Functional smoothing for risk management of energy assets

In this paper we propose a new method for constructing single-asset investment strategies that can be used for hedging and risk management, with emphasis on the highly volatile energy asset class. The method consists of exploiting three stylized facts of asset returns, momentum, mean reversion and bubbles, by taking non-overlapping segments of the data that are used in a functional-type of analysis. We illustrate the workings of the proposed method with real data on two of the largest energy ETFs and the ETF for the S&P500. Our results show that the proposed method can perform substantially better than a simple rebalancing strategy or the buy and hold benchmark as it exhibits better risk return characteristics. More importantly, it appears that it can identify turning points relatively fast and is thus suitable for being used as a hedging and risk management tool in the highly unstable energy markets.