<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In the study, the two-step EWS-GARCH models to forecast Value-at-Risk is presented. The EWS-GARCH allows different distributions of returns or Value-at-Risk forecasting models to be used in Value-at-Risk forecasting depending on a forecasted state of the financial time series. In the study EWS-GARCH with GARCH(1,1) and GARCH(1,1), with the amendment to the empirical distribution of random errors as a Value-at-Risk model in a state of tranquillity and empirical tail, exponential or Pareto distributions used to forecast Value-at-Risk in a state of turbulence were considered. The evaluation of Value-at-Risk forecasts was based on the Value-at-Risk forecasts and the analysis of loss functions. Obtained results indicate that EWS-GARCH models may improve the quality of Value-at-Risk forecasts generated using the benchmark models. However, the choice of best assumptions for the EWS-GARCH model should depend on the goals of the Value-at-Risk forecasting model. The final selection may depend on an expected level of adequacy, conservatism and costs of the model.</p></abstract>

In the study, the two-step EWS-GARCH models to forecast Value-at-Risk is presented. The EWS-GARCH allows different distributions of returns or Value-at-Risk forecasting models to be used in Value-at-Risk forecasting depending on a forecasted state of the financial time series. In the study EWS-GARCH with GARCH(1,1) and GARCH(1,1), with the amendment to the empirical distribution of random errors as a Value-at-Risk model in a state of tranquillity and empirical tail, exponential or Pareto distributions used to forecast Value-at-Risk in a state of turbulence were considered. The evaluation of Value-at-Risk forecasts was based on the Value-at-Risk forecasts and the analysis of loss functions. Obtained results indicate that EWS-GARCH models may improve the quality of Value-at-Risk forecasts generated using the benchmark models. However, the choice of best assumptions for the EWS-GARCH model should depend on the goals of the Value-at-Risk forecasting model. The final selection may depend on an expected level of adequacy, conservatism and costs of the model.

EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk

Central European Economic Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk"}

In the study, the two-step EWS-GARCH models to forecast Value-at-Risk is presented. The EWS-GARCH allows different distributions of returns or Value-at-Risk...

EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk

Data publikacji: 18 gru 2018

Zakres stron: 01 - 25

DOI: https://doi.org/10.1515/ceej-2017-0014

Słowa kluczowe
value-at-risk, state of turbulence, GARCH, tail distributions, market risk

© 2017 Marcin Chlebus published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk

Data publikacji: 18 gru 2018

Zakres stron: 01 - 25

DOI: https://doi.org/10.1515/ceej-2017-0014

Słowa kluczowevalue-at-risk, state of turbulence, GARCH, tail distributions, market risk

© 2017 Marcin Chlebus published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
value-at-risk, state of turbulence, GARCH, tail distributions, market risk