Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model
Ran Xiao and
Economic Modelling, 2017, vol. 67, issue C, 355-367
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.
Keywords: C32; C45; C53; Extreme learning machine; High-dimensional space; Value-at-Risk; Random mapping; GARCH model; Time series (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:67:y:2017:i:c:p:355-367
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