How Does Bitcoin React to Economic Uncertainty Volatility Shocks?
| Název práce v češtině: | Jak Bitcoin reaguje na období zvýšené volatility ekonomické nejistoty? |
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| Název v anglickém jazyce: | How Does Bitcoin React to Economic Uncertainty Volatility Shocks? |
| Klíčová slova: | Bitcoin, ekonomická nejistota, síťová struktura, přelévání volatility |
| Klíčová slova anglicky: | Bitcoin, economic uncertainty, network structure, volatility spillover |
| Akademický rok vypsání: | 2020/2021 |
| Typ práce: | bakalářská práce |
| Jazyk práce: | angličtina |
| Ústav: | Institut ekonomických studií (23-IES) |
| Vedoucí / školitel: | Mgr. Jan Šíla, M.Sc. |
| Řešitel: | skrytý - zadáno vedoucím/školitelem |
| Datum přihlášení: | 27.09.2021 |
| Datum zadání: | 27.09.2021 |
| Datum a čas obhajoby: | 06.09.2022 09:00 |
| Místo konání obhajoby: | Opletalova - Opletalova 26, O105, Opletalova - místn. č. 105 |
| Datum odevzdání elektronické podoby: | 02.08.2022 |
| Datum proběhlé obhajoby: | 06.09.2022 |
| Oponenti: | doc. PhDr. Jozef Baruník, Ph.D. |
| Kontrola URKUND: | |
| Seznam odborné literatury |
| (1) BAKER, Scott R.; BLOOM, Nicholas; DAVIS, Steven J. Measuring economic policy uncertainty. The quarterly journal of economics, 2016, 131.4: 1593-1636.
(2) BARUNIK, Jozef.; KREHLIK, Tomas. Dynamic Network Risk. arXiv:2006.04639, 2020. (3) DEMIR, Ender, et al. Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation. Finance Research Letters, 2018, 26: 145-149. (4) DIEBOLD, Francis X.; YILMAZ, Kamil. Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 2012, 28.1: 57-66. (5) WANG, Gang-Jin, et al. When Bitcoin meets economic policy uncertainty (EPU): Measuring risk spillover effect from EPU to Bitcoin. Finance Research Letters, 2019, 31. (6) WANG, Pengfei, et al. How does economic policy uncertainty affect the bitcoin market?. Research in International Business and Finance, 2020, 53: 101234. |
| Předběžná náplň práce v anglickém jazyce |
| Research question and motivation
I want to study connectedness of economic policy uncertainty represented by the EPU Index and Bitcoin. In 2008, peer-to-peer payment system called Bitcoin was created by a person/group going under the name of Satoshi Nakamoto. The supply of bitcoin is set to 21 million coins and new bitcoins are created approximately every 10 minutes, the number of new bitcoins created is based on predefined number that halves every 4 year. Therefore bitcoin’s monetary policy is considered as predictable. In oppose, fiat money, a state-issued money, has no limit of supply and decisions about its monetary policy are done by central authorities - usually a central bank. Considering all the above, it can be said that monetary policy of a fiat money is more unpredictable than bitcoin’s. Especially in times of uncertainty, central authorities controlling the fiat money can resort to decisions, which can be harmful to holders of the fiat money. Several events of this character happened in the past. It is no surprise that people during these events look for an alternative. This alternative can be Bitcoin. Using Baker et al. (2016) EPU (Economic Policy Uncertainty) Index, which makes possible to quantify changes in economic policy uncertainty, I can investigate the influence of economic policy uncertainty on Bitcoin. Contribution The research focusing on EPU and Bitcoin is relatively scarce. Most of the studies come to the conclusion that Bitcoin can serve as a hedging asset against the uncertainty: Demir et al. (2018) tested the hypothesis whether economic policy uncertainty predicts Bitcoin price during 2010 – 2017 period. The results of their work were that, relationship between EPU and BTC is mainly negative, but positive during extreme times of uncertainty, therefore Bitcoin can potentially serve as a hedging asset against uncertainty. Wang, Gang-Jin et al. (2019), who used EPU Index, equity market uncertainty index and VIX as proxy of EPU conclude that the risk spillover effect from EPU to Bitcoin is negligible in most conditions. This thesis aims to validate these results with different methodology and extended dataset. Also, provide more comprehensive view of the relationship by showing how connectedness between BTC and EPU develops over time and also across different time frequencies. Methodology The data I work with are obtained from Coinmarketcap (BTC) and www.policyuncertainty.com (EPU Index). To describe connection among variables I use Dynamics Networks framework developed by Baruník and Ellington (2020). It extends on Diebold and Yilmaz (2014) connectedness measures based on variance decomposition from vector autoregression model, which allows to determine how much of the future uncertainty in variable j is due to shocks in variable k. I will be particularly interested in the spillover from EPU index to Bitcoin. Baruník and Ellington (2020) uses locally stationary time-varying parameter VAR that estimates the parameters at each point in time, thus the results does not suffer from moving windows problems (one-off shock influences whole moving window period). This is a significant advantage as cryptocurrency markets are known to process information very quickly, hence Dynamics Networks yields more accurate results, which enable to precisely identify time-specific events that influenced the relationship. Also, it is possible to compute confidence intervals to view the magnitude of the effect. Further, Dynamics Networks allows to investigate whether the linkages are created over short-, medium- or long- term and present tools to test for statistical differences among these frequency-dependent network connections. Outline 1. Abstract 2. Introduction 3. Literature review 4. Methodology 5. Results and discussion 6. Conclusion 7. List of references |