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Purpose This paper aims to examine the frequency of co-movements and asymmetric dependencies between bitcoin (BTC), gold, Brent crude oil and the US economic policy uncertainty (EPU) index. Design/methodology/approach The authors use a wavelet approach and a quantile-on-quantile regression (QQR) method. Findings The results show a positive interdependence between BTC and commodity price returns at both medium and low frequencies over the sample period. In contrast, the dependence is negative between BTC and EPU index at both medium and low frequencies. Furthermore, the co-movements between markets are more pronounced during crises. The results show that strategic commodities and EPU index have the ability to predict BTC price returns at both medium- and long-terms. The QQR method reveals that higher gold returns tend to predict higher/lower BTC returns when the market is in a bullish/bearish state. Moreover, lower gold returns tend to predict lower (higher) BTC returns when the market is in a bearish (bullish) state (positive (negative) relationship). The lower Brent returns tend to predict higher/lower BTC returns when the market is in a bullish/bearish state. High Brent quantiles tend to predict the lower BTC returns in its extremely bearish states. Finally, higher and lower EPU changes tend to predict lower and higher BTC returns when the market is in a bearish/bullish state (negative relationship). Originality/value There is generally a lack of understanding of the linkages between BTC, gold, oil and uncertainty index across multiple frequencies. This is, as far as the authors know, the first attempt to apply both the wavelet approach and a QQR method to examine the multiscale linkages among markets under study. The findings should encourage the relevant policymakers to consider these co-movements which vary over time and in duration when setting up regulations that deem to enhance the market efficiency.

This article attempts to characterize the pattern of information flows between the stock markets by determining mean and variance causal relationships. A two-step procedure proposed by Cheung and Ng (1996) is used. Stock market returns are specified as Autoregressive-Generalized Autoregressive Conditional Heteroskedasticity (AR-GARCH) models with Monday and Friday effects. Stock markets of our sample are chosen to analyse the main causes of information flows documented in the literature: linkage between economic fundamentals and the time lag between the stock markets' opening hours. Results provide evidence of nonlinear causality between stock markets, even when linear Granger causality is rejected. Causality seems to be attributed to both economic linkage and time lag between market openings.

Purpose Earnings management (EM) plays a vital role in risk management. This paper aims to investigate the impact of real earning management (REM) on credit risk. Design/methodology/approach This paper measures the credit risk by the expected default frequency of Kealhofer, McQuown and Vasicek model. This paper uses data from 2011 to 2020 of Pakistani manufacturing listed firms. This paper applies the fixed effect to analyze the results and generalized methods of moments to handle the heterogeneity issue. Findings This paper finds that the impact of REM on corporate credit risk is positive and significant and that of sales manipulation is negative and significant. This paper also reports similar outcomes of the robustness test using dynamic panel regression. Originality/value The findings of this study may help managers to modify the EM strategy to minimize corporate credit risk. Furthermore, the findings of this study are important for investors to enhance their understanding of firms’ accounting information, REM activities and cash flow patterns. It further suggests the manager should consider credit risk as an important factor while practicing REM.

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