Reddy, Y. V.; Dhabolka , Pinkesh
A icle
P icing e iciency o exchange aded unds in India
O ganiza ions and Ma ke s in Eme ging Economies
P o ided in Coope a ion wi h:
Facul y o Economics and Business Adminis a ion, Vilnius Uni e si y
Sugges ed Ci a ion: Reddy, Y. V.; Dhabolka , Pinkesh (2020) : P icing e iciency o exchange aded
unds in India, O ganiza ions and Ma ke s in Eme ging Economies, ISSN 2345-0037, Vilnius
Uni e si y P ess, Vilnius, Vol. 11, Iss. 1, pp. 244-268,
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O ganiza ions and Ma ke s in Eme ging Economies ISSN 2029-4581 eISSN 2345-0037
2020, ol. 11, no. 1(21), pp. 244–268 DOI: h ps://doi.o g/10.15388/omee.2020.11.33
P icing E iciency o Exchange T aded Funds
in India
Y V Reddy (Co esponding au ho )
Goa Uni e si y, India
y [email protected]
h ps://o cid.o g/0000-0002-0805-6637
Pinkesh Dhabolka
Vidya P abhodini College, Pa a i, Goa, India
dhabolka [email protected]
h ps://o cid.o g/0000-0001-8858-9515
Abs ac . Exchange aded unds (ETFs) ha e wo p ices, he ma ke p ice and he ne asse alue
(NAV) p ice. ETFs NAV p ice ge s de e mined by he ne alue o he cons i uen asse s, whe eas he
ma ke p ice o ETFs depends upon he numbe o uni s bough o sold on he s ock exchange du ing
ading hou s. As pe he law o one p ice, he NAV and ma ke p ice o he ETF should be he same.
Howe e , due o demand and supply o ces, he ma ke p ice may di e om i s NAV. This p ice
di e ence may ha e signi ican epe cussions o in es o s, as i ep esen s a cos i hey buy o e alued
ETF sha es o sell unde alued ETF sha es. P icing e iciency is he speed a which he ma ke make s
co ec he de ia ions be ween ETFs NAV and ma ke p ice. The p esen s udy a emp s o in es iga e
he p icing e iciency o Indian equi y ETFs employing an au o eg ession model o e i s p ice de ia ion,
and also a emp s o unde s and he lead-lag ela ionship be ween he p ice and NAV using he ec o
e o co ec ion model (VECM).
Keywo ds: exchange- aded unds, p icing e iciency, p emium, discoun .
1. In oduc ion
Exchange- aded unds (ETFs) a e uni in es men us s designed o mimic an unde -
lying ma ke index. I is a s ock ha e lec s he composi ion o a chosen ma ke index;
each ETF sha e is a claim on a us ha holds a speci ied pool o asse s. An acc edi ed
Recei ed: 12/14/2019. Accep ed: 4/12/2020
Copy igh © 2020 Y V Reddy, Pinkesh Dhabolka . Published by Vilnius Uni e si y P ess. This is an Open Access a icle
dis ibu ed unde he e ms o he C ea i e Commons A ibu ion Licence, which pe mi s un es ic ed use, dis ibu ion, and
ep oduc ion in any medium, p o ided he o iginal au ho and sou ce a e c edi ed.
Con en s lis s a ailable a Vilnius Uni e si y P ess
245
Y V Reddy, Pinkesh Dhabolka .
P icing E iciency o Exchange T aded Funds in India
inancial ins i u ion (ma ke make o au ho ised pa icipan ) c ea es ETF sha es by
deposi ing a po olio o secu i ies wi h he T us and ecei es ETF sha es in e u n.
The c ea ed ETF sha es, in u n, a e sold o o he in es o s in he seconda y ma ke .
The objec i es and cha ac e is ics o ETF a e simila o Index mu ual unds managed by
asse managemen companies. Index unds a emp o eplica e pe o mance o chosen
ma ke index. The di e ence be ween he heo e ical e u n o a ge index and e u ns
o index und is called as acking e o . One can hink o ETFs as index mu ual unds
ha can be bough and sold in eal- ime a a p ice ha changes h oughou he day. The
di e ence be ween hese wo asse classes is ha unlike index mu ual unds which ade
a he end o he day a NAV, ETFs ade a eal- ime on s ock exchanges, and hence,
in es o s can de i e he bene i o ade-in ETF jus like any o dina y s ocks, which
means hey a e easie o buy and sell quickly, i need be. Secondly, ETFs a e a ailable
only on s ock exchanges. Hence, one needs a dema accoun o in es in an ETF, whe e-
as o an index mu ual und, one doesn’ need a dema accoun and may buy o sell he
uni s di ec ly om he mu ual und in small amoun s.
Though he e is no di e ence in he composi ion o index mu ual unds and ETFs,
he e is a signi ican di e ence as a as o ma ion and edemp ion o ETFs is conce ned.
As a esul , ETFs a e mo e ax-e icien and also ca y less expense a io compa ed o
mos index mu ual unds.
Ni y BeES (Ni y Benchma k Exchange T aded Scheme) based on he Ni y 50 in-
dex was he i s ETF launched in India in Decembe 2001 by Benchma k Mu ual Fund.
Al hough index in es ing is a a nascen s age in India, i has expe ienced a no able mo-
men um, mainly because o ins i u ional in es o s and Go e nmen ini ia i es. The
ecen decisions o he Go e nmen o India o consen Employees’ P o iden Fund
O ganisa ion o in es he inc emen al su plus in o equi y ETFs, and also o use he
ETF ou e o disin es men ha e u he gi en a push o ETFs in India.
13124 13204 1471522409
49916
77694
139073
0
20000
40000
60000
80000
100000
120000
140000
160000
2013 2014 2015 2016 2017 2018 2019
AUM Rs. in C o es
Yea
ETF AUM… FTE
FIGURE 1. Asse Unde Managemen (AUM) o ETFs in India (in C o es)
Sou ce: Compiled om AMFI newsle e s.
