No abstract

The composite quantile estimator is a robust and efficient alternative to the least-squares estimator in linear models. However, it is computationally demanding when the number of quantiles is large. We consider a model-averaged quantile estimator as a computationally cheaper alternative. We derive its asymptotic properties in high-dimensional linear models and compare its performance to the composite quantile estimator in both low-and high-dimensional settings. We also assess the effect on efficiency of using equal weights, theoretically optimal weights, and estimated optimal weights for combining the different quantiles. None of the estimators dominates in all settings under consideration, thus leaving room for both model-averaged and composite estimators, both with equal and estimated optimal weights in practice.

Salmon price is highly volatile and hard to predict. This obscures planning decisions and raises financing costs for market participants. This study considers hedging the spot price uncertainty with salmon futures contracts. It employs a new framework of hedging under square loss, consisting of a new objective function, an optimal hedge ratio and a measure of hedging effectiveness. The new framework aims at minimizing the expected squared forecast error. It generalizes the classical minimum variance hedging as it relaxes the assumption of known expected prices. The salmon futures contracts deliver satisfactory hedging performance, albeit constrained by low liquidity.Therefore, I suggest holding the contract through maturity rather than closing the futures and the spot positions simultaneously. This strategy alleviates the liquidity issue and saves transaction costs.All things considered, hedging with salmon futures is a moderately effective way of handling the salmon price uncertainty. Importantly, the empirical results differ starkly under the two different hedging frameworks. Hence, it is crucial to choose the new framework when expected prices are unknown.