Which GARCH Model for Option Valuation?

Characterizing asset return dynamics using volatility models is an important part of empirical finance. The existing literature favors some rather complex volatility specifications whose relative performance is usually assessed through their likelihood based on a time-series of asset returns. This paper compares a range of volatility models along a different dimension, using option prices and returns under the risk-neutral as well as the physical probability measure. We judge the relative performance of various models by evaluating an objective function based on option prices. In contrast with returns-based inference, we find that our option-based objective function favors a relatively parsimonious model. Specifically, when evaluated out-of-sample, our analysis favors a model that besides volatility clustering only allows for a standard leverage effect. This empirical analysis is part of a growing literature suggesting that discrete-time option pricing with time-varying volatility is practical and insightful. Caracteriser les dynamiques des rendements d'actifs a l'aide de modeles de volatilite est un champ important de la finance empirique. La litterature dans ce domaine privilegie des specifications de volatilite plutot complexes dont la performance relative est generalement estimee par leur vraisemblance a partir de series chronologiques de rendements d'actifs. Cet article compare plusieurs modeles de volatilite selon un critere different, utilisant les rendements et prix d'options dans une mesure neutre au risque et de probabilite physique. Nous estimons la performance relative des differents modeles en evaluant la fonction objective basee sur les prix d'options. Contrairement a l'inference basee sur les rendements, nous trouvons que notre fonction objective basee sur les options favorise un modele relativement parcimonieux. En particulier, lorsqu'elle est evaluee hors-echantillon, notre analyse favorise un modele qui, outre le groupement de volatilites, ne permet qu'un effet de levier standard. Cette analyse empirique fait partie d'une litterature en plein essor qui suggere que l'evaluation des prix d'options en temps discret, lorsque la volatilite varie dans le temps, est pratique et riche en enseignements.

[1]  F. Black,et al.  The Pricing of Options and Corporate Liabilities , 1973, Journal of Political Economy.

[2]  Michael J. Brennan,et al.  The Pricing of Contingent Claims in Discrete Time Models , 1979 .

[3]  R. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[4]  H. White Maximum Likelihood Estimation of Misspecified Models , 1982 .

[5]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[6]  Louis O. Scott Option Pricing when the Variance Changes Randomly: Theory, Estimation, and an Application , 1987, Journal of Financial and Quantitative Analysis.

[7]  James B. Wiggins Option values under stochastic volatility: Theory and empirical estimates , 1987 .

[8]  D. Shanno,et al.  Option Pricing when the Variance Is Changing , 1987, Journal of Financial and Quantitative Analysis.

[9]  K. French,et al.  Expected stock returns and volatility , 1987 .

[10]  Alan G. White,et al.  The Pricing of Options on Assets with Stochastic Volatilities , 1987 .

[11]  J. Wooldridge,et al.  A Capital Asset Pricing Model with Time-Varying Covariances , 1988, Journal of Political Economy.

[12]  G. Schwert Why Does Stock Market Volatility Change Over Time? , 1988 .

[13]  Adrian Pagan,et al.  Alternative Models for Conditional Stock Volatility , 1989 .

[14]  S. Turnbull,et al.  Pricing foreign currency options with stochastic volatility , 1990 .

[15]  John Y. Campbell,et al.  No News is Good News: An Asymmetric Model of Changing Volatility in Stock Returns , 1991 .

[16]  Daniel B. Nelson CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH , 1991 .

[17]  T. Day,et al.  Stock market volatility and the information content of stock index options , 1992 .

[18]  R. Engle,et al.  Implied ARCH models from options prices , 1992 .

[19]  R. Chou,et al.  ARCH modeling in finance: A review of the theory and empirical evidence , 1992 .

[20]  S. Heston A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options , 1993 .

[21]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

[22]  C. Granger,et al.  A long memory property of stock market returns and a new model , 1993 .

[23]  David S. Bates Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Thephlx Deutschemark Options , 1993 .

[24]  Christopher G. Lamoureux,et al.  Forecasting Stock-Return Variance: Toward an Understanding of Stochastic Implied Volatilities , 1993 .

[25]  Bruno Dupire Pricing with a Smile , 1994 .

[26]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[27]  Dean P. Foster,et al.  Asypmtotic Filtering Theory for Univariate Arch Models , 1994 .

[28]  Linda Anderson-Courtney Journal of econometrics : Subject and author index: volumes 51–60, 1992–1994 , 1994 .

[29]  Daniel B. Nelson,et al.  ARCH MODELS a , 1994 .

[30]  R. Engle,et al.  Forecasting Volatility and Option Prices of the S&P 500 Index , 1994 .