246
ISSN 2029-4581 eISSN 2345-0037 O ganiza ions and Ma ke s in Eme ging Economies
As pe he Associa ion o Mu ual Funds in India (AMFI) indus y ends June 2019
epo , ETFs de i ed 94% o hei asse s om ins i u ional in es o s and only 6% om
indi idual in es o s. As indi idual and ins i u ional in es o s end o di e si y hei in-
es men s ac oss di e en ma ke s, he esul o he s udy would be c ucial o in es o s
who look owa ds he ETF ma ke o po olio di e si ica ion. To a ac indi idual
in es o s, he Minis y o Finance and he Go e nmen o India ha e p oposed o in-
clude ETFs in es ing in Cen al Public Sec o En e p ises (CPSEs) in he Sec ion 80C
deduc ion o Income Tax Ac in he Union Budge o 2019-2020. Besides, he ma ke
egula o , Secu i ies and Exchange Boa d o India (SEBI), has es ic ed he To al Ex-
pense Ra io (TER) o index mu ual unds and ETFs o 1% o d i e cos down o in es-
o s. In India, Equi y, Deb , Gold and In e na ional Indices ETFs a e a ailable o ade.
As o May 2019, he e a e a o al o 79 ETFs lis ed on he Indian s ock ma ke .
ETF c ea ion/ edemp ion mechanism
The key o unde s anding how ETFs wo k is he c ea ion/ edemp ion mechanism.
When an ETF company wan s o c ea e new sha es o i s und, whe he o launch a new
p oduc o o mee inc easing ma ke demand, i app oaches an au ho ized pa icipan
(AP). An AP may be a ma ke make , a specialis , o any o he la ge inancial ins i u ion
wi h a lo o buying powe . I is ma ke make ’s du y o acqui e he sha es ha he ETF
wan s o hold. Fo ins ance, i an ETF is designed o ack he NSE Ni y 50 index, he
ma ke make will buy sha es in all he NSE Ni y 50 cons i uen s in he exac p o-
po ion as he index, hen deli e hose sha es o he ETF p o ide . In exchange, he
p o ide gi es he ma ke make a block o equally alued ETF sha es, called a c e-
a ion uni . The ma ke make deli e s a ce ain amoun o unde lying secu i ies and
ecei es he same alue in ETF sha es, p ice based on hei ne asse alue and no he
ma ke alue a which ETF happens o be ading. I is bene icial o bo h pa ies; he
ETF p o ide ge s he s ocks i needs o ack he index, and he ma ke make ecei es
ETF sha es o esell on exchange o a p o i . The edemp ion p ocess wo ks in e e se.
Ma ke make s can emo e ETF sha es om he ma ke by pu chasing enough o hose
sha es o o m a c ea ion uni and hen deli e ing hose sha es o he ETF issue . In
exchange, he ma ke make ecei es he same alue in he unde lying secu i ies o he
und. The c ea ion/ edemp ion p ocess is i al o he ETF. I is he p ocess ha keeps
sha e p ices ading in line wi h he und’s unde lying ne asse alue. Because an ETF
ades like a s ock, i s p ice will luc ua e du ing he ading hou s, due o ma ke de-
mand and supply. Fo ins ance, when he demand o ETF sha es inc eases, he ETF’s
sha e p ice may ise abo e he alue o i s unde lying secu i ies. When his happens, i is
he ma ke make who in e enes. Recognising he o e p iced ETF, he ma ke make
migh buy up he unde lying sha es ha compose he ETF and hen sell ETF sha es on
he open ma ke . This exe cise helps d i e he ETF’s sha e ma ke p ice back owa d ne
asse alue, while he ma ke make ea ns a isk ee a bi age p o i .
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Y V Reddy, Pinkesh Dhabolka .
P icing E iciency o Exchange T aded Funds in India
Likewise, i he ETF s a s ading a a discoun o he secu i ies i holds, he ma ke
make can buy ETF sha es equal o he c ea ion uni on he cheap and edeem hem
o he unde lying secu i ies, which can be esold on he exchange. By buying up he
unde alued ETF sha es, he AP d i es he ma ke p ice o he ETF back owa ds ne
asse alue while once again making a isk- ee a bi age p o i . The a bi age mecha-
nism helps o keep an ETF’s ma ke p ice in line wi h he ne asse alue o i s unde -
lying po olio. Wi h mul iple ma ke make s wa ching mos ETFs, ETF ma ke p ice
ypically s ays in line wi h i s ne asse alue.
2. P emium/Discoun , a bi age and p icing e iciency
ETF ades a a p emium i i s ma ke p ice is highe han he NAV, and a a discoun ,
i he ma ke p ice is lowe han he NAV. This p ice di e ence may ha e signi ican
epe cussions o in es o s, as i ep esen s a cos i hey buy o e alued ETF sha es o
sell unde alued ETF sha es (Cha e is, 2013). P icing e iciency is he speed a which
he ma ke make s co ec he de ia ions be ween ETFs NAV and he ma ke p ice.
Tse e al. (2006) indica ed ha a pe ec ly e icien ma ke p o ides g ea e liquidi y,
lowe ansac ion cos s, and ewe es ic ions, which plays a i al ole in p ice disco -
e y in o he s ock ma ke index and i s de i a i es. Thus, he p esen s udy a emp s o
in es iga e he p icing e iciency o Indian equi y ETFs employing an au o eg ession
model o e i s p ice de ia ion, and also a emp s o unde s and he lead-lag ela ion-
ship be ween ETF p ice and NAV using he ec o e o co ec ion model (VECM).
Ou s udy conside s a la ge pool o ETFs ha ack di e en indices, including o eign
ma ke indices. The p esen esea ch b idges he gap by ex ending he sample o all he
equi y ETFs lis ed in India. The s udy is aimed o con ibu e signi ican ly o he inance
li e a u e and assis ma ke egula o s, und houses, ma ke make s and esea ch ana-
lys s in e alua ing he Indian ETF ma ke .
3. Li e a u e e iew
The ea lies li e a u e on ETFs by El on e al. (2002), Po e ba e al. (2002), Bli z and
Huij (2012), Rompo is (2009) a emp s o unde s and he pe o mance o index unds
wi h ega d o i s e u ns and acking abili y o he chosen ma ke index. Gallaghe and
Sega a (2005) examined he abili y o ETFs on he Aus alian s ock exchange o ack
he unde lying benchma k index and o p o ide a compa ison o he acking e o
ola ili y. Wong and Shum (2010) examined he pe o mance o 15 wo ldwide ETFs
ac oss bullish and bea ish ma ke s.
3.1 Li e a u e on p icing e iciency
DeFusco e al. (2011) s udied he p icing de ia ions o Spide , Diamonds, and Cubes
om he p ice o he unde lying index. The s udy applied summa y s a is ics, simple

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ISSN 2029-4581 eISSN 2345-0037 O ganiza ions and Ma ke s in Eme ging Economies
OLS eg ession, and VECM model o analyze he da a. The s udy ound ha hei p ice
de ia ion is p edic able and nonze o. Ma shall e al. (2013) analyzed he SPDR S&P
500 and iSha es Co e S&P 500 ETF o he pe iod o Feb ua y 2001 o Augus 2010.
