Prior studies on the price formation in the Bitcoin market consider the role of Bitcoin transactions at the conditional mean of the returns distribution. This study employs in contrast a non-parametric causality-in-quantiles test to analyse the causal relation between trading volume and Bitcoin returns and volatility, over the whole of their respective conditional distributions. The nonparametric characteristics of our test control for misspecification due to nonlinearity and structural breaks, two features of our data that cover 19th December 2011 to 25th April 2016. The causality-in-quantiles test reveals that volume can predict returns-except in Bitcoin bear and bull market regimes. This result highlights the importance of modelling nonlinearity and accounting for the tail behaviour when analysing causal relationships between Bitcoin returns and trading volume. We show, however, that volume cannot help predict the volatility of Bitcoin returns at any point of the conditional distribution.
A recent literature emphasizes the role of news-based economic policy uncertainty (EPU) and equity market uncertainty (EMU) as drivers of oil-price movements. Against this backdrop, this paper uses a k-th order nonparametric quantile causality test, to analyze whether EPU and EMU predicts stock returns and volatility. Based on daily data covering the period of 2 nd January, 1986 to 8 th December, 2014, we find that, for oil returns, EPU and EMU have strong predictive power over the entire distribution barring regions around the median, but for volatility, the predictability virtually covers the entire distribution, with some exceptions in the tails. In other words, predictability based on measures of uncertainty is asymmetric over the distribution of oil returns and its volatility.JEL Codes: C22; C32; C53; Q41 Keywords: Uncertainty; Oil markets; Volatility; Quantile causality We would like to thank two anonymous referees for many helpful comments. However, any remaining errors are solely ours.
PurposeThe purpose of this paper is to investigate the factors affecting capital structure decisions of Istanbul Stock Exchange (ISE) lodging companies.Design/methodology/approachA model based on the trade‐off and pecking order theories is specified and implications of both theories are empirically tested. The model is estimated using a dynamic panel data approach for five ISE companies for the period of 1994‐2006.FindingsThe findings suggest that effective tax rates, tangibility of assets, and return on assets are related negatively to the debt ratio, while free cash flow, non‐debt tax shields, growth opportunities, net commercial credit position, and firm size do not appear to be related to the debt ratio. Although the findings partially support the pecking order theory, neither the trade‐off nor the pecking order theory exactly seem to explain the capital structure of Turkish lodging companies.Research limitations/implicationsThe data used in this paper are limited to five companies traded in the ISE, since the data on other companies are not available. A more detailed analysis would use data for other companies in the industry.Practical implicationsThe findings of the study clearly demonstrate the importance of capital structure decisions for financial sources.Originality/valueAlthough the capital structure theory is extensively examined in the finance literature, there are fewer studies covering the tourism industry, particularly Turkey. The paper establishes the determinants of the capital structure of Turkish lodging companies. The research findings should help managers to make optimal capital structure decisions.
Highlights• BRICS stock markets do not react to geopolitical risks (GPRs) in a uniform way.• GPRs generally drive stock market volatility rather than returns.• The effect of GPRs is particularly strong at return quantiles below the mean.
This paper examines the causal link between economic policy uncertainty and stock returns in China and India, using bootstrap Granger full-sample causality test and sub-sample rolling window estimation. We use monthly data covering from 1995:02 to 2013:02 for China and 2003:02-2013:02 for India. The bootstrap full-sample Granger causality test suggests no evidence of any causality between economic policy uncertainty and stock returns for the two countries. However, taking structural changes into account, we assess stability of parameters of the estimated vector autoregressive (VAR) models. We find both the short-run and long-run relationships between economic policy uncertainty and stock return estimated using full-sample data are unstable over the sample period. This suggests that full-sample causality tests cannot be relied upon. We turn to propose a time-varying (bootstrap) rolling window approach to revisit the dynamic causal relationship between the two variables. Using a rolling window of 24 months, we do find that there are bidirectional causal relationships between stock returns and EPU for several sub-periods in China and India. However, the association between EPU and stock returns is, in general, weak for these two emerging countries. These findings have important implications for policy makers as well as investors.
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