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Statistical Analysis of ETF Flows,
Prices, and Premiums
Aleksander Sobczyk
iShares Global Investments & Research
BlackRock
Matlab Computational Finance Conference
New York
April 9th, 2014
iS-12030 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION
Agenda
Rapid growth of exchange-traded funds (ETFs) has generated considerable interest in the dynamics of
flows, prices, and premiums:
Despite over $2 trillion of AUM world-wide as of 2013, there is not to our knowledge any previous systematic
statistical analysis of ETF flows
As ETFs are used by hedge funds and other sophisticated investors to quickly and cheaply express their views
across asset classes and regions, flows could be very informative about changes in investor sentiment
Concerns have been raised around ETF role in global financial markets, particularly about premiums and discounts
to NAV, as well as risks for overall market liquidity
Analyzing data that is increasing both in size and complexity (intra-day ETF prices and premiums, ETF taxonomy
and classifications, fund holdings information, auxiliary datasets, etc.) requires state-of-the-art tools like Matlab
Objectives:
Part One: Understand dynamics of ETF flows:
Are flows predictable? At what horizons? What drives flow dynamics?
How are flows related to past flows and returns within an asset class? Across asset classes?
Can flows or sentiment predict returns?
Part Two: Analyze the role of ETFs in the global financial markets:
Clarify common misconceptions about premiums and discounts to NAV.
Provide canonical framework to analyze liquidity and price discovery functions of ETFs.
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Part One: Statistical Analysis of ETF Flows
iShares Global Investments & Research
BlackRock
How to Define ETF Flows?
Unlike a traditional mutual fund, purchases/sales of ETFs do not necessarily require investors to interact with the
fund; offering lower costs and tax efficiency; transactions can also occur throughout the day.
While ETFs trade intraday on organized exchanges, like equities, the creation-redemption mechanism allows
additional ETF supply/demand through primary market transactions beyond visible secondary market.
Authorized
Participant ETF Issuer
Basket of underlying securities
ETF units
Creation Process: Authorized Participant delivers a basket of underlying securities to issuer
who in turn delivers the ETF units
Total ETF Flow in a month (or week) with T trading days
= Total Creations Minus Redemptions
= S i=1..T days (Shares Outi – Shares Outi-1) * NAVi
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Global ETF Flows and Assets
iS-12030 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION
Source: BlackRock “ETP Landscape” as of December 31, 2013
5
Predictability of Flows Within Asset Classes
Are flows predictable?
Simplest approach is to look at autocorrelation in scaled flows within an asset class
Examine weekly and monthly frequencies as the daily data is too noisy
Consistent patterns emerge:
Equity flows are not autocorrelated, indicating little persistence over weekly or monthly horizons
However, at weekly and monthly frequencies, both commodity and fixed income flows are strongly positive
autocorrelated
Autocorrelation is stronger during the period of the financial crisis suggesting that investors sentiment tended to be
persistent from week to week with no evidence of contrarian behavior
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January 2005 - July 2013
