Working Papers

The WNE Working Papers series has been published by the Faculty of Economic Sciences at the University of Warsaw since 2008.

The WNE Working Papers series provides a fast, open channel for disseminating research conducted at the Faculty of Economic Sciences, University of Warsaw. The papers hosted here are working versions (pre-prints) and may evolve as authors refine their analyses, incorporate feedback, or progress through formal peer-review. What you read is the current version released by the authors, timestamped and assigned DOI/ISSN identifiers to ensure precise citation and version tracking. Copyright remains with the authors, who may, at any time, upload a revised file or add a note directing readers to a later, peer-reviewed publication.

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Number of Results 3

WP(9/2020)315. Size does matter. A study on the required window size for optimal quality market risk models

Authors: Buczyński Mateusz, Chlebus Marcin
When it comes to market risk models, should we use full data that we possess or rather find a sufficient subsample? We have conducted a study of different fixed moving window's lengths (from 300 to 2000 observations) for three Value-at-Risk model…

WP315. Size does matter. A study on the required window size for optimal quality market risk models

When it comes to market risk models, should we use full data that we possess or rather find a sufficient subsample? We have conducted a study of different fixed moving window's lengths (from 300 to 2000 observations) for three Value-at-Risk models: historical simulation, GARCH and CAViaR model for three different indexes: WIG20, S&P500 and FTSE100. Testing samples contained 250 observations, each ending with the end of years 2015-2019. We have also addressed the subjectivity of choosing the window's size by testing change points detection algorithms: binary segmentation and Pelt; to find the best matching cut-off point. Results indicate that the size of the training sample greater than 900-1000 observations doesn't increase the quality of the model, while the lengths lower than such cut-off provide unsatisfactory results and decrease model's conservatism. Change point detection methods provide more accurate models. Applying the algorithms with every model's recalculation provides results better by on average 1 exceedance. Our recommendation is to use GARCH or CAViaR model with recalculated window size.


Buczyński Mateusz, Chlebus Marcin
Value at Risk; historical simulation; CAViaR; GARCH; forecast comparison; sample size

WP(12/2019)297. Old-fashioned parametric models are still the best. A comparison of Value-at-Risk approaches in several volatility states

Authors: Buczyński Mateusz, Chlebus Marcin
Numerous advances in the modelling techniques of Value-at-Risk (VaR) have provided the financial institutions with a wide scope of market risk approaches. Yet it remains unknown which of the models should be used depending on the state of volatility.…

WP297. Old-fashioned parametric models are still the best. A comparison of Value-at-Risk approaches in several volatility states

Numerous advances in the modelling techniques of Value-at-Risk (VaR) have provided the financial institutions with a wide scope of market risk approaches. Yet it remains unknown which of the models should be used depending on the state of volatility. In this article we present the backtesting results for 1% and 2.5% VaR of six indexes from emerging and developed countries using several most known VaR models, among many: GARCH, EVT, CAViaR and FHS with multiple sets of parameters. The backtesting procedure has been based on the excess ratio, Kupiec and Christoffersen tests for multiple thresholds and cost functions. The added value of this article is that we have compared the models in four different scenarios, with different states of volatility in training and testing samples. The results indicate that the best of the models that is the least affected by changes in the volatility is GARCH(1,1) with standardized student's t-distribution. Non-parmetric techniques (e.g. CAViaR with GARCH setup (see Engle and Manganelli, 2001) or FHS with skewed normal distribution) have very prominent results in testing periods with low volatility, but are relatively worse in the turbulent periods. We have also discussed an automatic method to setting a threshold of extreme distribution for EVT models, as well as several ensembling methods for VaR, among which minimum of best models has been proven to have very good results - in particular a minimum of GARCH(1,1) with standardized student's t-distribution and either EVT or CAViaR models.


Buczyński Mateusz, Chlebus Marcin
Value-at-Risk GARCH Extreme Value Theory Filtered Historical Simulation CAViaR market risk forecast comparison

WP(29/2017)258. Is CAViaR model really so good in Value at Risk forecasting? Evidence from evaluation of a quality of Value-at-Risk forecasts obtained based on the: GARCH(1,1), GARCH-t(1,1), GARCH-st(1,1), QML-GARCH(1,1), CAViaR and the historical simulation models depending on the stability of financial markets

Authors: Buczyński Mateusz, Chlebus Marcin
In the literature, there is no consensus which Value-at-Risk forecasting model is the best for measuring a market risk in banks. In the study an analysis of Value-at-Risk forecasting models quality over varying economic stability periods for main ind…

WP258. Is CAViaR model really so good in Value at Risk forecasting? Evidence from evaluation of a quality of Value-at-Risk forecasts obtained based on the: GARCH(1,1), GARCH-t(1,1), GARCH-st(1,1), QML-GARCH(1,1), CAViaR and the historical simulation models depending on the stability of financial markets

In the literature, there is no consensus which Value-at-Risk forecasting model is the best for measuring a market risk in banks. In the study an analysis of Value-at-Risk forecasting models quality over varying economic stability periods for main indices from stock exchanges was conducted. The VaR forecasts from GARCH(1,1), GARCH-t(1,1), GARCH-st(1,1), QML-GARCH(1,1), CAViaR and historical simulation models in periods with contrasting volatility trends (increasing, constantly high and decreasing) for countries economically developed (the USA – S&P 500, Germany - DAX and Japan – Nikkei 225) and economically developing (China – SSE COMP, Poland – WIG20 and Turkey – XU100) were compared. The data samples used in the analysis were selected from period 01.01.1999 – 24.03.2017. To assess the VaR forecasts quality: excess ratio, Basel traffic light test, coverage tests (Kupiec test, Christoffersen test), Dynamic Quantile test, cost functions and Diebold-Marino test were used. Obtained results shows that the quality of Value-at-Risk forecasts for the models varies depending on a volatility trend. However, GARCH-st (1,1) and QML-GARCH(1,1) were found as the most robust models to the different volatility periods. The results shows, as well that the CAViaR model forecasts were less appropriate in the increasing volatility period. Moreover, no significant differences for the VaR forecasts quality were found for the developed and developing countries.


DOI: https://doi.org/10.26405/WP/WNE/2017/258/029
Buczyński Mateusz, Chlebus Marcin
risk management value at risk GARCH CAViaR historical simulation quality of model assessment
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