at month t where (Rm-Rf) is the market factor and SMB is the 
monthly size factor.HML is the monthly value factor whileRMW and CMA are monthly profitability and 
investment risk factors respectively. ln σit2is log of the conditional variance of the stock returns of stock 
i at time t while αi bi , ci and   are constant in the EGARCH model, vector of coefficients and asymmetric 
coefficient respectively. Further, the conditional distribution of residuals (εit) in the mean equation is 
based on the set of information at t-1 which is assumed to be normal with the mean of zero and variance 
of σit2 whereas the conditional variance (σit2) in the variance equation is a function of past p-period of 
residual variance and past q-period of return shocks where α i 0, bi+ ci  1, and λ 0 if volatility is 
asymmetric.  
The idiosyncratic volatility (IVOL) of stocks is measured as the square root of the conditional variance 
of residuals of five-factor asset pricing model estimated using the EGARCH model. Furthermore, the 
selected firms in the sample of the current study have at least 30 monthly return observations in order 
to overcome the look-ahead biasness problem (Fu, 2009; Pukthuanthong-Le & Visaltanachoti, 2009).   
3.4 Portfolio Formation    
In order to draw inferences between idiosyncratic volatility and average stock returns, the authors have 
formed idiosyncratic volatility sorted portfolios in the current study. Accordingly, five equal-weight and 
value-weight idiosyncratic volatility sorted portfolios formed to analyze the association between 
average stock returns and idiosyncratic volatility.  
3.5 Gibbons, Ross and Shanken (1989) Test   
The null hypothesis of Gibbons, Ross, and Shanken (GRS) (1989) test notes that regression intercepts 
of different asset portfolios developed through the asset pricing models are not significantly different 
from zero. Thus, in order to achieve the objective of the current study the authors have used the GRS 
test for idiosyncratic volatility sorted portfolios.  
4. Summary Statistics  
4.1 Descriptive Statistics  
Table 2 shows the descriptive statistics of the variables used in the study. The average stock return is 
found to be 0.93 percent in Sri Lanka while Fu (2009) reports a mean return value of 1.18 percent with 
respect to the United States. Further, market factor is found to be highly volatile compared to other risk 
factors whereAbeysekera and Nimal (2017) note similar findings in relation to the CSE. Also, Ang et al. 
(2009) highlight that market factor seems to be highly volatile in the Asian context.     
Even though, the mean value of size factor (0.37 percent) slightly deviates from the previous findings, 
a mean size factor closer to zero is in line with the findings ofFama and French (2012) and Abeysekera 
and Nimal (2017). However, the average value factor of 0.6 percent (see Table 2) is found to be parallel 
with both local and international findings. For instance, Abeysekera and Nimal (2016) reports a mean 
value factor of 0.54 percent in the Sri Lankan context whileFama and French (2012) and Ang et al. 
(2009) report average value factors of 0.62 percent and 0.72 percent forAsia Pacific and Asia 
respectively.  
Table 2: Descriptive statistics  
  

   𝑅     R m - R f 
  SMB   HML   RMW   CMA   IVOL   

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Mean  0.93%  -8.89%  0.37% 0.60%  0.45%  0.06%  10.61% Std. Dev. 
 7.15%  7.42%  3.04%  4.22%  3.82%  3.27%  1.81% 

t-Mean 
 1.655 
Note: is the average stock returns.  Rm-Rfis the market factor where the market risk premium is the 
excess of ASTRI return over risk free rate of return (i.e. three-month government treasury bill rate). 
SMB is the monthly size factor where HML is the monthly value factor. RMW and CMA are monthly 
profitability and investment risk factors respectively. IVOL is the monthly idiosyncratic volatility of 
stocks estimated through the EGARCH model by using Fama and French (2015) five-factor asset 
pricing model.  
Despite the relative consistence with previous empirical findings on mean values of popular risk factors, 
the average values on profitability (0.45 percent) and investment (0.06 percent) factors are contrasted 
considerably to that of the previous findings. For instance, in the United States,the mean values of 
profitability and investment factors are found to be 0.25 percent and 0.33 percent respectively (Fama& 
French, 2015) while in the Asian Pacific region, the mean values of these factors are found to be  0.21 
percent and 0.39 percent respectively (Fama& French, 2017). Interestingly, the mean value of 
idiosyncratic volatility (10.61 percent) is slightly closer to the average value of 12.67 percent in the 
United States (Fu, 2009). Nevertheless, in a cross country analysis, Pukthuanthong-Le and 
Visaltanachoti (2009) record a mean value for idiosyncratic volatility as high as 15.98 percent for Sri 
Lanka.  
4.2 Equal-weight and Value-weight Portfolio Return Analysis  
Table 3 shows the results of portfolio return analysis where the Panel A shows the equal-weight average 
portfolio returns while Panel B shows the value-weight average portfolio returns.Accordingly, some 
interesting empirical findings can be observed with respect to idiosyncratic volatility of stocks. The 
empirical results in Panel A depict that portfolio 5 (stocks with highest idiosyncratic volatility) has 
generated substantially higher average return (1.90 percent) compared to the average return of 
portfolio 1 (lowest idiosyncratic volatility)(0.14 percent). Further, the average return differential of 1.76 
percent between portfolio 5 and portfolio 1 is found to be highly statistically significant. Hence, this 
confirms the existence of idiosyncratic volatility in the Sri Lankan context and it is statistically 
significant and positively related with the average stock returns. 

