Narrow Results

This chapter presents advancement of several widely applied portfolio models to ensure flexibility in their applications: Mean–variance (MV), Mean–absolute deviation (MAD), linearized value-at-risk (LVaR), conditional value-at-risk (CVaR), and Omega models. We include short-sales and transaction costs in modeling portfolios and further investigate their effectiveness. Using the daily data of international ETFs over 15 years, we generate the results of the rebalancing portfolios. The empirical findings show that the MV, MAD, and Omega models yield higher realized return with lower portfolio diversity than the LVaR and CVaR models. The outperformance of these risk-return-based models over the downside-risk-focused models comes from efficient asset allocation but not only the saving of transaction costs.

In this chapter, we apply various portfolio models to rebalance portfolios and further analyze their realized performance, including mean-variance (MV), conditional value-at-risk (CVaR), and Omega models. To ensure feasibility, we consider the transaction costs and the optimization of short-selling weights. The empirical results using the daily returns of international funds across 21 countries over 20 years show that the risky portfolios realize higher performance than a naïve diversification, particularly the CVaR model with a confidence level of 95% despite their higher trading costs. The CVaR model, mainly focusing on controlling loss, yields higher performance than those that are based on trade-off between return and volatility, such as the mean−variance and Omega models. The Omega model, however, generates lower downside risk. The superiority of risky portfolios over the equally weighted diversification varies intertemporarily across various models. The excess returns of risky portfolios over the equally weighted diversification are larger when the market is volatile, such as the periods of the subprime mortgage financial crisis and the 2020 COVID-19 recession.

This paper presents the advancement of several widely applied portfolio models to ensure flexibility in their applications: Mean-variance (MV), Mean-absolute deviation (MAD), Linearized value-at-risk (LVaR), Conditional value-at-risk (CVaR), and Omega models. We include short sales and transaction costs in modeling portfolios and further investigate their effectiveness. Using the daily data of international ETFs over 15 years, we generate the results of the rebalancing portfolios. The empirical findings show that the MV, MAD, and Omega models yield a higher realized return with lower portfolio diversity than the LVaR and CVaR models. The outperformance of these risk–return-based models over the downside-risk-focused models comes from efficient asset allocation and not only the saving of transaction costs.