Speaker
Description
In practice, we often face the challenge of multiple testing, which, without appropriate corrections, may lead to too many rejected true null hypothesis (type I error rate). The challenge also needs to be dealt with in the energy sector of the economy. One such case is trading with emission coupons, which we address in this study. The purpose of emission coupons is to reduce the emissions of carbon dioxide. Trading with coupons takes place at two levels: on the primary and secondary market. Here, companies which actively trade on the emission markets, strive to provide themselves with enough allowance for carbon dioxide emissions.
On the primary market, there are auctions every working day at 11 o’clock. The secondary market is active ten hours a day every working day. There is trading going on continuously on the secondary market and the traders want to know which day and which hour are optimal for selling or buying emission coupons (when is the actual price higher or lower than daily or weekly average).
There are various possible approaches for testing statistical significance of differences between average prices such as t-test, Wilcoxon sig-rank test, Tukey test and permutation test. In this study, we focus on comparison between paired t-test and a permutation test.
Our simulations show that both tests are appropriate and have about the same test power. But the paired t-test is faster and easier to use, so we choose it as more appropriate.
We also attempt to find out which multiple testing procedure is the most appropriate – with which procedure do we get the best test power and how big are differences between procedures.
Finally, we move away from simulations to real-life data in order to propose a relevant trading strategy.
