Output details
11 - Computer Science and Informatics
Goldsmiths' College
Heavy-tailed mixture GARCH volatility modeling and Value-at-Risk estimation
<13> This is published in the most important journal in expert systems, with an H-index of 73 (SCImago) it reports the first research to design a proper model for variance estimation in financial time series, and derives reliable algorithm for its estimation. The experiments with our low order MT(2)-GARCH(1,1) show that it yields results with improved statistical characteristics and economic performance compared to linear and nonlinear heavy-tail GARCH, as well as normal mixture GARCH. We demonstrate that our model leads to reliable Value-at-Risk performance in short and long
trading positions across different confidence levels.