







































Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

Indian Journal of Finance and Banking; Vol. 4, No. 1; 2020 
                                       ISSN 2574-6081   E-ISSN 2574-609X 

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

109 

 

Liquidity and Asset Pricing: Evidence from Indian Stock Market 
 
 

Sharad Nath Bhattacharya 
Associate Professor 

Indian Institute of Management, Shillong, India 
E-mail: snb@iimshillong.ac.in 

 
 Mousumi Bhattacharya 

Assistant Professor  
Indian Institute of Management, Shillong, India  

E-mail: msb@iimshillong.ac.in 
 

Sumit Kumar Jha 
Research Fellow 

Indian Institute of Management, Shillong, India  
E-mail: sumit.fpm14@iimshillong.ac.in 

 
 
Abstract 
In this research article, we present a liquidity premium based asset pricing model and test it in the Indian stock market. 
Using high-frequency data of stocks listed in the National Stock Exchange, we show that observed illiquidity has a 
significant negative impact on realized stock returns even after controlling for the up and down market, volatility, and 
effects of derivatives trading. The illiquidity measure is modified for its time variations, and then the modified measure is 
used to assess its impact on returns. Using a cross-section of stocks, we show the year wise results of the model and extend 
it to show that it has some role in explaining returns across industries. Findings show that the down market has 
contemporaneous systematic risk at higher levels, and the market risk premium is higher in down markets. Finance, utility 
and real estate sector companies have higher systematic risk in both up and down market and investors of these sectors has 
relatively higher expected higher returns in comparison to companies from the rest of the segments. 

 

 
 
1. Introduction 
Liquidity is an underlying concept without a universal definition. The most accepted notion of liquidity is the capability of 
an asset to trade in a market with minimal price disruption. A liquid market is expected to have a considerable number of 
orders both sides, i.e. above and below the last traded price of an asset. Investors of an illiquid asset face illiquidity risk, 
which is the possible inability to exit from the investment without incurring significant transaction costs. Risk-averse 
investors are expected to demand an illiquidity premium to undertake such illiquidity risk and invest in illiquid assets. 
Hence return on any asset is expected to include some illiquidity premium. 

    In a seminal work (Amihud & Mendelson, 1986) found that excess returns include a premium for the quoted spread 
(bid-ask spread divided by price) at a declining rate, thus emphasizing a concave relationship due to clientele effect. Datar et 
al. (1998) evidence that liquidity proxies like trading volume explains stock returns and conclude that the relationship 
between the size of an asset and the return generated out of that asset reflects the relationship between liquidity and return. 
Garleanu & Pedersen (2007) and Brunnermeier & Pedersen (2009) supported the idea that there is an asymmetric influence 
of illiquidity on different states of the market. They supported this on the grounds of liquidity tremors, margin-induced 
price spirals, and tighter risk management by institutions. Garleanu & Pedersen (2011) noted the importance of funding 
constraints in influencing risk and return dynamics. Hameed et al. (2010) evidenced that enormous negative returns have a 
stronger relationship with variations in liquidity measures than positive market returns. It is argued that liquidity premium is 
dependent or conditional upon the state of return, and an investor is asymmetrically concerned about liquidity during 
periods of positive and negative returns. 

    A market participant is more worried about the informational role of illiquidity during periods of negative returns for 
asset pricing than the periods of positive returns. Alternative theories supporting critical roles of illiquidity in explaining 
asset returns generally lay their foundation on supply-side arguments. Proponents of supply-side arguments evidence that 
during negative returns, the financial intermediaries especially market makers are impacted by their margin constraints due 
to a reduction in the value of the collaterals forcing them to liquidate their position and inducing an illiquidity spiral. 
Liquidity measures also suffer from the day of the week effect, month effect, holiday effect, and are also affected by global 

  
Keywords:    Asset Pricing, Down Market, Illiquidity Measure, Liquidity Risk, Liquidity Premium, Stock Market.  

 
    

  
JEL Classifications: 

   G10, G12. 
 

