Factor Structure in Equity Options
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Transcript of Factor Structure in Equity Options
Factor Structure in Equity Options Peter Christoffersen
(Rotman School, CBS and CREATES) Mathieu Fournier
(University of Toronto, PhD student) Kris Jacobs
(University of Houston)
Motivation • Black and Scholes (1973) derive their famous formula
in several ways including one in which the underlying assets (the stock) obey a CAPM-type factor structure.
• They show that in their setting the beta of the stock does not matter for the price of the option.
• They of course assume constant volatility. • We show that under SV the beta of the stock matters.
– Equity option valuation – Equity and index option risk management – Equity option expected returns
• We find strong empirical evidence for factor structure in equity option IV.
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Scale of Empirical Study • Principal component analysis
– 775,000 Index Options – 11 million Equity Options
• Estimation of structural model parameters – 6,000 Index Options – 150,000 Equity Options
• Estimation of spot variance processes – 130,000 Index Options – 3.1 million Equity Options
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Related Literature (Selective) • Bakshi, Kapadia and Madan (RFS, 2003) • Serban, Lehoczky and Seppi (WP, 2008) • Driessen, Maenhout and Vilkov (JF, 2009) • Duan and Wei (RFS, 2009) • Elkamhi and Ornthanalai (WP, 2010) • Buss and Vilkov (RFS, 2012) • Engle and Figlewski (WP, 2012) • Chang, Christoffersen, Jacobs and Vainberg
(RevFin, 2012) • Kelly, Lustig and Van Nieuwerburgh (WP, 2013)
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Paper Overview
• Part I: A model-free look at option data • Part II: Specifying a theoretical model • Part III: Properties of the model • Part IV: Model estimation and fit
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Part I: Data Exploration • Option Data from OptionMetrics
– Use S&P500 options for market index – Equity options on 29 stocks from Dow Jones 30
Index. – Kraft Foods only has data from 2001 so drop it. – Volatility surfaces. – 1996-2010 – Various standard data filters (IV <5%, IV>150%,
DTM<30, DTM>365, S/K<0.7, S/K>1.3, PV dividends > .04*S)
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Table 1: Companies, Tickers and Option Contracts, 1996-2010
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Table 2: Summary Statistics on Implied Volatility (IV). Puts (left) Calls (right) 1996-2010
Figure 1: Short-Term, At-the-money implied volatility. Simple average of available contracts each day. Sub-sample of six large firms 1996-2010
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PCA Analysis • On each day, t, using standardized regressors,
run the following regression for each firm, j,
• For the set of 29 firms do principal component analysis (PCA) on 10-day moving average of slope coefficients.
• Also do PCA index option IVs. • We use calls and puts here.
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Figure 2: Does the common factor in the time series of equity IV levels look anything like S&P500 index IV?
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Table 3: Firms’ loadings on the first 3 PCs of the matrix of constant terms from the IV regressions
Moments of PC Loadings IV Levels (Table 3)
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Figure 3: Moneyness slopes: S&P500 index versus 1st Principal Component. - Need firm-specific variation.
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Table 4: Firms’ loadings on the first 3 PCs of the matrix of moneyness slopes from the IV regressions
Moments of PC Loadings IV Moneyness Slopes (Table 4)
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Figure 4: IV term structure: common factor versus S&P500 index term structure?
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Table 5: Firms’ loadings on the first 3 PCs of the matrix of maturity slopes from the IV regressions
Moments of PC Loadings IV Maturity Slopes (Table 5)
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Part II: Theoretical Model
• Idea: Stochastic volatility (SV) in index and equity volatility gives you identification of beta.
• Black-Scholes-Merton: Impossible to identify beta.
• SV is a strong stylized fact in equity and index returns.
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Market Index Specification
• Assume the market factor index level evolves as
• With affine stochastic volatility
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Individual Equities • The stock price is assumed to follow these price
and idiosyncratic variance dynamics:
• Beta is the firm’s loading on the index. • Note that idiosyncratic variance is stochastic also. • Note that total firm variance has two components:
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Risk Premiums
• We allow for a standard equity risk premium (μI) as well as a variance risk premium (λI) on the index but not on the idiosyncratic volatility.
• The firm will inherit equity risk premium via its beta with the market.
• The firm will inherit the volatility risk premium from the index via beta.
