Visiting the effects of oil price shocks on exchange rates: Quantile-on-quantile and causality-in-quantiles approaches

We employ the relatively novel quantile-on-quantile and causality-in-quantiles approaches to empirically address the effects of oil price shocks on exchange rates of developed and developing countries. We find the evidence of the effects of oil shocks on exchange rates vary across quantiles. In addition, the effects and causality of oil price shocks are asymmetric and the slope of the coefficient in quantile-on-quantile analysis shows a relatively extreme fluctuation. Furthermore, for the developed currencies, the Granger causal relationship in both the mean and the variance running from oil shocks to exchange rates is always evident at all quantiles, while for the developing countries, the causal flow in the first and second moments is insignificant at middle quantiles.     

Does Bitcoin affect decomposed oil shocks differently? Evidence from a quantile-based framework

This paper studies the impact of Bitcoin on decomposed oil price shocks within a quantile-based framework, through which the underlying investment sheltering role of Bitcoin for various oil price fluctuations is explored. The aggregate oil price shock is decomposed into three perspectives of the demand, the supply, and the changing attitudes towards risk. A comparison of the sheltering role between Bitcoin and gold is further evaluated. By using a non-parametric causality test, we find that there exists an asymmetric and unidirectional causal relationship from Bitcoin/gold to oil shocks. Such the unidirectional causality appears only to the demand and supply shocks of oil instead of the risk-specific shocks, and is more evident at median quantiles. By jointly considering the data distribution of both dependent and independent variables realized by a quantiles-on-quantiles method, both Bitcoin and gold generally depict the hedge and safe haven abilities for oil shocks, and such the ability is shown to be different not only between Bitcoin and gold but also for various sources of oil shocks. The sheltering role of gold is found to be greater than that of Bitcoin for the supply shock, while the results reverse for the demand shock. Moreover, shocks from the identified shocks from the COVID-19 pandemic and the Russia–Ukraine conflict are found to not change the cross-market relationship. A series of robustness checks confirm our findings that possess important implications for various stakeholders.     

Evolving United States stock market volatility: The role of conventional and unconventional monetary policies

Despite the econometric advances of the last 30 years, the effects of monetary policy stance during the boom and busts of the stock market are not clearly defined. In this paper, we use a structural heterogeneous vector autoregressive (SHVAR) model with identified structural breaks to analyse the impact of both conventional and unconventional monetary policies on U.S. stock market volatility. We find that contractionary monetary policy enhances stock market volatility, but the importance of monetary policy shocks in explaining volatility evolves across different regimes and is relative to supply shocks (and shocks to volatility itself). In comparison to business cycle fluctuations, monetary policy shocks explain a greater fraction of the variance of stock market volatility at shorter horizons, as in medium to longer horizons. Our basic findings of a positive impact of monetary policy on equity market volatility (being relatively stronger during calmer stock market periods) are also corroborated by analyses conducted at the daily frequency based on an augmented heterogeneous autoregressive model of realised volatility (HAR-RV) and a multivariate k-th order nonparametric causality-in-quantiles framework. Our results have important implications both for investors and policymakers.     

Time–frequency quantile dependence between Bitcoin and global equity markets

In this paper, we examine return dependence between Bitcoin and stock market returns using a novel quantile cross-spectral dependence approach. The results suggest a right-tail (high return) dependence between Bitcoin and the stock markets in the long term and that said dependence decreases significantly from yearly to monthly investment horizons. Furthermore, right-tail dependence between Bitcoin and the US stock market is the strongest compared with other stock markets. We also extract information on the time-varying and time–frequency structure of co-movements between Bitcoin and the stock markets using wavelet-coherence analysis, the results of which suggest that the co-movement between Bitcoin and the US stock market is positive, whereas, for other stock markets, it is negative at certain frequencies and time periods. Overall, the findings highlight additional risk-management capabilities of Bitcoin according to different stock markets.     

Time-frequency causality and dependence structure between crude oil,EPU and Chinese industry stock: Evidence from multiscale quantile perspectives

This article investigates the time-frequency causality and dependence structure of Chinese industry stock returns on crude oil shocks and China's economic policy uncertainty (EPU) across quantiles over the period from January 2001 to June 2021. We use wavelet-based decomposition series to establish a multiscale causality-in-quantiles test and a quantile-on-quantile regression approach to reveal the complicated relationships involving crude oil, EPU and stock returns. Our empirical results are as follows: First, the predictability of crude oil and EPU on industry stock returns is significantly strong under extreme market conditions. Second, the explanatory ability of EPU on industry stock returns in the long term is stronger than EPU’s ability to explain short term returns. Third, the impacts of crude oil and EPU on industry stock returns remain remarkably asymmetric across quantile levels. Finally, nonenergy-intensive industries are also affected by crude oil shocks, but less than energy-intensive industries. Overall, these empirical findings can provide implications for policymakers to stabilize stock markets and investors to hedge the potential risks from crude oil and EPU.     

How does investor attention matter for crude oil prices and returns? Evidence from time-frequency quantile causality analysis

This paper studies the time–frequency, nonlinear quantile relationship between investor attention (GSVI) and crude oil over the period from January 2000 to April 2020. To do so, the wavelet coherency, wavelet-based causality-in-quantiles test and quantile-on-quantile method are employed. The results indicate that first, the correlation between investor attention and crude oil is relatively high, and the highly correlated regions are concentrated from 8 to 16 months. In most cases, the GSVI is negatively correlated with the crude oil market. Additionally, under extreme market conditions, the explanatory ability is stronger than in the normal market, and it is greater in the low-frequency domain than in the high-frequency domain. Finally, investor attention has an apparent asymmetric impact on crude oil prices and returns at each scale, displaying a positive effect on the low quantiles of crude oil but a negative effect on the high quantiles across all quantiles of the GSVI. In the short term, when crude oil prices and returns are in a bear market, the larger volume of the GSVI has a greater impact on them. Moreover, the impact becomes greatest under extreme market conditions.     

Dynamic and asymmetric effects between carbon emission trading,financial uncertainties,and Chinese industry stocks: Evidence from quantile-on-quantile and causality-in-quantiles analysis

This paper investigates the dynamic and asymmetric effects between carbon emission trading (CET), financial uncertainties, and Chinese stocks in different industries over the period from 19th December 2013 to 21st March 2022. We utilized a novel quantile framework including rolling window quantile regression method, quantile-on-quantile method, and causality-in-quantiles method to implement this research more comprehensively and accurately. Our contributions and findings, empirical in nature, are as follows: (i) In the early establishing stage of the carbon market, with a bullish market situation, carbon emission trading has a negative impact on most industry stocks. In the developing and improving stage of the carbon market, different industries have different impact situations. (ii) We find that the effects of financial uncertainty on stocks are stronger than CET on stocks. We also find that the dependence structures between CET, financial uncertainty, and industry stocks are asymmetric in most industries, and there are many mutation structures with significant risks in extreme situations. (iii) Carbon emissions trading, crude oil volatility, and US stock volatility all have strong causal relationships with Chinese industry stocks. (iv) We also provide policy suggestions to relevant countries to balance carbon market and stock markets and avoid risks from financial uncertainty in different industries.