    

mailto:sumit.fpm14@iimshillong.ac.in


Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

110 

 

or local events (Chordia et al., 2001a). Chordia et al. (2001b) studied the time variations in liquidity, and they foresighted 

the role of unexpected liquidity in asset pricing models. They stressed the importance of understanding of nature of the 
relationship between liquidity and stock returns to improve the level of market participant's confidence in the stock market. 
Acharya & Pederson (2005) showed that liquidity risk is priced. They argue that both individual stock level and market-
level liquidity have significant explanatory power in predicting stock returns and an adverse shock to a stock's liquidity leads 
to lower realized return.   

    It is debated that measures of liquidity may have some influence in determining the level of liquidity of firms as well as 
of markets of different asset classes. The transaction cost-related measures like bid-ask spreads look for order processing, 
and execution costs present in the markets. In contrast, volume-based measures capture the number of trades or trading 
volume and try to understand the depth and breadth of the markets. The price impact measures changes in prices caused by 
the sudden changes in volume or imbalance of orders in the market. The effect of the arrival of new information is captured 
by the measures of resiliency. Some measures are more suitable for low-frequency data while others are more robust when 
they are constructed using high-frequency data. Across the choices available (Amihud, 2002) measure of illiquidity is one of 
the most widely used measures across both the emerging and developed markets. Ahn et al. (2018) provide a good summary 
of various liquidity measures available in the literature and document those influential in explaining stock returns in 
emerging markets. 

    This research article evidences the contemporaneous relationship between Indian security returns and illiquidity factor. 
The association exists even after controlling for local and global factors that are expected to influence stock returns. Over 
the past decade, the social and economic prospects of India have improved, and it is not only one of the fastest-growing 
economies but also has one of the largest stock markets in the world. We use 30 minutes of stock price data, and we show 
that stock prices have a contemporaneous relation with market return, illiquidity, open interest, and volatility. We modify 
the illiquidity measure of (Amihud, 2002) for time-varying effects and show that unexpected illiquidity negatively impacts 
stock return in the Indian market. We evidence that market premium is higher during down market compared to up 
markets. We also show that an increase in volatility does not necessarily lead to an increase or decrease in coefficient of 
market risk premium (a measure of systematic risk) but an increase in open interest in the market increases in the coefficient 
of market risk premium.   

    The remainder of the research article is prepared as follows. In section 2, we discuss the relevant literature, while in 
section 3, we describe the data and methodology. Section 4 presents the results and findings. Section 5 concludes the paper. 
 
2. Literature Review 

In developed nations enormous amount of research on liquidity risk premium is available (Amihud & Mendelson, 1986; 

Chordia et al., 2001b; Acharya & Pedersen, 2005) but the same for emerging nations are comparatively limited. Numerous 

authors evidenced a significant positive association between expected security returns and illiquidity in US markets (Datar et 

al., 1998; Amihud, 2002). Chordia et al. (2001a) evidence that time-varying illiquidity has a negative effect on stock returns 

in the US. Using a price impact measure of liquidity (Amihud, 2002) finds that realized liquidity risk and expected returns 
are positively related. However, unanticipated illiquidity is negatively related to excess returns in the US. Easley et al. (2002) 
use volume and turnover as a measure of liquidity to support an adverse effect of liquidity on returns in developed stock 
markets. Pástor & Stambaugh (2003) show that stocks with higher sensitivities to market liquidity have higher returns on 
US stock exchanges; thus, supporting that market liquidity is an essential factor in asset pricing. Extending the standard 
Capital Asset Pricing Model (CAPM) to include security-specific and market-level liquidity premiums (Acharya & 
Pedersen, 2005) explain how liquidity variations result in low realized returns and high expected returns using the data of 
NYSE and AMEX. Lee (2011) shows that covariance of a stock's illiquidity and US market return hurts the expected 
return. Artikis (2018) showed that liquidity is a priced factor for asset pricing in the UK.  

