AI, Big Data and Quantitative Methods in Finance
Vol. 1 No. 1 (2026): ABQ Vol. 1 No. 1, April 2026

Spillovers Between Indonesia's Green Index, Conventional Index and Global Stock Index: Which is More Stable?

Ade Holis (School of Business, IPB University, Jl Pajajaran Bogor 16151, Indonesia)
Edi Sumanto (School of Business, IPB University, Jl Pajajaran Bogor 16151, Indonesia)
Roy Hendra Michael Sembel (IPMI-International Business School, Jl Rawajati Timur I No.1 Jakarta 12750, Indonesia)
Adler Haymans Manurung (Universitas Bhayangkara Jakarta Raya, Jl. Harsono RM No.67 Ragunan 12550 Jakarta, Indonesia)



Article Info

Publish Date
29 Apr 2026

Abstract

Background:  The increasing global concern over climate change has accelerated the growth of sustainable investment, including the development of green stock indices such as Indonesia’s SRI-KEHATI Index. However, despite their potential resilience and attractiveness, green indices remain exposed to volatility and interconnectedness with conventional and global financial markets. Therefore, understanding the dynamics of volatility and spillover effects between green, conventional, and global stock indices becomes crucial to assess their relative stability and investment potential. Purpose: This study aimed to measure the level volatility, connectedness and spillovers between Indonesia green index, Indonesia conventional stock index, and some global stock price indices in the last decade. Design/methodology/approach: Using daily return data from 2 February 2012 to 31 August 2022, this study used a rolling-samples of descriptive statistics approach and framework developed by Diebold and Yilmaz (2012) to measure the levels of volatility and spillovers between Indonesia's green index, Indonesia LQ45 index and several global stock indexes.Findings: The results of the analysis showed that volatility and spillovers that have occurred between variables are dynamic over time. When the volatility values of the variables tend to be low, Indonesia return green index tends to be higher, and on the other hand, when volatility is high, Indonesian return green index tends to be lower than the conventional return index volatility. In addition, during the analysis period, the spillovers that occurred between variables experienced a significant increase several times and then decreased again after a certain period of time. In the long run, the returns of Indonesia's green index tend to experience negative spillovers where the spillovers caused by the returns of the green stock index to all variables tend to be smaller than the spillovers received by the returns of the green stock index. In addition, the return movement of Indonesia's green stock index is generally more explained by the return movement of global stock price indexes compared to conventional stock indexes of Indonesia.Conclusion: The findings indicate that Indonesia’s green stock index delivers higher average returns compared to the conventional index, although it exhibits slightly higher volatility over the study period. Furthermore, the results reveal that volatility spillovers are dynamic and largely driven by global market movements, with the green index tending to receive more spillovers than it transmits in the long runOriginality/value: This study compares the volatility and connectedness between Indonesia green index and Indonesia conventional stock index with several global stock indexes. As an index that was just launched about ten years ago, research on the Indonesian green index was still limited. In addition, the rolling sample method that measures the dynamics of parameter changes over time such as time varying volatility and the Diebold and Yilmaz (2012) framework provide something new in the time series research method.  Keywords:green stock index, spillover, rolling samples, diebold-yielmaz, level volatility, descriptive statistics

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Journal Info

Abbrev

abq

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Subject

Description

AI, Big Data and Quantitative Methods in Finance (ABQ) covers theoretical and empirical research in finance that applies artificial intelligence, big data analytics, and advanced quantitative methods. The journal welcomes studies within, but not limited to, the following areas: Artificial ...