Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

Results found: 4

first rewind previous Page / 1 next fast forward last

Search results

Search:
in the keywords:  value-at-risk
help Sort By:

help Limit search:
first rewind previous Page / 1 next fast forward last
1
Publication available in full text mode
Content available

BOOSTING UNDER QUANTILE REGRESSION – CAN WE USE IT FOR MARKET RISK EVALUATION?

100%
Bień-Barkowska K.
Metody Ilościowe w Badaniach Ekonomicznych
|
2014
|
vol. 15
|
issue 1
7-17
XX
We consider boosting, i.e. one of popular statistical machine-learning meta-algorithms, as a possible tool for combining individual volatility estimates under a quantile regression (QR) framework. Short empirical exercise is carried out for the S&P500 daily return series in the period of 2004-2009. Our initial findings show that this novel approach is very promising and the in-sample goodness-of-fit of the QR model is very good. However much further research should be conducted as far as the out-of-sample quality of conditional quantile predictions is concerned.
2
Content available remote

Assessing the Efficiency of Investment Fund Management Using Quantile Risk Measures

88%
Rutkowska-Ziarko A., Garsztka P.
Olsztyn Economic Journal
|
2016
|
vol. 11
|
issue 3
277-298
EN
The aim of the research is to compare the efficiency of managing selected Polish investment funds in various phases of stock market condition. The Value at Risk (VaR) and Conditional Value at Risk (CVaR) is used to construct efficiency ratios of fund management. Those funds investing in financial instruments have the most stable expected rate of return and the lowest risk, in all the analysed periods which made them highly effective. The article also discusses the alternative methods to VaR and CVaR estimation which are used in the study. It is noted VaR and CVaR estimates obtained using backtesting and using APARCH models give similar results.
3
Publication available in full text mode
Content available

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

88%
Chlebus M.
Central European Economic Journal
|
2017
|
vol. 3
|
issue 50
EN
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.
4
Publication available in full text mode
Content available

Market risk, value-at-risk and exponential weighting

75%
Broll U., Förster A.
Economics and Business Review
|
2022
|
vol. 8
|
issue 2
80-91
EN
Banks and nfiancial intermediaries are exposed to market risk. The aim of the paper is to explore the implications of legal requirements on market risk valuation. The focus is on the calculation of the permissible weighting factor of the concept of value-at-risk (VaR). When measuring market risk, banks and nfiancial intermediaries may deviate from equally weighting historical data in their value-at-risk calculation and instead use an exponential time series weighting. eTh use of exponential weighting in the value-at-risk calculation is very popular because it takes into account changes in market volatility (immediately) and can therefore quickly adapt to VaR. In less volatile market phases this leads to a reduction in VaR and thus to lower own funds' requirements for banks and nfiancial intermediaries. However, in the exponential weighting a high volatility in the past is quickly forgotten and the VaR can be underestimated. To prevent this banks and nfiancial intermediaries are not completely free to choose a weighting (decay) factor. The exchange rate between Polish zloty and euro is used to estimate the value-at-risk as an example and exceptions to the general legal requirements are also discussed.
first rewind previous Page / 1 next fast forward last
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.