Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model
| Autor: | Fei Su, Heng-Guo Zhang, Shuqi Qiu, Yan Song, Ran Xiao, Chi-Wei Su |
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| Rok vydání: | 2017 |
| Předmět: |
Economics and Econometrics
Autoregressive conditional heteroskedasticity 05 social sciences Random mapping 02 engineering and technology High dimensional Support vector machine Nonlinear system 0502 economics and business 0202 electrical engineering electronic engineering information engineering Econometrics Economics Applied mathematics 020201 artificial intelligence & image processing 050207 economics Volatility (finance) Value at risk Extreme learning machine |
| Zdroj: | Economic Modelling. 67:355-367 |
| ISSN: | 0264-9993 |
| DOI: | 10.1016/j.econmod.2017.02.014 |
| Popis: | In this study, we propose a non-linear random mapping model called GELM. The proposed model is based on a combination of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the Extreme Learning Machine (ELM), and can be used to calculate Value-at-Risk (VaR). Alternatively, the GELM model is a non-parametric GARCH-type model. Compared with conventional models, such as the GARCH models, ELM, and Support Vector Machine (SVM), the computational results confirm that the GELM model performs better in volatility forecasting and VaR calculation in terms of efficiency and accuracy. Thus, the GELM model can be an essential tool for risk management and stress testing. |
| Databáze: | OpenAIRE |
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