The co-authored paper (Dr Mattia Bevilacqua, Dr David Morelli, Professor Radu Tunaru) titled ‘The Determinants of the Model-Free Positive and Negative Volatilities” examines the role of macroeconomic and financial determinants in explaining stock market volatilities in the U.S. market.
The research involved analysing how stock market volatility is affected by different factors. The researchers looked at both expected volatility (what people think will happen) and realised volatility (what actually happened). They then broke these down into positive and negative components, which allowed them to calculate the level of risk involved in different directions of the market.
The researchers found that negative volatility, which indicates a decline in the market, is more influenced by factors like uncertainty and geopolitical risk. Positive volatility, which indicates growth in the market, is more influenced by things like inflation and overall economic growth.
Interestingly, the study found that these factors changed in importance over time. Before the 2008 financial crisis, macroeconomic factors were more important in driving volatility, but after the crisis, financial conditions became more influential.
In addition to the above findings, the researchers used a more advanced statistical method called mixed frequency Granger causality approach to examine the relationships between volatility, risk premium and macroeconomic variables. They found that these relationships are complex and cannot be fully explained by traditional methods like low frequency VAR models.
This approach allowed them to uncover new insights about how different factors influence stock market volatility and risk. The findings suggest that a more nuanced understanding of these relationships is needed to accurately predict future market behavior.
These findings have been published in the Journal of International Money and Finance.