Multiperiod Portfolio Optimization with General Transaction Costs

DeMiguel, Victor; Mei, Xiaoling; Nogales, Francisco J.

doi:10.2139/ssrn.2295345

Cited by 6 publications

(10 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DeMiguel et al (2014) show analytically that the optimal trading strategies are confined by a no-trade region centered at a target portfolio in the presence of proportional transaction costs and they give close-form expression for the no-trade region when there is no predictability. With predictability, Lynch and Tan (2010) numerically find the optimal rebalancing rule for each period to be a no-trade region with rebalancing to the boundary.…”

Section: No-trade Region Policymentioning

confidence: 98%

“…With quadratic transaction costs, the closed-form expressions for the optimal number of shares can be obtained based on their framework. With proportional transaction costs and constant opportunity set, DeMiguel et al (2014) study analytically the properties of optimal trading strategies and provide closed-form expression for the notrade regions based on the G&P framework. They also show that the certainty equivalent loss incurred from using a mean-variance utility instead of a CRRA utility of intermediate consumption is small.…”

Section: Mean-variance Frameworkmentioning

confidence: 99%

“…Moreover, the target portfolio is a linear combination of the current optimal portfolio in the absence of transaction costs and the expected future target portfolios. With a finite horizon and a constant opportunity set, DeMiguel et al (2014) show the optimal policy is a linear combination of the Markowitz portfolio, the previous period portfolio and the next period portfolio. The combination of the Markowitz strategy and the next period portfolio can be considered as the investor's target for next period.…”

Section: Linear Policymentioning

confidence: 99%

“…Our work is related to Smith (2011) andDeMiguel et al (2014). Like Brown and Smith (2011), we propose some approximate trading strategies for a multiperiod investor with CRRA power utility, but instead of approximating the dynamic programming recursion (the continuation value functions) of the primal problem, we approximate the primal problem for each period with a quadratic program that can handle problems with more risky assets.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, we consider an investor that maximizes her power utility of intermediate consumption while Brown and Smith (2011) consider an investor maximizes her utility of terminal wealth. DeMiguel et al (2014) consider a mean-variance investor who faces general transaction costs and constant opportunity set. For the case with proportional transaction costs, they give closed-form expressions for the no-trade region.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Portfolio selection with proportional transaction costs and predictability

Mei

Nogales

2018

Journal of Banking & Finance

Self Cite

View full text Add to dashboard Cite

We consider the portfolio optimization problem for a multiperiod investor who seeks to maximize her utility of consumption facing multiple risky assets and proportional transaction costs in the presence of return predictability. Due to the curse of dimensionality, this problem is very difficult to solve even numerically. In this paper, we propose several feasible policies that are based on optimizing quadratic programs. These proposed feasible policies can be easily computed even for many risky assets. We show how to compute upper bounds and use them to study how the losses associated with using the approximate policies depend on different problem parameters. Portfolio Selection with Proportional Transaction Costs and Predictability Xiaoling MeiJavier Nogales AbstractWe consider the portfolio optimization problem for a multiperiod investor who seeks to maximize her utility of consumption facing multiple risky assets and proportional transaction costs in the presence of return predictability. Due to the curse of dimensionality, this problem is very di cult to solve even numerically. In this paper, we propose several feasible policies that are based on optimizing quadratic programs. These proposed feasible policies can be easily computed even for many risky assets. We show how to compute upper bounds and use them to study how the losses associated with using the approximate policies depend on di↵erent problem parameters.

show abstract

Section: No-trade Region Policymentioning

confidence: 98%

Section: Mean-variance Frameworkmentioning

confidence: 99%

Section: Linear Policymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Portfolio selection with proportional transaction costs and predictability

Mei

Nogales

2018

Journal of Banking & Finance

Self Cite

View full text Add to dashboard Cite

show abstract

Multi-period portfolio selection using kernel-based control policy with dimensionality reduction

Takano

Gotoh

2014

Expert Systems with Applications

View full text Add to dashboard Cite

Multiple Regression Model Averaging and the Focused Information Criterion With an Application to Portfolio Choice

Klimenka¹,

Wolter²

2018

Journal of Business & Economic Statistics

View full text Add to dashboard Cite

We consider multiple regression (MR) model averaging using the Focused Information Criterion (FIC). Our approach is motivated by the problem of implementing a mean-variance portfolio choice rule. The usual approach is to estimate parameters ignoring the intention to use them in portfolio choice. We develop an estimation method that focuses on the trading rule of interest.Asymptotic distributions of submodel estimators in the MR case are derived using a localization framework. The localization is of both regression coefficients and error covariances. Distributions of submodel estimators are used for model selection with the FIC. This allows comparison of submodels using the risk of portfolio rule estimators. FIC model averaging estimators are then characterized. This extension further improves risk properties. We show in simulations that applying these methods in the portfolio choice case results in improved estimates compared with several competitors. An application to futures data shows superior performance as well.

show abstract

Multiperiod Portfolio Optimization with General Transaction Costs

Cited by 6 publications

References 28 publications

Portfolio selection with proportional transaction costs and predictability

Portfolio selection with proportional transaction costs and predictability

Multi-period portfolio selection using kernel-based control policy with dimensionality reduction

Multiple Regression Model Averaging and the Focused Information Criterion With an Application to Portfolio Choice

Contact Info

Product

Resources

About