Unveiling Market Interdependencies: Volatility spill over Dynamics across Nifty, Dow, Gold, WTI, Bond Yields, and the Dollar Index

Introduction

In today\'s interconnected financial landscape, it is imperative to comprehend how movements in the Dow Jones, Nifty, WTI crude oil, gold, the Dollar Index, and bond yields affect each other\'s volatility. The examination of the volatility spillover among these pivotal indicators furnishes crucial discernments for investors and policymakers, moulding tactics to manoeuvre and apprehend the complex dynamics inherent in global financial markets.

Review of literature

Yilmaz (2010) reveals the return spillover indices for East Asia which demonstrate an increasing market integration over time, indicating stronger links and synchronised returns. Diebold & Yilmaz (2012) found that prior to the global financial crisis of 2007, there was little correlation between the fluctuations in the stock, bond, foreign currency, and commodities markets. Awartani & Maghyereh (2013) emphasise how crucial it is to look at how oil and stock markets affect each other. Other studies by Boubaker & Raza (2017), Roy & Sinha Roy (2017), Husain et al. (2019), Evrim Mandacı et al. (2020), Zhang et al. (2021), Patra & Panda (2021), and Shen et al. (2022) further examine volatility spillovers across various asset classes and markets.

Objective of study

• The objective of the study is to examine the extent of interdependencies across different markets.
• To know the overall return spillover and volatility spillover index among Nifty, Dow Jones, Dollar index, WTI, Bond Yield, and Gold.
• To find out the net receiver and net transmitter of volatility among the selected macroeconomic variable.

Research methodology

This section explains Diebold and Yilmaz\'s proposed directional spillover index measure (2009, 2012). The empirical analysis includes weekly data from 18th August 2013 to 17th Sept 2023 of Nifty, Dow Jones, Dollar index, Bond yield, WTI, and Gold (Dollar terms). Weekly returns are expressed as annualized percentages. Using Garman and Klass (1980), weekly return volatilities are determined.

Data analysis & Results

The empirical findings indicate that return series are not normally distributed, and WTI crude oil is more volatile. The total return spillover resulting from these variables is 22.3% of the variance in return forecast error. Across our entire sample of 6 markets, 40.1% of the volatility forecast error variance arises from transmissions. The highest volatility spillover toward Nifty arises from the US 10-year bond yield, followed by Dow Jones. The volatility spillover index burst in 2020 during Covid-19, but had already started rising before 2020.

Conclusion

The study analyzes weekly data from 18th August 2013 to 23rd September 2023 and finds significant return and volatility spillovers among Nifty, Dow Jones, Dollar index, US 10-year bond yields, Gold, and WTI crude oil. Fund managers and policy makers should consider these interdependencies before making important decisions.