Flow Change (%) Descriptive Statistics by Asset Class
Asset Class Mean Median Std Dev 1st Autocorr
Equity 0.07 0.05 0.12 -0.05
Fixed Income 0.15 0.14 0.12 0.26**
Commodities 0.16 0.09 0.42 0.24*
Monthly Exchange Traded Product (ETP) Flow Changes All US-domiciled ETPs, January 2005 – July 2013
Flow Change (%) = (1 / T) Flowk / AUMk-1
Source: Bloomberg, Thomson Reuters, BlackRock, as at July 31, 2013
05 06 07 08 09 10 11 12 13 14-0.5
0
0.5Equity ETPs
Year
Flo
w C
hange (
%)
05 06 07 08 09 10 11 12 13 14-0.5
0
0.5
1Fixed Income ETPs
Year
Flo
w C
hange (
%)
05 06 07 08 09 10 11 12 13 14-2
0
2
4Commodities ETPs
Year
Flo
w C
hange (
%)
*** Denotes statistical signif icance at 0.1%
** Denotes statistical signif icance at 1%
* Denotes statistical signif icance at 5%
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Weekly ETP Flow Changes All US-domiciled ETPs, January 2005 – July 2013
Flow Change (%) = (1 / T) Flowk / AUMk-1
Source: Bloomberg, Thomson Reuters, BlackRock , as at July 31, 2013
January 2005 - July 2013
Flow Change (%) Descriptive Statistics by Asset Class
Asset Class Mean Median Std Dev 1st Autocorr
Equity 0.07 0.05 0.23 -0.03
Fixed Income 0.15 0.14 0.20 0.17***
Commodities 0.13 0.07 0.38 0.36***
05 06 07 08 09 10 11 12 13 14-1
0
1
2Equity ETPs
Year
Flo
w C
hange (
%)
05 06 07 08 09 10 11 12 13 14-2
-1
0
1
2Fixed Income ETPs
Year
Flo
w C
hange (
%)
05 06 07 08 09 10 11 12 13 14-2
0
2
4Commodities ETPs
Year
Flo
w C
hange (
%)
*** Denotes statistical signif icance at 0.1%
** Denotes statistical signif icance at 1%
* Denotes statistical signif icance at 5%
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Predictability of Flows Across Asset Classes
Are flows predictable? What drives flows?
Autocorrelation within an asset class is useful but does not shed light on cross-asset or lead-lag relationships
We use autoregression (AR) framework to model flows within a given asset class as a function of past flows and past
returns
Extend to VAR model to account for cross-asset class effects (note: dimensionality is a challenge)
We include year-end effects as this seasonality is important in driving flows
Consistent results
Equity
Equity flows at weekly or monthly horizons are strongly correlated with contemporaneous returns, which is consistent
with anecdotal evidence
Fixed Income
At weekly and monthly frequencies, both fixed income and commodity flows are strongly positive correlated with lagged
flows
Model fit is especially strong post the financial crisis
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Equities - Monthly
Dependent Variable =
Independent Flow Change (%)
Variable: AR(1) AR(1) AR(2)
Constant 0.0005
(4.80)
0.0006
(4.88)
0.0007
(4.59)
Flow
Change (%)
Flow Lag 1 Month -0.0665
(0.70)
-0.0649
(0.66)
Flow Lag 2 Months -0.0690
(0.69)
Asset Class
Return
(Russell
3000 Index)
Return Lag 0
(Contemporaneous)
0.0037
(1.62)
Return Lag 1 Month -0.0033
(1.42)
-0.0034
(1.31)
Return Lag 2 Months -0.0023
(0.90)
Dummy is_year_end 0.0014
(3.45)
0.0014
(3.57)
0.0015
(3.57)
Adjusted R-square 11.80% 11.18% 11.21%
Observations 101 101 100
Autoregressive Models – Equity ETP Flows All Equity US-domiciled ETPs, January 2005 – July 2013
Flows vs. Lagged Flows and Lagged Russell 3000 Index Returns
Source: Bloomberg, Thomson Reuters, BlackRock , as at July 31, 2013
Equities - Weekly
Dependent Variable =
Independent Flow Change (%)
Variable AR(1) AR(1) AR(2)
Constant 0.0005
(4.95)
0.0006
(5.05)
0.0006
(4.69)
Flow
Change (%)
Flow Lag 1 Week -0.0451
(0.90)
-0.0442
(0.87)
Flow Lag 2 Weeks 0.0358
(0.71)
Asset Class
Return
(Russell
3000 Index)
Return Lag 0
(Contemporaneous)
0.0254
(6.91)
Return Lag 1 Week -0.0041
(1.00)
-0.0035
(0.84)
Return Lag 2 Weeks 0.0019
(0.46)
Dummy is_year_end 0.0012
(3.16)
0.0014
(3.44)
0.0013
(3.28)
Adjusted R-square 11.42% 2.21% 1.98%
Observations 445 445 444
Values:
Beta
(|T-Stat|) Each column is a autoregressive model; shading indicates statistical significance at 1% level
Strong contemporaneous correlation between weekly equity ETP flows and R3K returns
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Autoregressive Models – Fixed Income All Fixed Income US-domiciled ETPs, January 2005 – July 2013
Source: Bloomberg, Thomson Reuters, BlackRock , as at July 31, 2013
Flows vs. Lagged Flows and Lagged Barclays AGG Index Returns
Fixed Income - Weekly
Dependent Variable =
Independent Flow Change (%)
Variable AR(1) AR(1) AR(2)
Constant 0.0015
(15.10)
0.0013
(10.26)
0.0012
(8.58)
Flow
Change (%)
Flow Lag 1 Week 0.1671
(3.58)
0.1413
(3.00)
Flow Lag 2 Weeks 0.0579
(1.24)
Asset Class
Return
(Barclays
Aggregate
Index)
Return Lag 0
(Contemporaneous)
-0.0150
(0.77)
Return Lag 1 Week 0.0018
(0.09)
0.0016
(0.08)
Return Lag 2 Weeks 0.0230
(1.20)
Dummy is_year_end -0.0003
(0.74)
-0.0002
(0.68)
-0.0002
(0.60)
Adjusted R-square -0.20% 2.28% 1.99%
Observations 445 445 444
Fixed Income -
Monthly
Dependent Variable =
Independent Flow Change (%)
Variable AR(1) AR(1) AR(2)
Constant 0.0014
(11.01)
0.0010
(5.67)
0.0011
(5.22)
Flow
Change (%)
Flow Lag 1 Month 0.2399
(2.56)
0.1323
(1.41)
Flow Lag 2 Months 0.0441
(0.48)
Asset Class
Return
(Barclays
Aggregate
Index)
Return Lag 0
(Contemporaneous)
0.0096
(0.79)
Return Lag 1 Month 0.0213
(1.82)
0.0262
(2.38)
Return Lag 2 Months 0.0015
(0.13)
Dummy is_year_end -0.0003
(0.71)
-0.0004
(1.00)
-0.0004
(0.98)
Adjusted R-square -0.87% 7.38% 4.76%
Observations 101 101 100
Values:
Beta
(|T-Stat|) Strong autocorrelation in fixed income flows at both monthly and weekly horizons
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Vector Autoregressive Model (VAR) All US-domiciled ETPs, January 2005 – July 2013
Source: Bloomberg, Thomson Reuters, BlackRock , as at July 31, 2013
Values:
Beta
(|T-Stat|)
Dependent Variable:
1- Week Lagged
Independent Variable:
Equity
Flow FI Flow Cmd Flow
Russell
3000
Barclays
AGG
TR/J CRB
Index
Flow
Change
(%) Lag
1 Week
Equity -0.0238
(0.48)
-0.1266
(2.90)
-0.1455
(2.04)
-1.9798
(3.33)
0.1171
(1.12)
-2.2412
(3.77)
Fixed
Income
-0.0193
(0.36)
0.1679
(3.61)
0.0302
(0.40)
-0.0173
(0.03)
-0.0170
(0.15)
0.7562
(1.18)
Cmd -0.0019
(0.07)
0.0275
(1.10)
0.3343
(8.15)
-0.8686
(2.53)
-0.0013
(0.02)
0.0567
(0.17)
Asset
Returns
Lag 1
Week
Russell
3000
-0.0050
(1.07)
-0.0013
(0.31)
0.0090
(1.35)
-0.0105
(0.19)
0.0356
(3.62)
0.0849
(1.52)
Barclays
AGG
-0.0316
(1.43)
0.0016
(0.08)
0.0576
(1.81)
0.0621
(0.23)
-0.0514
(1.10)
-0.3000
(1.13)
TR/J CRB
Index
0.0003
(0.07)
0.0081
(2.10)
0.0042
(0.66)
0.0097
(0.18)
0.0060
(0.64)
-0.0696
(1.31)
Weekly 6-Dimensional VAR(1) Model solved in Matlab (using vgxvarx):
Strong autocorrelation in fixed income and commodity ETP flows
Equity ETP flows are drivers of fixed income and commodity flows
Interesting reversal pattern of Russell 3000 index on equity ETP flows, consistent with price
pressure hypothesis
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Equity ETP Flows and Russell 3000 Index Reversal All Equity US-domiciled ETPs, January 2005 – July 2013
Source: Bloomberg, Thomson Reuters, BlackRock , as at July 31, 2013
Russell 3000 Index Returns vs. Lagged Flows
Equities - Monthly
Dependent Variable =
Independent Russell 3000 Index Return
