Pricing European option with transaction costs under the fractional long memory stochastic volatility model

2012 ◽  
Vol 391 (4) ◽  
pp. 1469-1480 ◽  
Author(s):  
Xiao-Tian Wang ◽  
Min Wu ◽  
Ze-Min Zhou ◽  
Wei-Shu Jing
Keyword(s):  
Transaction Costs ◽  
Stochastic Volatility ◽  
Long Memory ◽  
Stochastic Volatility Model ◽  
European Option ◽  
Volatility Model

scholarly journals Moment based estimation of supOU processes and a related stochastic volatility model

2015 ◽  
Vol 32 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Robert Stelzer ◽  
Thomas Tosstorff ◽  
Marc Wittlinger
Keyword(s):  
Stochastic Volatility ◽  
Method Of Moments ◽  
Long Memory ◽  
Generalized Method Of Moments ◽  
Memory Effects ◽  
Stochastic Volatility Model ◽  
Volatility Model ◽  
Consistent Estimators

AbstractAfter a quick review of superpositions of OU (supOU) processes, integrated supOU processes and the supOU stochastic volatility model we estimate these processes by using the generalized method of moments (GMM). We show that the GMM approach yields consistent estimators and that it works very well in practice. Moreover, we discuss the influence of long memory effects.

scholarly journals ON THE LOG PERIODOGRAM REGRESSION ESTIMATOR OF THE MEMORY PARAMETER IN LONG MEMORY STOCHASTIC VOLATILITY MODELS

2001 ◽  
Vol 17 (4) ◽  
pp. 686-710 ◽  
Author(s):  
Rohit S. Deo ◽  
Clifford M. Hurvich
Keyword(s):  
Stochastic Volatility ◽  
Long Memory ◽  
Recent Literature ◽  
Stochastic Volatility Model ◽  
Asymptotic Bias ◽  
Stochastic Volatility Models ◽  
Volatility Models ◽  
Volatility Model ◽  
Memory Parameter ◽  
Memory Series

We consider semiparametric estimation of the memory parameter in a long memory stochastic volatility model. We study the estimator based on a log periodogram regression as originally proposed by Geweke and Porter-Hudak (1983, Journal of Time Series Analysis 4, 221–238). Expressions for the asymptotic bias and variance of the estimator are obtained, and the asymptotic distribution is shown to be the same as that obtained in recent literature for a Gaussian long memory series. The theoretical result does not require omission of a block of frequencies near the origin. We show that this ability to use the lowest frequencies is particularly desirable in the context of the long memory stochastic volatility model.

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