Risk aversion, prudence and temperance. It is a matter of gap between moments

Higher order risk preferences are important determinants of choices under uncertainty. We build a questionnaire different from usually adopted ones: our questionnaire is simpler in order to reduce the number of random choices, and it includes questions with largely diversified stake sizes to observe different gaps between moments. Moreover, we collect results from a large and heterogeneous population to provide more general and unbiased results. Our results confirm the preference of the majority of the respondents for higher odd and lower even moments of the expected return distribution. However, we highlight three features: (i) the importance of the gap between the values of the corresponding moments of the two choices, (ii) the behavioral change in presence of a positive/zero/negative expected value, (iii) the huge heterogeneity in behaviors, also due to the complexity of the choice as an important driver of the propensity to switch from choosing on the basis of preferences to choosing randomly. We also find that age and geographical location are important determinants of risk propensity

Skewness in energy returns: Estimation, testing and implications for tail risk

In this paper we estimate the skewness of the unconditional distribution of energy returns and test its statistical significance. We compare the performance of traditional and robust tests for skewness with those based on the implied unconditional skewness in a TGARCH model with Gram-Charlier (TGARCH-GC) innovations. We also analyze the implications of TGARCH-GC skewness for tail risk through evaluation of Value-at-Risk (VaR) and expected shortfall (ES) accuracy. Our results show that crude oil (Brent and WTI) and Gasoline returns are negatively skewed, while we do not find evidence of skewed distribution for other energy returns such as Heating oil, Kerosene and Natural gas. This indicates that the returns of the former are likely to encapsulate more largely the effect of negative shocks and so present higher tail risk than those of the latter. These results differ from traditional and robust tests for skewness providing important information on how to improve mean-variance risk management measures. Indeed, we find that the three-moment VaR and ES measures based on the third-order Cornish Fisher (CF3) expansion for the unconditional distribution of returns considerably improve their corresponding two-moment ones. We adopt CF3 to disentangle skewness effects from kurtosis in tail risk.Financial support from the Spanish Government under project PID2021-124860NB-I00 and from the Generalitat Valenciana under project CIPROM/2021/060 is gratefully acknowledged

Hyperparameter Learning via Distributional Transfer

Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We propose to transfer information across tasks using learnt representations of training datasets used in those tasks. This results in a joint Gaussian process model on hyperparameters and data representations. Representations make use of the framework of distribution embeddings into reproducing kernel Hilbert spaces. The developed method has a faster convergence compared to existing baselines, in some cases requiring only a few evaluations of the target objective