Time-frequency effect of crude oil and exchange rates on stock markets in BRICS countries: Evidence from wavelet quantile regression analysis

This study examines the effects of oil prices and exchange rates on stock market returns in BRICS countries (Brazil, Russia, China, India and South Africa) from a time–frequency perspective over the period 2009–2020. We use wavelet decomposition series to develop a threshold rolling window quantile regression to detect time–frequency effects at various scales. The empirical results are as follows. First, our findings confirm that the effects of both crude oil prices and exchange rates on BRICS stock returns are asymmetric. Positive shocks of crude oil have a greater impact on a bull market, whereas negative shocks have a greater impact on a bear market. Second, there is a short-term enhancement effect of crude oil and exchange rate on BRICS stock markets. In addition, volatility in the macro financial environment also exacerbates the impacts of oil prices and exchange rates on the stock market, and these fluctuations are heterogeneous. Overall, these findings provide useful insights for international investors and policy makers.     

Quantile dependencies and connectedness between the gold and cryptocurrency markets: Effects of the COVID-19 crisis

This paper examines the quantile dependence, connectedness, and return spillovers between gold and the price returns of leading cryptocurrencies, using quantile cross-spectral, the return spillovers based the quantile VAR, and quantile connectedness approaches. The results show that the dependencies within cryptocurrencies are highly symmetric and sensitive to different quantile arrangements. Under normal market conditions, we find a high positive dependence within cryptocurrencies and a low positive dependence between cryptocurrencies and gold. The dependence is higher at long term than intermediate- and short- terms before the pandemic during bearish market conditions. In contrast, the degree of dependence decreases at the intermediate- and long-terms during COVID-19 period than before. Moreover, the magnitude of return spillovers is higher at lower quantile (bearish market) than upper quantile (bullish market). Gold serves as a safe haven and diversifier asset for cryptocurrencies during COVID-19 outbreak at both intermediate and long terms.     

S&P 500 volatility,volatility regimes,and economic uncertainty

We assess the relationship between regime-dependent volatility in S&P 500, economic policy uncertainty, the S&P 500 bull and bear sentiment spread (bb_sp), as well as the Chicago Board Options Exchange's VIX over the period 2000–2018. Our findings from two-covariate GARCH–MIDAS (GM) methodology, regime switching Markov Chain, and quantile regressions suggest that the association of realized volatility and sentiment varies across high- and low-volatility regimes and depends on investors’ sensitivity toward incidents of market uncertainties under these regimes. The findings suggest that these indicators may not be useful in volatility forecasting, especially under high-volatility regimes.     

FDI与经济增长质量的变化——基于分位数回归方法的经验研究

引进FDI对不同经济发展质量的省市的影响差异较大，现有研究的门槛效应不能很好诠释。基于1992-2012年28个省域面板数据采用分位数回归的方法，研究发现：①依分位点由低到高，FDI 对经济增长质量的影响呈先增后降趋势，说明FDI双门槛效应存在；②在经济增长质量的0.5分位点周围，FDI的技术溢出效应最大且最为显著，表明中等发展水平的省市引进FDI最有利；③FDI技术溢出效应呈现明显的区域差异，对东部影响显著，中部不明显，西部有抑制作用。     

基于分位数回归的农村普惠金融发展增收效应研究——以广西40个县为例

农村普惠金融对改善农村地区经济、提高农村居民收入水平有重要作用.本文首先构建农村普惠金融发展指数的指标体系,并以广西40个县为例,对其在2013—2017年的普惠金融发展进行测度及聚类分析.在聚类结果的基础上,采用面板回归模型对普惠金融发展的增收效应进行分析;同时,考虑到不同地区的农村居民收入水平存在差异,进一步采用分位数回归模型对增收效应进行研究.结果发现:农村普惠金融发展对农村居民收入增长具有正向效应,但对收入层次较低的农村居民而言,普惠金融发展不利于收入的提高.鉴于此,本文建议进一步优化农村普惠金融的发展环境,推动不同农村地区普惠金融协同发展,切实增强农村普惠金融发展的增收效应.     

