| Paweł SAKOWSKI |  |
Assistant Professor at Department of Quantitative Finance, University of Warsaw, Faculty of Economic Sciences, Poland
Short bio: Assistant Professor at University of Warsaw, Head of Master in Quantitative Finance program at the University of Warsaw. His research interests concentrate on volatility modeling, algorithmic trading, derivatives pricing and financial time-series analysis. Paweł is also a professional data analyst, statistician and independent statistical consultant. 15+ years of experience in market data analysis focused on financial econometrics and machine learning solutions for high-frequency data. Experience in numerous commercial and academic research projects. He offers specialized courses at the University of Warsaw including C++ in Quantitative Finance, Time-Series Analysis and Machine Learning. He also spent several years in the market research industry, being responsible for developing quantitative tools for segmentation and conducting multivariate data analysis. He is an experienced R/C++ programmer and enthusiast of open source solutions.
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Web application link.
The aim of the study is to check if cryptocurrencies – a new investable asset class – improve performance of an optimal portfolio of equity indices. The strategies are constructed with the most important world equity indices and largest cryptocurrencies (in terms of market capitalization) on daily data for the last six years in the framework of Markowitz (1952). Results are presented in the form of an interactive web application. The user can easily get equity lines of analysed strategies and benchmark portfolios, as well as their performance and risk measures for selected strategy parameters. Our solution also reports historical portfolio composition at every point of time together with weights obtained in the optimization process and presents dynamics of historical correlation between assets from regular and crypto markets. Our application also allows us to perform sensitivity analysis with respect to the length of the historical window, frequency of portfolio rebalancing and degree of financial leverage. This gives a chance for the user to easily manipulate those parameters and to observe how they affect the strategy results. The results presented illustrate the large potential of risk diversification offered by the new class of investable assets. Robustness check confirms the findings and also advocates for the cryptocurrency to be added to the portfolio. Application is deployed in the cloud and the whole process of updating the data and portfolio rebalancing is performed automatically.
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