Conditional tail-risk in cryptocurrency markets

Borri, Nicola

doi:10.1016/j.jempfin.2018.11.002

Cited by 290 publications

(54 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These variables make up the Median Probability Model (MPM). Although the BTC and ETH are correlated, [18], the variables that affect the series (by means of the MPM) are not the same. The MPM of ETH consists of 8 covariates: the USD/EUR and USD/CNY exchange rates, the price of Crude Oil and Gold, the VIX and NASDAQ indices and both quasi-economic variables HR and AVS.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, the proliferation of cryptocurrencies other than BTC that are supported by different technologies, i.e., variations of the standard Proof-of-Work distributed consensus of the BTC blockchain-calls for a more comprehensive research approach. Despite the high documented correlation in the price of the various cryptocurrencies, [18], it is highly debated whether this trend will also continue into future or not, [19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach

Koki

Leonardos

Piliouras

2019

The 3rd Annual Decentralized Conference on Blockchain and Cryptocurrency

View full text Add to dashboard Cite

With Bitcoin, Ether and more than 2000 cryptocurrencies already forming a multi-billion dollar market, a proper understanding of their statistical and financial properties still remains elusive. Traditional economic theories do not explain their characteristics and standard financial models fail to capture their statistic and econometric attributes such as their extreme variability and heteroskedasticity. Motivated by these findings, we study Bitcoin and Ether prices via a Non-Homogeneous Pólya Gamma Hidden Markov (NHPG) model that has been shown to outperform its counterparts in conventional financial data. The NHPG algorithm has good in-sample performance and identifies both linear and non-linear effects of the predictors. Our results indicate that all price series are heteroskedastic with frequent changes between the two states of the underlying Markov process. In a somewhat unexpected result, the Bitcoin and Ether prices, although correlated, are significantly affected by different variables. We compare long term to short term Bitcoin data and find that significant covariates may change over time. Limitations of the current approach—as expressed by the large number of significant predictors and the poor out-of-sample predictions—back earlier findings that cryptocurrencies are unlike any other financial asset and hence, that their understanding requires novel tools and ideas.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach

Koki

Leonardos

Piliouras

2019

The 3rd Annual Decentralized Conference on Blockchain and Cryptocurrency

View full text Add to dashboard Cite

show abstract

“…So, on the basis of the modern portfolio theory, an optimal risk portfolio has been established and the effect of cryptocurrency on the usual investment portfolio of assets has been investigated. The results, obtained in the paper [14], show that the expected return on the cryptocurrency portfolio is greater than the return of separate cryptocurrency. The risk assessment was carried out according to the quantile method, but unlike our research, the distribution of assets return does not determined.…”

Section: Advances In Economics Business and Management Research Volmentioning

confidence: 89%

Cryptocurrency portfolio optimization using Value-at-Risk measure

Hrytsiuk¹,

Babych²,

Bachyshyna³

2019

Proceedings of the 6th International Conference on Strategies, Models and Technologies of Economic Systems Management (SMTESM 2

View full text Add to dashboard Cite

Current research has led to a rejection of the hypothesis of a normal distribution of financial assets returns. Under these conditions, portfolio variance cannot serve as a good risk measure. In this paper analyzed the daily returns of the most common cryptocurrencies: Bitcoin, Bitcoin Cash, Litecoin, XRP, Ethereum, NEM. It is shown that the asset returns are not normally distributed, but with good precision follow the Cauchy distribution. The analytical expressions for risk measure were obtained using the Cauchy distribution function and the VaR technique. The efficient frontiers of cryptocurrencies portfolios were constructed using modified Markowitz model. The purpose of the article is to assess the risks of major cryptocurrencies and to diversify the risk of cryptocurrency investing by applying a portfolio model Keywords-cryptocurrency, portfolio of assets, expected return, risk measure, variance, Value-at-Risk I.

show abstract

“…Bianchi (2020) finds that there is a mild relationship between returns on cryptocurrencies and precious metals, but such a relationship does not translate in volatility spillover effects. Borri (2019) finds that cryptocurrencies are not exposed to tail-risk with respect to other global assets, like U.S. stocks or gold. Weber (2016) explores the similarities and differences between the Bitcoin standard and the gold standard and describes the media of exchange that would exist under the Bitcoin standard.…”

Section: Gold Marketmentioning

confidence: 99%

Cryptocurrency shocks

Liu

Rahman

Serletis

2020

The Manchester School

View full text Add to dashboard Cite

The cryptocurrency market is fast-growing and is highly volatile. By January 2018, the market capitalization of cryptocurrencies reached about 770 billion U.S. dollars at its peak, equivalent to 20 per cent of the narrow money supply (M1) in the United States. Also, the smallest monthly volatility for cryptocurrency is more than the largest monthly volatility for gold and currencies. Considering the potential correlation between cryptocurrency and standard financial markets, the risk that the cryptocurrency imposes on financial markets and investors could be non-negligible-see Liu and Serletis (2019). Moreover, the cryptocurrency has the potential to disrupt traditional payment systems and currencies. It raises questions such as: what is the impact of cryptocurrency market shocks on traditional financial markets? What is the role of cryptocurrency in financial asset pricing?

show abstract

Conditional tail-risk in cryptocurrency markets

Cited by 290 publications

References 22 publications

Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach

Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach

Cryptocurrency portfolio optimization using Value-at-Risk measure

Cryptocurrency shocks

Contact Info

Product

Resources

About