This article applies a new statistical moment, Trimmed L-comoment, in modelling Expected Shortfall ( ES ) and exploits an empirical study on China's stock markets. In comparison with existing models, out-of-sample forecasts and backtests indicate superior accuracy and precision for the models based on Trimmed L-comoments, especially to those based on central moments.
In spite of their importance, third or higher moments of portfolio returns are often neglected in portfolio construction problems due to the computational difficulties associated with them. In this paper, we propose a new robust mean–variance approach that can control portfolio skewness and kurtosis without imposing higher moment terms. The key idea is that, if the uncertainty sets are properly constructed, robust portfolios based on the worst-case approach within the mean–variance setting favor skewness and penalize kurtosis.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.