The s udy ound ha sp eads inc ease jus be o e a bi age oppo uni ies, consis en
wi h a dec ease in liquidi y. The s udy also ound ha he ETFs ha e a daily e u n co -
ela ion o 0.99, and de ia ions co ec back ollowing misp icing.
Miu e al. (2013) examined he in o ma ional e iciency o p ices o 273 ETFs ha
ac i ely ade on he NYSE A ca, based on sho -ho izon e u n p edic abili y om
pas o de lows. The s udy ound ha p ice adjus men s o new in o ma ion o ETFs
occu in abou 30 minu es. The esea ch also shows ha he speed o con e gence o
ma ke e iciency o ETFs is no only signi ican ly d i en by olume, bu also by he
p obabili y o in o med ading. Hillia d (2014) s udied he ETF p emium/discoun
p ocess and de e minan s o domes ic equi y, in e na ional equi y, commodi y, ax-
able bond, cu ency, and municipal bond ETFs domiciled in he Uni ed S a es om
Ap il 2010 o Ap il 2011. The s udy ound ha eme ging ma ke ETFs end o ha e
mo e signi ican and mo e pe sis en p emiums han de eloped ma ke ETFs. The
s udy also documen ed illiquidi y o unde lying asse s, highe ola ili y o he eme g-
ing ma ke s, highe bid-ask sp eads, and o he ma ke ic ions as ac o s o misp ic-
ing o ETFs. K eis and Lich (2018) analyzed de ia ions in he Eu opean ETF ma ke s
using g oss and ne e u ns o a long-sho ading s a egy in he capi al asse p icing
model. The s udy ound a posi i e g oss excess e u ns o he long-sho s a egy in all
sample pe iods. Lin e al. (2006) in es iga ed he p icing e iciency o Taiwan Top 50
T acke Fund (TTT) using he de ia ion o p ice om he NAV and he absolu e alue
o misp icing. The s udy ound TTT ends o sell a a p emium; howe e , he p emium
is no signi ican .
Kayali (2007) in es iga ed he p icing de ia ions o p ice om NAV o he Dow
Jones Is anbul 20 (DJIST) o one yea and ound ha DJIST ades a a smalle dis-
coun on a e age, and p emiums o discoun s do no pe sis o e ime and disappea
wi hin wo days. Shin and Soydemi (2010) es ima ed acking e o s om 26 ETFs
u ilizing h ee di e en me hods and ound ha acking e o s a e signi ican ly di e -
en om ze o and display pe sis ence. The s udy using se ial co ela ion es s, uns es s,
and panel eg ession analysis also ound g ea e pe sis ence in ETFs p ice de ia ion.
Shanmugham and Zabiulla (2012) examined he p icing e iciency o Ni y BeES in
bullish and bea ish ma ke condi ions using da a o se en yea s. The s udy ound ha
p ice di e gence disappea s wi hin h ee days due o he a bi age mechanism. Cha -
e is (2013) examined he p icing e iciency o domes ic and o eign ETFs lis ed in
Sou h A ica and ound ha wo ou o se en unds we e ading a a discoun and e-
maining a a p emium. The s udy also sugges s ha di e ences, howe e , do no pe sis
o mo e han wo ading days. Cha e is e al. (2014) in es iga ed he ex en o which
ETFs p emiums and discoun s mo i a e eedback ading in eme ging ma ke s using
a sample o index ETFs. The s udy p o ides e idence deno ing ha eedback ading
249
Y V Reddy, Pinkesh Dhabolka .
P icing E iciency o Exchange T aded Funds in India
g ows signi ican ly in he p esence o lagged p emiums, and is mo e widesp ead when
lagged p emiums inc ease in magni ude.
Swa hy (2015) in es iga ed he p icing e iciency o i e ETFs o Benchma k/Gold-
man Sachs asse managemen company lis ed on NSE, India. The s udy pe iod is om
2010 o 2015. The da a we e analyzed using eg ession analysis; i was ound ha p emi-
ums and discoun s do no pe sis o e ime and, hus, he ETF ma ke was ound o be
e icien . Bas and Sa ioglu (2015) e alua ed he acking e o and p icing e iciency o
16 ETFs be ween 2005 and 2013 ope a ing in he Tu kish Capi al ma ke s. The p icing
e iciencies we e compu ed using he a e age p emium and discoun and ound o be
e icien . Adi ya and Desai (2015) examined he p icing e iciency and p ice disco e y o
equi y index ETFs in India. The esul showed ha Indian ETFs ake a minimum o 4 days
and a maximum o 10 days o he di e en ial be ween he NAV and p ice o disappea .
Kuma (2018) in es iga ed he p icing e iciency o CPSE ETF lis ed on he Na ional
S ock Exchange India. The esea che employed simple linea eg ession o unde s and
he ela ionship be ween ne asse alue and he ma ke p ice o ETF. The esea che also
made use o desc ip i e s a is ics o analyze p icing e iciency and concluded ha du ing
he s udy pe iod CPSE ETF aded a a discoun , bu he discoun was economically
insigni ican o he ma ke pa icipan o p o i om he a bi age oppo uni y.
4. Me hodology and Da a
The p esen s udy makes use o a ious s a is ical and econome ic ools and echniques
o suppo he analysis and o achie e he objec i es amed. Such me hods a e b ie ly
explained o ge an unde s anding o he ele ance o hese echniques in he p esen
s udy, and equa ions a e inco po a ed o suppo he analysis.
The s udy a emp s o in es iga e he p icing e iciency o domes ic equi y index
ETFs lis ed on he Na ional S ock Exchange and Bombay S ock Exchange. As di e -
en unds ha e di e en incep ion da es, he esea che s eel i will be inapp op ia e o
examine he p icing e iciency o ETFs ac oss di e en ime ho izons. Figu e 1 shows
he subs an ial in low o unds o he ETFs, especially om 2017 o 2019, which may
ha e a conside able impac on he pe o mance o ETFs. As such, we s udy he p icing
e iciency o he selec ed ETFs o wo yea s, i.e., om Ap il 2017 o Ma ch 2019. Table
1 shows he cha ac e is ics o he selec ed ETFs.
