Gold, platinum, and expected Bitcoin returns

Huynh, Toan Luu Duc; Burggraf, Tobias; Wang, Mei

doi:10.1016/j.mulfin.2020.100628

Cited by 31 publications

(19 citation statements)

References 71 publications

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“…We explain the link between cryptocurrency and gold prices via the aggregate market risk. The study by Huynh et al (2020) has indicated that the gold may play a proxy role for economic risk which causes the movements in cryptocurrencies, particularly in the Bitcoin. This mechanism explains how gold and cryptos prices are strongly connected.…”

Section: Methodology - Transfer Entropymentioning

confidence: 99%

“…Therefore, the interconnectedness between these cryptocurrencies and gold is intuitive. Furthermore, the study by Huynh et al (2020) has suggested that the investors tend to move their capital from the risky assets to the safer investment options when the market shakes. Therefore, the coins which are generated to refer to gold trading might suffer the 'flight-to-quality'.…”

Section: Methodology - Transfer Entropymentioning

confidence: 99%

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“Small things matter most”: The spillover effects in the cryptocurrency market and gold as a silver bullet

Huynh

Nasir

et al. 2020

The North American Journal of Economics and Finance

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Highlights - Cryptos with small market capitalization are more likely to be sources of shocks than their larger counterparts. - Bitcoins can be considered as a better hedge due to its relative independence. - USDT strong anchoring with US$ makes it very volatile. - The idiosyncrasy of Gold enables it to weather adverse crypto market’s movements. - Having gold in the portfolio with cryptocurrency helps fruitful diversification.

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Section: Methodology - Transfer Entropymentioning

confidence: 99%

Section: Methodology - Transfer Entropymentioning

confidence: 99%

“Small things matter most”: The spillover effects in the cryptocurrency market and gold as a silver bullet

Huynh

Nasir

et al. 2020

The North American Journal of Economics and Finance

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“…The related area to the first is pricing formation of cryptocurrencies and other possible measurement such as ratio of gold to platinum (GP) prices forecast Bitcoin return, e.g. Huynh et al [ 24 ]. The second area lies on cryptocurrencies empirical facts like other returns along with return-volatility correlation, volatility clustering or asymmetric volatility, see e.g.…”

Section: Introductionmentioning

confidence: 99%

Modeling risk dependence and portfolio VaR forecast through vine copula for cryptocurrencies

Syuhada

Hakim

2020

PLoS ONE

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Risk in finance may come from (negative) asset returns whilst payment loss is a typical risk in insurance. It is often that we encounter several risks, in practice, instead of single risk. In this paper, we construct a dependence modeling for financial risks and form a portfolio risk of cryptocurrencies. The marginal risk model is assumed to follow a heteroscedastic process of GARCH(1,1) model. The dependence structure is presented through vine copula. We carry out numerical analysis of cryptocurrencies returns and compute Value-at-Risk (VaR) forecast along with its accuracy assessed through different backtesting methods. It is found that the VaR forecast of returns, by considering vine copula-based dependence among different returns, has higher forecast accuracy than that of returns under prefect dependence assumption as benchmark. In addition, through vine copula, the aggregate VaR forecast has not only lower value but also higher accuracy than the simple sum of individual VaR forecasts. This shows that vine copula-based forecasting procedure not only performs better but also provides a well-diversified portfolio.

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“…The main criteria of selecting these cryptocurrencies were that they had the highest market capitalizations in all 30 cryptocurrencies on November 1, 2020, and that they were also continuous listings and on the CCI30 during the pre-COVID-19 and COVID-19 periods as defined by March 11, 2020 [the day the WHO declared COVID-19 as a global pandemic (WHO 2020)]. As suggested by Alexander and Dakos ( 2020 ) and Huynh et al ( 2020a ), the validity of the data set was checked using different data sources for crypto prices. Table 1 provides an overview of the variables, their abbreviations, and their data sources.…”

Section: Data Descriptionmentioning

confidence: 99%

Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods

Neslihanoğlu

2021

Financ Innov

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This research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods. Two extensions are offered to compare the performance of the linear specification of the market model (LMM), which allows for the measurement of the cryptocurrency price beta risk. The first is the generalized additive model, which permits flexibility in the rigid shape of the linearity of the LMM. The second is the time-varying linearity specification of the LMM (Tv-LMM), which is based on the state space model form via the Kalman filter, allowing for the measurement of the time-varying beta risk of the cryptocurrency price. The analysis is performed using daily data from both time periods on the top 10 cryptocurrencies by adjusted market capitalization, using the Crypto Currency Index 30 (CCI30) as a market proxy and 1-day and 7-day forward predictions. Such a comparison of cryptocurrency prices has yet to be undertaken in the literature. The empirical findings favor the Tv-LMM, which outperforms the others in terms of modeling and forecasting performance. This result suggests that the relationship between each cryptocurrency price and the CCI30 index should be locally instead of globally linear, especially during the COVID-19 period.

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Gold, platinum, and expected Bitcoin returns

Cited by 31 publications

References 71 publications

“Small things matter most”: The spillover effects in the cryptocurrency market and gold as a silver bullet

“Small things matter most”: The spillover effects in the cryptocurrency market and gold as a silver bullet

Modeling risk dependence and portfolio VaR forecast through vine copula for cryptocurrencies

Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods

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