On the Asymmetric Volatility Connectedness

TL;DR

This paper introduces an asymmetric method for measuring volatility spillovers between financial markets, capturing the directional impact more accurately than previous symmetric approaches.

Contribution

It develops an asymmetric variance decomposition approach based on Hatemi-J's method, improving the measurement of volatility spillovers in financial markets.

Findings

Asymmetric spillover effects are significant between major financial markets.

The new method reveals directional volatility impacts not captured by symmetric models.

Application demonstrates better alignment with real-world market behavior.

Abstract

Connectedness measures the degree at which a time-series variable spills over volatility to other variables compared to the rate that it is receiving. The idea is based on the percentage of variance decomposition from one variable to the others, which is estimated by making use of a VAR model. Diebold and Yilmaz (2012, 2014) suggested estimating this simple and useful measure of percentage risk spillover impact. Their method is symmetric by nature, however. The current paper offers an alternative asymmetric approach for measuring the volatility spillover direction, which is based on estimating the asymmetric variance decompositions introduced by Hatemi-J (2011, 2014). This approach accounts explicitly for the asymmetric property in the estimations, which accords better with reality. An application is provided to capture the potential asymmetric volatility spillover impacts between the three largest financial markets in the world.