Mixtures of generalized normal distributions and EGARCH models to analyse returns and volatility of ESG and traditional investments

Pierdomenico Duttilo (), Stefano Antonio Gattone () and Barbara Iannone ()
Additional contact information
Pierdomenico Duttilo: University “G. d’Annunzio” of Chieti-Pescara
Stefano Antonio Gattone: University “G. d’Annunzio” of Chieti-Pescara
Barbara Iannone: University “G. d’Annunzio” of Chieti-Pescara

AStA Advances in Statistical Analysis, 2024, vol. 108, issue 4, No 3, 755-775

Abstract: Abstract Environmental, social and governance (ESG) criteria are increasingly integrated into investment process to contribute to overcoming global sustainability challenges. Focusing on the reaction to turmoil periods, this work analyses returns and volatility of several ESG indices and makes a comparison with their traditional counterparts from 2016 to 2022. These indices comprise the following markets: Global, the US, Europe and emerging markets. Firstly, the two-component mixture of generalized normal distribution was exploited to objectively detect financial market turmoil periods with the Naïve Bayes’ classifier. Secondly, the EGARCH-in-mean model with exogenous dummy variables was applied to capture the turmoil period impact. Results show that returns and volatility are both affected by turmoil periods. The return–risk performance differs by index type and market: the European ESG index is less volatile than its traditional market benchmark, while in the other markets, the estimated volatility is approximately the same. Moreover, ESG and non-ESG indices differ in terms of turmoil periods impact, risk premium and leverage effect.

Keywords: Mixtures of GND; Naïve Bayes’ classifier; EGARCH model; Returns distribution; Volatility; ESG investments (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10182-023-00487-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:alstar:v:108:y:2024:i:4:d:10.1007_s10182-023-00487-7

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10182/PS2

DOI: 10.1007/s10182-023-00487-7

Access Statistics for this article

AStA Advances in Statistical Analysis is currently edited by Göran Kauermann and Yarema Okhrin

More articles in AStA Advances in Statistical Analysis from Springer, German Statistical Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().