Bitcoin, Litecoin, and the Euro: an annualized volatility analysis

Miglietti, Cynthia; Kubosova, Zdenka; Škuláňová, Nicole

doi:10.1108/sef-02-2019-0050

Cited by 30 publications

(16 citation statements)

References 14 publications

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“…Bitcoin reports the lowest risk measures of all coins, specifically, regarding standard, semi and downside deviation, historical VaR and ES. Similar results by Miglietti et al (2019) indicate that Bitcoin has a rather low-risk profile compared to other cryptocurrencies such as Litecoin. Because of the fact, Bitcoin was the first cryptocurrency launched and, thus, making it the most matured of all, investors highly appreciate it as a safe haven, which has also been empirically proved (Bouri et al, 2017;Chan et al, 2019).…”

Section: Descriptive Statisticssupporting

confidence: 67%

Optimization of special cryptocurrency portfolios

Schellinger

2020

JRF

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Purpose This paper aims to elaborate on the optimization of two particular cryptocurrency portfolios in a mean-variance framework. In general, cryptocurrencies can be classified to as coins and tokens where the first can be thought of as a medium of exchange and the latter accounts for security or utility tokens depending upon its design. Design/methodology/approach Against this backdrop, this empirical study distinguishes, in particular, between pure coin and token portfolios. Both portfolios are optimized by maximizing the Sharpe ratio and, subsequently, compared with alternative portfolio strategies. Findings The empirical findings demonstrate that the maximum utility portfolio of coins, with a risk aversion of λ = 10, outweighs alternative frameworks. The portfolios optimized by maximizing the Sharpe ratio for both coins and tokens indicate a rather poor performance. Testing the maximized utility for different levels of risk aversion confirms the findings of this empirical study and confers them more robustness. Research limitations/implications Further investigation is strongly recommended as tokens represent a new phenomenon in the cryptocurrency universe, for which only a limited amount of data are available, which restricts the sampling. Furthermore, future study is to include more sophisticated optimization models using different constraints in portfolio creation. Practical implications In light of the persistently substantial volatility in cryptocurrency markets, the empirical findings assert that portfolio managers are advised to construct a global minimum variance portfolio. In the absence of sophisticated optimization models, private investors can invest according to the market values of cryptocurrencies. Despite minor differences in the risk and reward ratios of the portfolios tested, tokens tend to be more speculative, especially, if the Tether token is excluded, which may require enhanced supervision and investor protection by regulating authorities. Originality/value As the current literature investigates on diversification effects of blended cryptocurrency portfolios rather than making an explicit distinction, this paper reflects one of the first to explore the investability and role of diversifying coins and tokens using a classic Markowitz approach.

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Section: Descriptive Statisticssupporting

confidence: 67%

Optimization of special cryptocurrency portfolios

Schellinger

2020

JRF

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“…As we know from the previous studies that cryptocurrency markets are highly volatile markets and the price of the crypto’s might change over time (Bouri et al , 2018; Miglietti et al , 2020; Sahoo, 2017). So, the study tries to check the break period of the data series.…”

Section: Empirical Analysismentioning

confidence: 99%

“…However, the COVID-19 relationship in cryptocurrency markets remains unexplored. Bitcoin and other cryptocurrencies are considered as financial asset and highly volatile (Bouoiyour et al , 2014; Bouri et al , 2018; Sahoo, 2017; Kostika and Laopodis, 2019; Hu et al , 2019; Miglietti et al , 2020; Pelster et al , 2019; Sahoo, 2020) and better hedging capabilities over other financial assets such as stocks and US dollar (Dyhrberg, 2016). As a result, during the pandemic, investors have tried to diversify their portfolio toward cryptocurrencies to take short-term gain.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 pandemic and cryptocurrency markets: an empirical analysis from a linear and nonlinear causal relationship

Sahoo

2021

SEF

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Purpose This paper aims to empirically examine the effect of Coronavirus disease 2019 (COVID-19) pandemic on cryptocurrency market returns with particular attention to top five cryptocurrencies and COVID-19 confirmed and death cases. Design/methodology/approach The study applies the linear Toda and Yamamoto and nonlinear Diks and Panchenko Granger causality test to know the causal relationship of cryptocurrencies with COVID-19 pandemic. The study also uses the Narayan and Popp endogenous two structural break tests to capture the break period of the sample. Findings The findings of the study confirm the existence of unidirectional causal relation from COVID-19 confirmed and death cases to cryptocurrency price returns. While examining the break periods, the post-break period result indicates the presence of unidirectional linear causality from COVID-19 confirmed cases to Bitcoin and Ethereum price returns. This shows that prior knowledge of COVID-19 pandemic growth helps to predict the return of cryptocurrencies. Originality/value The study suggests the investors or crypto lovers to observe the growth of COVID-19 situations during their investment in cryptocurrency markets.

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“…Buchholz, et al, (2012) found that before the peak of the bubble, volatility had a statistically significant positive effect on price of Bitcoin. Miglietti, et al, (2019) indicate that the Litecoin is more volatile than Bitcoin. Vardar and Aydogan (2019) reveal the existence of the positive unilateral return spillovers from the bond market to Bitcoin market.…”

Section: Literature Review:-mentioning

confidence: 99%

Forecasting Cryptocurrency Price Movement Using Moving Average Method : A Case Study of Bitcoin Cash

Bakar¹,

Rosbi²,

Uzaki³

2019

IJAR

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The aim of this study is to develop forecasting cryptocurrency price movement using moving average. The cryptocurreny that selected in this study is Bitcoin Cash. The observation periods involved in this study are starting from 1 st October 2019 until 20 th December 2019.The price of Bitcoin Cash are collected from https://www.coindesk.com. The moving average forecasting method implemented using 2-days, 3days, 4-days and 7-days calculation. The value of mean absolute error percentage for 2-days moving average forecasting method is 3.1 %. The significant of this study is it can help investors to determine the price movement of cryptocurrency in selecting best option for investment portfolio.

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Bitcoin, Litecoin, and the Euro: an annualized volatility analysis

Cited by 30 publications

References 14 publications

Optimization of special cryptocurrency portfolios

Optimization of special cryptocurrency portfolios

COVID-19 pandemic and cryptocurrency markets: an empirical analysis from a linear and nonlinear causal relationship

Forecasting Cryptocurrency Price Movement Using Moving Average Method : A Case Study of Bitcoin Cash

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