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Keywords = CKLS (Chan, Karolyi, Longstaff, and Sanders)

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14 pages, 2567 KiB  
Article
Simultaneous Identification of Volatility and Mean-Reverting Parameter for European Option under Fractional CKLS Model
by Jiajia Zhao and Zuoliang Xu
Fractal Fract. 2022, 6(7), 344; https://doi.org/10.3390/fractalfract6070344 - 21 Jun 2022
Cited by 3 | Viewed by 1788
Abstract
In this paper, we reconstruct the time-dependent volatility function of the underlying asset and the mean-reverting parameter γ of the interest rate for European options under the fractional Chan–Karolyi–Longstaff–Sanders (CKLS) stochastic interest rate model. Tikhonov regularization is used to solve the ill-posedness of [...] Read more.
In this paper, we reconstruct the time-dependent volatility function of the underlying asset and the mean-reverting parameter γ of the interest rate for European options under the fractional Chan–Karolyi–Longstaff–Sanders (CKLS) stochastic interest rate model. Tikhonov regularization is used to solve the ill-posedness of the inverse problem. The existence and stability of the solution of the regularization problem are given. We employ the alternating direction method of multipliers (ADMM) to iteratively optimize the volatility function and the parameter γ. Finally, numerical simulations and the empirical analysis are presented to illustrate the efficiency of the proposed method. Full article
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12 pages, 551 KiB  
Article
Are These Shocks for Real? Sensitivity Analysis of the Significance of the Wavelet Response to Some CKLS Processes
by Somayeh Kokabisaghi, Eric J. Pauwels, Katrien Van Meulder and André B. Dorsman
Int. J. Financial Stud. 2018, 6(3), 76; https://doi.org/10.3390/ijfs6030076 - 2 Sep 2018
Viewed by 4238
Abstract
The CKLS process (introduced by Chan, Karolyi, Longstaff, and Sanders) is a typical example of a mean-reverting process. It combines random fluctuations with an elastic attraction force that tends to restore the process to a central value. As such, it is widely used [...] Read more.
The CKLS process (introduced by Chan, Karolyi, Longstaff, and Sanders) is a typical example of a mean-reverting process. It combines random fluctuations with an elastic attraction force that tends to restore the process to a central value. As such, it is widely used to model the stochastic behaviour of various financial assets. However, the calibration of CKLS processes can be problematic, resulting in high levels of uncertainty on the parameter estimates. In this paper we show that it is still possible to draw solid conclusions about certain qualitative aspects of the time series, as the corresponding indicators are relatively insensitive to changes in the CKLS parameters. Full article
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