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Predicting the Equity Premium withImplied Volatility Spreads
Charles Cao†, Timothy Simin†, and Han Xiao‡
† Department of Finance, Smeal College of Business, Penn State University‡ Department of Economics, Penn State University
March 23, 2018
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Motivation and Research Questions
Stock return predictabilty is an important question in asset pricing
literature (uncondtional and conditional)
Conventional predictiors are based on backward-looking information
I Dividend yield, P/E, Book-to-market ratio, term spread, etc
Question
I What is the predictive ability of forward-looking information of options?
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Motivation and Research Questions
Can the call-put option implied volatility spread (CPIVS) predict the
aggregate market risk premium?
Can we improve the performance of conditional factor models by
incorporating CPIVS?
Why does CPIVS have predictive power?
Does CPIVS predict non-equity variables?
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Motivation and Research Questions
Many reasons to investigate predictive ability of CPIVS
Theory
I Chowdhry and Nanda (1991), Easley, O’Hara, and Srinivas (1998):
Informed traders chose option market first
I An, Ang, Bali, and Cakici (2014): Noisy rational expectations model of
informed trading in both markets ⇒ option volatilities can predict
stock returns
Empirical work
I Option market information: price, volume and volatility
I Information Content of Option Implied Volatility Spread
I Nonlinear risks
I Cross sectional predictability
I Time-series prediction
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Literature Review
Information Content of Option Implied Volatility Spread
I Doran, Fodor, and Jiang (2013), Christoffersen, Jacobs, and Chang
(2013), Cao, Gempeshaw, and Simin (2018)
Nonlinear risks
I Bollerslev and Todorov (2011), Kelly and Jiang (2014)
Cross sectional evidence
I Bali and Hovakimian (2009), Cremers and Weinbaum (2010), Xing,
Zhang and Zhao (2010) and An, Ang, Bali and Cakici (2014)
Time-series prediction
I Atilgan, Bali and Demirtas (2015), Cao, Gempeshaw, and Simin (2018)
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Motivation and Research Questions
We consider a quarterly horizon:
Options are 3-month contracts
Longer horizon prediction: market timing, transaction costs, and
bid-ask spread
Lower autocorrelation: less spurious regression bias
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Main results
CPIVS predicts
I Quarterly aggregate market returns in-sample and out-of-sample
F In-sample R2: 14.7%!
F Out-of-sample R2: 8.5% (29% during recessions!)
I Long-run in-sample prediction up to three years
CPIVS improves the conditional factor models
I 50% less pricing errors
Prediction power comes from
I Forward-looking information orthogonal to other predictors
I Net innovation between call option and put option implied volatility
Economic significance of our results
I Ability to forecast macroeconomic uncertainty
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Data and Methodology
Mkt risk premium = CRSP value-weighted excess market return
CPIVS: difference between call and put option implied volatility
CPIVSt = CVOLt − PVOLt
I OptionMetrics (1996 - 2016)
I At-the-Market (ATM) Option Implied volatility
I Delta: 0.5
I Days-to-expiration: 30 days
I Quarterly average of daily spread
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Data and Methodology
Other predictors: Goyal and Welch (2008)
Focus on Dividend Yield and Cay
Fundamental valuations:
I Logarithm of dividend-yield ratio (log(DY ))
Macroeconomic indicators
I Consumption-to-wealth ratio (Cay)
Kitchen Sink: stack all variables
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Data: Descriptive Statistics
Mean Std. Dev. ρ
Equity Premium 0.015 0.085 0.091
Equal Weighted CPIVS -0.008 0.010 0.159
Value Weighted CPIVS -0.006 0.076 0.168
log(DY) -4.008 0.216 0.915
Cay -0.006 0.022 0.882
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Time Series of CPIVS and Equity Premium
-0.06
-0.04
-0.02
0
0.02
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
CP
IVS
Eq
uit
y P
rem
ium
Equity Premium CPIVS
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Time Series of CPIVS and Log(DY)
-0.06
-0.04
-0.02
0
0.02
-4.6
-4.4
-4.2
-4
-3.8
-3.6
-3.4
1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
CP
IVS
Lo
g D
ivid
end
Yie
ld
log(DY) CPIVS
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Methodology
In-sample Prediction
OLS prediction for one-quarter, semiannual, and one-year ahead
aggregate market returns
rt+h = αi ,h + βi ,hXi ,t + εi ,t+h
where
I h = 1 (quarterly), 2 (semiannually), and 4 (annually)
I X represents individual predictors
I Newey-West and Hodrick standard errors
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Methodology
Out-of-sample Prediction
1 Compare predictor with the historical average
2 Mean Square Forecast Error (MSFE)
I Is one-step ahead forecast error using our predictors smaller than
forecast using historical average?
