Constraint Method for Possibilistic Mean-variance Portfolio with Transaction Costs and Lending

2010 ◽  
Vol 5 (9) ◽  
pp. 73-84
Author(s):  
Xue Deng ◽  
Junfeng Zhao ◽  
Lihong Yang ◽  
Rongjun Li
Keyword(s):  
Transaction Costs ◽  
Mean Variance Portfolio ◽  
Mean Variance ◽  
Constraint Method

It’s All in the Timing: Simple Active Portfolio Strategies that Outperform Naïve Diversification

2012 ◽  
Vol 47 (2) ◽  
pp. 437-467 ◽  
Author(s):  
Chris Kirby ◽  
Barbara Ostdiek
Keyword(s):  
Transaction Costs ◽  
Asset Allocation ◽  
Estimation Risk ◽  
Portfolio Strategies ◽  
Out Of Sample ◽  
Volatility Timing ◽  
Naïve Diversification ◽  
Mean Variance Portfolio ◽  
Timing Strategies ◽  
Mean Variance

AbstractDeMiguel, Garlappi, and Uppal (2009) report that naïve diversification dominates mean-variance optimization in out-of-sample asset allocation tests. Our analysis suggests that this is largely due to their research design, which focuses on portfolios that are subject to high estimation risk and extreme turnover. We find that mean-variance optimization often outperforms naïve diversification, but turnover can erode its advantage in the presence of transaction costs. To address this issue, we develop 2 new methods of mean-variance portfolio selection (volatility timing and reward-to-risk timing) that deliver portfolios characterized by low turnover. These timing strategies outperform naïve diversification even in the presence of high transaction costs.

scholarly journals Inclusão de Custos de Transação Não-Lineares na Otimização Média-Variância

2005 ◽  
Vol 3 (2) ◽  
pp. 195
Author(s):  
José Euclides De Melo Ferraz ◽  
Christian Johannes Zimmer
Keyword(s):  
Approximation Algorithm ◽  
Transaction Costs ◽  
Portfolio Optimization ◽  
Financial Market ◽  
Optimal Portfolio ◽  
Market Impact ◽  
Market Data ◽  
Bid Ask Spread ◽  
Mean Variance Portfolio ◽  
Mean Variance

In this article we propose a new way to include transaction costs into a mean-variance portfolio optimization. We consider brokerage fees, bid/ask spread and the market impact of the trade. A pragmatic algorithm is proposed, which approximates the optimal portfolio, and we can show that is converges in the absence of restrictions. Using Brazilian financial market data we compare our approximation algorithm with the results of a non-linear optimizer.

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