Asymmetric dependence structures for regional stock markets: An unconditional quantile regression approach

The Nonlinear Links Between Urbanization And CO2: Evidence From Unconditional Quantile And Threshold Regression

“…However, the distributions of the CQR (Conditional Quantile Regression) approach are defined as conditional on specific covariates, which leads to an important limitation that it cannot capture dependence structures in its entirety (Dong et al, 2020). Sometimes researchers find it beyond the scope of the study to use conditional quantiles and prefer unconditional quantiles (Porter, 2015).…”

COVID-19 and stock returns: Evidence from the Markov switching dependence approach

“…Two recent economic and political crises had magnificent impact on the level of stock market dependence and volatility spillovers between Qatar and its GCC neighbours ( Charfeddine and Refai, 2019 ). Economic policy uncertainty and global economic factors explain the asymmetric dependence structure with stock markets in Asia, America and Europe ( Guo et al, 2018 , Dong et al, 2020 ).…”

“…For example, the major stock indexes in the US reveal links of an incline in dependence structure to the increasing popularity of index products, and this aligns with Asian and European market behaviour ( Baltussen et al, 2019 , Ji et al, 2019 ). Moreover, the US stock markets show an asymmetric association/dependence, and the link depends on both the sign and size of the stock market shocks ( Shahzad et al, 2018 ), influenced by various factors related to uncertain economic policy and unfavourable global events ( Guo et al, 2018 , Dong et al, 2020 ). The US stock markets also exhibit high contagion risks from Asian and European markets ( Abbara and Zevallos, 2014 , Shim et al, 2016 , Bensaida et al, 2018 , Luo and Chen, 2018 ), which aligns well with Akhtaruzzaman et al’s (2020) and Onali’s (2020) observations, as discussed above.…”

Probability of informed trading during the COVID-19 pandemic: the case of the Romanian stock market

“…However, as Dong et al ( 2020 ) argued, in the CQR the dependent variable distribution is specified conditional on a certain set of factors, leading to a serious limitation, since it cannot capture the dependence structures in its entirety. To overcome this issue, Firpo et al ( 2009 ) developed unconditional quantile regression (UQR) by using the influence function (IF) and the recentred influence function (RIF).…”

“…This suggests that regressing a particular statistic, such as the mean, generates the same coefficients as the OLS estimates, and this principle applies to any statistics of interest along the dependent variable distribution. Furthermore, according to Dong et al ( 2020 ), the conditional expectation of the can be designed as a function of the explanatory variables, i.e., . In addition, if we select the τth quantile as the statistic of interest and choose to estimate the density functions for each quantile based on Kernel density techniques, the RIF, given is specified as follows: …”