    Findings for emerging markets often contrasts the conclusion from the developed markets. In a causality study across 
27 emerging stock markets (Jun et al., 2003) observe higher market illiquidity does not always lead to a higher return. Dey 
(2005) notes that turnover, as a liquidity measure, positively affects stock returns in the emerging market, but it is no longer 
significant in developed markets. One of the issues with emerging markets is the availability of data for the construction of 
liquidity proxies, and most of the emerging markets are order-driven markets compared to their quote-driven peers in the 
developed world. Amihud (2002) build an illiquidity measure (AI) that uses the ‘absolute value of the daily return-to-
volume’ ratio to capture the price impact of liquidity. This measure allowed the study of the time-series effects of illiquidity 
shocks on realized stock returns. Using AI (Bekaert et al., 2007) describe a negative return-illiquidity relationship in 18 
emerging markets. Hearn (2010) reports the existence of illiquidity factor in stock markets of SAARC countries like 
Bangladesh, Pakistan, and India but not in Sri Lanka. However, he could not support any time trends or unexpected changes 
in illiquidity. Narayan & Zheng (2011) conducted a study on the Chinese stock market. They found mixed results on the 
relationship between liquidity and returns. They observe a negative relationship between stock return and liquidity, but the 
negative relation is more rooted in the Shanghai stock exchange compared to the Shenzhen stock exchange. Lee (2011) and 
Liang & Wei (2012) added another dimension to the study of illiquidity-return nexus in emerging markets where they 
supported a more significant role of local illiquidity risk in emerging stock markets. In developed markets, they argued that 
global illiquidity risks outweigh local illiquidity risk. Existence of more substantial illiquidity premium in the emerging 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

111 

 

markets compared to their developed peers are reported by (Amihud et al., 2015). Bhattacharya et al. (2016) report that 
multiple dimensions of liquidity collectively explain variations in the Indian stock market. Bhattacharya et al. (2019) report 
that market liquidity and returns exhibit both long-term and short-term relationships in India and added that trading 
activity and market resiliency (measured by market efficiency coefficient) affect the stock market positively while the spread 
has a negative influence on returns. Kumar & Mishra (2019) provide empirical results of (Acharya & Pedersen, 2005) 
model. They used (Fama & MacBeth, 1973) regression in the Indian context and shows that liquidity is a priced factor in 

Indian Market. Stereńczak et al. (2020) investigated the influence of the absence of liquidity across frontier markets which 
are expected to be less integrated with other markets. They used a battery of liquidity proxies and covered both pre and 
post-global financial crisis period to conclude that there is no liquidity premium for investors investing in those stock 
markets. This finding is contrary to those observed in developed and emerging financial markets. 

    The present work extends the earlier studies in the Indian context. We build the daily measures of return and 
illiquidity using high-frequency firm-level data of the stocks consisting of the NIFTY500 to investigate the impact of 
illiquidity on returns during both up and down markets. We remove the time-varying components of AI to use the 

modified version of AI (AI.m) in the presence of global liquidity factors like market volatility (vix) and open interest as 
advocated by (Donadelli & Prosperi, 2012) in explaining stock returns. 
 
3. Data and Methodology 

The firms covered in the NIFTY 500 index for 2008 to 2017 were considered. The average return for each day for each 

stock is computed by aggregation of fourteen 30 minutes return for each day. The daily AI𝑖 is constructed using the 

relation 𝐴I𝑖 = Average (
rt

Vt
⁄ ), where 𝑟𝑡 denotes return on stock on day t and 𝑉𝑡 represents the volume of the stock (in 

INR) traded in the National Stock Exchange on day t. The modified version of (Amihud, 2002) measure (AIm) is 
obtained as a residual of the equation I after controlling for the day of the week, month, holiday, and other critical financial 
events. 

AIi,t = ∑ di,k

4

𝑘=1

. Dk,t + ∑ ei.k

11

k=1

. Mk,t + β1,i . Ht + β2,i. DCYt + β3,i. SPt  + β4,i. ATt + AIi,t
m   … (I) 

    As regressors, we considered weekday dummies (Dk,t); month dummy (Mk,t); weekends or holiday dummy (Ht); time 
trend variables (DCYt and SPt ). DCYt variable is introduced to capture the impact of DotCom mania (Ofek & Richardson, 
2003) that resulted in significant regulatory changes with time. DCYt is calculated as the difference between the current 
calendar year and the year 2000 (dotcom year). The year 2000 is replaced by the year of listing for a stock if it was not 
listed in 2000. SPt is expected to capture the impact of the sub-prime crisis and is calculated as the difference between the 
current year and the year 2008 (replaced by the year of listing for those stocks not listed in 2008). Algorithmic trading 

