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.
We analyze the impact of institutional quality on firm survival in 15 Central and Eastern European (CEE) countries. We employ the Cox proportional hazards model with a large dataset of firms from 2006-2015 and control for firm-specific determinants and country differences. Our results show that institutional quality (IQ) is a significant preventive factor for firm survival, and the result is robust to different measures of IQ and industry sectors. Furthermore, we document the existence of diminishing productivity of IQ, as the economic effect upon institutions is largest for low-level IQ countries and smallest for high-level IQ countries. In terms of firm-specific controls, ownership structure plays a vital role in strengthening the probability of firm survival. Notably, foreign ownership helps firms survive in all three country groups, and the effect is again larger for countries with low-and mid-level IQs. ROA, profit margin, solvency ratio, and firm age represent additional significant preventive factors, albeit with smaller economic effects.
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.
Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We show significant temporal proximity effects between markets and somewhat weaker temporal effects with regard to the US equity marketvolatility spillovers decrease when markets are characterized by greater temporal proximity.
Documents inVolatility spillovers also present a high degree of interconnectedness. Our results also link spillovers of escalating magnitude with increasing market size, market liquidity and economic openness.JEL-Codes: C310, C580, F010, G010, G150.Keywords: volatility spillovers, stock markets, shock transmission, Granger causality network, spatial regression, financial crisis. We benefited from helpful comments we received from two anonymous referees and participants at several presentations.
Eduard Baumöhl University of Economics in Bratislava
This paper aims to elucidate the connectedness between major forex currencies and cryptocurrencies using the quantile cross-spectral approach recently proposed by Baruník and Kley (2015). The sample covers six forex currencies and six cryptocurrencies over the period of 1 September 2015 to 29 December 2017. Compared with the results obtained from standard correlations and detrended moving-average cross-correlation analysis (DMCA), the quantile cross-spectral approach provides richer information on the dependence structure across different quantiles and frequencies. The most interesting result is that the intra-group dependencies are positive in the lower extreme quantiles, while inter-group dependencies are negative. This result holds in both the short-and long-term perspectives. Thus, it is worth diversifying between these two currency groups.
We address the safe haven properties of gold relative to US stock market sector indices using the bivariate cross-quantilogram of Han et al. (2016). Splitting our sample into pre-and post-crisis periods, our results show that the safe haven properties of gold have a changing nature. Before and after the financial crisis, we find only limited quantile dependence and that gold can be considered a safe haven for most of the sectors, except Industrials. On a full sample (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016), there are only three sectors -Healthcare, IT, and Telecommunication services -for which gold can be considered a safe haven.
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