Time series Analysis of Cryptocurrencies Market

Author: Elizaveta Lebedeva

Originally, cryptocurrencies were created to allow fast and secure transactions without any middlemen (such as bank, for example) controlling the creation every additional unit of cryptocurrencies. But in present time, they are mostly used not as a payment method, but as financial assets. And as with any financial asset, it is rational to assume that there are interactions between cryptocurrencies’ returns and volatilities.

Source: Flickr.Namecoin

The field of cryptocurrencies connectedness (that is how price shock of one affects prices of others) is not well discovered since the topic itself is young. Mostly, researchers investigate Bitcoin, and there are very few papers dedicated to altcoins. Among others, I would note the paper by Halaburda and Gandal (2014) which examines competition between 4 coins. Authors explore correlations in daily closing prices and conduct Vector Autoregression analysis to see whether movements in the USD/BTC exchange rate ‘predict’ future changes in other digital currencies. But the size of used data is small, so results seem incomplete.

Since more data is available now, I apply the similar approach to a larger dataset. I use VAR analysis to investigate how cryptocurrency returns interact with each other, conduct the impulse response analysis and the forecast error variance decomposition.

My dataset consists of daily, hourly and 5-min returns for 23 TOP-cryptocurrencies obtained from cryptocompare.com (see the list of coins in the Table below). 

• Daily data - August 1, 2017 to January 18, 2018(171 observations) 

• Hourly prices - October 27, 2017 to January 18, 2018 (2000 observations)

• 5-min prices - January 17 12:25 to January 18 21:45 (400 observations)

Cryptocurrencies used for analysis (based on data from cryptocompare.com, 15.01.2018)

On Figures below, you can see daily, hourly and 5-min returns for selected coins.

Based on data from cryptocompare.com

I start VAR(1) analysis with daily data [1] (all tables are available here). Results show that lag 1 of Bitcoin price return positively affects other prices [2]. BitcoinCash has a different direction of lagged influence (positive effect for Ethereum, Dash, EthereumClassic and negative for Bitcoin, Bytecoin, OmiseGO) and Miota negatively affects returns of other coins on average.

To investigate the dynamic effect of returns, I conduct impulse response analysis. It shows: if there is a shock in Bitcoin price, majority of coins reacts in opposite direction, but after 3-5 days it wears off (figure below).

Orthogonal impulse response takes into account the fact that shocks from coins don’t occur in isolation (comparing to simple impulse response)

In contrast, most of the coins have positive initial response for a shock in Ethereum, which decreases after day 1 (Figure 7). Also, impulse response analysis shows statistically significant results for Ripple, Miota, NEM, etc.

Results of forecast error variance decomposition demonstrate that most of the variance of the forecast errors for coins using daily data comes from its own innovations. In addition: 

• Bitcoin explains about 10-15% of forecast error variance for such cryptocurrencies as Ethereum, NEM, Dash, Monero, Zcash (but interestingly, Bitcoin explain less for BitcoinCash – 6.8% even though it is obvious to expect high impact for forked coin). 
• Ethereum contributes more in the forecast error variance of Ripple (13.4%), Litecoin (23.6%), Monero (25.8%), Ethereum Classic (28.8%), Zcash (31.6%) – that is higher than Bitcoin does. 
• Bytecoin contribute to Siacoin (14.7%) and Doge (12.1%).

To sum up the first part of analysis using daily data: altcoins are influenced more by Ethereum rather that by Bitcoin and there exist internal interactions between TOP-20 cryptocoins besides their connections to the two largest cryptocurrencies.

Now let’s look at VAR(2) model for hourly data.

• Lag 1: returns of Bitcoin, Lisk, BitShares have negative effect on returns of other cryptocurrencies on average, Ethereum has positive significant effect on Litecoin, Dash, Zcash, OmiseGO;

• Lag 2: coefficients of Ethereum are negative in the equations for Bitcoin, Litecoin, NEM, Stellar, Miota, Monero, Lisk, Zcashs, Bitecoin, BitShares. Coefficients for Bitcoin are smaller and statistically significant in less number of equations. Among others, I would note VeChain with its negative effect on Ethereum, BitcoinCash, Litecoin, NEM, Stellar.