The daily closing p ice o ETFs was sou ced om he Na ional S ock Exchange and
Bombay S ock Exchange. The daily Ne Asse Value o ETFs was sou ced om he As
-
socia ion o Mu ual Funds in India (AMFI). The da a cleaning p ocess was unde aken
o missing alues, and he p ice de ia ion se ies, o u he esea ch, was calcula ed as
he di e ence be ween he daily closing p ice o an ETF and i s daily NAV.
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ISSN 2029-4581 eISSN 2345-0037 O ganiza ions and Ma ke s in Eme ging Economies
TABLE I: Cha ac e is ics o selec ed ETFs
S .
No. ETF Issue Unde lying Index Incep ion Da e
01 Adi ya Bi la Sun Li e
Ni y ETF
Adi ya Bi la Sun Li e
Mu ual Fund NIFTY 50 TRI 21-Jul-11
02 Edelweiss ETF - Ni y
100 Quali y 30 Edelweiss Mu ual Fund NIFTY 100 Quali y 30 TRI 25-May-16
03 Edelweiss ETF - Ni y
Bank Edelweiss Mu ual Fund NIFTY Bank TRI 15-Dec-15
04 Edelweiss ETF - Ni y
50 Edelweiss Mu ual Fund NIFTY 50 TRI 08-May-15
05 HDFC Ni y 50 ETF HDFC Mu ual Fund NIFTY 50 TRI 09-Dec-15
06 HDFC Sensex ETF HDFC Mu ual Fund S&P BSE Sensex TRI 09-Dec-15
07 ICICI P uden ial Ni y
100 ETF
ICICI P uden ial Mu-
ual Fund NIFTY 100 TRI 20-Aug-13
08 ICICI P uden ial Ni y
ETF
ICICI P uden ial Mu-
ual Fund NIFTY 50 TRI 20-Ma -13
09 ICICI P uden ial NV20
ETF
ICICI P uden ial Mu-
ual Fund NIFTY 50 Value 20 TRI 17-Jun-16
10 ICICI P uden ial Sensex
ETF
ICICI P uden ial Mu-
ual Fund S&P BSE Sensex TRI 10-Jan-03
11 IDFC Ni y ETF IDFC Mu ual Fund NIFTY 50 TRI 07-Oc -16
12 IDFC Sensex ETF IDFC Mu ual Fund S&P BSE Sensex TRI 07-Oc -16
13 In esco India Ni y ETF In esco Mu ual Fund NIFTY 50 TRI 13-Jun-11
14 Ko ak Banking ETF Ko ak Mahind a Mu ual
Fund NIFTY Bank TRI 04-Dec-14
15 Ko ak Ni y ETF Ko ak Mahind a Mu ual
Fund NIFTY 50 TRI 02-Feb-10
16 Ko ak PSU Bank ETF Ko ak Mahind a Mu ual
Fund NIFTY PSU Bank TRI 08-No -07
17 Ko ak Sensex ETF Ko ak Mahind a Mu ual
Fund S&P BSE Sensex TRI 06-Jun-08
18 LIC MF Exchange
T aded Fund-Ni y 50 LIC Mu ual Fund NIFTY 50 TRI 20-No -15
19 LIC MF Exchange
T aded Fund-Ni y 100 LIC Mu ual Fund NIFTY 100 TRI 17-Ma -16
20 LIC MF Exchange
T aded Fund-Sensex LIC Mu ual Fund S&P BSE Sensex TRI 30-No -15
21 Mo ilal Oswal M50 ETF Mo ilal Oswal Mu ual
Fund NIFTY 50 TRI 28-Jul-10
22 Mo ilal Oswal Midcap
100 ETF
Mo ilal Oswal Mu ual
Fund NIFTY Midcap 100 TRI 31-Jan-11
23 Mo ilal Oswal Nasdaq
100 ETF
Mo ilal Oswal Mu ual
Fund Nasdaq 100 29-Ma -11
24 Quan um Ni y ETF Quan um Mu ual Fund NIFTY 50 TRI 10-Jul-08
25 Nippon ETF Bank BeES Nippon Mu ual Fund NIFTY Bank TRI 27-May-04
251
Y V Reddy, Pinkesh Dhabolka .
P icing E iciency o Exchange T aded Funds in India
S .
No. ETF Issue Unde lying Index Incep ion Da e
26 Nippon ETF Hang Seng
BeES Nippon Mu ual Fund HangSeng 09-Ma -10
27 Nippon ETF In a BeES Nippon Mu ual Fund NIFTY In as uc u e TRI 29-Sep-10
28 Nippon ETF Junio
BeES Nippon Mu ual Fund NIFTY Nex 50 TRI 21-Feb-03
29 Nippon ETF Ni y 100 Nippon Mu ual Fund NIFTY 100 TRI 22-Ma -13
30 Nippon ETF Ni y BeES Nippon Mu ual Fund NIFTY 50 TRI 28-Dec-01
31 Nippon ETF NV20 ETF Nippon Mu ual Fund NIFTY 50 Value 20 TRI 08-Jun-15
32 Nippon ETF PSU Bank
BeES Nippon Mu ual Fund NIFTY PSU Bank TRI 25-Oc -07
33 Nippon ETF Sensex Nippon Mu ual Fund S&P BSE Sensex TRI 24-Sep-14
34 SBI-ETF BSE 100 SBI Mu ual Fund S&P BSE 100 TRI 16-Ma -15
35 SBI-ETF Ni y 50 SBI Mu ual Fund NIFTY 50 TRI 23-Jul-15
36 SBI-ETF Ni y Nex 50 SBI Mu ual Fund NIFTY Nex 50 TRI 16-Ma -15
37 SBI-ETF Ni y Bank SBI Mu ual Fund NIFTY Bank TRI 20-Ma -15
38 UTI NIFTY Exhcange
T aded Fund UTI Mu ual Fund NIFTY 50 TRI 03-Sep-15
39 UTI SENSEX Exchange
T aded Fund UTI Mu ual Fund S&P BSE Sensex TRI 03-Sep-15
Au ho s’ compila ion
The a bi age pe sis ence was cap u ed o check o he p icing e iciency o ETFs.
A bi age is he simul aneous buying and selling secu i ies o ake ad an age o a p ice
di e ence. The p esence o a bi age is deno ed by he p ice de ia ion be ween he ma -
ke p ice o he ETF and i s NAV. The p ice de ia ion is equa ed as (1)
D = P – NAV (1)
whe e
D – p ice de ia ion
P – closing p ice o he ETF and,
NAV – NAV o he ETF.