 
Market share  29.04%  20.90%  17.29%  16.82%  15.94%   
Profitability 11.32% 9.30% 7.24% 3.05% -0.09%  Investment 133.71% 46.73% 61.34% 86.79% 
31.56%   
              

  - 15.233   1.542   1.798   1.506   0.241   74.758   

Table 3: Idiosyncratic volatility ( IVOL  sorted portfolios )   

Panel A: Equal - weight average returns   

  Portfolios formed on IVOL   

  )  (Low 1   2   3   4   5  (High )   (5 - 1)   

𝑅     % 0.14   % 0.16   0.42 %   0.94 %   1.90 %**   1.76 %*   

  (0.0014)   (0.0016)   (0.0042)   (1.3530)   (2.0308)   (2.9720)   

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𝑅   

 
Profitability  11.32%  9.30%  7.24%  3.05%  -0.09%    
Investment  133.71%  46.73%  61.34%  86.79%  31.56%    
Note: is the average stock returns. The market share of each IVOL sorted portfolio is calculated by using 
the market capitalisation of each IVOL portfolio as a percentage of the total market capitalisation of all 
IVOL sorted portfolios. Profitability is the average of the net profit-to-book equity ratio of each IVOL 
sorted portfolio. Investment is the average of the growth of total assets of each IVOL sorted portfolio. 
Newey-West (1987) adjusted t-statistics are reported in parentheses. * and ** indicate 1 percent and 5 
percent significance levels respectively.  
Interestingly, the empirical results of the value-weight average returns in Panel B of Table 3 present a 
contradictory argument for the positive relation between average stock returns and idiosyncratic 
volatility.   
The empirical results depict that portfolio 5 (stocks with highest idiosyncratic volatility) has generated 
substantially lower average return (-0.24 percent) compared to the average return of portfolio 1 (lowest 
idiosyncratic volatility) (0.07 percent). Moreover, the difference of value-weight average returns of 
portfolio 5 and portfolio 1 is 0.30 percent with a t statistic of -1.2576. However, this average return 
differential is found to be economically and statistically insignificant.  
Additional to the above empirical findings, Table 3 demonstrates more striking evidence on 
idiosyncratic volatility of stocks. The results depict that stocks with highest idiosyncratic volatility have 
the lowest market share of 15.94 percent compared to the stocks with lowest idiosyncratic volatility 
(29.04 percent). This indicates that the stocks with high idiosyncratic volatility tend to be small in the 
CSE. In fact, this empirical finding is consistent with the previous studies where Hou and Moskowitz 
(2005), Ang et al. (2006), Bali and Cakici (2008) and Fu (2009) also note that idiosyncratic volatility 
is high with small stocks.  
Moreover, the empirical findings on profitability and investment yield novel evidence in relation to the 
idiosyncratic volatility. The empirical evidence in Table 3 shows that profitability of the idiosyncratic 
volatility sorted portfolios has drastically declined as the idiosyncratic volatility of stocks increases. For 
instance, the profitability of the lowest idiosyncratic volatility sorted portfolio (portfolio 1) is found to 
be 11.32 percent while the profitability of the highest idiosyncratic volatility sorted portfolio (portfolio5) 
is found to be -0.09 percent. In other words, this implies that when the idiosyncratic volatility of stocks 
increases the profitability of stocks starts to fall. This confirms the argument of Fu (2009) on the 
idiosyncratic volatility where he notes that idiosyncratic volatility is firm specific and it does not move 
in line with the market. Hence, the impact of idiosyncratic volatility varies from one firm to another 
where the results show that when the idiosyncratic volatility becomes high, it negatively affects the 
profitability of the firms.  

Panel B: Value - weight  average returns   

  Portfolios formed on IVOL   

  )  (Low 1   2   3   4   5  (High )   (5 - 1)   

𝑅     % 0.07   - % 0.05   % 0.15   % 0.25   - % 0.24 -   - 0.30 %    

  (0.4790)   ( - 0.0005)   (0.0015)   (0.0025)   ( - 0.0024)   ( - 1.2576)    

