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.

The Working Papers series accepts articles by research employees of the Faculty and publications from conferences organised at the Faculty of Economic Sciences at the University of Warsaw. Articles should be original research papers which have not been previously published, on the subject of economics.

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Number of Results 7

WP(20/2026)514. Hedging Auction Volatility with Gap Call Options

Authors: Mbara Gilbert
We investigate the feasibility of hedging commodity price risk using gap call options within an auction-based market devoid of traditional derivatives. Using a novel, high-frequency dataset from the Nairobi Coffee Exchange (NCE), we model spot price …

WP514. Hedging Auction Volatility with Gap Call Options

We investigate the feasibility of hedging commodity price risk using gap call options within an auction-based market devoid of traditional derivatives. Using a novel, high-frequency dataset from the Nairobi Coffee Exchange (NCE), we model spot price dynamics by deriving a Geometric Brownian Motion process from the independent private values paradigm. The estimated model captures the unique microstructure of the NCE, where discrete weekly auctions generate prices characterized by extreme volatility (146.5% annualized). We utilize Monte Carlo simulation to price and evaluate the performance of gap call options for buyers seeking protection against catastrophic price spikes. Our results demonstrate that gap options – characterized by a trigger price higher than the strike – provide superior risk-adjusted returns compared to standard European calls. Our study offers a practical framework for developing tailored risk management instruments in emerging commodity exchanges, and provides empirical evidence for the viability of gap options as a cost-effective hedging tool in high-volatility, institutionally constrained markets.


DOI: https://doi.org/10.33138/2957-0506.2026.20.514
Mbara Gilbert
risk management hedging commodity price risk gap options Nairobi Coffee Exchange geometric Brownian motion Monte Carlo simulation

WP(28/2021)376. The effectiveness of Value-at-Risk models in various volatility regimes

Authors: Schiffers Aleksander, Chlebus Marcin
There is an ongoing discussion, what is the most efficient approach to Value-at-Risk estimation. Subsequent studies and meta-analyzes show that there is no scientific consensus in this field and the necessity of further research is frequently underli…

WP376. The effectiveness of Value-at-Risk models in various volatility regimes

There is an ongoing discussion, what is the most efficient approach to Value-at-Risk estimation. Subsequent studies and meta-analyzes show that there is no scientific consensus in this field and the necessity of further research is frequently underlined. In this study, authors try to assess the comparative performance of models used for Value-at-Risk estimations in changing market volatility regimes. The models considered are: the Historical Simulation, the Risk Metrics®, the GARCH(1,1)-n, the GARCH(1,1)-t, the GARCH(1,1)-st, the GARCH(1,1)-QML. GARCH models are additionally enriched with additional, exogenous regressors in the form of lagged commodity futures contracts returns. The analysis is conducted on a set of utility sector stock indices from six developed countries across the globe: WIG Energia (Poland), Dow Jones Utilities Average (USA), CAC Utilities (France), Tokyo SE Topix-17 Power & Gas (Japan), S&P ASX 200 Utilities (Australia), and DAX All Utilities (Germany). Three samples of different characteristics are distinguished from the last 10 years of data and one of them covers the upsurge in market volatility caused by the Covid-19 pandemic. In order to evaluate the VaR forecasts performance of each model, conditional/unconditional coverage tests of Kupiec and Christoffersen, Dynamic Quantile test, and Diebold-Marino test were used. Empirical results of the study indicate that in the volatile market periods, overall quality of forecasts deteriorates for all models to a varying degree. However, the GARCH(1,1)-st with external regressors is considered the most efficient and robust model due to its ability to capture stylized facts of data distribution. Exogenous variables are worth considering but their contribution to performance improvement may be model and market dependent.


Schiffers Aleksander, Chlebus Marcin
risk management market risk Value-at-risk GARCH Historical Simulation Risk Metrics® risk modelling benchmark model quality assessment

WP(28/2020)334. Value-at-risk — the comparison of state-of-the-art models on various assets

Authors: Kielak Karol, Ślepaczuk Robert
This paper compares different approaches to Value-at-Risk measurement based on parametric and non-parametric approaches. Three portfolios are taken into consideration — the first one containing only stocks from the London Stock Exchange, the se…

WP334. Value-at-risk — the comparison of state-of-the-art models on various assets

This paper compares different approaches to Value-at-Risk measurement based on parametric and non-parametric approaches. Three portfolios are taken into consideration — the first one containing only stocks from the London Stock Exchange, the second one based on different assets of various origins and the third one consisting of cryptocurrencies. Data used cover the period of more than 20y. In the empirical part of the study, parametric methods based on mean-variance framework are compared with GARCH(1,1) and EGARCH(1,1) models. Different assumptions concerning returns' distribution are taken into consideration. Adjustment for the fat tails effect is made by using Student t distribution in the analysis. One-day-ahead 95%VaR estimation is then calculated. Thereafter, models are validated using Kupiec and Christoffersen tests and Monte Carlo Simulation for reliable verification of the hypotheses. The overall goal of this paper is to establish if analyzed models accurately estimate Value-at-Risk measure, especially if we take into account assets with various returns distribution characteristics.


