Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Series Vol. 17 , 13 September 2023
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The emergence of financial derivatives complicates traditional financial products and increases financial market volatility. Individuals and financial institutions are both exposed to more complex and uncontrollable risks in this environment. Because of the risk's uncertainty, we must use reasonable methods to predict and estimate it in order to achieve the goal of risk control. This paper discusses three new VaR (Value at Risk) models that have emerged in recent years based on the ARCH family model using a method of literature review. The ARMA-EGARCH model, for example, combines the ARMA model to describe constant variance time series and the EGARCH model to describe heteroscedasticity phenomena, and theoretically can better describe the fluctuations of financial time series and obtain an independent time series with the same distribution. The sequence is processed using extreme value theory, which is the ARMA-EGARCH-GPPD model, in conjunction with the GPD model. We used the ARMA-EGARCH-semi-parametric method in conjunction with the historical simulation method and the parameter method to avoid cumbersome quantile calculation because the model algorithm is more complex. The generalized EWMA risk value prediction model has more advantages for financial data with large peaks.
VaR, ARCH series model, ARMA-EGARCH-GPPD model, generalized-EWMA model
1. Liu Dingci. The comparison of different approaches to estimate value at risk and optimization [D]. Jilin university, 2020. The DOI: 10.27162 /, dc nki. Gjlin. 2020.005143.
2. Tang Ning. Statistical research and Empirical analysis based on Value at Risk (VaR) model and backtest test [D]. China West Normal University,2016.
3. Jing Yongqiang. A study on the Value at risk model of portfolio investment [J]. North University of China,2017.
4. He Ying. Research on Financial risk Assessment of Small and medium-sized listed Companies based on VAR [D]. Shenyang Institute of Technology,2016.
5. Thavaneswaran Aerambamoorthy,Paseka Alex,Frank Julieta. Generalized value at risk forecasting[J]. Communications in Statistics - Theory and Methods,2020,49(20).
6. Wang Tianyi,Liang Fang,Huang Zhuo,Yan Hong. Do realized higher moments have information content? - VaR forecasting based on the realized GARCH-RSRK model[J]. Economic Modelling,2022,109.
7. Badaye Hemant Kumar,Narsoo Jason. Forecasting multivariate VaR and ES using MC-GARCH-Copula model[J]. The Journal of Risk Finance,2020,ahead-of-print(ahead-of-print).
8. Tian Yahao. Using Convolutional neural network to determine ARCH time series model [D]. Xiamen university, 2020. DOI: 10.27424 /, dc nki. Gxmdu. 2020.001647.
9. YU Huiqin. Comparative study on industry risk of listed companies based on VaR model of extreme value Theory [D]. Zhejiang University,2013.
10. Wang Liming, Wang Lian, et al. Applied Time Series Analysis [M]. Shanghai: Fudan University Press,2012.
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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