Time series analysis of GSS bonds Part 2 – further univariate analysis of S&P Green Bond Index

Debashish Dey

British Actuarial Journal, 2024, vol. 29, -

Abstract: The popularity of green, social and sustainability-linked bonds (GSS bonds) continues to rise, with circa US$939 billion of such bonds issued globally in 2023. Given the rising popularity of ESG-related investment solutions, their relatively recent emergence, and limited research in this field, continued investigation is essential. Extending non-traditional techniques such as neural networks to these fields creates a good blend of innovation and potential. This paper follows on from our initial publication, where we aim to replicate the S&P Green Bond Index (i.e. this is a time series problem) over a period using non-traditional techniques (neural networks) predicting 1 day ahead. We take a novel approach of applying an N-BEATS model architecture. N-BEATS is a complex feedforward neural network architecture, consisting of basic building blocks and stacks, introducing the novel doubly residual stacking of backcasts and forecasts. In this paper, we also revisit the neural network architectures from our initial publication, which include DNNs, CNNs, GRUs and LSTMs. We continue the univariate time series problem, increasing the data input window from 1 day to 2 and 5 days respectively, whilst still aiming to predict 1 day ahead.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

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:cup:bracjl:v:29:y:2024:i::p:-_20

Access Statistics for this article

More articles in British Actuarial Journal from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().