ABSTRACT
Although the spillover effects of return and volatility risk across commodity markets have been demonstrated, evidence of extreme risk spillovers is limited. Using an autoregressive conditional density model, this study estimates the conditional skewness of nine S&P Goldman Sachs Commodity indices and then applies the Diebold-Yilmaz TVP-VAR-based approach to investigate the higher moment spillovers across commodity markets. Our findings provide evidence of extreme risk transfers from one commodity index to another. Among three energy indices including crude oil, natural gas and gasoil, crude oil transmits the most return, volatility risk and extreme risk to the agricultural indices and precious metal indices. Furthermore, our results confirm that spillovers in all three moments were significantly strengthened by extreme events such as the September 11 attacks, the global financial crisis, the food price crisis, the violent shock of international oil prices and the coronavirus disease of 2019. However, different events may have different impacts on spillovers. Finally, the results indicate that return spillover and skewness are affected by extreme events with almost the same intensity and direction for most periods.
ABSTRACT
PurposeThe purpose of this research is to analyze the Bitcoin (BTC) and Ether (ETH) long memory and conditional volatility.Design/methodology/approachThe empirical approach includes ARFIMA-HYGARCH and ARFIMA-FIGARCH, both models under Student‘s t-distribution, during the period (ETH: November 9, 2017 to November 25, 2021 and BTC: September 17, 2014 to November 25, 2021).FindingsFindings suggest that ARFIMA-HYGARCH is the best model to analyze BTC volatility, and ARFIMA-FIGARCH is the best approach to model ETH volatility. Empirical evidence also confirms the existence of long memory on returns and on BTC volatility parameters. Results evidence that the models proposed are not as suitable for modeling ETH volatility as they are for the BTC.Originality/valueFindings allow to confirm the fractal market hypothesis in BTC market. The data confirm that, despite the impact of the Covid-19 crisis, the dynamics of BTC returns, and volatility maintained their patterns, i.e. the way in which they evolve, in relation to the prepandemic era, did not change, but it is rather reaffirmed. Yet, ETH conditional volatility was more affected, as it is apparently higher during Covid-19. The originality of the research lies in the focus of the analysis, the proposed methodology and the variables and periods of study.
ABSTRACT
PurposeThe purpose of this research is to analyze the Bitcoin (BTC) and Ether (ETH) long memory and conditional volatility. Design/methodology/approachThe empirical approach includes ARFIMA-HYGARCH and ARFIMA-FIGARCH, both models under Student's t-distribution, during the period (ETH: November 9, 2017 to November 25, 2021 and BTC: September 17, 2014 to November 25, 2021). FindingsFindings suggest that ARFIMA-HYGARCH is the best model to analyze BTC volatility, and ARFIMA-FIGARCH is the best approach to model ETH volatility. Empirical evidence also confirms the existence of long memory on returns and on BTC volatility parameters. Results evidence that the models proposed are not as suitable for modeling ETH volatility as they are for the BTC. Originality/valueFindings allow to confirm the fractal market hypothesis in BTC market. The data confirm that, despite the impact of the Covid-19 crisis, the dynamics of BTC returns, and volatility maintained their patterns, i.e. the way in which they evolve, in relation to the prepandemic era, did not change, but it is rather reaffirmed. Yet, ETH conditional volatility was more affected, as it is apparently higher during Covid-19. The originality of the research lies in the focus of the analysis, the proposed methodology and the variables and periods of study.
ABSTRACT
Purpose The study's purpose is to investigate the price volatility of four dairy commodities (skim milk powder [SMP], whole milk powder [WMP], butter and cheddar cheese) in the three most significant regional markets (EU, Oceania and US) in the international dairy market. Design/methodology/approach The study uses a panel-Generalized Autoregressive Conditional Heteroskedastic (panel-GARCH) modeling technique and data from January 12, 2001, to April 28, 2017. Findings Conditional volatility was higher during subperiods 2007-2010 and 2014-2016 when conditional cross-correlations between prices had the lowest values. In some cases, they were negative (i.e. between the EU and the USA and between Oceania and the USA for both butter and cheese). Interdependence across the three dairy markets, especially for SMP and WMP markets and for the butter market between EU and Oceania is also strongly evidenced. Interdependence is responsible for the spillover of price shocks across the three regions. Research limitations/implications The data set used should be extended to cover the COVID-19 pandemic period. Originality/value This is the first study to use panel-GARCH to examine international dairy prices and volatility linkages, where previous studies mainly used multivariate GARCH models. Panel-GARCH allows a high-dimensional data series (i.e. 12 dairy prices) and generates potential efficiency gains in estimating conditional variances and covariances by incorporating information about heterogeneity across markets and considering their interdependence.
ABSTRACT
This paper analyzes the role of COVID-19 pandemic crisis in determining and forecasting conditional volatility returns for a set of eight cryptocurrencies through an asymmetric GARCH modeling approach. The findings report that the COVID-19 pandemic exerts a positive effect on the conditional volatility of those returns, while explicitly considering the pandemic event improves volatility predictions.