I also perform impulse response analysis which provides similar results to daily specification. Looking at the forecast error variance decomposition: as for daily data, most of the variance of the forecast errors for coins come from its own innovations. But the difference is that now Bitcoin shocks are leading in contributing to forecast error variance for other cryptocurrencies (20-30%), compared to Ethereum (10-15%). Shares of other altcoins are less than 1% on average. 

Thus, based on VAR(2) analysis for hourly data, Bitcoin and Ethereum play the central role in explaining the forecast error variance of cryptocurrencies.

Next, I estimate VAR(2) for 5-min data. Main things to notice:

• While lag 1 of Bitcoin return has positive effect on return of other coins (statistically significant for Stellar, Monero, EthereumClassic, VeChain and EOS), lag 2 influences in opposite direction;

• Lag 1 and lag 2 of Ethereum are statistically significant only in Dash equation (positive effect);

• Both lags of NEM provide negative influence on coins, Litecoin and Stellar lag 1 coefficients show negative effect on Bitcoin, BitcoinCash, Miota, Monero.

Forecast error variance decomposition shows that Bitcoin contributes to more than 30% to forecast error variance for the majority of coins, reaching 60-70% for Ethereum, BitcoinCash, EthereumClassic, EOS. The coins that are not affected by such high influence are Lisk, VeChain, Veritaseum and Verge. Forecast error variance of currencies explained by Ethereum is about 1-3% on average, which is much less than was observed in daily and hourly data.

Most probably, such results are due to data and the situation on the market itself that was happening on January, 17 (South Korea announced possible bans of cryptocurrencies trading). During this shock, Bitcoin and Ethereum lost about 20% and 30% of its value respectively, as well as other coins. Anyway, this example also provides useful insights: analysis of the shock time period shows that Bitcoin remains the most central player on cryptocurrencies market, and even Ethereum still cannot reach it.

To sum up:

Estimated models suggest that within daily periodicity, there are more interactions between cryptocurrencies returns, meaning that altcoins can affect and influence each other. In contrast, for hourly specification, the impact of Bitcoin increases and, it contributes 3-4 or even 5 times more explaining other coins variances compared to the daily model. The example of VAR modelling during shock period exhibits strong central role of Bitcoin at cryptocurrencies market. Thus, regarding chosen strategy (long-term HODL or short-term trading), investors should take into account existing connectedness of coins market.

Presented analysis can be considered as a preliminary work for future research where one can apply more advanced models which would provide deeper insights into cryptocurrencies market networks.

                                             

[1] - Choosing between VAR(1) and VAR (2), AIC for lag 2 is less, but considering higher degrees of freedom, lag 1 is more preferable

[2] - Explanations are given for statistically significant coefficients at least at confidence level 90%

Invisible hand of blockchain

Author: Yuliia Puzanova

Can you imagine that your laptop or smartphone will participate in blockchain very soon? Yes, you, probably, observe recent years a lot of discussions about cryptocurrency, Bitcoin especially, the blockchain, mining and how different governments express their opinion regarding the legalization of mining, cryptocurrency, and blockchain overall.  

The future of blockchain technology and the Bitcoin should be considered separately. The blockchain is promising technology which can be used in many spheres, while the future of Bitcoin is ambiguous. There are some skeptics who do not believe in a good future of Bitcoin. Especially, Cecilia Skingsley, the Swedish Central Bank Deputy Governor, expressed her point of view at World Economic Forum in Davos this year against Bitcoin as a currency and said that it can be possibly used only as an asset. One of the reasons for this statements is its high volatility (Figure 1). Moreover, The Nobel laureate economist Robert Shiller also said that he tends ‘to think of Bitcoin as an interesting experiment, it’s not a permanent feature of our lives’ and ‘we should broaden it out to blockchain, which will have other applications’ and emphasized several times that it seems to be the next bubble in financial system rather than a future for it. In this blog post, we will try to figure out the main aspects of blockchain technology and how different countries are preparing for this future.