Variable AR(1) AR(1) AR(2)
Constant 0.0031
(0.65)
0.0110
(2.16)
0.0190
(3.39)
Flow
Change (%)
Flow Lag 1 Month -13.4674
(3.50)
-14.9108
(3.87)
Flow Lag 2 Months -9.5286
(2.43)
Russell
3000 Index
Return
Return Lag 1 Month 0.2329
(2.36)
0.2879
(3.04)
0.2531
(2.52)
Return Lag 2 Months -0.0769
(0.78)
Dummy is_year_end 0.0136
(0.80)
0.0206
(1.27)
0.0231
(1.45)
Adjusted R-square 3.94% 13.86% 19.80%
Observations 101 101 100
Equities - Weekly
Dependent Variable =
Independent Russell 3000 Index Return
Variable AR(1) AR(1) AR(2)
Constant 0.0010
(0.75)
0.0021
(1.54)
0.0024
(1.71)
Flow
Change (%)
Flow Lag 1 Week -2.1898
(3.59)
-2.1521
(3.53)
Flow Lag 2 Weeks -0.6374
(1.04)
Russell
3000 Index
Return
Return Lag 1 Week -0.0593
(1.25)
-0.0055
(0.11)
-0.0115
(0.23)
Return Lag 2 Weeks 0.0741
(1.50)
Dummy is_year_end 0.0039
(0.81)
0.0071
(1.47)
0.0074
(1.53)
Adjusted R-square 0.01% 2.63% 2.78%
Observations 445 445 444
Russell 3000 Index reversal persistent at both monthly and weekly horizon
This effect is consistent with price pressure hypothesis
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Risk Flow Sentiment Measure
Are flows indicative of investor sentiment?
With over $2 trillion in worldwide AUM, ETPs are used by sophisticated investors to express their views across asset
classes and regions
We derived a simple metric that captures shifts in investor sentiment as expressed via primary ETP flows
BlackRock Risk Flow Sentiment measure:
BlackRock Risk Flow Sentiment measure sorts ETPs within each asset class based on risk of each fund
Risk Flow Sentiment is then defined as total dollar inflows/outflows for the riskers group of ETPs less those of safer
group, scaled by the dispersion of all flows
Our research shows that over longer horizons (i.e. monthly), Risk Flow Sentiment exhibits greater persistence than
raw flows alone, consistent with the idea that composition of flows is indicative of investor sentiment
BlackRock Risk Flow Sentiment measure has been regularly included in the ETF Landscape starting in July 2013
Source: BlackRock , as at July 31, 2013
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BlackRock Risk Flow Sentiment Measure Introduced in July’s 2013 Issue of ETP Landscape
BlackRock Risk Flow Sentiment Measure is
derived from raw monthly risk flow metric as deviation from one-year moving average
iS-12030 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION
Source: BlackRock “ETP Landscape”
as of July 31, 2013
15
Monthly Sentiment Flows Statistics (01/05 - 07/13)
Asset Class Mean Median Std Dev 1st Autocorr
Equities -6.17 -6.24 26.05 0.20*
Fixed Income -6.34 -3.91 11.25 0.43***
Commodities -1.74 -1.75 5.70 0.21*
Risk Flow Sentiment exhibits statistically significant persistence in all ETP asset classes
Risk Sentiment Flow = (High – Low Risk Flowk ) / StdDev(Net Flowk)
Source: Bloomberg, Thomson Reuters, BlackRock , as at July 31, 2013
05 06 07 08 09 10 11 12 13 14-100
-50
0
50Equity ETPs
Year
Sentim
ent
Flo
w
05 06 07 08 09 10 11 12 13 14-60
-40
-20
0
20Fixed Income ETPs
Year
Sentim
ent
Flo
w
05 06 07 08 09 10 11 12 13 14-20
-10
0
10
20Commodities ETPs
Year
Sentim
ent
Flo
w
*** Denotes statistical significance at 0.1%
** Denotes statistical significance at 1%
* Denotes statistical significance at 5%
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Part Two: Liquidity and Price Discovery in ETFs
iShares Global Investments & Research
BlackRock
ETFs in Global Financial Markets
Key objectives and questions:
• Clarify common misconceptions about ETF prices, premiums, and liquidity.
