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Recent Developments in Cryptocurrency Markets: Co-Movements, Spillovers and Forecasting

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The emergence of Bitcoin and other cryptocurrencies has led to an explosion of trading and speculation in once nontraditional markets [...]
Journal of
Risk and Financial
Management
Editorial
Recent Developments in Cryptocurrency Markets:
Co-Movements, Spillovers and Forecasting
Thanasis Stengos


Citation: Stengos, Thanasis. 2021.
Recent Developments in
Cryptocurrency Markets:
Co-Movements, Spillovers and
Forecasting. Journal of Risk and
Financial Management 14: 91.
https://doi.org/10.3390/jrfm
14030091
Received: 23 February 2021
Accepted: 23 February 2021
Published: 26 February 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
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iations.
Copyright: © 2021 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Department of Economics, University of Guelph, Guelph, ON N1G 2W1, Canada; tstengos@uoguelph.ca
The emergence of Bitcoin and other cryptocurrencies has led to an explosion of trading
and speculation in once nontraditional markets. There is a large number of cryptocurrencies
in existence; see, for example, the website Coin Market Cap, which has a complete list:
https://coinmarketcap.com/all/views/all/. Of those, four stand apart from the rest in
terms of market capitalization and volume. These are Bitcoin, Ethereum, XRP, and Litecoin,
and as of 27 March 2019, their market capitalizations stood at USD 71.9 billion, 17.8 billion,
13.0 billion, and 3.8 billion, respectively. Each of these has its own unique features and
purpose, and even though there is a huge and ever-growing literature on their individual
behavior, there has been considerably less work on investigating their interactions and
interrelationships when taken together as a group. In this Special Issue, the emphasis is
primarily on investigating the relationship between the different cryptocurrencies over
time, by identifying the co-movement patterns, forecasting ability, and leading trends of
individual currencies that cause spillover effects. The papers in the Special Issue bring
together different aspects of the above research questions and relationships from different
perspectives using some of the most up-to-date statistical and econometric techniques.
Below, I will briefly summarize the main points raised by these papers, not necessarily in
the order that they have been published but mostly by their thematic connection.
The papers by Rambaccussing and Mazibas (2020) “True versus Spurious Long Mem-
ory in Cryptocurrencies” and Soylu et al. (2020) “Long Memory in the Volatility of Selected
Cryptocurrencies: Bitcoin, Ethereum and Ripple” examine the behaviour of certain cryp-
tocurrencies by testing for the presence of long memory behavior. The former paper does
not find much evidence in the returns for long memory, and any persistence found in
volatility is borderline nonstationary, while the later paper finds that the squared returns of
three cryptocurrencies have a significant long memory, supporting the use of fractional
generalized auto regressive conditional heteroscedasticity (GARCH) extensions as a suit-
able modelling approach. Similarly, the paper by Jha and Baur (2020) “Regime-Dependent
Good and Bad Volatility of Bitcoin” analyzes high-frequency estimates of the good and bad
realized volatility of Bitcoin and finds that any volatility asymmetry depends on the volatil-
ity regime and the forecast horizon, and compared with stock markets, the persistence and
predictability of the volatility is low. The paper by Ozturk (2020) “Dynamic Connectedness
between Bitcoin, Gold and Crude Oil Volatilities and Returns” examines the connectedness
among Bitcoin, gold, and crude oil between 3 January 2017 and 31 December 2019 based
on the argument that Bitcoin can be similar to gold in terms of its hedging properties and
that it can be used for hedging for different assets. The results indicate that the volatility
connectedness is higher than the return connectedness among these assets, suggesting
that although diversification among these three assets is more difficult in the short- and
medium-term, investors may benefit from diversification in the long run. In a similar
vein, the paper by Hoang et al. (2020) “Does Bitcoin Hedge Commodity Uncertainty?”
examines the connectedness between Bitcoin and commodity volatilities, including those
of oil, wheat, and corn, during the period Oct. 2013–Jun. 2018, using time- and frequency-
domain frameworks, also finding that Bitcoin could be a hedger for commodity volatilities.
Finally, the paper by Kyriazis (2020) “Is Bitcoin Similar to Gold? An Integrated Overview
J. Risk Financial Manag. 2021,14, 91. https://doi.org/10.3390/jrfm14030091 https://www.mdpi.com/journal/jrfm
J. Risk Financial Manag. 2021,14, 91 2 of 3
of Empirical Findings” explores whether Bitcoin can be considered as a globally accepted
asset that has a resemblance to gold, which is widely considered to be the safest choice.
The majority of evidence reveals that Bitcoin has a long way to go before it will acquire the
same characteristics as the safe-haven asset of gold, and even though Bitcoin is found to be
an efficient hedge against oil and stock market indices, it is so to a lesser extent than gold,
which turns out to be a better and more stable safe-haven investment than Bitcoin.
