Detecting instability in the volatility of carbon prices

By Julien Chevallier. 2011

128 citations recorded by the source corpus.

This article investigates the presence of outliers in the volatility of carbon prices. We compute three different measures of volatility for European Union Allowances, based on daily data (EGARCH model), option prices (implied volatility), and intraday data (realized volatility). Based on the methodology developed by Zeileis et al. (2003) and Zeileis (2006), we detect instability in the volatility of carbon prices based on two kinds of tests: retrospective tests (OLS-/Recursive-based CUSUM processes, F-statistics, and residual sum of squares), and forward-looking tests (by monitoring structural changes recursively or with moving estimates). We show evidence of strong shifts mainly for the EGARCH and IV models during the time period. Overall, we suggest that yearly compliance events, and growing uncertainties in post-Kyoto international agreements, may explain the instability in the volatility of carbon prices.

Subfields: Economics and Econometrics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/3552cc89cb3a731844584b7faed2541f0b6bb5ee

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Citations in the corpus

A model of carbon price interactions with macroeconomic and energy dynamics (2011, methodology): “…shown in the Appendix (Table 14), the results reported in this section are robust to the selection of other proxies for macroeconomic performance.40 Overall, our results tend to confirm that the carbon market adjusts to the macroeconomic environment only with a delay (see Chevallier (2009, 2011a)).”
Forecasting carbon futures volatility using GARCH models with energy volatilities (2013, methodology): “However, since the advent of phase II, this restriction has moderated and the amount of the allocation has been adjusted (Chevallier, 2011b).”
Does the Indian Financial Market Nosedive because of the COVID-19 Outbreak, in Comparison to after Demonetisation and the GST? (2020, background): “…Markov process with transition probabilities Pr St ¼ jjSt 1 ¼ ið Þ ¼ Pij;i; j ¼ 1; . . . ;M: The transition probabilities of the Markov Switching process determine the probability of the volatility switching to another regime, and thus the expected duration of each regime (Chevallier, 2011a).”
Dynamics of China’s carbon prices in the pilot trading phase (2017, background): “In terms of economic activities, Chevallier (2009, 2011) show that there is a weak link between macro factors (equity dividend yield and junk bond premium) and carbon prices.”