• Provide sound quantitative view into the role that ETFs play as price discovery tools in global financial markets
• Can the dynamics of ETF prices, volatilities, and premiums be described through systematic econometric framework?
• Are ETFs efficiently priced?
• What is the role of liquidity in pricing ETFs?
• What are the key drivers behind ETF premiums and how do they change with the nature of the ETF exposure (asset class,
geography)?
• Is there a persistence in ETF premiums?
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Key Drivers of ETP Market and NAV prices
𝑝𝑡 = 𝑚𝑡 + 𝑢𝑡
𝑢𝑡 = 𝜑𝑢𝑡−1 + 𝜖𝑡
𝑛𝑡 = 1 − 𝜃 𝑚𝑡 + 𝜃𝑛𝑡−1 + 𝛾𝑡
𝑑𝑚𝑡 = 𝑚𝑡 − 𝑚𝑡−1 = 𝐶 + 𝛿𝑡
ETF market price pt is the sum of true (unobserved) asset price mt and liquidity shocks (noise) ut:
True (unobserved) asset price mt follows a martingale process:
Error correction in liquidity shocks ut is modeled as AR(1) process:
Net Asset Value (NAV) (denoted as nt) can deviate from true asset price because of various pricing errors gt:
Staleness parameter q > 0 indicates to what degree stale NAV quotes were used to determine current NAV.
To better understand ETF price dynamics, we specifically model one type of error, staleness, arising from
using stale quotes in calculation of NAV:
𝑛𝑡 = 𝑚𝑡 + 𝛾𝑡
* For more extensive theoretical analysis, see:
Madhavan (2013) or Engle & Sarkar (2002)
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ETF Transaction Costs and Spreads
iS-12030 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION
Source: Bloomberg,
BlackRock, and
FINRA TRACE as of
June 30, 2013
𝑝𝑡 = 𝑞𝑚𝑖𝑑,𝑡 +𝑐𝑡
2𝑝𝑡
The actual transaction price of the ETF at time t is the midquote qmid,t plus or minus the effective bid-ask spread ct:
𝑝𝑡 ∈ (−1,1) where
Since quotations are two-sided, it is reasonable to posit that midquote qmid,t reflects “true” asset price mt , and the
microstructure noise term ut is interpreted as (ct / 2) pt.
For an individual security, in the absence of other costs, the spread arises because order flow is informative and
market makers protect themselves against adverse selection.
In the context of ETF, the fact that a broad index portfolio is being traded, flow is unlikely to be informative at the
individual security level, hence expectation for lower spreads.
ETFs indeed offer lower spreads compared to underlying exposure:
Average Time-Weighted Bid-Ask Spreads (bps)
20
State-Space Model Representation of ETF Price Dynamics
The cointegrated evolution of ETF market and NAV prices can be described in State-Space Model (SSM) form:
𝑛𝑡
𝑝𝑡=
1 − 𝜃 01 −𝜑
𝑚𝑡
𝑚𝑡−1+
𝜃 00 𝜑
𝑛𝑡−1
𝑝𝑡−1+
𝛾𝑡
𝜖𝑡 SSM measurement equation:
SSM transition equation: 𝑚𝑡
𝑚𝑡−1=
1 01 0
𝑚𝑡−1
𝑚𝑡−2+
𝐶 00 0
10
+𝛿𝑡
0
ETF premium (discount) (or P/D) can be written as:
𝜋𝑡 = 𝑝𝑡 − 𝑛𝑡 = 𝑢𝑡 + 𝜃 𝑚𝑡 − 𝑛𝑡−1 − 𝛾𝑡
Assuming all innovations (e t, gt, and dt) are i.i.d., the variance of ETF premium can be approximated as:
var 𝜋 ≅ var 𝜖
1 − 𝜑2 + 𝜃2var 𝛿
1 − 𝜃2 + var(𝛾)
Transitory
Liquidity
NAV
Staleness
NAV Pricing
Errors NAV Measurements Errors (price discovery)
The last equation allows to attribute variance of P/D into portions related to transitory liquidity and price discovery.