3 Utility Gain
I Do investors see any utility gains using the predictors?
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Methodology
Out-of-sample Prediction: MSFE and R2OS
Predictors (Xi ) Historical Average (X0)
One-step Forecast ri ,t+s r0,t+s
Forecast error ei ,t+s = rt+s − ri ,t+s e0,t+s = rt+s − r0,t+s
MSFE MSFE 2i = 1
S
∑Ss=1 e
2i ,t+s MSFE 2
0 = 1S
∑Ss=1 e
20,t+s
Statistics R2OS = 1− MSFEi
MSFE0
Evaluation If R2OS > 0, then MSFEi < MSFE0 ⇒
Predictor beats historical average
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Out-of-sample Prediction: Utility Gains
For a quadratic utility investor, the optimal weight in the market is
w =(
1γ
)(E(rm)σ2rm
)Setting γ = 5, compute utility gains from using CPIVS as follows:
At each period t,
I σ2 = trailing sample variance of the market,
I w0 uses E (rm) = sample average, w1 uses E (rm) = E (rm|CPIVS).
I Keep the returns from rc = (1− w)rf + wrm using the two w ’s
At time T
I Calculate utility using mean and variance of the two portfolios
I Utility gain = U(rc |CPIVS)− U(rc)
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Empirical Results: In-sample Prediction
h = 1 (quarter) h = 2 (semiannual) h = 4 (annual)CPIVS 3.58 2.00 1.05
(4.12) (3.14) (2.04)
log(DY) 0.10 0.11 0.11(2.18) (2.75) (4.01)
Cay 0.21 0.38 0.48(0.68) (1.22) (1.28)
R2(%) 14.7 5.7 -1.0 7.7 11.8 0.2 3.1 22.3 2.4
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Empirical Results: Out-of-sample Prediction
Overall Expansion Recession
R2OS U-Gain R2
OS U-Gain R2OS U-Gain
CPIVS 8.48∗∗ 6.31 -7.11 2.15 29.02∗∗ 21.67
(0.01) (0.11) (0.01)
log(DY) 3.00 1.13 14.63∗∗∗ 5.16 -12.32 -14.53
(0.10) (0.00) (0.77)
Cay -5.27 2.05 -20.08 -2.21 14.24∗∗∗ 17.92
(0.31) (0.79) (0.00)
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Empirical Results: Out-of-sample Prediction
Robustness
Test the predictability of CPIVS on the following portfolios
Size, operating profitability, and investment-to-asset
I up to 90% out-of-sample significance (29/32)
Industry portfolios
I up to 90% out-of-sample significance (15/17)
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Empirical Results: Conditional Asset Pricing Models
Incorporate the information from CPIVS, log(DY) and Cay into conditionalversions of AP model
Intuition
Log(DY) and CPIVS predict at different segments of business cycle
Cover equity market, option market, and overall economy information
Time-varying moments may help model
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Empirical Results: Conditional Asset Pricing Models
The generic conditional asset pricing is
Et(rt+1|Zt) = α(Zt) + β(Zt)Et(Ft+1|Zt)
where
I Zt = lagged instruments = {log(DY )t , Cayt , CPIVSt}
Three versions:
I α fixed, β = b0 + b1Zt ;
I α = a0 + a1Zt , β = b0 + b1Zt ;
I α fixed, β fixed, Et(Ft+1|Zt) = d0 + d1Zt
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Empirical Results: Conditional Asset Pricing Models
Operating profitability portfolios: Annual abnormal return (%)
U-FF3 β(Z ) α(Z ), β(Z ) F(Z)
LOW -6.69∗∗∗ -3.66∗∗ -5.51∗∗ -6.04∗∗∗
D2 -3.44∗∗∗ -4.30∗∗∗ -3.72∗∗∗ -2.41D3 -2.45∗ -1.86 -0.80 -0.96D4 0.26 0.22 0.26 0.60D5 -1.70 -0.82 -0.86 -2.88∗∗
D6 0.11 0.69 0.99 -0.36D7 -0.51 -0.45 -0.04 -1.36D8 3.00∗∗∗ 1.76∗∗∗ 2.87∗∗∗ 2.24∗∗
D9 2.51∗∗∗ 1.77 1.44 1.44HIGH 2.24∗∗ 0.59 -0.16 2.96∗∗∗
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Empirical Results
The Source of Prediction
1 CPIVS contains forward-looking information not captured by
backward-looking predictors
2 CPIVS captures the net innovation between call and put option
implied volatility
3 CPIVS can predict innovation in discount rate and cash flow
4 CPIVS predicts macroeconomic uncertainty
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Empirical Results: Two-step Orthogonality
Method:
Step 1 Predictor i : obtain the residual εi ,t+1 from
rt+1 = αi + βiXi ,t + εi ,t+1
Step 2 Predictor j : Regress the residual εi ,t+1 on other predictors
Xj ,t , j 6= i ,
εi ,t+1 = δj + γjXj ,t + µj ,t+1, j 6= i
Evaluation: If γj is significant, then the predictor j contains further
information than predictor i .