(ATt) is allowed to capture the effect of algorithmic trading started at NSE on 4th April 2008. The residual AIi,t
m is 

interpreted as abnormal or unexpected illiquidity, which is existing in the stock market.  Next, we run our asset pricing 
model as pooled OLS: 

Ri,t − Rf,t =  αi +  βi,m(Rm,t −  Rf,t) +  βi,down(Rm,t − Rf,t). Ddown i,t + βi,up(Rm,t − Rf,t). Dup i,t +  γi. AIi,t−1
m

+ θi,m[(Rm,t − Rf,t). Cvix,t] + φi,m[(Rm,t − Rf,t). Coi,t] … . (II) 

    Ri,t − Rf,t is the excess return over risk-free rate while Ri,t −  Rf,t represents the market risk premium. The risk-free 
rate is the annualized treasury bill rate applicable at time t. Based on the suggestion of (Donadelli & Prosperi, 2012) open 
interest and volatility index are included as the two global liquidity factors. Open interest is the number of outstanding 
derivatives contracts for options or futures contracts that have not been settled on the National Stock Exchange. Both the 

open interest (oi) and volatility index (vix) values for the Indian Stock Market are obtained from Bloomberg. Cvix,t and 

Coi,t  denotes changes in vix and oi respectively from period t-1 to t, Ddown i,t and Dup i,t denotes dummy variables to 
accommodate asymmetry. The dummy variable Ddown i,t (Dup i,t) is 1 if the Ri,t is less (more) than 1.5 standard deviation of its 
unconditional mean. For the remaining values of Ri,t, the dummy variables value is 0 (neutral market). 

4. Result 

The findings are presented in Table I and Table II that reports market risk premium coefficients in three market scenarios 

(down, neutral, and up) through βdown, β, and βup respectively where all three are statistically significant. The, β + 

βdown (β + βup) represents the estimate of the sensitivity towards the market risk premium in Down (Up) market. 

 

 

https://www.tandfonline.com/doi/full/10.1080/23322039.2019.1573471


Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

112 

 

     Table I. Year-wise parameter estimates of equation II 

Year 𝜶 𝜷 𝜷up 𝜷down θ φ γ 
All 

Years 
-0.00423***  

[0.0001] 
0.9645*** 
[0.0017] 

0.1025***  
[0.002] 

1.5*** 
[0.0019] 

0.0454***  
[0.037] 

11.10*** 
[0.0482] 

-0.3134***  
[0.065] 

2008 -0.0187***  
[0.0005] 

0.8036*** 
[0.0064] 

-0.1138***  
[0.0088] 

1.314*** 
[0.0071] 

-0.0365***  
[0.01] 

9.119*** 
[0.1394] 

-0.3652* 
[0.2172] 

2009 -0.0097***  
[ 0.0003] 

0.896*** 
[ 0.004] 

-0.1682***  
[ 0.0052] 

1.4855*** 
[ 0.0047] 

- 0.1086***  
[0.0181] 

13.2908 
[0.1172] 

-0.208**  
[ 0.0967] 

2010 0.0044***  
[ 0.0011] 

1.0712*** 
[ 0.0147] 

0.2371***  
[ 0.0153] 

1.5665*** 
[ 0.015] 

- 0.087*** 
 [ 0.0165] 

11.3031*** 
[ 0.3981] 

1.4474*  
[ 0.7476] 

2011 0.0054***  
[ 0.001] 

1.0867*** 
[ 0.0118] 

0.3147***  
[ 0.0126] 

1.5806*** 
[ 0.012] 

0.0132  
[ 0.0144] 

12.2402 
[ 0.3178] 

-1.2353***  
[ 0.3425] 

2012 -0.001  
[ 0.0014] 

1.0005*** 
[ 0.0166] 

0.2182***  
[ 0.0171] 

1.5384*** 
[ 0.0169] 

0.02  
[ 0.0155] 

9.8357*** 
[ 0.4582] 

0.1912  
[ 0.2725] 

2013 -0.0057***  
[ 0.0007] 

0.9485*** 
[ 0.009] 

0.1419***  
[ 0.0095] 

1.5029*** 
[ 0.0094] 

 0.0581***  
[ 0.0153] 

