This study examines long-run relationships and short-run dynamic causal linkages among the U.S., Japanese, and ten Asian emerging stock markets, with the particular attention to the 1997-1998 Asian financial crisis. Extending related empirical studies, comparative analyses of pre-crisis, crisis,...

This study proposes a semiparametric estimate and a test for base-independence equivalence scale. Our semiparametric approach is based on nondensity weighted loss function in contrast to Pendakur's (1999) density weighted loss function. Simulation results indicate that our specification tends to...

This study examines long-run relationships and short-run dynamic causal linkages among the US, Japanese, and ten Asian emerging stock markets, with the particular attention to the 1997-1998 Asian financial crisis. Extending related empirical studies, comparative analyses of pre-crisis, crisis,...

We construct twelve marginal--copula combinations using three marginal distributions (normal, <italic>t</italic> and <italic>S_U </italic>-normal) and four types of copulas (normal, skewed normal, <italic>t</italic> and skewed <italic>t</italic>). Bivariate empirical evidence shows that the choice of marginal distribution plays a more important role in the Value...

We have developed a measure for systemic risk under the bivariate SU-normal distribution, and estimated systemic risk conditional upon the VaR of financial institutions. Simulation results show that both the normal and the quantile regression estimates are downward biased relative to the...

This study provides a nonparametric test for the parametric conditional distribution in the sample selection model. The nonparametric test statistic proposed by Fan et al. (2006) is applied to the Korean housing demand model. Test results indicate that the conditional normality in the housing...

This study compares parametric and nonparametric quantile regression methods using Monte Carlo simulations. Simulation results indicate that the nonparametric quantile regression approach is more appropriate, particularly when the underlying model is nonlinear or the error term follows a...