I D is nega i e, he und is said o be ading a a discoun o i s NAV and, a a p e-
mium, i i is posi i e.
To begin wi h, we made use o summa y s a is ics o analyse and unde s and he
na u e o he ob ained p ice de ia ion se ies. The summa y s a is ics shows he numbe
o obse a ions o each ETF, i s mean de ia ion amoun , minimum de ia ion amoun ,
maximum de ia ion amoun , s anda d de ia ion, skewness, and ku osis o he da a
se ies. The mean implies a e age p ice de ia ion du ing he pe iod. S anda d de ia ion
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g ui y in imings o Indian s ock ma ke s is-a- is he o eign ma ke . The s udy ound
he Mo ilal Oswal Nasdaq 100 ETF equi es wo days o i s de ia ion o disappea ,
while Nippon ETF Hang Seng BeES needs h ee days o align p ice wi h NAV. Ou ind-
ing is pa ly in line wi h Cha e is (2013), who ound ha he de ia ion o domes ic and
o eign ETFs lis ed in Sou h A ica does no pe sis o mo e han wo days. The a ying
le els o p icing e iciency o ETFs acking he same indices highligh he c i ical ole o
be played by each ma ke -make ied up wi h he und house. The s udy also documen s
di e se le els o e iciency o he di e en ETF schemes belonging o he same und
house, posing a ques ion on he ole played by liquidi y in he ETF ma ke .
5.4 Uni oo es
The s udy also a emp s o unde s and he p ice disco e y p ocess be ween he ma ke
p ice and NAV o ETFs. The exis ence o a long- un ela ionship be ween he ma ke
p ice and NAV o ETFs needs o be examined be o e cap u ing he p ice disco e y
p ocess o he ETFs. The long- un ela ionship can be examined by using he Johansen
co-in eg a ion es . To check o he exis ence o any long- un ela ionship using he
co-in eg a ion echnique equi es checking o he s a iona i y o da a a he le el. Fo
he applica ion o he co-in eg a ion echnique, he da a ha e o be non-s a iona y a
he le el and should be s a iona y a he same di e ence. The ADF es is used o es
o s a iona i y o da a. Table 5 p esen s he esul s o he ADF es applied on he le els
and he i s di e ence o ETFs daily ma ke p ice and NAV se ies, espec i ely.
E idence om ADF uni oo es s sugges s ha ma ke p ice and NAV ge s a ion-
a y a he i s di e ence and a le els hey a e non-s a iona y as can be in e ed om
Table 5. This means ha bo h he a iables ollow an I (1) p ocess. Since bo h he se ies
a e in eg a ed a he same o de , he co-in eg a ion es can be applied o he p ice and
NAV o he ETF.
TABLE 5. Uni oo es s o he P ice and NAV o ETF
Scheme name P ice NAV
Le el Fi s Di Le el Fi s Di
Adi ya Bi la Sun Li e Ni y ETF 0.5325 0.0000 0.5862 0.0000
Edelweiss ETF - Ni y 100 Quali y 30 0.7294 0.0000 0.6116 0.0000
Edelweiss ETF - Ni y Bank 0.0707 0.0000 0.5744 0.0000
Edelweiss ETF - Ni y 50 0.1818 0.0000 0.3785 0.0000
HDFC Ni y 50 ETF 0.6542 0.0000 0.5946 0.0000
HDFC Sensex ETF 0.5015 0.0000 0.4323 0.0000
ICICI P uden ial Ni y 100 ETF 0.3523 0.0000 0.4207 0.0000
ICICI P uden ial Ni y ETF 0.5715 0.0000 0.5881 0.0000
ICICI P uden ial NV20 ETF 0.7527 0.0000 0.7172 0.0000
ICICI P uden ial Sensex ETF 0.4224 0.0000 0.5261 0.0000
IDFC Ni y ETF 0.6311 0.0000 0.5143 0.0000
IDFC Sensex ETF 0.5451 0.0000 0.6759 0.0000

259
Y V Reddy, Pinkesh Dhabolka .
P icing E iciency o Exchange T aded Funds in India
Scheme name P ice NAV
Le el Fi s Di Le el Fi s Di
In esco India Ni y ETF 0.1770 0.0000 0.5923 0.0000
Ko ak Banking ETF 0.9453 0.0000 0.7446 0.0000
Ko ak Ni y ETF 0.1873 0.0000 0.1920 0.0000
Ko ak PSU Bank ETF 0.3267 0.0000 0.2057 0.0000
Ko ak Sensex ETF 0.6338 0.0000 0.7490 0.0000
LIC MF Exchange T aded Fund-Ni y 50 0.3074 0.0000 0.6230 0.0000
LIC MF Exchange T aded Fund-Ni y 100 0.2276 0.0000 0.3881 0.0000
LIC MF Exchange T aded Fund-Sensex 0.0772 0.0000 0.1881 0.0000
Mo ilal Oswal M50 ETF 0.3671 0.0000 0.5768 0.0000
Mo ilal Oswal Midcap 100 ETF 0.2390 0.0000 0.2377 0.0000
Mo ilal Oswal Nasdaq 100 ETF 0.3898 0.0000 0.5897 0.0000
Quan um Ni y ETF 0.2712 0.0000 0.2644 0.0000
Nippon ETF Bank BeES 0.7875 0.0000 0.7503 0.0000
Nippon ETF Hang Seng BeES 0.3689 0.0000 0.1847 0.0000
Nippon ETF In a BeES 0.3249 0.0000 0.2653 0.0000
Nippon ETF Junio BeES 0.0707 0.0000 0.0774 0.0000
Nippon ETF Ni y 100 0.2650 0.0000 0.3474 0.0000
Nippon ETF Ni y BeES 0.5878 0.0000 0.5929 0.0000
Nippon ETF NV20 ETF 0.4992 0.0000 0.3576 0.0000
Nippon ETF PSU Bank BeES 0.3122 0.0000 0.2075 0.0000
Nippon ETF Sensex 0.2877 0.0000 0.4130 0.0000
SBI-ETF BSE 100 0.1692 0.0000 0.3016 0.0000
SBI-ETF Ni y 50 0.5870 0.0000 0.3713 0.0000
SBI-ETF Ni y Nex 50 0.0682 0.0000 0.0704 0.0000
SBI-ETF Ni y Bank 0.7166 0.0000 0.7403 0.0000
UTI NIFTY Exchange T aded Fund 0.5802 0.0000 0.5776 0.0000
UTI SENSEX Exchange T aded Fund 0.6656 0.0000 0.8370 0.0000
Sou ce: Compiled using EViews and MS Excel
No e: Values in he able a e P- alues o ADF es .
