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.

Please send your paper by e-mail: LCKFy[a.7B'U|*G+dYAgSX18}2N]#[D/=.cJIys/hHpE?uJb3_\=p.+}7 

Please send 2 files:

(1) the main text without the title of the article and the authors (DOC/DOCX file) and

(2) the title page including: the title of the paper, the authors and their affiliation (DOC/DOCX file).

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Number of Results 3

WP(11/2021)359. Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts

Authors: Lis Szymon, Chlebus Marcin
No model dominates existing VaR forecasting comparisons. This problem may be solved by combine forecasts. This study investigates the daily volatility forecasting for commodities (gold, silver, oil, gas, copper) from 2000-2020 and identifies the sour…

WP359. Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts

No model dominates existing VaR forecasting comparisons. This problem may be solved by combine forecasts. This study investigates the daily volatility forecasting for commodities (gold, silver, oil, gas, copper) from 2000-2020 and identifies the source of performance improvements between individual GARCH models and combining forecasts methods (mean, the lowest, the highest, CQOM, quantile regression with the elastic net or LASSO regularization, random forests, gradient boosting, neural network) through the MCS. Results indicate that individual models achieve more accurate VaR forecasts for the confidence level of 0.975, but combined forecasts are more precise for 0.99. In most cases simple combining methods (mean or the lowest VaR) are the best. Such evidence demonstrates that combining forecasts is important to get better results from the existing models. The study shows that combining the forecasts allows for more accurate VaR forecasting, although it's difficult to find accurate, complex methods.


Lis Szymon, Chlebus Marcin
Combining forecasts Econometric models Finance Financial markets GARCH models Neural networks Regression Time series Risk Value-at-Risk Machine learning Model Confidence Set

WP(33/2020)339. The impact of the content of Federal Open Market Committee post-meeting statements on financial markets – text mining approach

Authors: Osowska Ewelina, Wójcik Piotr
This article examines the impact of FOMC statements on stock and foreign exchange markets with the use of text mining and modelling methods including linear and non-linear algorithms. Proposed methodology is based on calculating the FOMC statements&#…

WP339. The impact of the content of Federal Open Market Committee post-meeting statements on financial markets – text mining approach

This article examines the impact of FOMC statements on stock and foreign exchange markets with the use of text mining and modelling methods including linear and non-linear algorithms. Proposed methodology is based on calculating the FOMC statements' tone called as sentiment and incorporate it as a potential predictor in the modelling process. Additionally, we incorporate the market surprise component as well as two financial indicators namely Purchasing Managers' Index and Consumer confidence index that gauge for corporate managers and retail customers assessment of the economic situation and potential fluctuations. Eight event windows around the event are considered: 60-minute and 20-minute windows before the event and also 15-minute, 20-minute, 25-minute, 30-minute, 60-minute and 120-minute windows after the event. Research has shown that given linear models the sentiment of FOMC statements does not generate a significant response in any of the analyzed event windows neither for the S&P 500 Index nor for the spot price on the EUR/USD currency pair. However, significant predictors occurred to be market shock in case of both S&P 500 Index and EUR/USD spot price, PMI in case of EUR/USD spot price and also CCI in case of EUR/USD spot price. Given non-linear models, the negative relation of statement's sentiment score and the model prediction is observed for EUR/USD spot price.


Osowska Ewelina, Wójcik Piotr
FOMC Statements event arbitrage sentiment analysis financial markets prediction

WP(9/2018)268. Momentum and contrarian effects on the cryptocurrency market

Authors: Krzysztof Kość, Sakowski Paweł, Ślepaczuk Robert
We report the results of investigation of the momentum and contrarian effects on cryptocurrency markets. The investigated investment strategies involve 100 (amongst over 1200 present as of date Nov 2017) cryptocurrencies with the largest market cap a…

WP268. Momentum and contrarian effects on the cryptocurrency market

We report the results of investigation of the momentum and contrarian effects on cryptocurrency markets. The investigated investment strategies involve 100 (amongst over 1200 present as of date Nov 2017) cryptocurrencies with the largest market cap and average 14-day daily volume exceeding a given threshold value. Investment portfolios are constructed using different assumptions regarding the portfolio reallocation period, width of the ranking window, the number of cryptocurrencies in the portfolio, and the percent transaction costs. The performance is benchmarked against: (1) equally weighted and (2) market-cap weighted investments in all of the ranked assets, as well as against the buy and hold strategies based on (3) S&P500 index, and (4) BTCUSD price. Our results show a clear and significant dominance of the short-term contrarian effect over both momentum effect and the benchmark portfolios. The information ratio coefficient for the contrarian strategies often exceeds two-digit values depending on the assumed reallocation period and the width of the ranking window. Additionally, we observe a very significant diversification potential for all cryptocurrency portfolios with relation to the S&P500 index.


DOI: http://doi.org.10.26405/WP/WNE/2018/268/009
Krzysztof Kość, Sakowski Paweł, Ślepaczuk Robert
cryptocurrencies bitcoin blockchain momentum effect contrarian effect investment strategy efficiency of financial markets new asset class asset allocation
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