Kielak Karol, Ślepaczuk Robert
risk management Value-at-Risk GARCH models returns distribution Monte Carlo Simulation asset class cryptocurrencies

WP(21/2020)327. HRP performance comparison in portfolio optimization under various codependence and distance metrics

Authors: Barziy Illya, Chlebus Marcin
Problem of portfolio optimization was formulated almost 70 years ago in the works of Harry Markowitz. However, the studies of possible optimization methods are still being provided in order to obtain better results of asset allocation using the empir…

WP327. HRP performance comparison in portfolio optimization under various codependence and distance metrics

Problem of portfolio optimization was formulated almost 70 years ago in the works of Harry Markowitz. However, the studies of possible optimization methods are still being provided in order to obtain better results of asset allocation using the empirical approximations of codependences between assets. In this work various codependences and metrics are tested in the Hierarchical Risk Parity algorithm to determine whether the results obtained are superior to those of the standard Pearson correlation as a measure of codependence. In order to compare how HRP uses the information from alternative codependence metrics, the MV, IVP, and CLA optimization algorithms were used on the same data. Dataset used for comparison consisted of 32 ETFs representing equity of different regions and sectors as well as bonds and commodities. The time period tested was 01.01.2007-20.12.2019. Results show that alternative codependence metrics show worse results in terms of Sharpe ratios and maximum drawdowns in comparison
to the standard Pearson correlation for each optimization method used. The added value of this work is using alternative codependence and distance metrics on real data, and including transaction costs to determine their impact on the result of each algorithm.


Barziy Illya, Chlebus Marcin
Hierarchical Risk Parity portfolio optimization ETF hierarchical structure clustering backtesting distance metrics risk management machine learning

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

WP(6/2016)197. EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk

Authors: Chlebus Marcin
In the study a proposal of two-step EWS-GARCH models to forecast Value-at-Risk is presented. The EWS-GARCH allows different distributions of returns to be used in Value-at-Risk forecasting depending on a forecasted state of the financial time series.…

WP197. EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk

In the study a proposal of two-step EWS-GARCH models to forecast Value-at-Risk is presented. The EWS-GARCH allows different distributions of returns 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 returns 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 the quality of the Value-at-Risk forecasts was based on the Value-at-Risk forecasts adequacy (the excess ratio, the Kupiec test, the Christoffersen test, the asymptotic test of unconditional coverage and the back-testing criteria defined by the Basel committee) and the analysis of loss functions (the Lopez quadratic loss function, the Abad & Benito absolute loss function, the 3rd version of Caporin loss function and proposed in the study the function of excessive costs). Obtained results indicate that the EWS-GARCH models may improve the quality of the Value-at-Risk forecasts generated using benchmark models. However, the choice of best assumptions for an 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 a model.


Chlebus Marcin
Value-at-Risk GARCH forecasting state of turbulence regime switching risk management risk measure market risk.

WP(1/2016)192. One-Day Prediction of State of Turbulence for Portfolio. Models for Binary Dependent Variable.

Authors: Chlebus Marcin
This paper proposes an approach to predict states (states of tranquillity and turbulence) for a current portfolio in a one-day horizon. The prediction is made using 3 different models for a binary variable (LOGIT, PROBIT, CLOGLOG), 4 definitions of a…

WP192. One-Day Prediction of State of Turbulence for Portfolio. Models for Binary Dependent Variable.

This paper proposes an approach to predict states (states of tranquillity and turbulence) for a current portfolio in a one-day horizon. The prediction is made using 3 different models for a binary variable (LOGIT, PROBIT, CLOGLOG), 4 definitions of a dependent variable (1%, 5%, 10%, 20% of worst realization of returns), 3 sets of independent variables (untransformed data, PCA analysis and factor analysis). Additionally an optimal cut-off point analysis is performed. The evaluation of the models was based on the LR test, Hosmer-Lemeshow test, GINI coefficient analysis and KROC criterion based on the ROC curve. 
Six combinations of assumptions have been chosen as appropriate (any model for a binary variable, the dependent variable defined as 5% or 10% of worst realization of returns, untransformed data, 5% or 10% cut-off point respectively). Models built on these assumptions meet all the formal requirements and have a high predictive and discriminant ability.


Chlebus Marcin
prediction state of turbulence regime switching risk management risk measure market risk.
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