Forecasting price of financial market crash via a new nonlinear potential GARCH model

Xing, Dun-Zhong; Li, Haifeng; Li, Jiang-Cheng; Long, Chao

doi:10.1016/j.physa.2020.125649

Cited by 18 publications

(12 citation statements)

References 55 publications

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“…, n is noise. Similarly, instead of y k, first finite differences of their logarithms could be used (i.e., the ARCH or GARCH models) [11,12], but this complicates returning to an estimation of the initial y k . Let us highlight two important features of the presented observation model:…”

Section: Data Modelmentioning

confidence: 99%

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Statistical Analysis of Current Financial Instrument Quotes in the Conditions of Market Chaos

2022

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In this paper, the problem of estimating the current value of financial instruments using multidimensional statistical analysis is considered. The research considers various approaches to constructing regression computational schemes using quotes of financial instruments correlated to the data as regressors. An essential feature of the problem is the chaotic nature of its observation series, which is due to the instability of the probabilistic structure of the initial data. These conditions invalidate the constraints under which traditional statistical estimates remain non-biased and effective. Violation of experiment repeatability requirements obstructs the use of the conventional data averaging approach. In this case, numeric experiments become the main method for investigating the efficiency of forecasting and analysis algorithms of observation series. The empirical approach does not provide guaranteed results. However, it can be used to build sufficiently effective rational strategies for managing trading operations.

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Section: Data Modelmentioning

confidence: 99%

“…. , n is a non-stationary random process roughly described by the Gaussian model with fluctuating parameters [9][10][11][12][13][14][15].…”

Section: Data Modelmentioning

confidence: 99%

Statistical Analysis of Current Financial Instrument Quotes in the Conditions of Market Chaos

2022

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“…From the review, GARCH has been deployed in different domains for forecasting data with high volatility, such as the prices of gold [28,29], stocks and the financial market [30][31][32][33], agricultural products [34], and power and electricity [12,35] from 2005 to 2021. GARCH is proven to have better performance than ARIMA if the data have high volatility.…”

Section: Generalized Autoregressive Conditional Heteroscedasticity (Garch)mentioning

confidence: 99%

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

et al. 2021

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Audience attention is vital in Digital Signage Advertising (DSA), as it has a significant impact on the pricing decision to advertise on those media. Various environmental factors affect the audience attention level toward advertising signage. Fixed-price strategies, which have been applied in DSA for pricing decisions, are generally inefficient at maximizing the potential profit of the service provider, as the environmental factors that could affect the audience attention are changing fast and are generally not considered in the current pricing solutions in a timely manner. Therefore, the time-series forecasting method is a suitable pricing solution for DSA, as it improves the pricing decision by modeling the changes in the environmental factors and audience attention level toward signage for optimal pricing. However, it is difficult to determine an optimal price forecasting model for DSA with the increasing number of available time-series forecasting models in recent years. Based on the 84 research articles reviewed, the data characteristics analysis in terms of linearity, stationarity, volatility, and dataset size is helpful in determining the optimal model for time-series price forecasting. This paper has reviewed the widely used time-series forecasting models and identified the related data characteristics of each model. A framework is proposed to demonstrate the model selection process for dynamic pricing in DSA based on its data characteristics analysis, paving the way for future research of pricing solutions for DSA.

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“…Zhang & Jia, 2021), GARCH (Aras, 2021;Hung et al, 2020;Z. Liu & Huang, 2021;Marchese et al, 2020;Paul & Sharma, 2021;Salisu et al, 2020;Xing et al, 2021), vector autoregressive moving average (Dias & Kapetanios, 2018;Guefano et al, 2021;Jeong et al, 2021), etc.…”

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GSTARIMA Model with Missing Value for Forecasting Gold Price

Mubarak

Aslanargun

Sıklar³

2022

IJSA

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Gold is one of the investments that be a great demand. Selecting and applying the best GSTARIMA model for gold price forecasting was the aim of this study. However, the gold price data that has been obtained missing values. Missing value data has been imputed by the last data before the missing value and moving average techniques. The GSTAR (1) and GSTARI (1, 1) models have been combined with an imputation technique solved this problem. Based on the smallest RMSE value, the GSTARI (1, 1) model which has been combined with the imputation technique that used the last value was the best method because it produced the smallest RMSE when compared to other methods. Forecasting results shown that gold prices in the United States, United Kingdom, and Indonesia increased but gold prices in Turkey actually decreased. Forecasting gold prices in each of these countries become one of the references in investing in gold. Based on the results of gold price forecasting, gold prices changed but not significantly.

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Forecasting price of financial market crash via a new nonlinear potential GARCH model

Cited by 18 publications

References 55 publications

Statistical Analysis of Current Financial Instrument Quotes in the Conditions of Market Chaos

Statistical Analysis of Current Financial Instrument Quotes in the Conditions of Market Chaos

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

GSTARIMA Model with Missing Value for Forecasting Gold Price

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