Advancement of Optimal Portfolio Models with Short-Sales and Transaction Costs: Methodology and Effectiveness

Wan-Jiun Paul Chiou and Jing-Rung Yu

Chapter 104 in Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning:(In 4 Volumes), 2020, pp 3649-3674 from World Scientific Publishing Co. Pte. Ltd.

Abstract: 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.

Keywords: Financial Econometrics; Financial Mathematics; Financial Statistics; Financial Technology; Machine Learning; Covariance Regression; Cluster Effect; Option Bound; Dynamic Capital Budgeting; Big Data (search for similar items in EconPapers)
JEL-codes: C01 C1 G32 (search for similar items in EconPapers)
Date: 2020
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