Tail risk forecasting with semiparametric regression models by incorporating overnight information
Year of publication: |
2024
|
---|---|
Authors: | Chen, Cathy W. S. ; Koike, Takaaki ; Shau, Wei-Hsuan |
Published in: |
Journal of forecasting. - New York, NY : Wiley Interscience, ISSN 1099-131X, ZDB-ID 2001645-1. - Vol. 43.2024, 5, p. 1492-1512
|
Subject: | CAViaR model | expected shortfall | Markov chain Monte Carlo method | nowcasting | realized measures | Value-at-Risk | Risikomaß | Risk measure | Monte-Carlo-Simulation | Monte Carlo simulation | Prognoseverfahren | Forecasting model | Theorie | Theory | Markov-Kette | Markov chain | Schätzung | Estimation | Nichtparametrisches Verfahren | Nonparametric statistics | Kapitaleinkommen | Capital income | Risiko | Risk | Statistische Verteilung | Statistical distribution |
-
Improving quantile forecasts via realized double hysteretic GARCH model in stock markets
Chen, Cathy W. S., (2024)
-
Gerlach, Richard, (2020)
-
A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting
Wang, Chao, (2024)
- More ...
-
Subset selection of autoregressive time series models
Chen, Cathy W. S., (1999)
-
Bayesian non‐linear quantile effects on modelling realized kernels
Dong, Manh Cuong, (2021)
-
Bayesian modeling and forecasting of Value‐at‐Risk via threshold realized volatility
Chen, Cathy W. S., (2018)
- More ...