The Vaz de Melo Mendes and Carneiro (2020) paper “A Comprehensive Statistical
Analysis of Six Major Crypto-Currencies from August 2015 through June 2020” presents
a comprehensive statistical analysis of the six most important cryptocurrencies from the
period 2015–2020. Using daily data, their analysis indicates that the strength of the depen-
dence among the cryptocurrencies has increased over the recent years in the co-integrated
crypto market, something that may be of help to investors for managing risk while iden-
tifying opportunities for alternative diversified and profitable investments. Similarly,
Xiao and Sun (2020), in the paper “Forecasting the Returns of Cryptocurrency: A Model
Averaging Approach”, investigate major cryptocurrencies’ return determinants and fore-
cast their returns using methods that deal with model uncertainty. In particular, they
propose a shrinkage Mallows model averaging (SMMA) estimator for forecasting, and
they find that the returns for most cryptocurrencies are sensitive to volatilities from major
financial markets. Deniz and Stengos (2020), in “Cryptocurrency Returns before and after
the Introduction of Bitcoin Futures”, also examined the behaviour of Bitcoin returns and
those of several other cryptocurrencies in the periods before and after the introduction
of the Bitcoin futures market by using a principal-component-guided sparse regression
(PC-LASSO) model to analyze several sample sizes for the before and after periods, and
they found that the top-five cryptocurrencies were substitutes before the launch of Bitcoin
futures. However, this effect was lost, and moreover, there were spillover effects on altcoins
during both the after and the recovery periods. Similarly, Panagiotidis et al. (2020), in the
paper “A Principal Component-Guided Sparse Regression Approach to the Determination
of Bitcoin Returns”, examined the significance of forty-one potential covariates of Bitcoin
returns for the period 2010–2018, and they found that economic policy uncertainty and
stock market volatility are among the most important variables for Bitcoin; they also traced
strong evidence of bubbly Bitcoin behavior in the 2017–2018 period.
The paper by Venter and Maré(2020) “GARCH Generated Volatility Indices of Bitcoin
and CRIX” examines the pricing performance of the GARCH option pricing model when
applied to Bitcoin (BTCUSD) and the implied volatility indices (30, 60 and 90 days) of
BTCUSD and the Cyptocurrency Index (CRIX). The findings suggest that the GARCH
option pricing model produces accurate European option prices when compared to market
prices, and the term structure of the volatility indices indicates that the short-term volatility
(30 days) is generally lower when compared to longer maturities. Finally, Kyriazis (2019),
in “A Survey on Empirical Findings about Spillovers in Cryptocurrency Markets”, offers
a comprehensive survey of the return and volatility spillovers of cryptocurrencies based
on the empirical results of relevant academic literature. The overall evidence reveals that
Bitcoin is the most influential among digital coins, mainly as a transmitter toward digital
currencies but also as a receiver of spillovers from virtual currencies and alternative assets.
This survey provides useful guidance regarding the hotly debated issue of the reform and
decentralization of financial systems.
Funding: This research received no external funding.
Conflicts of Interest: The author declares no conflict of interest.
References
Deniz, Pinar, and Thanasis Stengos. 2020. Cryptocurrency Returns before and after the Introduction of Bitcoin Futures. Journal of Risk
and Financial Management 13: 116. [CrossRef]
Hoang, Khanh, Cuong C. Nguyen, Kongchheng Poch, and Thang X. Nguyen. 2020. Does Bitcoin Hedge Commodity Uncertainty?
Journal of Risk and Financial Management 13: 119. [CrossRef]
J. Risk Financial Manag. 2021,14, 91 3 of 3
Jha, Kislay Kumar, and Dirk G. Baur. 2020. Regime-Dependent Good and Bad Volatility of Bitcoin. Journal of Risk and Financial
Management 13: 312. [CrossRef]
Kyriazis, Nikolaos A. 2019. A Survey on Empirical Findings about Spillovers in Cryptocurrency Markets. Journal of Risk and Financial
Management 12: 170. [CrossRef]
Kyriazis, Nikolaos A. 2020. Is Bitcoin Similar to Gold? An Integrated Overview of Empirical Findings. Journal of Risk and Financial
Management 13: 88. [CrossRef]
Ozturk, Serda S. 2020. Dynamic Connectedness between Bitcoin, Gold, and Crude Oil Volatilities and Returns. Journal of Risk and
Financial Management 13: 275. [CrossRef]
Panagiotidis, Theodore, Thanasis Stengos, and Orestis Vravosinos. 2020. A Principal Component-Guided Sparse Regression Approach
for the Determination of Bitcoin Returns. Journal of Risk and Financial Management 13: 33. [CrossRef]
Rambaccussing, Dooruj, and Murat Mazibas. 2020. True versus Spurious Long Memory in Cryptocurrencies. Journal of Risk and
Financial Management 13: 186. [CrossRef]
Soylu, Pınar Kaya, Mustafa Okur, Özgür Çatıkka¸s, and Z. Ayca Altintig. 2020. Long Memory in the Volatility of Selected Cryptocurren-
cies: Bitcoin, Ethereum and Ripple. Journal of Risk and Financial Management 13: 107. [CrossRef]
Vaz de Melo Mendes, Beatriz, and AndréFluminense Carneiro. 2020. A Comprehensive Statistical Analysis of the Six Major
Crypto-Currencies from August 2015 through June 2020. Journal of Risk and Financial Management 13: 192. [CrossRef]
Venter, Pierre J., and Eben Maré. 2020. GARCH Generated Volatility Indices of Bitcoin and CRIX. Journal of Risk and Financial
Management 13: 121. [CrossRef]
Xiao, Hui, and Yiguo Sun. 2020. Forecasting the Returns of Cryptocurrency: A Model Averaging Approach. Journal of Risk and Financial
Management 13: 278. [CrossRef]
... Though Tanwar et al. noticed interrelationships, they still neglect the importance of Bitcoin. The co-integrated crypto market's strength of dependence among cryptocurrencies has grown in recent years, which may aid investors in risk management while identifying opportunities for alternative diversified and profitable investments [11]. Crypto-coins have been shown to be highly linear and non-linearly correlated [12]. ...
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This paper analyzes high-frequency estimates of good and bad realized volatility of Bitcoin. We show that volatility asymmetry depends on the volatility regime and the forecast horizon. For one-day ahead forecasts, good volatility commands a stronger impact on future volatility than bad volatility on average and in extreme volatility regimes but not across all quantiles and volatility regimes. For 7-day ahead forecasting horizons the asymmetry is similar to that observed in stock markets and becomes stronger with increasing volatility. Compared with stock markets, the persistence and predictability of volatility is low indicating high variations of volatility.
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