Prior research tends to propose and examine the negative relationship between market segmentation and energy efficiency. Does market segmentation necessarily impair energy efficiency? Considering the critical role that Chinaese government play in managing erergy efficiency, we propose a non-linear relationship between market segmentation and energy efficiency. Using data of 30 provinces in Mainland China during 2000 to 2017, we find an inverse U-shaped relationship between market segmentation and energy efficiency. Our findings remain robust after controlling endogeneity issues. Therefore, a moderate level of market segmentation is acceptable and beneficial for long-term improvement of energy efficiency in emerging economies.

In the presence of non-normally distributed asset returns, an optimal portfolio selection should consider higher-order (co-)moments when no sampling errors exist. However, the curse of dimensionality has already been a serious concern in mean-variance analyses; higher-order (co-)moments also face the same dilemma. This study uses mixed-frequency (MF) data under the assumption that stock returns are generated by a mixed-data sampling (MIDAS) regression model, which shows to provide well-improved estimates for the covariance, co-skewness, and co-kurtosis matrices for higher dimension. We discover that the new, improved estimates used in higher-order (co-)moment portfolio selections can dominate the other existing structured estimates from an out-of-sample perspective in most instances.

The traditional estimation of higher order co-moments of non-normal random variables by the sample analog of the expectation faces a curse of dimensionality, as the number of parameters increases steeply when the dimension increases. Imposing a factor structure on the process solves this problem; however, it leads to the challenging task of selecting an appropriate factor model. This paper contributes by proposing a test that exploits the following feature: when the factor model is correctly specified, the higher order co-moments of the unexplained return variation are sparse. It recommends a general to specific approach for selecting the factor model by choosing the most parsimonious specification for which the sparsity assumption is satisfied. This approach uses a Wald or Gumbel test statistic for testing the joint statistical significance of the co-moments that are zero when the factor model is correctly specified. The asymptotic distribution of the test is derived. An extensive simulation study confirms the good finite sample properties of the approach. This paper illustrates the practical usefulness of factor selection on daily returns of random subsets of S&P 100 constituents.