10.3473*** 
[0.2717] 

-0.416**  
[0.207] 

2014 0.0083***  
[ 0.0012] 

1.1115*** 
[ 0.0146] 

0.3351***  
[ 0.015] 

1.6332*** 
[ 0.0147] 

- 0.0878***  
[ 0.0126] 

12.6862*** 
[0.4212] 

-0.1201  
[ 0.3023] 

2015 0.0145***  
[ 0.0013] 

1.206*** 
[ 0.0171] 

0.3388*** 
 [ 0.0177] 

1.7472*** 
[ 0.0176] 

0.0215  
[ 0.0148] 

14.9418*** 
[ 0.494] 

-2.4331  
[ 1.577] 

2016 0.0073***  
[ 0.0007] 

1.1173*** 
[ 0.0092] 

0.2297***  
[ 0.01] 

1.6802*** 
[ 0.0096] 

-0.0088  
[ 0.0169] 

13.0725*** 
[ 0.3355] 

-56.3764***  
[ 13.5369] 

2017 0.0163***  
[ 0.0012] 

1.2623*** 
[ 0.018] 

0.2728***  
[ 0.0186] 

1.971*** 
[ 0.0184] 

0.2209***  
[ 0.0248] 

18.9006*** 
[ 0.7568] 

3.7834  
[ 13.531] 

Table I presents the parameter estimates of equation II. [.] shows the standard errors. *,**, and *** shows significance at  90%, 95% and 99% 
respectively.  

     

Table I shows that AIm co-efficient γ is significant and negative considering the entire period. By assuming that there is 

no association between market liquidity and corporate cash flow, it is established that unexpected illiquidity (AIm) has an 
adverse influence on equity returns. Following (Amihud, 2002) we maintain that observed illiquidity raises expected 
illiquidity. Furthermore, the compensation for an investor who accepts this expected illiquidity is reflected in higher 

expected returns and lower contemporaneous stock prices for the investors. Downmarket beta (β + βdown) is consistently 

higher than upmarket beta (β + βup) in all the years. The down market has contemporaneous systematic risk at a higher 
level (measured by beta). This higher beta would lead to higher expected returns as investors would get into the market at 
lower levels (down market), making expected returns higher on the recovery of the market. The upmarket beta was lower 
than neutral beta during 2008 and 2009. It appears that pre-emptive actions by market regulators like SEBI and RBI have 
some role in reducing the systematic risk during that time (subprime crisis and its aftermath). The yearly trends of varying 
marketing risk premium coefficient (beta) in different market conditions in the presence of the illiquidity factor is presented 

in Figure 1. Global liquidity factors coefficients θ (φ) is small (large), positive, and significant for the overall period. Φvix 
turns negative and significant in 2008, 2009, and 2010 – that coincides with the years of government policy paralysis under 
the UPA-II regime in India.  

 

 

Figure 1. Trends of market risk premium sensitivity 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

113 

 

Next, we present the estimates from equation II for industries.   

     Table 2. Sector-wise parameter estimates of equation II 

Sector Α β Βup βdown Φvix Φoi γ 
CD -0.0062***  

[ 0.0003 ] 
0.941*** 
[ 0.0044 ] 

0.0627*** 
[ 0.0051 ] 

1.4939*** 
[ 0.0049 ] 

0.0597*** 
[ 0.0094 ] 

11.4521*** 
[ 0.1221 ] 

-0.5116*** 
[ 0.1181 ] 

CS -0.0117***  
[ 0.0005 ] 

0.8658*** 
[ 0.0062 ] 

0.0139* 
[ 0.0074 ] 

1.4245*** 
[ 0.0071 ] 

0.0522*** 
[ 0.0134 ] 

10.7922*** 
[ 0.1687 ] 

0.2047* 
[ 0.1133 ] 

ENR -0.0009  
[ 0.0007 ] 

1.0038*** 
[ 0.009 ] 

0.1607*** 
[ 0.0114 ] 

1.504*** 
[ 0.0104 ] 

0.0206 
[ 0.0196 ] 

10.7814*** 
[ 0.2525 ] 

-234.4878*** 
[ 80.8294 ] 

FIN 0.0004  
[ 0.0003 ] 

