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Since the outbreak of the COVID-19 pandemic, stock markets around the world have experienced unprecedented declines, which have resulted in extremely high stock market uncertainty, measured as price variation. In this paper, we show that during such periods, Google Trends data represent a timely and valuable data source for forecasting price variation. Fear of the coronavirus, as measured by Google searches is predictive of future stock market uncertainty for stock markets around the world. Google searches were also strongly correlated with the evolution of physical contagion (the number of new cases), and with implemented nonpharmaceutical interventions. The effect of pandemic-related policies on investors' attention and fear is thus very well captured by Google Trends data.

In this study, we construct financial networks in which nodes are represented by assets and where edges are based on long-run correlations. We construct four networks (complete graph, a minimum spanning tree, a planar maximally filtered graph, and a threshold significance graph) and use three centrality measures (betweenness, eigenvalue centrality, and the expected force). To improve risk-return characteristics of well-known return maximization and risk minimization benchmark portfolios, we propose simple adjustments to portfolio selection strategies that utilize centralization measures from financial networks. From a sample of 45 assets (stock market indices, bond and money market instruments, commodities, and foreign exchange rates) and from data for 1999 to 2015, we show that irrespective of the network and centrality employed, the proposed network-based asset allocation strategies improve key portfolio return characteristics in an out-of-sample framework, most notably, risk and left-tail risk-adjusted returns. Resolving portfolio model selection uncertainties further improves risk-return characteristics. Improvements made to portfolio strategies based on risk minimization are also robust to transaction costs.

We examine whether there is contagion from the U.S. stock market to six Central and Eastern European stock markets. We use a novel measure of contagion that examines whether volatility shocks in the U.S. stock market coupled with negative returns are followed by higher co-exceedance between U.S. and emerging stock markets. Using our approach and controlling for a set of marketrelated variables, we show that during the period from 1998 to 2014, financial contagion occurred, i.e., unexpected negative events in the U.S. market are followed by higher co-exceedance between U.S. and Central and Eastern European stock markets. Even though contagion is stronger during the financial crisis, it also occurs in tranquil times.