• These assumptions imply the following risk-neutral dynamics
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Risk Neutral Processes (tildes)
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Variance risk premium < 0
Option Valuation • Index option valuation follows Heston (1993) • Using the affine structure of the index variance, the
affine idiosyncratic equity variance, and the linear factor model, we derive the closed-form solution for the conditional characteristic function of the stock price.
• From this we can price equity options using Fourier inversion which requires numerical integration. Call price:
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Part III: Model Properties
• Equity Volatility Level • Equity Option Skew and Skew Premium • Equity Volatility Term Structure • Equity Option Risk Management • Equity Option Expected Returns
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Equity Volatility
• The total spot variance for the firm is
• The total integrated RN variance is
• Where
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Model Property 1: Beta Matters for the IV Levels
• When the market risk premium is negative we have that
• We can show that for two firms with same levels of total physical variance we have
• Upshot: Beta matters for total RN variance.
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Model Property 2: Beta Matters for the IV Slope across Moneyness
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Figure 5: Beta and model based BS IV across moneyness Unconditional total P variance is held fixed. Index ρ =-0.8 and firm-specific ρ =0.
Model Property 3: Beta Matters for the IV Slope across Maturity
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Figure 6: Beta and model based BS IV across maturity Unconditional total P variance is held fixed. Index κ = 5 and firm-specific κ = 1.
Model Property 4: Risk Management
• Equity option sensitivity “Greeks” with market level and volatility
• Market “Delta”:
• Market “Vega”:
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Model Property 5: Expected Returns
• The model implies the following simple structure for expected equity option returns
• Where we have assumed that αj = 0.
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Part IV: Estimation and Fit
• We need to estimate the structural parameters
• We also need on each day to estimate/filter the latent volatility processes
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Estimation Step 1: Index • For a fixed set of starting values for the
structural index parameters, on each day solve
• Then keep sequence of vols fixed and solve
• Then iterate between these two optimizations.
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Estimation Step 2: Each Equity
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• Take index parameters as given. For a fixed set of starting values for the structural equity parameters, on each day solve
• Then keep sequence of vols fixed and solve
• Then iterate between these two optimizations. Do this for each equity…
Parameter Estimates
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Definitions
• Average total spot volatility (ATSV)
• Systematic risk ratio (SSR)
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Model Fit
• To measure model fit we compute
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IV Smiles. Market (solid) and Model (dashed). High Vol (black) and Low Vol (grey) Days.
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• Conclusion: The “smiles” vary considerably across firms and we fit them quite well.
• We also fit index quite well. 46
IV Term Slopes: Up and Down. Market (solid) and Model (dashed)
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• Conclusion: IV term structures vary considerably across firms. Model seems to adequately capture persistence.
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Beta: Cross-Sectional Implications
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• The model fits equity options well. • What are the cross sectional implications of
the factor structure? • Recall our IV regression from the model-free
analysis in the beginning:
Betas versus IV Levels
• Regress time-averaged constant terms from daily IV regressions on betas.
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Betas versus Moneyness Slopes
• Regress time-averaged moneyness slopes from daily IV regressions on betas.
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Beta and Maturity Slopes
• Regress time-averaged maturity slopes from daily IV regressions on betas.
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OLS Beta versus Option Beta
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OLS betas are estimated on daily Returns. 1996-2010. Regression line 45 degree line
Additional Factor Structure?
• We have modeled a factor structure in returns which implies a factor structure in equity total volatility.
• Engle and Figlewski (WP, 2012) • Kelly, Lustig and Van Nieuwerburgh (WP, 2013) • Is there a factor structure in the idiosyncratic
volatility paths estimated in our model? • Yes: The average correlation of idiosyncratic
volatility is 45%.
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Conclusions • Model-free PCA analysis reveals strong factor
structure in equity index option implied volatility and thus price.
• We develop a market-factor model based on two SV processes: Market and idiosyncratic.
• Theoretical model properties broadly consistent with market data.
• Model fits data reasonably well. • Firm betas are related to IV levels, moneyness
slopes and maturity slopes.
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Current / Future Work
• Add firms. • Study cross-sectional properties of beta
estimates. • Add a second volatility factor to the market
index. • Time-varying betas. • Add jumps to index and/or to idiosyncratic
process.
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