Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range

Autor: Chen, Cathy W. S., Gerlach, Richard, Hwang, Bruce B. K., McAleer, Michael

Publikováno v: E-Prints Complutense: Archivo Institucional de la UCM
Universidad Complutense de Madrid
E-Prints Complutense. Archivo Institucional de la UCM
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Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 2008-09 global financial crisis. We pro-pose some novel nonlinear threshold conditional autoregressive VaR (CAViaR)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::8af8564b64d5a628ef17bf9d9c02eefb
https://eprints.ucm.es/id/eprint/12755/