Assessing the risk of the European Union carbon allowance market : structural breaks and forecasting performance

Purpose - The purpose of this paper is to examine the impact of structural breaks on the conditional variance of carbon emission allowance prices. Design/methodology/approach - The authors employ the symmetric GARCH model, and two asymmetric models, namely the exponential GARCH and the threshold GARCH. Findings - The authors show that the forecast performance of GARCH models improves after accounting for potential structural changes. Importantly, we observe a significant drop in the volatility persistence of emission prices. In addition, the effects of positive and negative shocks on carbon market volatility increase when breaks are taken into account. Overall, the findings reveal that when structural breaks are ignored in the emission price risk, the volatility persistence is overestimated and the news impact is underestimated. Originality/value - The authors are the first to examine how the conditional variance of carbon emission allowance prices reacts to structural breaks.fi=vertaisarvioitu|en=peerReviewed

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Bitcoin for energy commodities before and after the December 2013 crash: diversifier, hedge or safe haven?

International audienceWe study the relationship between Bitcoin and commodities by assessing the ability of Bitcoin to act as a diversifier, hedge, or safe haven against daily movements in commodities in general, and energy commodities in particular. We focus on energy commodities because energy, in the form of electricity, is an essential input in the Bitcoin production. For the entire period, results show that Bitcoin is a strong hedge and a safe-haven against movements in both commodity indices. We further examine whether that ability is also present for non-energy commodities and our analysis show insignificant results when energy commodities are excluded from the general commodity index. We also account for the December 2013 Bitcoin price crash and our results reveal that Bitcoin hedge and safe-haven properties against commodities and energy commodities are only present in the pre-crash period, whereas in the post-crash period Bitcoin is no more than a diversifier. In addition to uncovering the time-varying role of Bitcoin, we highlight the dissimilarity in the dynamic correlations between the extreme downward and extreme upward movements

Systemic risk spillover across global and country stock markets during the COVID-19 pandemic

Uncovering the tail risk spillover among global financial markets helps provide a more comprehensive understanding of the information transmission in extreme market conditions such as the COVID-19 outbreak. In this paper, we examine systemic distress risk spillover between the global stock market and individual stock markets in the countries most affected by the COVID-19 pandemic. Using two important measures of tail dependence risk: conditional value at risk (CoVaR) and delta conditional VaR (CoVaR), we apply the bivariate dynamic conditional correlation (DCC) conditional autoregressive heteroscedastic (GARCH) model. The empirical results reveal that bivariate systemic risk contagion between the global stock market and each individual stock market evolved during the sample period and intensified as COVID-19 spread worldwide. During the COVID-19 period, the developed markets in Europe and North America transmitted and received more marginal extreme risk to and from the entire global market than Asian stock markets. Further analysis involving the connectedness among the value at risk (VaR) series of the sampled stock market indices and the global stock index, shows a high degree of integration in the extreme downside risk of the stock market system, especially during the COVID-19 period. These findings offer practical implications for regulators, policymakers, and portfolio risk managers during the unprecedented uncertainty period provoked by the COVID-19 pandemic. 2021 Economic Society of Australia, QueenslandScopu

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants