Document Type : Research Paper

Authors

• Reza Taleblou ¹
• Parisa Mohajeri ¹
• Maedeh Samadi ²

¹ Associate Professor of Economics, Allameh Tabataba’i University, Tehran, Iran

² Master's Student in Economics, Faculty of Economics Allamah Tabataba'i University, Tehran, Iran

Abstract

This study employs the Diebold-Yilmaz spillover index within the framework of a time-varying parameter vector autoregressive model (TVP-VAR) to analyze the dynamic connectedness between exchange rates and the Iranian stock market amidst the COVID-19 pandemic. Utilizing daily data spanning from October 2014 to October 2023, we examine the volatility spillovers between the US dollar and the stock indices of eight industries, including "chemicals," "basic metals," "petroleum products," "extraction of metal ores," "agriculture," "sugar," "cement," and "ceramics." Our findings reveal that systemic risk, represented by total connectedness within the network, averaged approximately 50% before the onset of the COVID-19 pandemic. However, following the emergence of the pandemic, network connections intensified significantly, surpassing 70% at times. The US dollar variable exhibits the highest idiosyncratic risk (75.62%), while the indices of basic metal industries (34.52%) and metal ores (34.59%) demonstrate the lowest idiosyncratic risk. Analysis of the network dynamics indicates that volatility originating from export-oriented commodity industries, particularly basic metals, predominantly influences the US dollar variable, acting as a net transmitter of volatilities to smaller industries, notably ceramics. Moreover, the basic metal industry emerges as the primary transmitter of volatilities within the network, with the agricultural and ceramic industries identified as significant recipients of shocks.

Keywords

• Systemic Risk
• Volatility Spillovers
• Time-Varying Parameter Vector Autoregressive (TVP-VAR) Model
• Stock Market

Main Subjects

• Financial Economics