Figure 1. Average USD market price across major Bitcoin exchanges

Data source: blockchain.info

The blockchain is not only about cryptocurrency. It is a transparent, credible, and immutable record of transactions. Put simply, it remembers all operations which were made with information contained in some storage. This data repository is not located just inside of one particular computer but is copied among millions of them. Blockchain technology provides the decentralized database which contains blocks with data and it is impossible to change it insensibly. Block can contain the following information: a vote, birth certificates, titles of ownership, money, contracts etc. Also, blockchain allows making a transaction without any intermediate party. For example, in a case of money transfer, we should include now the financial institution between sender and receiver of money, the third trusted party could be eliminated using blockchain, no need to rely on the governmental or financial institution to oversee the transaction or confirm the property rights. That is why blockchain is a very promising investment last 5 years and it continues to accumulate investments (Figure 2).

Figure 2. Blockchain annual global financing. 

Equity funding (excl. initial coin offerings)

Data source: CBINSIGHTS

In WEF Report ‘Realizing the Potential of Blockchain’, Don Tapscott and Alex Tapscott wrote that ‘this second era will be powered by a clever combination of cryptography, mathematics, software engineering and behavioural economics’ and some governments are preparing actively for this new era and trying to implement technology in some of the possible directions: contract, asset or identity management, financial transaction management and even voting. 

Estonia can be fairly claimed as a leading blockchain nation. Estonians data is decentralized and distributed ledger cannot be erased or changed as Kaspar Korjus, e-Residency Managing Director, stated in the blog post. But blockchain is not only used to make Estonians life clear and open. We also have to remember about economic benefits. In 2014, Estonia launched the e-Residency program. Its aim is to give a possibility to anyone receive a digital ID secured on blockchain to get an access to Estonian online services for registering a business, to manage it remotely, declare taxes and have an access for banking and international online payment services. As a result, 4272 companies from 143 countries¹ were established in Estonia and brought to the country EUR 2.5 million (as at end of October 2017) total direct taxes, according to the analysis conducted by Deloitte². By 2025 it is expected to reach nearly 1 million e-Residents and each company can bring to Estonia approximately EUR 70,000 of direct (state fees and taxes) and indirect (job creation, incremental demand, increased administrative efficiencies and country brand) net profit, as reported by Deloitte. Moreover, in prospect, each euro invested for the program development will bring 100 euro of the total return. Summary of the assumptions behind direct benefits and cost base conducted by Deloitte Estonia in 2017 is represented in Figure 3. 

Figure 3. e-Residency financial analysis

Data source: 'Key Stakeholders’ Report. Estimated Economic Impact Analysis of the Estonian E-Residency Programme', Deloitte Estonia, 2017

Georgia also made a breakthrough using blockchain. In 2016, the country launched a project with The Bitfury Group and the Blockchain Trust Accelerator for registering the land titles in blockchain to eliminate the manipulation of the property registration data. Moreover, in 2017 a new agreement with Bitfury about the usage of blockchain technology to other government departments was announced. Marc Taverner of Bitfury said at the Legal Tech conference that new registration system helps to ‘have the whole process done in 10 minutes instead of three days’ and to reduce operational costs up to 90%. Furthermore, blockchain was applied for land registry in Ghana, Sweden, and Honduras. 

Not only mentioned countries are testing new technology. China, Canada, the United Kingdom, Brazil, USA, and others are running pilot blockchain-based applications for government services (Figure 4).

Figure 4: Blockchain in the public sector (as of March 2017)

Source: Deloitte analysis in conjunction with the Fletcher School at Tufts University.

There are many of experts’ views regarding the future of blockchain. IBM Institute for Business Value and The Economist Intelligence Unit conducted a research on the basis of 200 surveys of government leaders in 16 countries and according to Executive Report 'Building trust in government. Exploring the potential of blockchains' 90% of surveyed government executives plan to invest in blockchain technology for applying in financial transactions, asset and contract management, regulatory compliance by 2018. 

One thing is clear: we cannot count out the transformational influence of blockchain technology on economic development. However, the future impact of it is difficult to be evaluated due to not very long track record. Most of the projects presented now are as a trial or in a beta testing stage.

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^{[1] https://e-resident.gov.ee/↩
[2] "Key Stakeholders’ Report. Estimated Economic Impact Analysis of the Estonian E-Residency Programme". Deloitte Estonia, December 2017 (provided by e-Residency)↩}