Optimized Portfolio Using a Forward-Looking Expected Tail Loss

In this paper, I construct an optimal portfolio by minimizing the expected tail loss (ETL) derived from the forward-looking natural distribution of the Recovery Theorem (RT). The RT is one of the first successful attempts at deriving an unparameterized natural distribution of future asset returns. This distribution can be used as the criterion function in an expected tail loss (ETL) portfolio optimization problem. I find that the portfolio constructed using the RT outperforms both the equally-weighted portfolio and a portfolio constructed using historical ETL. The portfolio constructed using the RT has the smallest historical tail loss, smallest maximum drawdown, highest Sortino Ratio, and highest Sharpe Ratio.