[31]  J. Duan,et al.  Série Scientifique Scientific Series Empirical Martingale Simulation for Asset Prices Empirical Martingale Simulation for Asset Prices , 2022 .

[32]  David S. Bates Testing Option Pricing Models , 1995 .

[33]  Philippe Jorion Predicting Volatility in the Foreign Exchange Market , 1995 .

[34]  Tim Bollerslev,et al.  Long-term equity anticipation securities and stock market volatility dynamics , 1999 .

[35]  J. Duan THE GARCH OPTION PRICING MODEL , 1995 .

[36]  Andrew W. Lo,et al.  Nonparametric estimation of state-price densities implicit in financial asset prices , 1995, Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr).

[37]  Ludger Hentschel All in the family Nesting symmetric and asymmetric GARCH models , 1995 .

[38]  É. Renault Econometric Models of Option Pricing Errors , 1996 .

[39]  T. Bollerslev,et al.  MODELING AND PRICING LONG- MEMORY IN STOCK MARKET VOLATILITY , 1996 .

[40]  George Tauchen New Minimum Chi-Square Methods in Empirical Finance , 1996 .

[41]  Jeff Fleming,et al.  Implied volatility functions: empirical tests , 1996, IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr).

[42]  Jin-Chuan Duan,et al.  Augmented GARCH (p,q) process and its diffusion limit , 1997 .

[43]  David M. Kreps,et al.  Advances In Economics and Econometrics: Theory And Applications: Seventh World Congress , 1997 .

[44]  David S. Bates Post-&Apos;87 Crash Fears in S&P 500 Futures Options , 1997 .

[45]  John M. Olin,et al.  A Closed-Form GARCH Option Pricing Model , 1997 .

[46]  Gurdip Bakshi,et al.  Empirical Performance of Alternative Option Pricing Models , 1997 .

[47]  Saikat Nandi,et al.  How important is the correlation between returns and volatility in a stochastic volatility model? Empirical evidence from pricing and hedging in the S&P 500 index options market , 1998 .

[48]  Eric Renault,et al.  A Note on Hedging in ARCH and Stochastic Volatility Option Pricing Models , 1998 .

[49]  David S. Bates Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Deutsche Mark Options , 1998 .

[50]  T. Andersen THE ECONOMETRICS OF FINANCIAL MARKETS , 1998, Econometric Theory.

[51]  D. Duffie,et al.  Transform Analysis and Asset Pricing for Affine Jump-Diffusions , 1999 .

[52]  Peter H. Ritchken,et al.  Pricing Options under Generalized GARCH and Stochastic Volatility Processes , 1999 .

[53]  E. Ghysels,et al.  A study towards a unified approach to the joint estimation of objective and risk neutral measures for the purpose of options valuation , 2000 .

[54]  Valentina Corradi,et al.  Reconsidering the continuous time limit of the GARCH(1, 1) process , 2000 .

[55]  S. Heston,et al.  A Closed-Form GARCH Option Valuation Model , 2000 .

[56]  David S. Bates Post-'87 crash fears in the S&P 500 futures option market , 2000 .

[57]  Wolfgang K. Härdle,et al.  Discrete time option pricing with flexible volatility estimation , 2000, Finance Stochastics.

[58]  Recovering Risk-Neutral Densities: A New Nonparametric Approach , 2000 .

[59]  Luca Benzoni,et al.  An Empirical Investigation of Continuous-Time Equity Return Models , 2001 .

[60]  Jun Pan The Jump-Risk Premia Implicit in Options : Evidence from an Integrated Time-Series Study , 2001 .

[61]  J. Detemple,et al.  Série Scientifique Scientific Series the Importance of the Loss Function in Option Pricing the Importance of the Loss Function in Option Pricing , 2022 .

[62]  Peter Christoffersen,et al.  Série Scientifique Scientific Series the Importance of the Loss Function in Option Valuation the Importance of the Loss Function in Option Valuation , 2022 .

[63]  Jun Pan The jump-risk premia implicit in options: evidence from an integrated time-series study $ , 2002 .

[64]  Peter Christoffersen,et al.  Série Scientifique Scientific Series Option Valuation with Conditional Skewness Option Valuation with Conditional Skewness , 2022 .

[65]  C. S. Jones The dynamics of stochastic volatility: evidence from underlying and options markets , 2003 .

[66]  Thomas H. McCurdy,et al.  News Arrival, Jump Dynamics and Volatility Components for Individual Stock Returns , 2003 .

[67]  Pascal J. Maenhout,et al.  A Portfolio Perspective on Option Pricing Anomalies , 2004 .

[68]  Bjørn Eraker Do Stock Prices and Volatility Jump? Reconciling Evidence from Spot and Option Prices , 2004 .