1.0216*** 
[ 0.004 ] 

0.2015*** 
[ 0.005 ] 

1.5088*** 
[ 0.0046 ] 

0.0444*** 
[ 0.009 ] 

9.82*** 
[ 0.1127 ] 

-2.653*** 
[ 0.6506 ] 

HC -0.0114***  
[ 0.0004 ] 

0.8692*** 
[ 0.0058 ] 

0.0138** 
[ 0.007 ] 

1.441*** 
[ 0.0069 ] 

0.0531*** 
[ 0.0126 ] 

11.5007*** 
[ 0.1604 ] 

0.1803 
[ 0.2682 ] 

IND -0.0029***  
[ 0.0004 ] 

0.9843*** 
[ 0.0046 ] 

0.1201*** 
[ 0.0055 ] 

1.525*** 
[ 0.0052 ] 

0.0425*** 
[ 0.0101 ] 

11.6142*** 
[ 0.1287 ] 

-0.6329*** 
[ 0.1426 ] 

IT -0.0079***  
[ 0.0005 ] 

0.917*** 
[ 0.0067 ] 

0.0369*** 
[ 0.008 ] 

1.4783*** 
[ 0.0076 ] 

0.0424*** 
[ 0.0146 ] 

11.2432*** 
[ 0.1864 ] 

-45.4849*** 
[ 8.7994 ] 

MAT -0.0038***  
[ 0.0003 ] 

0.9709*** 
[ 0.0042 ] 

0.0996*** 
[ 0.005 ] 

1.4941*** 
[ 0.0048 ] 

0.0577*** 
[ 0.0091 ] 

11.4719*** 
[ 0.1161 ] 

-0.3229 
[ 0.24 ] 

RE 0.007***  
[ 0.0009 ] 

1.1126*** 
[ 0.0112 ] 

0.2303*** 
[ 0.0132 ] 

1.6733*** 
[ 0.0121 ] 

-0.1246*** 
[ 0.0246 ] 

9.8929*** 
[ 0.3232 ] 

-150.6398*** 
[ 37.2249 ] 

TEL  0.001 
 [ 0.0011 ] 

1.0411*** 
[ 0.0139 ] 

0.1595*** 
[ 0.0166 ] 

1.553*** 
[ 0.0156 ] 

0.0003 
[ 0.0306 ] 

10.5171*** 
[ 0.3901 ] 

-21.023 
[ 61.1635 ] 

UTIL 0.0022*** 
 [ 0.0006 ] 

1.0444*** 
[ 0.0077 ] 

0.2298*** 
[ 0.0096 ] 

1.5323*** 
[ 0.0088 ] 

0.068*** 
[ 0.017 ] 

10.2193*** 
[ 0.223 ] 

-629.1086*** 
[ 92.4152 ] 

[.] shows the Standard errors. *,**, and *** shows significance at  90%, 95% and 99% respectively.  

 
    Sector-wise regression coefficient estimates presented in Table 2 reveal that Finance, utility, and real estate exhibits 

maximum systematic risk (β) in normal market conditions. In an up-market too finance, utility and real estate sector have 

maximum β + βup compared to other industries. Similarly, values of β + βdown reveal that in a down-market, investors of 
firms belonging to finance, industry, utility, telecom, and real estate sectors anticipate relatively higher returns in comparison 
to companies of the rest of eleven sectors. Therefore, investments in utility and real estate firms give better excess returns to 
all three market conditions. It may be noted here that Government is the majority shareholder in most of the utility 
companies. These are generally high dividend-paying companies, and they often play some role in reducing the fiscal deficit. 
The variation in risk premium can be visualized in figure 2. 

 

Figure 2. Trends of market risk premium sensitivities 

    Adjusted illiquidity co-efficient γ is negative and significant for aggregated data and sectoral data; it is negative and 
significant for all but three sectors (HC, MAT, and TEL). The global illiquidity factors (oi and vix) are impacting returns 

in Indian stock market along with local liquidity parameter (γ). At least one of the three is significant in each year and each 
industry. To understand the interaction between market risk premium (MRP or Rm-Rf) and global liquidity factors 
(volatility index and open interest), we draw “interplots” as shown in Figure 3.  



