Working Papers

Seria wydawnicza WNE WORKING PAPERS prowadzona jest przez WNE UW od 2008 r. i jak dotąd opublikowano w niej ponad 400 prac.

Seria WNE Working Papers służy szybkiemu upowszechnianiu wyników badań prowadzonych na Wydziale Nauk Ekonomicznych UW. Publikowane tu teksty mają charakter wersji roboczych (pre-printów): mogą być uzupełniane lub poprawiane w miarę postępu prac nad tekstem i procesu wydawniczego. Prezentujemy aktualną wersję udostępnioną przez autorów, opatrzoną datą oraz identyfikatorami DOI/ISSN – co ułatwia jej cytowanie i śledzenie kolejnych iteracji. Każdy dokument zachowuje pełne prawa autorskie swoich twórców, a w przypadku późniejszych zmian, autorzy mogą w dowolnym momencie zamieścić zaktualizowany plik lub wskazać miejsce ostatecznego wydania.

Do serii WORKING PAPERS przyjmowane są artykuły pracowników naukowych WNE UW oraz publikacje z konferencji organizowanych na WNE UW. Artykuły powinny dotyczyć ekonomii, mieć charakter oryginalnych prac badawczych, nie być wcześniej publikowane. Przyjmowane są wyłącznie teksty w języku angielskim.

Prace prosimy przesyłać e-mailem na adres: SVtR7#N\z^4%X_BE!D`Zv}iq$+e]#[KFg={t2M\OttLv60eMVR!cNg/wR

Prosimy przesyłać dwa pliki:

(1) główny tekst bez podania tytułu artykułu oraz autorów (plik DOC/DOCX) oraz

(2) stronę tytułową zawierającą: tytuł opracowania, autorów oraz ich afiliację (plik DOC/DOCX). 

Przed przesłaniem tekstu prosimy zapoznać się z szczegółowymi wymogami edycyjnymi.

Redaktorem wydawniczym serii Working Papers jest dr Maciej Bukowski.

Ilość wyników 4

WP(42/2020)348. Does Bitcoin Improve Investment Portfolio Efficiency?

Autorzy: Sakowski Paweł, Turovtseva Daria
The aim of the paper is to check if cryptocurrency Bitcoin – a new investable asset class representative – is able to improve the performance of an optimal portfolio. Using two Markowitz criteria of optimization – expected return ma…

WP348. Does Bitcoin Improve Investment Portfolio Efficiency?

The aim of the paper is to check if cryptocurrency Bitcoin – a new investable asset class representative – is able to improve the performance of an optimal portfolio. Using two Markowitz criteria of optimization – expected return maximization and expected shortfall (CVaR) minimization – we test the investment opportunities after adding Bitcoin to the portfolio of 10 traditional assets (among them equity, fixed income, money, commodities and money market indices). Using daily observations from 1.05.2013 till 24.05.2019, we examine the behavior of the portfolios without and with Bitcoin and check if the return-risk ratio improves for the latter. Discussing the results, we conduct the sensitivity analysis by changing the lookback window (LB) and rebalancing frequency (RB) parameters. Empirical analysis suggests that Bitcoin-inclusive portfolios provide an investor with wider diversification opportunities. Robustness check confirms the findings and also advocates for the cryptocurrency to be added to the portfolio.


Sakowski Paweł, Turovtseva Daria
Portfolio optimization portfolio theory cryptocurrency Bitcoin Markowitz model asset allocation portfolio diversification investment opportunities

WP(15/2019)300. Lévy processes on the cryptocurrency market

Autorzy: Zięba Damian
Lévy processes are very often used in financial modelling since they address various characteristics of financial data. One of those characteristics is the heavy-tailedness of probability density functions - a very common empirical stylized fa…

WP300. Lévy processes on the cryptocurrency market

Lévy processes are very often used in financial modelling since they address various characteristics of financial data. One of those characteristics is the heavy-tailedness of probability density functions - a very common empirical stylized fact on the cryptocurrency market. The aim of this study was to determine which type of Lévy motion fits the data of cryptocurrencies better, namely Alpha-Stable distribution or one of distributions from the family of generalized hyperbolic motions. The log-returns of 227 cryptocurrencies, standardized by the realized volatility estimated with the GARCH (1,1), were fitted to 11 types of distributions. The results show that the generalized hyperbolic motions fit the cryptocurrency data much more accurately than the Alpha-Stable distribution, similarly as in the case of TOP100 NASDAQ stocks. In the further stage of the analysis, it is shown how the distribution of cryptocurrency data varies over time, i.e. before, during, and after the 'boom-period' of 2017/2018.


Zięba Damian
cryptocurrency market distribution fitting Generalized Hyperbolic distribution Alpha-Stable distribution Lévy process

WP(2/2019)287. Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

Autorzy: Zenkova Maryna, Ślepaczuk Robert
This study investigates the profitability of a algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns ar…

WP287. Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

This study investigates the profitability of a algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns are in the highest or lowest quantile. Each cryptocurrency is represented by a set of six technical features. SVM is trained on historical tail sets and tested on the current data. The classifier is chosen to be a nonlinear support vector machine. Portfolio is formed by ranking coins using SVM output. The highest ranked coins are used for long positions to be included in the portfolio for one reallocation period. The following metrics were used to estimate the portfolio profitability: %ARC (the annualized rate of change), %ASD (the annualized standard deviation of daily returns), MDD (the maximum drawdown coefficient), IR1, IR2 (the information ratio coefficients). The performance of the SVM portfolio is compared to the performance of the four benchmark strategies based on the values of the information ratio coefficient IR1 which quantifies the risk-weighted gain. The question on how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.


Zenkova Maryna, Ślepaczuk Robert
machine learning support vector machines investment algorithm algorithmic trading strategy optimization cross-validation overfitting cryptocurrency market technical analysis meta parameters

WP(17/2018)276. Are demand shocks in Bitcoin contagious?

Autorzy: Zięba Damian, Śledziewska Katarzyna
The main aim of this paper is to examine interdependencies between prices of cryptocurrencies, with the special focus on Bitcoin. The analysis is conducted in two stages and results are compared between two consequent sub-periods. In order to analyze…

WP276. Are demand shocks in Bitcoin contagious?

The main aim of this paper is to examine interdependencies between prices of cryptocurrencies, with the special focus on Bitcoin. The analysis is conducted in two stages and results are compared between two consequent sub-periods. In order to analyze topological properties of cryptocurrency market, the Minimum-Spanning Tree technique is used. Results indicate that Bitcoin plays one of the most important roles in the cryptocurrency market, while other cryptocurrencies form clusters and such forming has a sensible economic interpretation. In the second stage, main cryptocurrencies from each of formed clusters are analyzed using the Vector Autoregression methodology. The results from VAR (1) indicate that demand shocks in Bitcoin price are not contagious to other cryptocurrencies, while some interdependencies within the formed clusters may be observed. Overall, results indicate that conclusions drawn from the analysis of Bitcoin shall not be generalized to the entire cryptocurrency market.


DOI: https://doi.org.10.26405/WP/WNE/2018/276/017
Zięba Damian, Śledziewska Katarzyna
cryptocurrency market Bitcoin Minimum-Spanning Tree Vector Autoregression
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