Connectedness between stocks of cryptocurrency-linked US companies and the Cryptocurrency market
Název práce v češtině: | Propojenost mezi akciemi amerických společností spojených s kryptoměnami a kryptoměnovým trhem |
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Název v anglickém jazyce: | Connectedness between stocks of cryptocurrency-linked US companies and the Cryptocurrency market |
Klíčová slova anglicky: | Spillover effects Cryptocurrencies Bitcoin Dynamic Networks Cryptocurrency-linked companies Stock market |
Akademický rok vypsání: | 2021/2022 |
Typ práce: | bakalářská práce |
Jazyk práce: | angličtina |
Ústav: | Institut ekonomických studií (23-IES) |
Vedoucí / školitel: | Mgr. Jan Šíla, M.Sc. |
Řešitel: | skrytý![]() |
Datum přihlášení: | 30.06.2022 |
Datum zadání: | 30.06.2022 |
Datum a čas obhajoby: | 12.09.2023 09:00 |
Místo konání obhajoby: | Opletalova, O105, místnost č. 105 |
Datum odevzdání elektronické podoby: | 01.08.2023 |
Datum proběhlé obhajoby: | 12.09.2023 |
Oponenti: | PhDr. František Čech, Ph.D. |
Seznam odborné literatury |
[1] Härdle, W.K., Harvey, C.R. & Reule, R.C.G., 2020. Understanding Cryptocurrencies. Journal of financial econometrics, 18(2), pp.181-208.
[2] Frankovic, J., Liu, B. & Suardi, S., 2021. On spillover effects between cryptocurrency-linked stocks and the cryptocurrency market: Evidence from Australia. Global finance journal, p.100642. [3] Diebold, F.X. & Yilmaz, K., 2009. Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. The Economic journal (London), 119(534), pp.158-171. [4] Diebold, F.X. & Yilmaz, K., 2012. Better to give than to receive: Predictive directional measurement of volatility spillovers. International journal of forecasting, 28(1), pp.57-66. [5] Diebold, F.X. & Yılmaz, K., 2014. On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of econometrics, 182(1), pp.119-134. [6] Cahill, D. et al., 2020. I am a blockchain too: How does the market respond to companies’ interest in blockchain? Journal of banking & finance, 113, p.105740. [7] Moratis, G., 2021. Quantifying the spillover effect in the cryptocurrency market. Finance research letters, 38, p.101534. [8] Kumar, A. et al., 2022. Connectedness among major cryptocurrencies in standard times and during the COVID-19 outbreak. Journal of international financial markets, institutions & money, 77, p.101523 [9] Baruník, J. & Křehlík, T., 2018. Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk. Journal of financial econometrics, 16(2), pp.271-296. [10] Baruník, J. & Ellington, M., 2020. Dynamic Network Risk, SSRN Electronic Journal. [11] Baruník, J. & Ellington, M., 2021. Dynamic Networks in Large Financial and Economic Systems, SSRN Electronic Journal. [12] Xu, F., Bouri, E. & Cepni, O., 2022. Blockchain and crypto-exposed US companies and major cryptocurrencies: The role of jumps and co-jumps. Finance research letters, 50. [13] Baruník, J., Bevilacqua, M. & Tunaru, R., 2020. Asymmetric Network Connectedness of Fears. The review of economics and statistics, pp.1-41. |
Předběžná náplň práce v anglickém jazyce |
Research question and motivation
The main research question that I would like to study is the connectedness between stocks of companies with high exposure to the cryptocurrency market and the cryptocurrency market itself. The popularity of cryptocurrencies is growing rapidly, still following the trend of the last few years. Thus, it is not surprising that the biggest cryptocurrencies (especially Bitcoin), as well as the underlying technology of blockchain, attracted many companies that in some way started to participate in the crypto market. As a result, a few companies with high exposure to the cryptocurrency market went public in previous years, such as BITF and COIN (both had an IPO on Nasdaq in 2021). But there are also some companies with high crypto exposure that have been historically listed on stock exchanges (e.g. MSTR was listed on Nasdaq in 1998). These companies would provide valuable examples for studying desired effects in a longer timeframe. Studying these effects might be interesting since the crypto sector is known for its volatility and unexplored methods of valuation [1]. Thus, the main motivation lies in contributing to research in the unexplored field of cryptocurrencies. My research could provide useful findings about the valuation and risk management of crypto-exposed companies. In my thesis, I would like to study spillover effects of shocks in returns and volatility between cryptocurrency-linked stocks (CLS) and the crypto market. Previous research [2] studied these effects on publicly listed Australian companies but is missing in the context of crypto. My bachelor thesis would follow conceptually the previous research by examining the effects on US-listed companies and crypto, thus working with a potentially larger dataset, and describing findings about the biggest stock market worldwide. Recent research [12] examined jumps and co-jumps between Blockchain and crypto-exposed US companies and major cryptocurrencies, which further motivates my topic. Contribution The main contribution of this thesis would be the examination of return and volatility spillover effects of the cryptocurrency market and US-listed CLS. I aim to model the dynamics between the volatile crypto market and traditional stocks and drivers of valuation of CLS. By doing so, my thesis would enlarge previous research, which is limited only to Australian companies. However, recent research examined jumps and co-jumps of CLS and cryptocurrencies also in the US. The thesis would also contribute by studying the relatively unexplored field of cryptocurrencies (especially the field of valuation of CLS and examination of volatility effects in the cryptocurrency market). Methodology The work will examine the spillover effect using Dynamic Networks [10,11,13], a novel methodology expanding a widely used Diebold-Yilmaz framework [3,4,5]. The Diebold-Yilmaz framework is a vector autoregression system (VAR system), which is a statistical model used to capture relationships between multiple variables that change over time. Since I aim to study multiple variables, a VAR system is an appropriate choice. Based on published literature [10,11,13], I will build a Dynamic Network system, which models this dynamic and should indicate to us, whether these CLS are driven more by traditional stocks or by the cryptocurrency market. The results will have implications for the valuation and risk management of studied CLS. For my research I would like to use data about several US-listed CLS (e.g., COIN, MSTR, BITF), US stock indices (e.g., S&P 500, NASDAQ), biggest cryptocurrencies (BTC, ETH, BNB, ADA) and crypto-market indices. Outline Abstract Introduction a. introduction into cryptocurrencies b. a brief overview of existing knowledge (research in Australia by J. Frankovic et al. [2]) c. the contribution of my thesis to the field of studies d. main results and the following indications e. structure of the thesis Literature review and hypotheses a. literature on: cryptocurrencies, measuring spillover effects (Diebold-Yilmaz [3],[4],[5]; Dynamic Networks [9],[10],[11]) b. main hypothesis c. motivation of the research (why is it interesting to examine these effects) Methodology a. description of data b. explaining the choice of variables c. explaining the process of examining spillover effects Results a. rejecting / not rejecting hypotheses b. interpretation of the results Conclusion a. detailed interpretation of results b. implications for the real world c. suggested topics for further research that could follow the thesis |