Investigating the Dynamics of Volatility Spillovers across Sectors’ Returns Utilizing a Time-Varying Parameter Vector Autoregressive Connectedness Approach; Evidence from Iranian Stock Market

Identifying the connection between different economic sectors is pivotal to policy-making and portfolio management, particularly in a developing country such as Iran. This study incorporates the high-frequency data of the daily returns in the Iranian stock market sector (four clusters, including 12 sectors that constitute over 70% of the stock market capitalization) from November 2009 to October 2022 to estimate the total and sectoral static and dynamic connectedness indices using the vector autoregression model (VAR) with time-varying parameter (TVP) and Diebold-Yilmaz connectedness index (DYCI). The findings indicate that 56% of the forecast error variance can be attributed to cross-sectoral innovations within the network, demonstrating a fairly strong co-movement across different sectors. Also, the connectedness between sectoral performances varies significantly over time. The strongest connectedness and spillovers have been observed in recent years when the stock market experienced extraordinary ups and downs, reaching its peak of 85% in the total connectedness index in early 2022. It was also found that the base metal industry and investment sector have acted as permanent transmitters of shocks, and the sugar and ceramic sectors were the permanent receptors of volatilities. This finding confirms the existence of the lead-lag effect in the Iranian stock market. Lastly, the strong pairwise connectedness, especially between “base metal and metal ore sectors” and “food and sugar industries,” indicates that shocks are transferred from downstream to upstream industries in the studied clusters.