5.5 Co-in eg a ion es
Resul s in Table 6 demons a e he exis ence o only one co-in eg a ing ela ionship be-
ween he ma ke p ice and NAV du ing he pe iod using he Johansen co-in eg a ion
es . The lag selec ion is based upon he Akaike in o ma ion c i e ia. Typically, one o
he a iables is used o no malize he co-in eg a ing ec o by ixing i s coe icien a
uni y. We make use o ma ke p ice as he no malizing (dependen ) a iable and NAV
as an independen a iable. The esul s o he Johansen co-in eg a ion es based on he
ace es and he max-eigen alue es a e epo ed in Table 6. The esul s indica e ha
all ETFs show a long- un ela ionship be ween he p ice and NAV. Based on he esul s
o he Johansen co-in eg a ion es , he null hypo hesis ha he p ice and NAV o Indi-
an ETFs do no ha e any log un ela ionship ge s ejec ed a he 5% signi icance le el.
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TABLE 6. Johansen Co-In eg a ion Tes : One Vec o
Scheme name Co-In eg a ing Vec o * T ace Tes Max-Eigen Tes Lags
Adi ya Bi la Sun Li e Ni y ETF None
A mos one
(0.0002)
(0.1003)
(0.003)
(0.1003) 2
Edelweiss ETF - Ni y 100 Quali y 30 None
A mos one
(0.0377)
(0.1016)
(0.0630)
(0.1016) 4
Edelweiss ETF - Ni y Bank None
A mos one
(0.0893)
(0.0426)
(0.2351)
(0.0426) 3
Edelweiss ETF - Ni y 50 None
A mos one
(0.0121)
(0.0264)
(0.0542)
(0.0264) 14
HDFC Ni y 50 ETF None
A mos one
(0.0000
(0.0930)
(0.0000)
(0.0930) 3
HDFC Sensex ETF None
A mos one
(0.0000
(0.0938)
(0.0000
(0.0938) 1
ICICI P uden ial Ni y 100 ETF None
A mos one
(0.0000
(0.0711)
(0.0000
(0.0711) 1
ICICI P uden ial Ni y ETF None
A mos one
(0.0000
(0.1261)
(0.0000
(0.1261) 2
ICICI P uden ial NV20 ETF None
A mos one
(0.0000
(0.2886)
(0.0000
(0.2886) 1
ICICI P uden ial Sensex ETF None
A mos one
(0.0000)
(0.0872)
(0.0000
(0.0872) 1
IDFC Ni y ETF None
A mos one
(0.0011)
(0.0664)
(0.0023)
(0.0664) 7
IDFC Sensex ETF None
A mos one
(0.0000)
(0.2135)
(0.0000)
(0.2135) 2
In esco India Ni y ETF None
A mos one
(0.0000)
(0.0952)
(0.0000)
(0.0952) 2
Ko ak Banking ETF None
A mos one
(0.0000)
(0.2451)
(0.0000)
(0.2451) 2
Ko ak Ni y ETF None
A mos one
(0.0000)
(0.0200)
(0.0000)
(0.0200) 2
Ko ak PSU Bank ETF None
A mos one
(0.0000)
(0.0330)
(0.0000)
(0.0330) 2
Ko ak Sensex ETF None
A mos one
(0.0000)
(0.2010)
(0.0000)
(0.2010) 2
LIC MF Exchange T aded Fund-Ni y
50
None
A mos one
(0.0000)
(0.1055)
(0.0000)
(0.1055) 2
LIC MF Exchange T aded Fund-Ni y
100
None
A mos one
(0.0000)
(0.0554)
(0.0000)
(0.0554) 1
LIC MF Exchange T aded Fund-
Sensex
None
A mos one
(0.0015)
(0.0156)
(0.0085)
(0.0156) 3
Mo ilal Oswal M50 ETF None
A mos one
(0.0000)
(0.1334)
(0.0000)
(0.1334) 3
Mo ilal Oswal Midcap 100 ETF None
A mos one
(0.0081)
(0.0338)
(0.0266)
(0.0338) 2
Mo ilal Oswal Nasdaq 100 ETF None
A mos one
(0.0386)
(0.1304)
(0.0388)
(0.1304) 3
Quan um Ni y ETF None
A mos one
(0.0000)
(0.0259)
(0.0000)
(0.0259) 2
261
Y V Reddy, Pinkesh Dhabolka .
P icing E iciency o Exchange T aded Funds in India
Scheme name Co-In eg a ing Vec o * T ace Tes Max-Eigen Tes Lags
Nippon ETF Bank BeES None
A mos one
(0.0000)
(0.2167)
(0.0000)
(0.2167) 2
Nippon ETF Hang Seng BeES None
A mos one
(0.0000)
(0.0376)
(0.0021)
(0.0376) 2
Nippon ETF In a BeES None
A mos one
(0.0000)
(0.0604)
(0.0000)
(0.0604) 1
Nippon ETF Junio BeES None
A mos one
(0.0003)
(0.0126)
(0.0017)
(0.0126) 6
Nippon ETF Ni y 100 None
A mos one
(0.0000)
(0.0420)
(0.0000)
(0.0420)
4
Nippon ETF Ni y BeES None
A mos one
(0.0000)
(0.1325)
(0.0000)
(0.1325) 2
Nippon ETF NV20 ETF None
A mos one
(0.0000)
(0.0309)
(0.0000)
(0.0309) 1
Nippon ETF PSU Bank BeES None
A mos one
(0.0000)
(0.0324)
(0.0000)
(0.0324) 2
Nippon ETF Sensex None
A mos one
(0.0000)
(0.0228)
(0.0000)
(0.0228) 1
SBI-ETF BSE 100 None
A mos one
(0.0000)
(0.0435)
(0.0000)
(0.0435) 2
SBI-ETF Ni y 50 None
A mos one
(0.0000)
(0.0576)
(0.0000)
(0.0576) 2
SBI-ETF Ni y Nex 50 None
A mos one
(0.0000)
(0.0043)
(0.0000)
(0.0043) 2
SBI-ETF Ni y Bank None
A mos one
(0.0000)
(0.1398)
(0.0000)
(0.1398) 2
UTI NIFTY Exchange T aded Fund None
A mos one
(0.0000)
(0.1010)
(0.0000)
(0.1010) 3
UTI SENSEX Exchange T aded Fund None
A mos one
(0.0002)
(0.2643)
(0.0002)
(0.2643) 12
Sou ce: Compiled using EViews and MS Excel
No es: *Null hypo hesis is a es o he p esence o co-in eg a ion ec o be ween he p ice o he ETF and
NAV o he ETF.**Tes p o es signi ican a he 5% con idence le el.
