Interlinkages across US sectoral returns: time-varying interconnectedness and hedging effectiveness


Polat O.

Financial Innovation, vol.10, no.51, pp.1-27, 2024 (SSCI)

  • Publication Type: Article / Article
  • Volume: 10 Issue: 51
  • Publication Date: 2024
  • Doi Number: 10.1186/s40854-023-00581-4
  • Journal Name: Financial Innovation
  • Journal Indexes: Social Sciences Citation Index (SSCI)
  • Page Numbers: pp.1-27
  • Bilecik Şeyh Edebali University Affiliated: Yes

Abstract

This study examines the time-varying asymmetric interlinkages between nine US sectoral returns from January 2020 to January 2023. To this end, we used the time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach of Adekoya et al. (Resour Policy 77:102728, 2022a, Resour Policy 78:102877, 2022b) and analyzed the time-varying transmitting/receiving roles of sectors, considering the positive and negative impacts of the spillovers. We further estimate negative spillovers networks at two burst times (the declaration of the COVID-19 pandemic by the World Health Organization on 11 March 2020 and the start of Russian-Ukrainian war on 24 February 2022, respectively). Moreover, we performed a portfolio back-testing analysis to determine the time-varying portfolio allocations and hedging the effectiveness of different portfolio construction techniques. Our results reveal that (i) the sectoral return series are strongly interconnected, and negative spillovers dominate the study period; (ii) US sectoral returns are more sensitive to negative shocks, particularly during the burst times; (iii) the overall, positive, and negative connectedness indices reached their maximums on March 16, 2020; (iv) the industry sector is the largest transmitter/recipient of return shocks on average; and (v) the minimum correlation and connectedness portfolio approaches robustly capture asymmetries. Our findings provide suggestions for investors, portfolio managers, and policymakers regarding optimal portfolio strategies and risk supervision.