The transformed Gram Charlier distribution: Parametric properties and financial risk applications

León, Ángel; Ñíguez, Trino-Manuel

doi:10.1016/j.jempfin.2021.07.004

Cited by 9 publications

(3 citation statements)

References 70 publications

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“…Qualitative analysis refers to evaluating the vagueness of the index, and quantitative refers to calculating the relative weight of the index. Then, dynamic analysis is carried out according to the weights, and the influence of all levels of indexes is determined (León and Ñíguez, 2021) The "edge" in EC is a relative concept, which mainly refers to any resource in the network except the cloud. Regarding the basic functions such as data collection, terminal equipment can perform predictive analysis and intelligent processing (Ding et al, 2021), which has accelerated network marginalization.…”

Section: Financial Risk Evaluation Model Based On Bcmentioning

confidence: 99%

Analysis of Safe Storage of Network Information Data and Financial Risks Under Blockchain Combined With Edge Computing

Liang¹,

Ruan

et al. 2022

Journal of Global Information Management

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To discuss the control of financial risks (FRs) under blockchain (BC) and improve network information security (NIS) and data security, edge computing (EC) combined with BC is proposed to control the risks of the big data (BD) financial system. Firstly, the BC-based financial system is introduced, and the characteristics of BC such as decentralization, tamper-resistant, and smart contract are analyzed. Secondly, the development status of NIS and the characteristics of marginal computing are explained, and the control model of NIS is established. Then, EC is used to encrypt the identity authentication system to ensure data security, and the BC-based FR evaluation model is established. Finally, a questionnaire is designed regarding the NIS model, and the results are analyzed. A simulation experiment is conducted regarding the index evaluation of the BC-based FR evaluation model. The experimental results indicate that network personnel control, environment, and technology have positive effects on NIS, and the impact factors are 0.26, 0.24, and 0.33, respectively.

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Section: Financial Risk Evaluation Model Based On Bcmentioning

confidence: 99%

Analysis of Safe Storage of Network Information Data and Financial Risks Under Blockchain Combined With Edge Computing

Liang¹,

Ruan

et al. 2022

Journal of Global Information Management

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“…The above description shows that, unlike the activation function of the hidden layer and output layer of BPNN, the sigmoid function is mainly used for edge detection. Its learning process is mainly concentrated in the hidden layer [ 21 , 22 ]. The main reason for choosing this function as a function of hidden layers is to avoid the saturation of hidden layers.…”

Section: Cell Recognition Algorithm Based On Bpnn Combined With Edge ...mentioning

confidence: 99%

Cell Recognition Using BP Neural Network Edge Computing

Du¹,

Liu²,

Sun

2022

Contrast Media & Molecular Imaging

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This exploration is to solve the efficiency and accuracy of cell recognition in biological experiments. Neural network technology is applied to the research of cell image recognition. The cell image recognition problem is solved by constructing an image recognition algorithm. First, with an in-depth understanding of computer functions, as a basic intelligent algorithm, the artificial neural network (ANN) is widely used to solve the problem of image recognition. Recently, the backpropagation neural network (BPNN) algorithm has developed into a powerful pattern recognition tool and has been widely used in image edge detection. Then, the structural model of BPNN is introduced in detail. Given the complexity of cell image recognition, an algorithm based on the ANN and BPNN is used to solve this problem. The BPNN algorithm has multiple advantages, such as simple structure, easy hardware implementation, and good learning effect. Next, an image recognition algorithm based on the BPNN is designed and the image recognition process is optimized in combination with edge computing technology to improve the efficiency of algorithm recognition. The experimental results show that compared with the traditional image pattern recognition algorithm, the recognition accuracy of the designed algorithm for cell images is higher than 93.12%, so it has more advantages for processing the cell image algorithm. The results show that the BPNN edge computing can improve the scientific accuracy of cell recognition results, suggesting that edge computing based on the BPNN has a significant practical value for the research and application of cell recognition.

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“…In practice, truncated GC expansions (also known as Edgeworth-Sargan densities) have to be used (see [10] for the analysis of these densities with financial data), and this often implies negative densities over some interval of their domain. Several solutions have been proposed to handle this problem: (i) restricting the parameter space and then estimating the parameters by constrained ML, see [11]; (ii) using certain transformations in order to guarantee positivity of the expansion, see [12,13]; (iii) considering semi-nonparametric densities which are always positive by construction, and hence the parameters can be easily estimated by using ML instead of constrained ML, see [14][15][16]. In this work we use the third approach, and consider the standardized semi-nonparametric distribution of fourth order, which is a two-parameter distribution.…”

Section: Introductionmentioning

confidence: 99%

Estimating Value-at-Risk and Expected Shortfall: Do Polynomial Expansions Outperform Parametric Densities?

2022

Self Cite

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We assess Value-at-Risk (VaR) and Expected Shortfall (ES) estimates assuming different models for the standardized returns: distributions based on polynomial expansions such as Cornish-Fisher and Gram-Charlier, and well-known parametric densities such as normal, skewed-t and Johnson. This paper aims to analyze whether models based on polynomial expansions outperform the parametric ones. We carry out the model performance comparison in two stages: first, with a backtesting analysis of VaR and ES; and second, using loss functions. Our backtesting results show that all distributions, except for normal ones, perform quite well in VaR and ES estimations. Regarding the loss function analysis, we conclude that polynomial expansions (specifically, the Cornish-Fisher one) usually outperform parametric densities in VaR estimation, but the latter (specifically, the Johnson density) slightly outperform the former in ES estimation; however, the gains of using one approach or the other are modest.

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The transformed Gram Charlier distribution: Parametric properties and financial risk applications

Cited by 9 publications

References 70 publications

Analysis of Safe Storage of Network Information Data and Financial Risks Under Blockchain Combined With Edge Computing

Analysis of Safe Storage of Network Information Data and Financial Risks Under Blockchain Combined With Edge Computing

Cell Recognition Using BP Neural Network Edge Computing

Estimating Value-at-Risk and Expected Shortfall: Do Polynomial Expansions Outperform Parametric Densities?

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