On the determinants of bitcoin returns: A LASSO approach

Panagiotidis, Theodore; Stengos, Thanasis; Vravosinos, Orestis

doi:10.1016/j.frl.2018.03.016

Cited by 181 publications

(132 citation statements)

References 16 publications

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“…Similar to Panagiotidis et al (2018a), we consider the full sample, as well as three sub-periods of it separately: (1) July 21st 2010 -December 10th 2013, (2) December 11th 2013 -March 24th 2017 and (3) March 25th 2017 -May 31st 2018. The selection of these three periods is motivated by (1) the different phases the bitcoin market has been through and (2) the rolling window generalized supremum ADF test for bubbles (GSADF; Phillips et al, 2015) (see Figure 2 in the Appendix).…”

Section: Methods and Datamentioning

confidence: 99%

“…Looking into the fundamental drivers of the Bitcoin and Ethereum price, Corbet et al (2018) test for the existence and dates of pricing bubbles employing the earlier methodology introduced in Phillips et al (2011). the sub-periods obtained in Panagiotidis et al (2018a) under different methods, namely an ADF Breakpoint unit root test.…”

Section: Methods and Datamentioning

confidence: 99%

“…We thus consider far more variables possibly important for bitcoin than in earlier studies (e.g., Panagiotidis et al, 2018a,b). PC-LASSO also exploits the correlation and the group structure of the independent variables by shrinking each group-wise component of the solution towards the leading principal components of that group (e.g., the group of stock market returns variables), which is not the case for standard LASSO employed in Panagiotidis et al (2018a). Doing so it yields results contradicting some of those in Panagiotidis et al (2018a,b).…”

mentioning

confidence: 96%

“…1 We examine the potential covariates of bitcoin returns (e.g., stock market returns, stock market volatility, exchange rates, commodities, central bank rates, internet trends and policy uncertainty). Panagiotidis et al (2018a) have also considered a variety of potentially important variables for bitcoin returns (21 variables in total whereas here we consider 41) employing the Least Absolute Shrinkage and Selection Operator (LASSO). We examine a larger set of potential variables (41) and account for the group structure of the independent variables.…”

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confidence: 99%

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A Principal Component-Guided Sparse Regression Approach for the Determination of Bitcoin Returns

2019

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We examine the significance of fourty-one potential covariates of bitcoin returns for the period 2010-2018 (2,872 daily observations). The principal component-guided sparse regression is employed, introduced by Tay et al. (2018). We reveal that economic policy uncertainty and stock market volatility are among the most important variables for bitcoin. We also trace strong evidence of bubbly bitcoin behavior in the 2017-2018 period.

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Section: Methods and Datamentioning

confidence: 99%

Section: Methods and Datamentioning

confidence: 99%

mentioning

confidence: 96%

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confidence: 99%

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A Principal Component-Guided Sparse Regression Approach for the Determination of Bitcoin Returns

2019

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“…The main problem is how to predict future Bitcoin prices. Panagiotidis et al, (2018) used a Lasso regression approach to analyze twenty-one factors that can have an influence on Bitcoin returns. The authors discovered that uncertainty in the European economic policy, the NIKKEI index and a negative google trend were the main determinants for Bitcoin returns during the analyzed period.…”

Section: Introductionmentioning

confidence: 99%

Price–volume Dependence of Bitcoin and Its Fractal Analysis

Bohdalová

Greguš

2019

CBUP

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Nowadays Bitcoin as cryptocurrency takes a significant place on the global financial markets. This paper analyzes the Bitcoin closing prices and traded volume during the period from December 28, 2013 to January 22, 2019. This period is known as a period with rapid increasing of the Bitcoin closing prices, mainly in the second half of the year 2017. The aim of this paper is twofold. First, we compute the Hurst coefficient to discover the close price dynamics and traded volume using a fractal point of view. We have discovered an anti-persistent behavior in the traded volume and random character of bitcoin closing prices. Second, we propose an analysis of the relationship between the close prices and traded volume. Our findings show how changes in the high-price period differ from changes in the low-price period. We also found that high prices caused investors to be afraid to trade due to possible rapid decrease in bitcoin closing prices. JEL Classification Numbers: C21, G12, D53,

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A note on the determinants of non‐fungible tokens returns

Panagiotidis,

Papapanagiotou

2024

Int. J Fin Econ

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We aim to identify the determinants of non‐fungible tokens non‐fungible tokens (NFTs) returns. The 10 most popular NFTs based on their price, trading volume, and market capitalisation are examined. Twenty‐three potential drivers of the returns of each NFT are considered. We employ a Bayesian LASSO model which takes into account stochastic volatility and leverage effect. The results indicate that NFTs returns are primarily driven by volatility and ethereum returns. We find a weak connection between NFTs returns and conventional assets, such as stock, oil, and gold markets.

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On the determinants of bitcoin returns: A LASSO approach

Cited by 181 publications

References 16 publications

A Principal Component-Guided Sparse Regression Approach for the Determination of Bitcoin Returns

A Principal Component-Guided Sparse Regression Approach for the Determination of Bitcoin Returns

Price–volume Dependence of Bitcoin and Its Fractal Analysis

A note on the determinants of non‐fungible tokens returns

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