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State-Space Model Estimation: Examples
Source: Bloomberg, BlackRock
for period 1/1/2005 – 12/31/2013
Analyzed three US-listed ETF funds (IVV, EWJ, and HYG) using daily price and NAV data for period
1/1/2005 through 12/31/2013
Model is solved in Matlab using Kalman Filter and maximum likelihood estimation.
ETF Ticker IVV EWJ HYG
ETF Name iShares S&P500
ETF
iShares MSCI Japan
ETF
iShares iBoxx High
Yield Bond ETF
q (NAV Staleness) -0.015 0.325 0.772
j (Autocorrelation) 0.109 0.179 0.857
Fundamental Innovations: St.Dev.(d) (annualized) 0.207 0.192 0.139
Price Innovation: St.Dev.(e) (annualized) 0.011 0.099 0.087
NAV Innovations: St.Dev.(g) (annualized) 0.015 0.145 0.046
Realized St.Dev.(P/D) (annualized) 0.019 0.200 0.230
Realized St.Dev.(Px) (annualized) 0.208 0.238 0.150
Realized St.Dev.(NAV) (annualized) 0.212 0.232 0.070
% Realized P/D variance explained by Transitory Liquidity 35.5% 25.2% 54.3%
% Realized P/D variance explained by NAV Staleness 2.6% 10.9% 54.0%
% Realized P/D variance explained by NAV Pricing Errors 61.4% 52.8% 4.1%
iS-12030 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 22
Drivers of ETF Premiums: Domestic Equity ETFs
Source: Bloomberg, BlackRock
for period 1/1/2005 – 12/31/2013
Analyzed all US-listed, domestic equity ETF funds with above $100MM in AUM (as of 12/31/2013) using
daily price and NAV data for period 1/1/2005 through 12/31/2013
Model is solved in Matlab using Kalman Filter and maximum likelihood estimation.
iS-12030 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION
Domestic Equity ETFs
% Contribution to P/D Variance
Mean AUM
Weighted
Transitory Liquidity 55.5% 52.3%
NAV Staleness 7.6% 3.7%
NAV Price Errors 37.2% 44.0%
0 50 100 150 20010
2
103
104
105
ETF AUM ($MM, Log-scale)
0 50 100 150 2000
10
20P/D Volatility (%)
0 50 100 150 2000
0.5
1
ETF (ranked from the highest to lowest in AUM)
Fraction P/D Variance Explained by SSM
Px LiquidNAV Stale
NAV Error
23
Drivers of ETF Premiums: Developed Markets (ex-US) Equity ETFs
Analyzed all US-listed developed markets (ex-US) equity ETF funds with above $100MM in AUM (as of 12/31/2013)
using daily price and NAV data for period 1/1/2005 through 12/31/2013
Model is solved in Matlab using Kalman Filter and maximum likelihood estimation.