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

114 

 

 

Figure 3. Interplot for an interaction effect between the coefficient of the market risk premium and “volatility” (left) and 
“open interest” (right) during 2008-2017 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

115 

 

    An increase in volatility leads to a decrease in the coefficient of market risk premium during 2008-2010, 2014, and 
2016. 2008-2010 is the sub-prime crisis and recovery period from the crisis. In 2014 the Indian stock market experienced 
high volatility during the general election. 2016 was the year of regulatory changes about financial inclusion initiatives by 
the Government of India which culminated in demonization drive. Millions of Indians experienced banking for the first 
time during that period and banks used technology to reach a large number of customers in a short time. The findings are in 
line with (Ang et al., 2009) where they supported the presence of broad non-diversifiable factors behind the phenomenon. 
Supporting (Bhuyan & Chaudhury, 2005; Fodor et al., 2011) figure 3 reveals that an increase in open interest in the market 
positively impacts returns through the increase in the coefficient of market risk premium. Hence investors in the Indian 
stock market need to keep an eye on the changes in open interest in the Indian derivatives market as well as on changes in 
the volatility index. 

5. Conclusion 

The modified (Amihud, 2002) measure is significant in explaining stock market liquidity in the Indian context. It is 
successful in capturing variations in illiquidity fluctuations during periods of financial shocks like dotcom year, sub-prime 
crisis, and other India specific events. The realized excess stock returns are negatively related to contemporaneous illiquidity. 
In line with (Amihud, 2002) we argue that higher illiquidity or AI raises expected illiquidity in the Indian market resulting 

in lower stock prices. The negative impact of AI on excess return; i.e., significant and negative γ, in the Indian stock market, 
appears to be the largest in 2016- the year of financial inclusion drive by the Indian Government. The global illiquidity 
factors (volatility index and open interest) also impact returns in the Indian stock market. The interaction effect of changes 
in volatility index with market risk premium shows an increase in volatility leads to a decrease in coefficient of market risk 
premium, especially during periods of exogenous shocks. The open interest impacts excess returns positively. The down-
market systematic risk is consistently higher than neutral and up-market systematic risks. Across sectors, finance, industry, 
utility, telecom, and real estate has a higher systematic risk and thus higher expected return for investors in these sectors. 

    Modified (Amihud, 2002) measure and consideration of this measure in portfolio construction strategies may help 
investors make better investment decisions. The study provides enough support in the lines of (Claessens et al., 2012) and 
advocates liquidity-based asset pricing models for the financial markets. A further investigation into the different networks 
of liquidity risk after considering a large number of stocks for a relatively long period across different countries can give a 
possible future direction of research.  
 
References 

Acharya, V. V., & Pedersen, L. H. (2005). Asset pricing with liquidity risk. Journal of financial Economics, 77(2), 375-
410. 

Ahn, H. J., Cai, J., & Yang, C. W. (2018). Which liquidity proxy measures liquidity best in emerging markets?. Economies, 
6(4), 1-29. 

Artikis, P. G. (2018). Liquidity as an asset pricing factor in the UK. Journal of Financial Management, Markets and 
Institutions, 6(2). https://doi.org/10.1142/S2282717X18500081 

Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of financial markets, 5(1), 
31-56. 

Amihud, Y., & Mendelson, H. (1986). Asset pricing and the bid-ask spread. Journal of financial Economics, 17(2), 223-
249. 

Amihud, Y., Hameed, A., Kang, W., & Zhang, H. (2015). The illiquidity premium: International evidence. Journal of 
Financial Economics, 117(2), 350-368. 

Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2009). High idiosyncratic volatility and low returns: International and 
further US evidence. Journal of Financial Economics, 91(1), 1-23. 
https://doi.org/10.1016/J.JFINECO.2007.12.005 

Bekaert, G., Harvey, C. R., & Lundblad, C. (2007). Liquidity and expected returns: Lessons from emerging markets. The 
review of financial studies, 20(6), 1783-1831. https://doi.org/10.1093/rfs/hhm030. 

