On the asymmetric impact of macro–variables on volatility

Amendola, Alessandra; Candila, Vincenzo; Gallo, Giampiero M.

doi:10.1016/j.econmod.2018.07.025

Cited by 47 publications

(24 citation statements)

References 35 publications

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“…Thus, a possible limitation of our work is that we do not take into account any possible impacts of other volatility determinants in the relationship between series j and i. Therefore, a third possible extension to this work could be evaluating the presence of volatility spillovers including the Industrial Production as an additional volatility determinant, in the spirit of Engle et al (2013) and Amendola et al (2018), among others.…”

Section: Discussionmentioning

confidence: 99%

On the Volatility Spillover between Agricultural Commodities and Latin American Stock Markets

Candila

Farace

2018

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Addressing the volatility spillovers of agricultural commodities is important for at least two reasons. First, for the last several years, the volatility of agricultural commodity prices seems to have increased. Second, according to the Food and Agriculture Organization, there is a strong need for understanding the potential (negative) impacts on food security caused by food commodity volatilities. This paper aims at investigating the presence, the size, and the persistence of volatility spillovers among five agricultural commodities (corn, sugar, wheat, soybean, and bioethanol) and five Latin American (Argentina, Brazil, Chile, Colombia, Peru) stock market indexes. Overall, when a negative shock hits the commodity market, Latin American stock market volatility tends to increase. This happens, for instance, for the relationships from corn to Chile and Colombia and from wheat to Peru and Chile.

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Section: Discussionmentioning

confidence: 99%

On the Volatility Spillover between Agricultural Commodities and Latin American Stock Markets

Candila

Farace

2018

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“…distributed variables with mean zero and unit variance and ℎ , is the conditional standard deviation for series . The GARCH specification to model the conditional variance ℎ , 2 for the series is:…”

Section: Methodsmentioning

confidence: 99%

Energy and non–energy Commodities: Spillover Effects on African Stock Markets.

Amendola¹,

Boccia²,

Candila³

et al. 2020

Journal of Statistical and Econometric Methods

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This paper examines the volatility transmission from energy and metal commodities to six major African exporters’ stock markets (Egypt for oil and gold, Nigeria for oil and gas, South Africa for coal and gold, Tunisia for oil, Uganda for gold and Zambia for copper). Modelling commodity volatility with the Double Asymmetric GARCH-MIDAS model with a Student’s t-distribution allows to detect the presence of impact and inertial stock market volatility spillovers at different lags and to take into account the leptokurtosis of the commodity series. We then derive the profile of Volatility Impulse Responses of the stock markets to commodity shocks.

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“…The use of such a variable sampled at a lower frequency than daily energy prices leads us to rely on the class of MIDAS models (Ghysels et al 2007). In particular, we exploit the number of US weekly deaths in the univariate specifications of the GARCH-MIDAS (GM, Engle et al 2013) and Double Asymmetric GARCH-MIDAS (DAGM, Amendola et al 2019). The inclusion of the DAGM model allows us to evaluate the effects of positive and negative variations of the COVID-19-related deaths in the US on the daily volatility of commodity returns.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach

Andreani

Candila

Morelli

et al. 2021

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This paper shows the effects of the COVID-19 pandemic on energy markets. We estimate daily volatilities and correlations among energy commodities relying on a mixed-frequency approach that exploits information from the number of weekly deaths related to COVID-19 in the United States. The mixed-frequency approach takes advantage of the MIxing-Data Sampling (MIDAS) methods. We compare our results to those obtained by employing two well-known models that do not account for the COVID-19 low-frequency variable, namely the Dynamic EquiCorrelation (DECO) and corrected Dynamic Conditional Correlation (cDCC). Moreover, we consider four possible specifications of the volatility: GARCH, GJR, GARCH-MIDAS, and Double-Asymmetric GARCH-MIDAS. The empirical results show that our approach is statistically superior to other models and represents a valuable methodology that can be used for risk managers, investors, and policy makers to assess the effects of the pandemic on spillovers effects in energy markets.

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On the asymmetric impact of macro–variables on volatility

Cited by 47 publications

References 35 publications

On the Volatility Spillover between Agricultural Commodities and Latin American Stock Markets

On the Volatility Spillover between Agricultural Commodities and Latin American Stock Markets

Energy and non–energy Commodities: Spillover Effects on African Stock Markets.

Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach

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