5.6 Vec o e o co ec ion model (VECM)
A VECM model is commonly used o da a whe e he unde lying a iables ha e a
long- un s ochas ic end, also known as co-in eg a ion. The VECM has co-in eg a ion
ela ions buil in o he speci ica ion so ha i es ic s he long- un beha iou o he
endogenous a iables o con e ge o hei co-in eg a ion while allowing o sho - un
adjus men dynamics. The co-in eg a ion e m known as e o co ec ion e m since
he de ia ion om long- un equilib ium is co ec ed g adually h ough a se ies o pa -
ial sho - un adjus men s. Table 7 epo s he VECM es ima ion esul s. Coe icien s
o he equilib ium e o co ec ion e m ep esen he speed a which he sho - un
de ia ion om he long un equilib ium is co ec ed in he subsequen pe iod. The e-
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ISSN 2029-4581 eISSN 2345-0037 O ganiza ions and Ma ke s in Eme ging Economies
sul sugges s ha o all he ETFs, he NAV leads he ma ke p ice in in o ma ion ans-
mission and p ice disco e y p ocesses. The ma ke p ice o en de ia es subs an ially
om he long- un equilib ium. The esul s help o unde s and he lead-lag ela ionship
be ween he ma ke p ice and NAV o he ETFs. The ma ke p ice co ec s i sel based
on he mo emen s o he NAV. Hence, we can conclude ha his o ical NAV da a can be
used o p edic ing u u e ma ke p ice disco e y o ETFs. In es o s can de ise p o i -
able s a egies based on he NAV-ma ke p ice mo emen , which would be e lec ed in
u u e ETF p ice le els.
TABLE 7. Vec o E o Co ec ion Model Es ima es o One Co-In eg a ion Vec o
Scheme Name Va iable E o
Coe icien
S anda d
E o P-Value Lag In e ence
Adi ya Bi la
Sun Li e Ni y
ETF
NAV
P ice
-0.0058
-0.1742
0.0128
0.0331
0.811
0.000 2
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
Edelweiss
ETF– Ni y
100 Quali y 30
NAV
P ice
0.000578
0.118513
0.00755
0.03197
0.938
0.002 4
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
Edelweiss
ETF – Ni y
Bank
NAV
P ice
-0.01123
-0.16198
0.01402
0.04892
0.424
0.001 3
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
Edelweiss
ETF – Ni y 50
NAV
P ice
0.01840
0.03166
0.0071
0.0327
0.0101
0.3343 14
E o coe icien o p ice is
highe han NAV and bo h a e
signi ican . Hence, he NAV
leads he p ice.
HDFC Ni y
50 ETF
NAV
P ice
0.07984
0.44438
0.1477
0.1410
0.5892
0.0017 3
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
HDFC Sensex
ETF
NAV
P ice
-0.03732
0.77236
0.0402
0.0709
0.3537
0.0000 1
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
ICICI P uden-
ial Ni y 100
ETF
NAV
P ice
0.00901
1.02137
0.05579
0.08167
0.8717
0.0000 1
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
263
Y V Reddy, Pinkesh Dhabolka .
P icing E iciency o Exchange T aded Funds in India
Scheme Name Va iable E o
Coe icien
S anda d
E o P-Value Lag In e ence
ICICI P uden-
ial Ni y ETF
NAV
P ice
-0.2368
0.23907
0.22129
0.21065
0.2851
0.2570 2
E o coe icien o p ice is
highe han NAV and bo h a e
no signi ican . Hence, he NAV
leads he p ice.
ICICI P uden-
ial NV20 ETF
NAV
P ice
-0.0259
0.97596
0.06772
0.09037
0.7013
0.0000 1
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
ICICI P uden-
ial Sensex ETF
NAV
P ice
0.04070
1.09185
0.04198
0.07533
0.3328
0.0000 1
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
IDFC Ni y
ETF
NAV
P ice
0.04370
0.32131
0.03801
0.06879
0.2509
0.0000 7
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
IDFC Sensex
ETF
NAV
P ice
-0.0444
0.62708
0.03469
0.09039
0.2018
0.0000 2
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
In esco India
Ni y ETF
NAV
P ice
-0.02583
0.39232
0.02827
0.07290
0.3615
0.0000 2
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
Ko ak Banking
ETF
NAV
P ice
-0.2018
0.33325
0.15508
0.15753
0.1937
0.0349 2
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
Ko ak Ni y
ETF
NAV
P ice
-0.70206
0.03992
0.05628
0.79573
0.0000
0.9600 2
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is signi ican .
Hence, he NAV leads he
p ice.
Ko ak PSU
Bank ETF
NAV
P ice
-0.42018
0.51724
0.19505
0.16446
0.0317
0.0018 2
E o coe icien o p ice is
highe han NAV and bo h a e
signi ican . Hence, he NAV
leads he p ice.
Ko ak Sensex
ETF
NAV
P ice
-0.01094
0.81884
0.0780
0.0921
0.8886
0.0000 2
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.

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ISSN 2029-4581 eISSN 2345-0037 O ganiza ions and Ma ke s in Eme ging Economies
Scheme Name Va iable E o
Coe icien
S anda d
E o P-Value Lag In e ence
LIC MF Ex-
change T aded
Fund-Ni y 50
NAV
P ice
-0.04357
0.73787
0.0340
0.0750
0.2013
0.0000 2
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
LIC MF Ex-
change T aded
Fund-Ni y 100
NAV
P ice
-0.0151
0.2595
0.0159
0.0442
0.3415
0.0000 1
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
LIC MF Ex-
change T aded
Fund-Sensex
NAV
P ice
0.0227
0.2860
0.0183
0.0666
0.2162
0.0000 3
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
Mo ilal Oswal
M50 ETF
NAV
P ice
-0.0912
0.2300
0.0357
0.0449
0.0110
0.0000 3
E o coe icien o p ice is
highe han NAV and bo h a e
signi ican . Hence, he NAV
leads he p ice.