Market share   29.04 %   20.90 %   17.29 %   16.82 %   15.94 %     

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Furthermore, Fama and French (2015) highlight that small stocks tend to be less profitable compared 
to big stocks; the profitability premium is higher for small stocks compared to big stocks. The empirical 
findings of Table 3 pertinent to characteristics of the idiosyncratic volatility sorted stock portfolios lend 
direct support for this argument. For instance, as discussed earlier, small stocks tend to have higher 
idiosyncratic volatility compared to big stocks, indicating less profitability of small stocks due to their 
high idiosyncratic volatility. Hence, this clearly supports the argument of Fama and French (2015) on 
higher profitability premium on small stocks.  
On the other hand, Table 3 demonstrates another piece of interesting evidence on idiosyncratic 
volatility of stocks. That is, as per the results, it can be observed that stocks with higher idiosyncratic 
volatility have the lowest investment value compared to stocks with lower idiosyncratic volatility. 
Hence, it seems that stocks with higher idiosyncratic volatility suffer from future growth prospects due 
to higher level of volatility in the firm specific risks which hinder the capital investments of such firms.  
Furthermore, Fama and French (2015) argue that expected investment premium is quite larger for 
small firms. The findings of this study clearly in line with this argument where the stocks with higher 
idiosyncratic volatility tend to be small and their investment values are relatively lower compared to 
big stocks. Hence, the investors expect a higher investment premium (Fama& French, 2015). Moreover, 
Fama and French (2015) report that small firms tend to invest more despite their lower level of 
profitability. Perhaps as shown in the results of Table 3, presence of high idiosyncratic volatility with 
small stocks might be the reason which hinders the ability of such firms to reap benefits from their 
investments. 
4.3 GRS Test  
In the GRS test, the null hypothesis denotes that there is no significant difference between the intercepts 
of the asset returns under consideration. In other words, tailoring to the current study, this indicates 
that the intercepts of the idiosyncratic volatility sorted stock portfolios are not significantly different 
from each other. Thus, it rejects the presence of idiosyncratic volatility of stocks. Moreover, it should 
be noted that GRS test has been carried out only for equal-weight portfolios as value-weight portfolio 
returns generate statistically insignificant results (see Table 3).  
According to empirical results depicted in Table 4,Fama and French five-factor (FF 5) alpha of lowest 
IVOL portfolio is -4.44 percent while it is as high as 1.03 percent for the highest IVOL portfolio. Similar 
to a hedging portfolio strategy highlighted by Fu (2009), longing highest IVOL portfolio and shorting 
lowest IVOL portfolio produces a statistically significant monthly return of 5.47 percent. The GRS test 
statistic of 26.28 strongly rejects the null hypothesis of GRS test which states that all intercepts are not 
significantly different from zero. In other words, GRS test reconfirms the findings of the portfolio 
analysis of this study and it validates the presence of idiosyncratic volatility of stocks in the CSE.  
Table 4: Fama and French five-factor (FF 5) alpha values  

  
  

 Portfolios formed onIVOL     
1 (Low)   2   3   4   5 (High)   

FF 5 Alpha   -4.44%*   -3.37%*   -2.70%*   -1.47%   1.03%   
  (-5.2669)   (-3.2757)   (-2.4387)   (-1.3003)   (0.7089)   

Note:Newey-West (1986) adjusted t-statistics are reported in parentheses. * indicates 1 percent level of 
significance.  

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5. Conclusion   
All empirical asset pricing models assume that the role of idiosyncratic volatility is irrelevant as the 
investors can avoid the exposure to the idiosyncratic volatility by holding well-diversified portfolios 
with many securities (Bali, Engle& Murray, 2016). Further, in the absence of market imperfections 
Merton (1987) notes that theoretically investors have zero level of exposure to the firm specific risk. 
However, the empirical studies provide strong evidence against this theoretical stance and highlight 
that investors are commanding reasonable compensation for bearing the idiosyncratic volatility (Ang 
et al., 2006; Bali & Cakici, 2008; Ang et al., 2009; Fu, 2009).  
This study attempted to shed a light on the idiosyncratic volatility puzzle from a South Asian market 
point view where both portfolio analysis and GRS test results confirmed the presence of idiosyncratic 
volatility in the Sri Lankan context. Furthermore, the empirical results revealed that idiosyncratic 
volatility has a statistically strong and positive influence on the average stock returns. Therefore, it 
indicates that investors expect an adequate return for bearing idiosyncratic risk.  
Moreover, the current study yields some novel striking empirical evidences in terms of the 
characteristics of the idiosyncratic volatility of stocks. Accordingly, the results of the portfolio analysis 
demonstrated that the stocks with higheridiosyncratic volatility are less profitable while having lower 
growth prospects. Hence, it seems high idiosyncratic volatility is coupled with less profitable firms with 
lower level of investments. Moreover, it is also found that idiosyncratic volatility is high with small 
stocks. In other words, this indicates that small stocks carry high idiosyncratic volatility while being 
exposed to lower level of profits and investments. Hence, as Fama and French (2015) argue, the results 
of the current study also document that critical issues in asset pricing models are coupled with small 
stocks. Thus, one of the key messages of this study is that it is still questionable as to why there is a high 
demand for small stocks in the market despite their lower level of exposure to profits and investments 
while bearing a higher level of idiosyncratic volatility.         
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Impact Factor: 4.85 

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