The performance of time-varying volatility and regime switching models in estimating Value-at-Risk
(2012) BUSP69 20121Department of Business Administration
- Abstract (Swedish)
- Markov Regime-Switching GARCH (MRS-GARCH) models have been gaining popularity due to their ability to account for shifts volatility regimes that tend to characterize returns series. Previous empirical studies have shown that this capacity to capture the volatility dynamics leads to a superior forecasting power of the MRS models. We investigate the performance of these models in quantifying and managing market risk for financial institutions. To this purpose, Klaasen’s (2002) MRS-GARCH model both under the normal and t-distribution assumptions, was applied in order to calculate VaR for four real trading portfolios, obtained from American and European banks, as well as a mimicking portfolio built with constant weights. The results concluded... (More)
- Markov Regime-Switching GARCH (MRS-GARCH) models have been gaining popularity due to their ability to account for shifts volatility regimes that tend to characterize returns series. Previous empirical studies have shown that this capacity to capture the volatility dynamics leads to a superior forecasting power of the MRS models. We investigate the performance of these models in quantifying and managing market risk for financial institutions. To this purpose, Klaasen’s (2002) MRS-GARCH model both under the normal and t-distribution assumptions, was applied in order to calculate VaR for four real trading portfolios, obtained from American and European banks, as well as a mimicking portfolio built with constant weights. The results concluded that the MRS model has not had a consistent performance over the five data series, and, in many cases was outperformed by the more simplistic GARCH models. The model does perform better in terms of exceptions compared to the in-house VaR models employed by the banks analyzed. However, we must keep in mind that the MRS-GARCH specification can be sensitive to the length of the rolling window used, and a larger in-sample period could have added more precision to the variance forecasts. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/2856253
- author
- Birtoiu, Alina LU and Dragu, Florin George
- supervisor
- organization
- course
- BUSP69 20121
- year
- 2012
- type
- H2 - Master's Degree (Two Years)
- subject
- language
- English
- id
- 2856253
- date added to LUP
- 2012-06-28 10:51:40
- date last changed
- 2012-06-28 10:51:40
@misc{2856253,
abstract = {{Markov Regime-Switching GARCH (MRS-GARCH) models have been gaining popularity due to their ability to account for shifts volatility regimes that tend to characterize returns series. Previous empirical studies have shown that this capacity to capture the volatility dynamics leads to a superior forecasting power of the MRS models. We investigate the performance of these models in quantifying and managing market risk for financial institutions. To this purpose, Klaasen’s (2002) MRS-GARCH model both under the normal and t-distribution assumptions, was applied in order to calculate VaR for four real trading portfolios, obtained from American and European banks, as well as a mimicking portfolio built with constant weights. The results concluded that the MRS model has not had a consistent performance over the five data series, and, in many cases was outperformed by the more simplistic GARCH models. The model does perform better in terms of exceptions compared to the in-house VaR models employed by the banks analyzed. However, we must keep in mind that the MRS-GARCH specification can be sensitive to the length of the rolling window used, and a larger in-sample period could have added more precision to the variance forecasts.}},
author = {{Birtoiu, Alina and Dragu, Florin George}},
language = {{eng}},
note = {{Student Paper}},
title = {{The performance of time-varying volatility and regime switching models in estimating Value-at-Risk}},
year = {{2012}},
}