Similar Search Results

Narrow search

Estimation of extreme value-at-risk : an EVT approach for quantile GARCH model

Yi, Yanping; Feng, Xingdong; Huang, Zhuo - In: Economics letters 124 (2014) 3, pp. 378-381

Persistent link: https://www.econbiz.de/10010495168

Modeling dynamic higher moments of crude oil futures

Huang, Zhuo; Liang, Fang; Wang, Tianyi; Li, Chao - In: Finance research letters 39 (2021), pp. 1-5

Persistent link: https://www.econbiz.de/10012805140

Do realized higher moments have information content? : VaR forecasting based on the realized GARCH-RSRK model

Wang, Tianyi; Liang, Fang; Huang, Zhuo; Yan, Hong - In: Economic modelling 109 (2022), pp. 1-13

Persistent link: https://www.econbiz.de/10013348237

Measuring systemic risk with high-frequency data : a realized GARCH approach

Chen, Qihao; Huang, Zhuo; Liang, Fang - In: Finance research letters 54 (2023), pp. 1-9

Persistent link: https://www.econbiz.de/10014472723

Estimation of Extreme Value-at-Risk : An EVT Approach for Quantile GARCH Model

Yi, Yanping - 2014

We proposed a method to estimate extreme conditional quantiles by combining quantile GARCH model of Xiao and Koenker (2009) and extreme value theory (EVT) approach. We first estimate the latent volatility process using the information of intermediate quantiles. We then apply EVT to the tail...

Persistent link: https://www.econbiz.de/10013055646

Probability Distribution in Volatility and Value-at-Risk Forecasting for Cryptocurrency

Chen, Qihao; Huang, Zhuo; Liang, Fang - 2022

This study investigates the role of probability distribution in forecasting volatility and Value-at-Risk (VaR). We use the Realized GARCH model and high-frequency data from the cryptocurrency market and show that the role of probability distribution varies across different situations. A skewed-t...

Persistent link: https://www.econbiz.de/10014239198