Highlights We model stock price variation around the world during the corona crash. We use Google search volume activity as a gauge of panic and fear. Search terms are specific to the coronavirus crisis. Our sample covers 10 stock market indices. Excess search volume predicts price variation during the corona crash.
The structure of return spillovers is examined by constructing Granger causality networks using daily closing prices of 20 developed markets from 2 nd January 2006 to 31 st December 2013. The data is properly aligned to take into account non-synchronous trading effects. The study of the resulting networks of over 94 sub-samples revealed three significant findings.First, after the recent financial crisis the impact of the US stock market has declined. Second, spatial probit models confirmed the role of the temporal proximity between market closing times for return spillovers, i.e. the time distance between national stock markets matters.Third, preferential attachment between stock markets exists, i.e. spillover from market j to market i is more likely if A) market j influences other markets other than i, or when B) market i is influenced by other markets other than j.Keywords: stock market networks, Granger causality, emerging and frontier markets, nonsynchronous trading, preferential attachment, JEL classification: L14, G1Highlights: Granger causality networks are constructed among 20 developed stock markets. A detailed procedure of handling the non-synchronicity of daily data is proposed. The spatial probit model is used to study the structure of the created networks. Relationships between markets depend on a temporal proximity of closing times.
Using a rolling windows analysis of filtered and aligned stock index returns from 40 countries during the period 2006-2014, we construct Granger causality networks and investigate the ensuing structure of the relationships by studying network properties and fitting spatial probit models. We provide evidence that stock market volatility and market size increases, while foreign exchange volatility decreases the probability of return spillover from a given market. We also show that market development and returns on the foreign exchange market and stock market also matter, but they exhibit significant time-varying behaviour with alternating effects. These results suggest that higher market integration periods are alternated with periods where investors appear to be chasing returns. Despite the significance of market characteristics and market conditions, what in reality matters for information propagation is the temporal distance between closing hours, i.e. the temporal proximity effect. This implies that choosing markets which trade in similar hours bears additional costs to investors, as the probability of return spillovers increases. The same effect was observed with regard to the temporal distance to the US market. Finally, we confirm the existence of the preferential attachment effect, i.e. the probability of a given market to propagate return spillovers to a new market depends endogenously and positively on the existing number of return spillovers from that market. JEL Classification: G01, L14 40 developed, emerging, and frontier markets, we test for Granger causalities among returns while controlling for the size of multiple Granger causality tests and taking care of return alignment with respect to non-synchronous trading effects. A possibly high number of return spillovers creates a complex network of relationships, which depicts world-wide market linkages. This is described via measures used in the network theory.The remainder of the paper is organized as follows. In Section 1 we briefly introduce the reader to the networks and their use in finance. Section 2 describes the data, including the return alignment procedure used to deal with non-synchronous trading effects. Econometric and network methodology is described in Section 3. Section 4 describes the results and the last section concludes.
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