Lévy Process-Driven Asymmetric Heteroscedastic Option Pricing Model and Empirical Analysis

Zhang, Gaoxun; Zheng, Yi; Zhang, Honglei; Xie, X. C.

doi:10.1155/2018/6042830

Cited by 2 publications

(5 citation statements)

References 27 publications

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“…As for the FGM model, most optimal α are obtained in (0, 1), which take 72% in all the cases. But there still exist 25% points obtained in (1,2). According to the results shown above, it is clear to see that the FGM model needs a wider range to select the optimal α than CFGM.…”

Section: About the Parameter αmentioning

confidence: 90%

“…In total, we can see that there are 99% values are obtained in [0, 1). And only a few optimal α are obtained in (1,2], with just 1.22%.…”

Section: About the Parameter αmentioning

confidence: 99%

“…According to the TSCV shown in Fig. 4, the CFGM and FGM model would be built on 15 subcases in each case 2 , and this means we need to build these models for 15 × 11 = 165 times in total.…”

Section: Overall Performance In Comparison To the Existing Fractional...mentioning

confidence: 99%

“…The main idea of grey forecasting was initially proposed by Deng in 1983, and the first grey model was developed in 1984 [1]. The commonly used statistical prediction models, such as the empirical models [2], semiparametric models [3], artificial neural networks (ANN) [4], and other hybrid models [5,6], often need large amount of sample data to make convinced predictions. But the idea of grey models can often built on very simple samples.…”

Section: Introductionmentioning

confidence: 99%

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The conformable fractional grey system model

et al. 2020

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The fractional order grey models (FGM) have appealed considerable interest of research in recent years due to its higher effectiveness and flexibility than the conventional grey models and other prediction models. However, the definitions of the fractional order accumulation (FOA) and difference (FOD) is computationally complex, which leads to difficulties for the theoretical analysis and applications. In this paper, the new definition of the FOA are proposed based on the definitions of Conformable Fractional Derivative, which is called the Conformable Fractional Accumulation (CFA), along with its inverse operation, the Conformable Fractional Difference (CFD). Then the new Conformable Fractional Grey Model (CFGM) based on CFA and CFD is introduced with detailed modelling procedures. The feasibility and simplicity and the CFGM are shown in the numerical example. And the at last the comprehensive real-world case studies of natural gas production forecasting in 11 countries are presented, and results show that the CFGM is much more effective than the existing FGM model in the 165 subcases.

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Section: About the Parameter αmentioning

confidence: 90%

“…In total, we can see that there are 99% values are obtained in [0, 1). And only a few optimal α are obtained in (1,2], with just 1.22%.…”

Section: About the Parameter αmentioning

confidence: 99%

Section: Overall Performance In Comparison To the Existing Fractional...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The conformable fractional grey system model

et al. 2020

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“…Energy consumption environment in China is changing rapidly, resulting in very little effective energy data available for prediction. While reliable predictions made by some popular statistical models (Zhang et al ., 2018; Ma et al ., 2019a; Ma and Liu, 2018; Du et al ., 2017, 2019; Wu et al ., 2019a; Fan et al ., 2019; Yang et al ., 2019) often rely on large samples, which shows they are not suitable for annual energy demand forecasting. According to a large number of empirical studies (Ma et al ., 2019b, c; Wang and Ye, 2017; Wu et al ., 2018), the grey model is efficient in small sample prediction and has been widely used in agriculture (Zeng et al ., 2019), energy (Ma et al ., 2019c), tourism (Ma et al ., 2019b), environmental pollutants (Wu et al ., 2020), and geological disasters (Zeng et al ., 2019), etc.…”

Section: Introductionmentioning

confidence: 99%

Application of a novel hybrid accumulation grey model to forecast total energy consumption of Southwest Provinces in China

Zhao

Cai

et al. 2023

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PurposeConsidering the small sample size and non-linear characteristics of historical energy consumption data from certain provinces in Southwest China, the authors propose a hybrid accumulation operator and a hybrid accumulation grey univariate model as a more accurate and reliable methodology for forecasting energy consumption. This method can provide valuable decision-making support for policy makers involved in energy management and planning.Design/methodology/approachThe hybrid accumulation operator is proposed by linearly combining the fractional-order accumulation operator and the new information priority accumulation. The new operator is then used to build a new grey system model, named the hybrid accumulation grey model (HAGM). An optimization algorithm based on the JAYA optimizer is then designed to solve the non-linear parameters θ, r, and γ of the proposed model. Four different types of curves are used to verify the prediction performance of the model for data series with completely different trends. Finally, the prediction performance of the model is applied to forecast the total energy consumption of Southwest Provinces in China using the real world data sets from 2010 to 2020.FindingsThe proposed HAGM is a general formulation of existing grey system models, including the fractional-order accumulation and new information priority accumulation. Results from the validation cases and real-world cases on forecasting the total energy consumption of Southwest Provinces in China illustrate that the proposed model outperforms the other seven models based on different modelling methods.Research limitations/implicationsThe HAGM is used to forecast the total energy consumption of the Southwest Provinces of China from 2010 to 2020. The results indicate that the HAGM with HA has higher prediction accuracy and broader applicability than the seven comparative models, demonstrating its potential for use in the energy field.Practical implicationsThe HAGM(1,1) is used to predict energy consumption of Southwest Provinces in China with the raw data from 2010 to 2020. The HAGM(1,1) with HA has higher prediction accuracy and wider applicability compared with some existing models, implying its high potential to be used in energy field.Originality/valueTheoretically, this paper presents, for the first time, a hybrid accumulation grey univariate model based on a new hybrid accumulation operator. In terms of application, this work provides a new method for accurate forecasting of the total energy consumption for southwest provinces in China.

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Lévy Process-Driven Asymmetric Heteroscedastic Option Pricing Model and Empirical Analysis

Cited by 2 publications

References 27 publications

The conformable fractional grey system model

The conformable fractional grey system model

Application of a novel hybrid accumulation grey model to forecast total energy consumption of Southwest Provinces in China

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