Mo ilal Oswal
Midcap 100
ETF
NAV
P ice
-0.0164
0.0598
0.0173
0.0200
0.3477
0.0030 2
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
Mo ilal Os-
wal Nasdaq 100
ETF
NAV
P ice
-0.0012
0.0219
0.0084
0.0111
0.8786
0.0555 3
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
Quan um Ni y
ETF
NAV
P ice
-0.4119
0.4452
0.1506
0.1394
0.0066
0.0016 2
E o coe icien o p ice is
highe han NAV and bo h a e
signi ican . Hence, he NAV
leads he p ice.
Nippon ETF
Bank BeES
NAV
P ice
-0.4932
0.0223
0.2859
0.2817
0.0852
0.9368 2
E o coe icien o p ice is
highe han NAV and bo h a e
no signi ican . Hence, he NAV
leads he p ice
Nippon ETF
Hang Seng
BeES
NAV
P ice
-0.0393
0.1192
0.0157
0.0311
0.0131
0.0001 2
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
Nippon ETF
In a BeES
NAV
P ice
-0.1849
0.7536
0.1147
0.1274
0.1076
0.0000 1
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
265
Y V Reddy, Pinkesh Dhabolka .
P icing E iciency o Exchange T aded Funds in India
Scheme Name Va iable E o
Coe icien
S anda d
E o P-Value Lag In e ence
Nippon ETF
Junio BeES
NAV
P ice
0.1468
0.4501
0.3361
0.3250
0.6624
0.1666 6
E o coe icien o p ice is
highe han NAV and bo h a e
no signi ican . Hence, he NAV
leads he p ice.
Nippon ETF
Ni y 100
NAV
P ice
0.0916
0.8893
0.1222
0.1282
0.4540
0.0000 4
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
Nippon ETF
Ni y BeES
NAV
P ice
-0.3911
0.0670
0.2537
0.2411
0.1238
0.7811 2
E o coe icien o p ice is
highe han NAV and bo h a e
no signi ican . Hence, he NAV
leads he p ice.
Nippon ETF
NV20 ETF
NAV
P ice
0.1098
0.8764
0.1248
0.1235
0.3794
0.000 1
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
Nippon ETF
PSU Bank
BeES
NAV
P ice
0.2513
0.5118
0.2110
0.1846
0.2342
0.0058 2
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
Nippon ETF
Sensex
NAV
P ice
-0.0319
0.9553
0.0806
0.1160
0.6924
0.0000 1
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
SBI-ETF BSE
100
NAV
P ice
0.0081
0.2109
0.0285
0.0345
0.7745
0.0000 2
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
SBI-ETF Ni y
50
NAV
P ice
-0.2517
0.3338
0.2307
0.2190
0.2764
0.1288 2
E o coe icien o p ice is
highe han NAV and bo h a e
no signi ican . Hence, he NAV
leads he p ice.
SBI-ETF Ni y
Nex 50
NAV
P ice
0.1474
0.8400
0.1741
0.1585
0.3976
0.0000 2
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
SBI-ETF Ni y
Bank
NAV
P ice
-0.3451
0.4163
0.2294
0.2195
0.1332
0.0585 2
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
266
ISSN 2029-4581 eISSN 2345-0037 O ganiza ions and Ma ke s in Eme ging Economies
Scheme Name Va iable E o
Coe icien
S anda d
E o P-Value Lag In e ence
UTI NIFTY
Exchange
T aded Fund
NAV
P ice
-0.0804
0.5404
0.0870
0.0963
0.3558
0.0000 3
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
UTI SENSEX
Exchange
T aded Fund
NAV
P ice
-0.0261
0.5070
0.0720
0.1131
0.7171
0.0000 12
E o coe icien o p ice is
highe han NAV and NAV’s
e o coe icien is no signi i-
can . Hence, he NAV leads he
p ice.
Sou ce: Compiled using EViews and MS Excel
6. Conclusion
Ou indings con ibu e o he unde s anding o equi y ETFs lis ed in India acking
domes ic as well as o eign ma ke indices by s udying he ela ion be ween he ma ke
p ice and he NAV o ETFs. The ecen subs an ial inc ease in he low o unds o ETFs
signi ies he ise in he popula i y o ETF as an in es men ool in India. The p esen
s udy con ibu es o he exis ing li e a u e on ETFs in India, and also ies o in es i-
ga e he p icing e iciency achie ed h ough he c ea ion- edemp ion mechanism by
he ETF ma ke make s.
The esul s o au o eg ession analysis showed ha du ing he s udy pe iod, ETFs
lis ed in India ake a minimum o one day and a maximum o ou days o he de i-
a ion be ween he NAV and ma ke p ice o disappea . The esul s o au o eg ession
a e in con as wi h El on e al. (2002) and Rompo is (2010), whe e pe sis ence in
de ia ion was obse ed o a day. The p esence o de ia ion be ween he ma ke p ice
and NAV o ETF o mo e han one day ep esen s an addi ional cos o he in es o s,
bu also p o ides a bi age s wi h an oppo uni y o book low- isk p o i . The VECM
esul s demons a e he sho e m dynamics and help o unde s and he lead-lag ela-
ionship; hey indica e he NAV as he lead a iable, which is ollowed by he ma ke
p ice (lag a iable). The pe sis ence o de ia ion be ween p ice and NAV, along wi h
he unde s anding o lead-lag mo emen , can be used by in es o s o ame p o i able
in es men s a egies in he Indian ETF ma ke . Though p icing e iciency o ETFs in
India has subs an ially imp o ed o e he pe iod, he e is s ill a need o ETF p o ide s
o pa ne wi h ma ke make s o e icien ly aligning p ice and NAV using he c ea-
ion- edemp ion mechanism e ec i ely. Thus, we can conclude ha he ETF ma ke in
India is pa ially e icien , and he e s ill exis a bi age oppo uni ies o ma ke make s
and in es o s. Yada and Pope (1994) epo ed ha misp icing is mo e likely o ep e-
sen p o i oppo uni ies a he han isk p emia. The ma ke egula o s in India mus
inc ease hei e o s o educa e in es o s abou he bene i s o in es ing in ETFs, which
will help imp o e p icing e iciency in he u u e.
267
Y V Reddy, Pinkesh Dhabolka .
P icing E iciency o Exchange T aded Funds in India
Acknowledgemen s
The au ho s acknowledge he aluable sugges ions ecei ed om anonymous e iewe s.
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