iS-12030 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION
Source: Bloomberg, BlackRock
for period 1/1/2005 – 12/31/2013
0 10 20 30 40 50 6010
2
103
104
105
ETF AUM ($MM, Log-scale)
0 10 20 30 40 50 600
20
40P/D Volatility (%)
0 10 20 30 40 50 600
0.5
1
ETF (ranked from the highest to lowest in AUM)
Fraction P/D Variance Explained by SSM
Px LiquidNAV Stale
NAV Error
Developed Markets (ex-US) Equity ETFs
% Contribution to P/D Variance
Mean AUM
Weighted
Transitory Liquidity 31.7% 25.4%
NAV Staleness 16.6% 17.4%
NAV Price Errors 47.6% 51.8%
24
Drivers of ETF Premiums: Emerging Markets Equity ETFs
Analyzed all US-listed emerging markets equity ETF funds with above $100MM in AUM (as of 12/31/2013)
using daily price and NAV data for period 1/1/2005 through 12/31/2013
Model is solved in Matlab using Kalman Filter and maximum likelihood estimation.
iS-12030 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION
Source: Bloomberg, BlackRock
for period 1/1/2005 – 12/31/2013
Emerging Markets Equity ETFs
% Contribution to P/D Variance
Mean AUM
Weighted
Transitory Liquidity 31.4% 20.3%
NAV Staleness 25.7% 43.9%
NAV Price Errors 38.1% 34.3%
0 10 20 30 40 5010
2
103
104
105
ETF AUM ($MM, Log-scale)
0 10 20 30 40 500
25
50P/D Volatility (%)
0 10 20 30 40 500
0.5
1
ETF (ranked from the highest to lowest in AUM)
Fraction P/D Variance Explained by SSM
Px LiquidNAV Stale
NAV Error
25
Summary and Conclusions
Contributions to Premium/Discount Variance
Source: Bloomberg, BlackRock, daily data for period 1/1/2005 – 12/31/2013.
Includes all US-listed ETFs with more than $100MM in AUM as of 12/31/2013
iS-12030 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION
Summary of key findings:
• Described ETF prices, volatilities, and premiums in State-Space Model (SSM) representation.
• Key drivers of ETF premiums identified as:
• Transitory liquidity observed ETF market prices (i.e. bid-ask bounce, or liquidity shocks);
• Staleness and other error measurements in NAV prices.
• Attributed variance of premiums and discounts into liquidity and NAV error measurements for various categories of ETFs
• Provided quantitative argument for ETFs serving as price discovery tools, particularly for international ETFs
0%
25%
50%
75%
100%
Domestic Equity DM (ex-US) Equity EM Equity
% Transitory Liquidity % NAV Staleness % Nav Price Error
26
Thank you
iShares Global Investments & Research
BlackRock
Carefully consider the iShares Funds’ investment objectives, risk factors, and charges and expenses before
investing. This and other information can be found in the Funds’ prospectuses, which may be obtained by calling 1-
800-iShares (1-800-474-2737) or by visiting www.iShares.com. Read the prospectus carefully before investing.
Investing involves risk, including possible loss of principal.
Transactions in shares of ETFs will result in brokerage commissions and will generate tax consequences. All regulated
investment companies are obliged to distribute portfolio gains to shareholders.
Although market makers will generally take advantage of differences between the NAV and the trading price of iShares Fund
shares through arbitrage opportunities, there is no guarantee that they will do so.
iS-12030 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION
The iShares Funds are distributed by BlackRock Investments, LLC (together with its affiliates, “BlackRock”).
The iShares Funds are not sponsored, endorsed, issued, sold or promoted by Cohen & Steers Capital Management, Inc.,
European Public Real Estate Association (“EPRA® ”), FTSE International Limited (“FTSE”), India Index Services & Products
Limited, JPMorgan Chase & Co., MSCI Inc., Markit Indices Limited, Morningstar, Inc., The NASDAQ OMX Group, Inc.,
National Association of Real Estate Investment Trusts (“NAREIT”), New York Stock Exchange, Inc., Russell Investment Group
or S&P Dow Jones Indices LLC, nor are they sponsored, endorsed or issued by Barclays Capital Inc. None of these
companies make any representation regarding the advisability of investing in the Funds. BlackRock is not affiliated with the
companies listed above.
©2014 BlackRock. All rights reserved. iSHARES and BLACKROCK are registered trademarks of BlackRock. All other marks
are the property of their respective owners. iS-12030-0314
Not FDIC Insured • No Bank Guarantee • May Lose Value
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