A european call options pricing model using the infinite pure jump levy process in a fuzzy environment

2018 ◽  
Vol 13 (10) ◽  
pp. 1468-1482 ◽  
Author(s):  
Huiming Zhang ◽  
Junzo Watada
2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Gaoxun Zhang ◽  
Yi Zheng ◽  
Honglei Zhang ◽  
Xinchen Xie

This paper describes the peak, fat tail, and skewness characteristics of asset price via a Lévy process. It applies asymmetric GARCH model to depict asset price’s random volatility characteristics and builds a GARCH-Lévy option pricing model with random jump characteristics. It also uses circular maximum likelihood estimation technology to improve the stability of model parameter estimation. In order to test the model’s pricing results, we use Hong Kong Hang Seng Index (HSI) price data and its option data to carry out empirical studies. Results prove that the pricing bias of EGARCH-Lévy model is lower than that of standard Heston-Nandi (HN) model in the financial industry. For short-term, middle-term, and long-term European-style options, the pricing error of EGARCH-Lévy model is the lowest.


2014 ◽  
Vol 352 (10) ◽  
pp. 859-864 ◽  
Author(s):  
Arturo Kohatsu-Higa ◽  
Eulalia Nualart ◽  
Ngoc Khue Tran
Keyword(s):  

2007 ◽  
Vol 17 (1) ◽  
pp. 156-180 ◽  
Author(s):  
Florin Avram ◽  
Zbigniew Palmowski ◽  
Martijn R. Pistorius

2014 ◽  
Vol 46 (3) ◽  
pp. 846-877 ◽  
Author(s):  
Vicky Fasen

We consider a multivariate continuous-time ARMA (MCARMA) process sampled at a high-frequency time grid {hn, 2hn,…, nhn}, where hn ↓ 0 and nhn → ∞ as n → ∞, or at a constant time grid where hn = h. For this model, we present the asymptotic behavior of the properly normalized partial sum to a multivariate stable or a multivariate normal random vector depending on the domain of attraction of the driving Lévy process. Furthermore, we derive the asymptotic behavior of the sample variance. In the case of finite second moments of the driving Lévy process the sample variance is a consistent estimator. Moreover, we embed the MCARMA process in a cointegrated model. For this model, we propose a parameter estimator and derive its asymptotic behavior. The results are given for more general processes than MCARMA processes and contain some asymptotic properties of stochastic integrals.


2009 ◽  
Vol 46 (02) ◽  
pp. 542-558 ◽  
Author(s):  
E. J. Baurdoux

Chiu and Yin (2005) found the Laplace transform of the last time a spectrally negative Lévy process, which drifts to ∞, is below some level. The main motivation for the study of this random time stems from risk theory: what is the last time the risk process, modeled by a spectrally negative Lévy process drifting to ∞, is 0? In this paper we extend the result of Chiu and Yin, and we derive the Laplace transform of the last time, before an independent, exponentially distributed time, that a spectrally negative Lévy process (without any further conditions) exceeds (upwards or downwards) or hits a certain level. As an application, we extend a result found in Doney (1991).


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