Bhattacharya, S. N., Bhattacharya, M., & Basu, S. (2019). Stock market and its liquidity: Evidence from ARDL bound 
testing approach in the Indian context. Cogent Economics & Finance, 7(1), 1586297. 
https://doi.org/10.1080/23322039.2019.1586297 

Bhattacharya, S. N., Sengupta, P., Bhattacharya, M., & Roychoudhury, B. (2016). Multidimensional liquidity: Evidences 
from Indian stock market. Applied Finance Letters, 5(2), 28-44. 

Bhuyan, R., & Chaudhury, M. (2005). Trading on the information content of open interest: Evidence from the US equity 
options market. Derivatives Use, Trading & Regulation, 11(1), 16-36. 

Brunnermeier, M. K., & Pedersen, L. H. (2009). Market liquidity and funding liquidity. The review of financial studies, 
22(6), 2201-2238. 

Chordia, T., Roll, R., & Subrahmanyam, A. (2001a). Market liquidity and trading activity. The journal of finance, 56(2), 

501-530. 

Chordia, T., Subrahmanyam, A., & Anshuman, V. R. (2001b). Trading activity and expected stock returns. Journal of 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

116 

 

financial Economics, 59(1), 3-32. 
Claessens, S., Kose, M. A., & Terrones, M. E. (2012). How do business and financial cycles interact?. Journal of 

International economics, 87(1), 178-190. 
Datar, V. T., Naik, N. Y., & Radcliffe, R. (1998). Liquidity and stock returns: An alternative test. Journal of Financial 

Markets, 1(2), 203-219. 
Dey, M. K. (2005). Turnover and return in global stock markets. Emerging Markets Review, 6(1), 45-67. 
Donadelli, M., & Prosperi, L. (2012). On the role of liquidity in emerging markets stock prices. Research in Economics, 

66(4), 320-348. 
Easley, D., Hvidkjaer, S., & O'hara, M. (2002). Is information risk a determinant of asset returns?. The journal of finance, 

57(5), 2185-2221. 
Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of political economy, 81(3), 

607-636. 
Fodor, A., Krieger, K., & Doran, J. S. (2011). Do option open-interest changes foreshadow future equity returns?. Financial 

markets and portfolio management, 25(3), 265-280. 
Garleanu, N., & Pedersen, L. H. (2007). Liquidity and risk management. American Economic Review, 97(2), 193-197. 
Garleanu, N., & Pedersen, L. H. (2011). Margin-based asset pricing and deviations from the law of one price. The Review 

of Financial Studies, 24(6), 1980-2022. 
Hameed, A., Kang, W., & Viswanathan, S. (2010). Stock market declines and liquidity. The Journal of finance, 65(1), 

257-293. 
Hearn, B. (2010). Time varying size and liquidity effects in South Asian equity markets: A study of blue-chip industry 

stocks. International Review of Financial Analysis, 19(4), 242-257. 
Jun, S. G., Marathe, A., & Shawky, H. A. (2003). Liquidity and stock returns in emerging equity markets. Emerging 

Markets Review, 4(1), 1-24. 
Kumar, G., & Misra, A. K. (2019). Liquidity-adjusted CAPM—An empirical analysis on Indian stock market. Cogent 

Economics & Finance, 7(1), 1573471. 
Lee, K. H. (2011). The world price of liquidity risk. Journal of Financial Economics, 99(1), 136-161. 
Liang, S. X., & Wei, J. K. (2012). Liquidity risk and stock returns around the world. Journal of Banking & Finance, 

36(12), 3274-3288. 
Narayan, P. K., & Zheng, X. (2011). The relationship between liquidity and returns on the Chinese stock market. Journal 

of Asian Economics, 22(3), 259-266. 
Ofek, E., & Richardson, M. (2003). Dotcom mania: The rise and fall of internet stock prices. The Journal of Finance, 

58(3), 1113-1137. 

Pástor, Ľ., & Stambaugh, R. F. (2003). Liquidity risk and expected stock returns. Journal of Political economy, 111(3), 
642-685. 

Stereńczak, S., Zaremba, A., & Umar, Z. (2020). Is there an illiquidity premium in frontier markets?. Emerging Markets 
Review, 42, 100673. 

 
 
 
Copyrights  
Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-
access article distributed under the terms and conditions of the Creative Commons Attribution license 
(http://creativecommons.org/licenses/by/4.0/). 
 