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Empirical Evidence: Two-step Orthogonality
Standardize the predictors: comparable coefficients
i = CPIVS i = log(DY)
βlog(DY ) R2
β(CPIVS) R2
0.22∗ 3.5% 0.37∗∗∗ 12.5%
(1.81) (3.20)
i = CPIVS i = Cay
βCay R2
β(CPIVS) R2
0.16∗ 1.1% 0.41∗∗∗ 15.8%
(1.82) (4.34)
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Empirical Evidence: Net Innovation
Intuition
Innovation predicts returns
Innovation in both options
I ∆CVOL: capture innovation in calls
I ∆PVOL: capture innovation in puts
I CPIVS : approximately the difference between calls and puts
Call-put parity: the difference between calls and puts
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Empirical Evidence: Net Innovation
Information from Call and Put Options
Overall RecessionR2OS U-Gain R2
OS U-Gain
CPIVS 8.48∗∗∗ 6.31 29.02∗∗∗ 21.67(0.01) (0.01)
∆CVOL 31.93∗∗∗ 6.03 27.89 7.86(0.01) (0.10)
∆PVOL 26.36∗∗∗ 5.64 20.21 6.32(0.01) (0.15)
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Empirical Results: Campbell and Shiller Decomposition
Decompose market returns into three components:
I Expected returns, Cash flow, and Discount rate
Determine which component is being predicted by CPIVS
Method:Campbell(1991) and Campbell and Ammer(1993)
Step 1: Use VAR to estimate innovations representing each
component
Step 2: Regress each innovation on CPIVS and compare coefficients
Evaluation: significance and magnitude
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Empirical Results: Decomposition
Standardize the predictors:
Panel A: Predictive Regression: rβCPIVS 0.034∗∗∗
(4.118)
Panel B: VAR residual using {log(DP)}Expected Return Cash Flow Discounted Rate
βCPIVS 0.004 0.006∗∗ −0.024∗∗∗
(1.35) (2.45) (-2.81)
Panel C: VAR residual using {log(DP), log(DY), Cay}Expected Return inn Cash Flow inn Discounted Rate inn
βCPIVS 0.003 0.020∗∗∗ -0.012(0.80) (4.89) (-1.07)
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Empirical Results: What else does CVIPS predict?
Macro Uncertainty is defined as Jurado, Ludvigson, and Ng (2015)
Macroeconomic uncertainty is related to market returns
CPIVS predicts macroeconomic uncertainty
Regression model:
Macro Uncertaintyt+h = αi + βiXi ,t + εi ,t+h
where h = 1 (one-quarter ahead), (h = 2) (two quarters ahean)
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Empirical Results: Macro Uncertainty
1Q ahead Macro-U 2Q ahead Macro-U
CPIVS -3.40∗∗∗ -3.48∗∗∗
(2.62) (-2.68)
log(DY) 0.04 0.03(0.52) (0.32)
Cay 0.12 0.11(0.25) (0.19)
R2
(%) 14.4 0.8 0.1 14.5 0.5 0.1
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Conclusion
Call-put option implied volatility spread predicts quarterly returns
I Significant in-sample and out-of-sample
CPIVS improves conditional asset pricing model
Forward-looking information within CPIVS contributes:
I through cash flow and discounted rate channels
I predicts lower overall uncertainty
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