Cryptocurrencies during COVID - Marcin Wątorek Cracow University of Technology Faculty of Computer Science and Telecommunications
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Cryptocurrencies during COVID Marcin Wątorek Cracow University of Technology Faculty of Computer Science and Telecommunications
Short money history • Barter - goods or services for other goods or services • Symmetry breaking–> one universal commodity–> money • universal features: durable, homogeneous, rare • Commodity money: grain, salt, tobacco, beads, leather • Coins: bronze, copper, iron, precious metal: gold and silver • Paper money -> fiat currencies -> digital currency (credit cards, Paypal)- loss of intrinsic value • Next stage - cryptocurrencies?
Cryptocurrency idea • 2008 – Bitcoin - Satoshi Nakamoto • transactions without central governing number of publications per year system MW • confidence in the state/central banks Bitcoin(t) replaced by confidence in technology • double spending problem • information about transactions stored in a distributed public ledger -> Blockchain MW P2P network Cryptography PoW
Assymetric cryptography How do cryptocurrencies work? Blockchain P2P network Security: Proof of Work Miners validate transactions, then combine them into blocks and receive a reward for adding a block to the chain (mining process).
Bitcoin vs fiat currencies Purchasing power index of the USD BTC supply Monetary base in US [USD] BTC- fixed max supply – 21 000 000 - algorithm USD – unlimited supply – FED policy hedge against printing money and the depreciation of fiat currencies
Stylized facts observed on mature financial markets: • „fat tails” in return distributions RΔt(t) • volatility clustering • long memory of the autocorrelation function • nonlinear correlations Financial time series • fractality and even multifractality Time RΔt(t)=log(P(t+ Δt))-log(P(t)) P(t) – price in t Random time series with normal distrbution RΔt(t) Time
Comparison of cryptocurrencies with currencies in Forex There are no significant differences in the characteristics of exchange rates for currencies and cryptocurrencies Cummulative distributions of normalized log-reurns Autorrelation function of the normalized R(Δt)=log(P(t+ Δt))-log(P(t)) rΔt=(RΔt-μ)/σ absolute log-returns Autorrelation function of the normalized log-returns 2017-2018 2017-2018
Multifractal spectrum Nonlinear correlations MFCCA/MFDFA: Time series Xk= [x(t1), x(t2),…, x(tn)] 2017-2018 1. Detrending: No scaling in 2. Covariance: previous years 3. Fluctuation function: 4. Scaling: q>0 large fluctuations For single time series: Market maturation Slope of F(q,s) – q Hurst scaling exponent- h(q) fluctuations exponent ≈ 0.5
Bitcoin price changes in US dollar from 2019 Return distributions Multifractal spectra Aug 2019 R(Δt)=log(P(t+ Δt))-log(P(t)) High volatility Mar 2020 clusters Bifractal-like shape of the spectrum related to exteme volatility Δt=1 min Symmetric spectrum during normal periods Date
Cross-correlations between cryptocurrencies and traditional assets in 2018 Pearson correlation coefficient Noise level ( , ) Average Cij= ( ) fluctuations Time scale dependence Large fluctuations Fluctuation size dependence Cryptocurrencies not correlated with traditional assets Can be used as a hedge?
What happened on financial markets in 2020? (1) The first identified case of Covid-19 in the United States (Jan 21, 2020) (2) Global market panic (March 2020) (3) The 2nd wave of the US pandemic (June-Jul 2020) (4) The pandemic slowdown in US, all time highs on S&P500 and NQ (Aug 2020) Time series of bitcoin (BTC), ethereum (ETH) and 20 conventional assets: fiat currencies: Australian dollar (AUD), euro (EUR), British pound (GBP), New Zealand dollar (NZD), Canadian dollar (CAD), Swiss franc (CHF), Chinese offshore yuan (CNH), Japanese yen (JPY), Mexican peso (MXN), Norwegian krone (NOK), Polish zloty (PLN), Turkish lira (TRY), and South African rand (ZAR), stock market indices: Dow Jones Industrial Average (DJIA), NASDAQ100, S&P500, and commodities: gold (XAU), crude oil (CL), silver (XAG), and copper (HG), all expressed in US dollar.
Cross-correlations between BTC and traditional assets in 1 month rolling window Statistically significant cross- correlation started in Jan 2020 (1) Negative ρ(q=4, s=360) between BTC and S&P500, positive with JPY, CHF, and gold. BTC hedge for risky assets? (2) Positive correlations between BTC and all assets except JPY during global panic in March 2020. (3) Positive correlations between BTC and all assets except JPY during the 2nd wave of the US pandemic (June-Jul 2020). (4) BTC positively correlated with all assets, even EUR, CHF and JPY during Covid-19 pandemic slowdown and bull market on all assets expressed in USD.
Central banks policy - true factor behind emergence of correlation between cryptocurrencies and traditional assets? Bitcoin(t) Are cryptocurrencies fulfilling their intended role as a hedge against printing money and the depreciation of fiat currencies?
Network representation of the cryptocurrency market Exchange rates of a form X/BTC, where X stands for one of 128 cryptocurrencies traded on the Binance exchange Minimal spanning tree from ρ(q,s) correlation matrix metric from the correlation coefficient: MST structure represents the strongest connections between cryptocurrencies Centralized MST Decentralized MST Jan 2019 Jan 2019 Central node -> Ethereum Ethereum ρ(q=1,s=10 min) ρ(q=1,s=360 min)
Changes in MST structure in 30-day rolling window ρ(q=1,s=10 min) Jan 2020 ρ(q=1,s=10 min) March 2020 Extreme Average volatility volatility Ethereum Tether dollar 1 USDT~1 USD Centralized MST structure during extreme volatility
Conclusions • Return distributions, autocorrelation function, Hurst exponent for cryptocurrencies are now similar to those observed in mature financial markets • Global market panic in March 2020 affected cryptocurrencies return distributions and caused bifractal-like singularity spectra but this was no different the behavior of traditional markets • Centralized cryptocurrency market network structure and much stronger cross- correlations between the nodes were observed during turbulent periods, especially the sudden dropdowns • Events connected with Covid-19 triggered the emergence of cross-correlations between the major cryptocurrencies and the traditional markets • Cross-correlations occurred not only during the sharp market fall, but also during a recovery phase in the summer 2020 • It seems that the cryptocurrency market have ceased to be an island detached from the traditional markets and become a connected part of the world’s financial markets
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