A Study on Cross－Effects of Prices of Bitcoin, Traditional Assets, and Traditional Currencies

Kim, Jungmu; Naeyoung, Kang; Park, Yuen Jung

doi:10.23839/kabe.2019.34.5.151

Cited by 2 publications

(6 citation statements)

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“…When analyzing the opposite direction, we find that only DXY granger causes Bitcoin at a 5% significance level. This outcome contradicts the previous results in Kim et al (2019). Specifically, they reported a reciprocal Granger causality between VIX and the Bitcoin, while we found a mutual Granger causality between DXY and Bitcoin [5] (Table 9).…”

Section: Jdqscontrasting

confidence: 99%

“…When analyzing the opposite direction, we find that only DXY granger causes Bitcoin at a 5% significance level. This outcome contradicts the previous results in Kim et al. (2019).…”

Section: Empirical Analysiscontrasting

confidence: 99%

“…This finding implied the role of Bitcoin as an alternative investment from retail investors' standpoint. Kim et al (2019) analyzed the mutual relationship between Bitcoin and real, speculative and currency assets using VECM and daily data from July 2010 to April 2018. They found that Bitcoin is more closely associated with the speculative than with real or currency assets.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Kim et al. (2019) analyzed the mutual relationship between Bitcoin and real, speculative and currency assets using VECM and daily data from July 2010 to April 2018.…”

Section: Literature Reviewmentioning

confidence: 99%

“…5. Kim et al . (2019) used the daily data from July 2010 to April 2018, while used monthly data from August 2010 to Feb 2022.…”

Section: Notesmentioning

confidence: 99%

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A VECM analysis of Bitcoin price using time-varying cointegration approach

Lee

Rhee²

2022

JDQS

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This study proposed an optimal model to examine the relationship between the Bitcoin price and six macroeconomic variables – the Bitcoin price, Standard and Poor's 500 volatility index, US treasury 10-year yield, US consumer price index, gold price and dollar index. It also examined the effectiveness of the vector error correction model (VECM) in analyzing the interrelationship among these variables. The authors employed the following approach: first, the authors sampled the period August 2010–February 2022. This is because Bitcoin achieved a market capitalization of more than US$1 tn over this period, gaining market attention and acceptance from retail, corporate and institutional investors. Second, the authors employed a VECM with the six macroeconomic variables. Finally, the authors expanded the long-run equilibrium relationship (time-invariant cointegration)-based VECM to develop a time-varying cointegration (TVC) VECM. The authors estimated the TVC VECM using the Chebyshev polynomial specification based on various information criteria. The results showed that the Bitcoin price can be modeled with the VECM (p = 1, r = 1). The TVC approach generated more explanatory power for Bitcoin pricing, indicating the effectiveness of the approach for modeling the long-run relationship between Bitcoin price and macroeconomic variables.

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Section: Jdqscontrasting

confidence: 99%

“…When analyzing the opposite direction, we find that only DXY granger causes Bitcoin at a 5% significance level. This outcome contradicts the previous results in Kim et al. (2019).…”

Section: Empirical Analysiscontrasting

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…Kim et al. (2019) analyzed the mutual relationship between Bitcoin and real, speculative and currency assets using VECM and daily data from July 2010 to April 2018.…”

Section: Literature Reviewmentioning

confidence: 99%

“…5. Kim et al . (2019) used the daily data from July 2010 to April 2018, while used monthly data from August 2010 to Feb 2022.…”

Section: Notesmentioning

confidence: 99%

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A VECM analysis of Bitcoin price using time-varying cointegration approach

Lee

Rhee²

2022

JDQS

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Does Bitcoin Contribute to Portfolio Performance?

Lim¹,

Kim²,

Han³

2022

Korean J Financ Stud

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This study analyzes the role of Bitcoin as an investment asset in a global multi-asset portfolio. For portfolio construction, we use a risk-based portfolio strategy that excludes estimates of future expected returns. We derive the optimal asset allocation ratio of Bitcoin included in the global multi-asset portfolio and compare the performance with the general portfolio. We find that the average investment weight of Bitcoin in the portfolio is 1.8%, and the portfolio containing Bitcoin shows superior investment performance compared to the portfolio without Bitcoin.

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A Study on Cross－Effects of Prices of Bitcoin, Traditional Assets, and Traditional Currencies

Cited by 2 publications

References 0 publications

A VECM analysis of Bitcoin price using time-varying cointegration approach

A VECM analysis of Bitcoin price using time-varying cointegration approach

Does Bitcoin Contribute to Portfolio Performance?

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