Multifractal analysis of implied volatility in index options

In this paper, we analyze the statistical and the non-linear properties of the log-variations in implied volatility for the CAC40, DAX and S&P500 daily index options. The price of an index option is generally represented by its implied volatility surface, including its smile and skew properties. We utilize a Lévy process model as the underlying asset to deepen our understanding of the intrinsic property of the implied volatility in the index options and estimate the implied volatility surface. We find that the options pricing models with the exponential Lévy model can reproduce the smile or sneer features of the implied volatility that are observed in real options markets. We study the variation in the implied volatility for at-the-money index call and put options, and we find that the distribution function follows a power-law distribution with an exponent of 3.5 ≤ γ ≤ 4.5. Especially, the variation in the implied volatility exhibits multifractal spectral characteristics, and the global financial crisis has influenced the complexity of the option markets.

[1]  J. Bouchaud,et al.  Fokker-Planck description for the queue dynamics of large tick stocks. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  R. Mantegna,et al.  Scaling behaviour in the dynamics of an economic index , 1995, Nature.

[3]  Gabjin Oh,et al.  Market efficiency in foreign exchange markets , 2007 .

[4]  B. Dumas,et al.  Implied volatility functions: empirical tests , 1996, IEEE Conference on Computational Intelligence for Financial Engineering & Economics.

[5]  R. Cont,et al.  Financial Modelling with Jump Processes , 2003 .

[6]  Shlomo Havlin,et al.  Financial factor influence on scaling and memory of trading volume in stock market. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[8]  P. Carr,et al.  Option valuation using the fast Fourier transform , 1999 .

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

[10]  Rama Cont,et al.  Dynamics of implied volatility surfaces , 2002 .

[11]  Rosario N. Mantegna,et al.  Book Review: An Introduction to Econophysics, Correlations, and Complexity in Finance, N. Rosario, H. Mantegna, and H. E. Stanley, Cambridge University Press, Cambridge, 2000. , 2000 .

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

[13]  Rosario N. Mantegna,et al.  Evolution of Worldwide Stock Markets, Correlation Structure and Correlation Based Graphs , 2011 .

[14]  V. Plerou,et al.  A theory of power-law distributions in financial market fluctuations , 2003, Nature.

[15]  H. Stanley,et al.  Statistical properties of cross-correlation in the Korean stock market , 2010, 1010.2048.

[16]  H. Stanley,et al.  Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series , 2002, physics/0202070.

[17]  Gabjin Oh,et al.  Asymmetric information flow between market index and individual stocks in several stock markets , 2012 .

[18]  H. Stanley,et al.  A multifractal analysis of Asian foreign exchange markets , 2008 .

[19]  P. Carr,et al.  The Variance Gamma Process and Option Pricing , 1998 .

[20]  R. Lourie,et al.  The Statistical Mechanics of Financial Markets , 2002 .

[21]  Gabjin Oh,et al.  Deterministic factors of stock networks based on cross-correlation in financial market , 2007, 0705.0076.

[22]  Seung-Ho Yang,et al.  Calibrating parametric exponential Lévy models to option market data by incorporating statistical moments priors , 2011, Expert Syst. Appl..