Effects of tax benefits on the price elasticity of charitable contributions in South Korea

Using South Korean panel data from 2008 to 2019 and censored quantile regression method, this study calculates the effects of different tax incentives on charitable contributions. We observe price elasticity under two different tax-benefit systems in South Korea and find that, first, taxpayers tend to be more sensitive to tax incentives under a tax deduction system than a tax credit system. The price elasticity gap between a tax deduction and tax credit is approximately −2.3 to −1.0. Second, we show the existence of heterogeneity in taxpayers’ behaviour: the price elasticity of charitable contributions exhibits a convex shape, where more significant donors have lesser reactions to tax incentives. We further show that socioeconomic contexts, such as income, gender, marital status, and education, affect people's attitudes. In sum, the results are as expected: tax deductions work more efficiently than tax credits.     

Reassessing the dependence between economic growth and financial conditions since 1973

Adrian, Boyarchenko and Giannone ((2019), ABG) adapt quantile regression (QR) methods to examine the relationship between US economic growth and financial conditions. We confirm their empirical findings, using their methodology and their pre-2016 sample. Mindful of the importance of the Covid-19 pandemic, we extend the sample to 2021Q3 and find attenuation of the key estimated coefficients using ABG's empirical methods. Given the pandemic observations, we provide robust QR analysis of dependence based on ranked data and explain the relationship with extant copula modelling methods.     

Neural network approximation for superhedging prices

This article examines neural network-based approximations for the superhedging price process of a contingent claim in a discrete time market model. First we prove that the α-quantile hedging price converges to the superhedging price at time 0 for α tending to 1, and show that the α-quantile hedging price can be approximated by a neural network-based price. This provides a neural network-based approximation for the superhedging price at time 0 and also the superhedging strategy up to maturity. To obtain the superhedging price process for $t>0$, by using the Doob decomposition, it is sufficient to determine the process of consumption. We show that it can be approximated by the essential supremum over a set of neural networks. Finally, we present numerical results.     

Distributional heterogeneity in climate change impacts and adaptation: Evidence from Indian agriculture

This study estimates the distributional heterogeneity in the effects of climate change on yields of three major cereal crops: rice, maize, and wheat in India using district-level information for the period 1966–2015. We distinguish between the effects of changes in growing season weather from those due to changes in long-term climate trends and the heterogeneity in these effects across the distribution of crop yields by estimating naïve and climate penalty inclusive models using fixed-effect quantile panel models. We observe an absence of adaptation against rising temperatures for rice and wheat. However, we find a statistically significant presence of adaptation for wheat and maize for changes in precipitation, though the magnitude is small. Moreover, we find that the effects are asymmetric, and are larger at the lower tail of productivity distribution and smaller at the upper tail of the distribution. A 1°C increase in temperature lowers rice and wheat productivity by 23% and 9%, respectively at the first quantile, but the damage is only 6% and 5% at the ninth quantile. Heterogeneity in impacts and adaptation estimates over the yield distribution curve and across crops suggests the importance of customizing strategies for adaptation to changing weather and climate conditions across regions, crops, and current productivity levels.     

The Great Recession,Household Income,and Children's Test Scores

Economic downturns may have important implications for the educational attainment and human capital accumulation of children. We examine how income losses during the Great Recession were associated with children's educational performance in Ireland, one of the countries most severely affected by the global financial crisis. Using longitudinal data from a nationally representative child cohort study, collected before and after the recession at ages 9 and 13 years, we estimate panel models to examine the impact of income changes on standardized tests. We explore both objective and subjective measures of recession impact, and investigate non-linearities and effect heterogeneity using quantile regression. While income is strongly associated with educational performance overall, there is little evidence of a short-run negative impact of income shocks during the Great Recession on children's test scores.