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 4

WP(27/2022)403. What makes Punks worthy? Valuation of Non-Fungible Tokens based on the CryptoPunks collection using the hedonic pricing method

Authors: Plachimowicz Ewelina, Wójcik Piotr
This article focuses on an attempt to value Non-Fungible Tokens from the CryptoPunks collection. Based on the data from January 2021 to July 2021, a hedonic pricing model was built, based on the transaction history and characteristics of a given NFT,…

WP403. What makes Punks worthy? Valuation of Non-Fungible Tokens based on the CryptoPunks collection using the hedonic pricing method

This article focuses on an attempt to value Non-Fungible Tokens from the CryptoPunks collection. Based on the data from January 2021 to July 2021, a hedonic pricing model was built, based on the transaction history and characteristics of a given NFT, as well as external markets variables - cryptocurrency prices (Bitcoin and Ethereum), natural gas prices and the popularity of the collection in social media (Twitter). According to the literature, we decided to build three regression models: Ordinary Least Squares model, XGBoost algorithm and bidirectional Long Short-Term Memory Model. Based on the results, we were able to prove that such complex issues as NFT valuation require more advanced methods than the classical regression model. In addition, we proved that one of the most important categories of variables in the case of NFT valuation is the history of token sales and its characteristics, indicating a particular rarity. Moreover, we have shown that the cryptocurrency and natural gas market is not an important factor in the NFT valuation. Finally, we proved that the increase in the popularity of tokens in social media translates into an increase in NFT prices, and this is an important element when trying to valuate tokens.


Plachimowicz Ewelina, Wójcik Piotr
NFT Non-Fungible Token pricing valuation luxury goods cryptocurrencies

WP(41/2020)347. Verification of Investment Opportunities on the Cryptocurrency Market within the Markowitz Framework

Authors: Sakowski Paweł, Turovtseva Anna
The aim of the paper is to reveal if the classical approach for asset allocation can be reflected on an innovative market of cryptocurrencies. Markowitz rebalanced portfolio technique is employed for this purpose. The filtering of coins for optimizat…

WP347. Verification of Investment Opportunities on the Cryptocurrency Market within the Markowitz Framework

The aim of the paper is to reveal if the classical approach for asset allocation can be reflected on an innovative market of cryptocurrencies. Markowitz rebalanced portfolio technique is employed for this purpose. The filtering of coins for optimization is done on the whole scope of cryptocurrencies available for the time horizon of the study and only 52 coins get into the portfolio at least once. There are four primary strategies produced within a set of assumed optimization parameters together with four benchmarks for each. The benchmarks are Bitcoin buy-and-hold, S&P500 buy-and-hold, equally weighted portfolio and portfolio weighted by market capitalization. While looking at the performance measures, it is concluded that Markowitz strategies outperform their benchmarks for every set of parameters. The results of the sensitivity analysis suggest that there is a big potential in finding profitable strategy of investment on cryptocurrencies. The change of parameters: look-back period, rebalancing window, transaction cost as well as optimization objective impact the strategies performance significantly. Eventually, there appear a strategy in the sensitivity analysis, which performs better than the primary ones due to the prolonged parameters of look-back period and rebalancing window.


Sakowski Paweł, Turovtseva Anna
Portfolio analysis Markowitz framework cryptocurrencies investment strategies asset allocation

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(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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