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Do commodity markets catch a cold from stock markets? Modelling uncertainty spillovers using Google search trends and wavelet coherence
International Review of Financial Analysis ; : 102304, 2022.
Article in English | ScienceDirect | ID: covidwho-1956181
ABSTRACT
We investigate stock market uncertainty spillovers to commodity markets using wavelet coherence and a general stock market-related Google Search Trends (GST)-based index to proxy for uncertainty. GST reflect stock market uncertainty over short-, medium- and long-term horizons and periods of association between GST and the VIX, a widely used proxy for stock market uncertainty, coincide with economic, financial, and geopolitical events. We show that the association between the VIX and GST has grown over time. In line with economic psychology, this implies that during times of heightened uncertainty investors increasingly search for stock market-related information. Our analysis further reveals that some commodities are more susceptible to uncertainty spillovers from stock markets, most notably energy commodities. We go onto demonstrate how GST may be used to isolate the impact of specific events and show that COVID-19 had a disproportionate impact on commodity price volatility. We also find that energy, livestock and precious metals are increasingly integrated with stock markets. Spillover analysis repeated using the VIX produces similar results and reflects information that is also reflected in GST, confirming an uncertainty narrative. The use of wavelet analysis and GST to proxy for general and event specific uncertainty offers an alternative perspective to traditional econometric approaches and may be of interest to econometricians, analysts, investors and researchers broadly.
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Full text: Available Collection: Databases of international organizations Database: ScienceDirect Language: English Journal: International Review of Financial Analysis Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: ScienceDirect Language: English Journal: International Review of Financial Analysis Year: 2022 Document Type: Article