Optimal Beats Naive Diversification: Asset Allocation Using High-Frequency Data
| Autor: | Nuria Alemany, Enrique Salvador, Vicent Aragó |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Transaction cost
portfolio construction 010407 polymers Economics and Econometrics Computer science Covariance matrix Diversification (finance) Asset allocation performance measurement 01 natural sciences General Business Management and Accounting Stock market index 0104 chemical sciences 03 medical and health sciences 0302 clinical medicine 030220 oncology & carcinogenesis Accounting portfolio theory Econometrics Portfolio Performance measurement Finance Modern portfolio theory |
| Popis: | This article evaluates the usefulness of high-frequency data in optimal portfolio choice. The authors use a comprehensive list of major stock indexes and different frequencies of observations. Furthermore, they consider the impact of economic cycles, microstructure noise, and seasonality on performance. Their results show the ability of high-frequency data–based strategies to beat both monthly and daily based strategies and the benchmark equally weighted portfolio, even in presence of transaction costs. The authors also find that the outperformance arises from the reduction in the estimation error of the covariance matrix, which offsets the increase in transaction costs. TOPICS:Performance measurement, portfolio construction, portfolio theory Key Findings • The authors evaluate the usefulness of high-frequency data in optimal portfolio choice. • They employ several stock indexes and different frequencies of observations. • The authors consider the impact of economic cycles, microstructure noise, and intraday seasonality on performance. |
| Databáze: | OpenAIRE |
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