The research concerns modelling of several financial time series. Due to presence of different type of outliers in the data the robust estimation of ARMA-GARCH-type models is required. A Monte Carlo simulation is used to measure sensitivity of volatility forecast on different fraction of outliers in the time series. For comparison purposes the QML estimation results were uses as a benchmark. The analysis is based on several type of instruments  from FX, capital, and commodity market. To illustrate the behavior of robust estimate, the VaR forecast is simulated, and for comparison purposes we use tests based on the decision-making aspect, i.e. Binary Loss, Regulatory Loss, Firm Loss and the Expected Shortfall.

• Legal notice:
  

  Copyright (c) Pielaszek Research, all rights reserved.
  The above materials, including auxiliary resources, are subject to Publisher's copyright and the Author(s) intellectual rights. Without limiting Author(s) rights under respective Copyright Transfer Agreement, no part of the above documents may be reproduced without the express written permission of Pielaszek Research, the Publisher. Express permission from the Author(s) is required to use the above materials for academic purposes, such as lectures or scientific presentations.
  In every case, proper references including Author(s) name(s) and URL of this webpage: https://science24.com/paper/29943 must be provided.

1. Risk modeling using the Extreme Value Theory. Application of Ecompassing Method and Combined Forecasts to VaR quantification.