Multiperiod portfolio optimization with multiple risky assets and general transaction costs

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

doi:10.1016/j.jbankfin.2016.04.002

Cited by 52 publications

(17 citation statements)

References 20 publications

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“…Thirdly, excess returns for the UC portfolios are the lowest across all Ns. These findings appear to corroborate the idea that ignoring transaction costs would be detrimental to portfolio outcome, as documented by Yoshimoto (1996) [10] and Mei et al (2016) [11]. Accordingly, reward to variability is affected for all Ns, further supporting the importance of integrating trading costs in formulating the portfolio selection problem during the training phase.…”

Section: Resultssupporting

confidence: 74%

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Further examination of the 1/N portfolio rule: a comparison against Sharpe-optimal portfolios under varying constraints

Nor¹,

Islam²

2017

EA-XXI

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Practical trading constraints (such as asset bounds and transaction costs) are known to affect the efficient frontier of an investment portfolio. In this study, we investigate out-of-sample trading performances of tangency portfolios against the naïve 1/N policy under varying constraints. Our aim is to deliberate if such constraints are influential to the relative (individual) performances between (of) the two competing strategies. Using FTSE Bursa Malaysia KLCI constituent stocks of 30 companies listed, we form several portfolios with different Ns and constraint specifications. Sample period spans 2006 through 2015, in order to alleviate possible confounding affects on risk/return dynamics caused by the 1MDB financial scandal and the U.S. Federal Reserve increasing its key interest rate starting from December 2015. Performance metrics exhibit sensitivity of portfolios to the degree (variability) of constraints, specifically floor, ceiling and the consideration of trading cost. Among other valuable findings, it has been found out that in all the cases researched the simple 1/N portfolio selection rule offers superior outcome as compared to the tangency portfolios. Generally stated, the naïve policy outperforms the more sophisticated portfolio optimization model in terms of the Sharpe criterion, information ratio and maximum drawdown during the period under investigation. Relative performances remain consistent regardless of the number of stocks included in the portfolio. Нор С. М. кандидат економічних наук, фондовий менеджер, Малайзійський університет Теренггану, Теренггану, Малайзія, дослідник, Інститут стратегічних економічних досліджень Вікторії, Університет Вікторії, Мельбурн, Австралія Іслам С. М. Н. кандидат економічних наук, професор, Інститут стратегічних економічних досліджень Вікторії, Університет Вікторії, Мельбурн, Австралія Подальший розгляд правила 1/N портфоліо: порівняння з оптимальними за Шарпом портфелями за різних обмежень Анотація. Відомо, що практичні торгові обмеження впливають на ефективну межу інвестиційного портфеля. У цій роботі ми досліджуємо позабіржові торговельні показники дотичних портфелів проти наївної політики 1/N за різних обмежень. Наша мета -виявити, чи впливають такі обмеження на дії між конкуруючими стратегіями. Використовуючи акції 30 компаній на фондовій біржі FTSE Bursa Malaysia KLCI, ми формуємо кілька портфелів із різними специфікаціями N та обмеженнями. Вибірка охоплює період з 2006 по 2015 рік, коли в кінці року усувається можливе змішування впливу на динаміку ризику/ прибутковості, викликане фінансовим скандалом 1 МБР, та початком підвищення ключової процентної ставки Федеральною резервною системою США починаючи з грудня 2015 року. Показники ефективності демонструють чутливість портфелів до ступеня (мінливості) обмежень, зокрема, нижньої межі, верхньої межі й урахування вартості торгівлі. Ключові слова: оптимізація портфеля; коефіцієнт Шарпа; коефіцієнт інформації; максимальна просадка; наївна диверсифікація; практичні обмеження. Нор С. М. кандидат экономических наук...

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Section: Resultssupporting

confidence: 74%

“…Although advancements have been made in this area, including the use of complex constraints (e.g. Mei et al, 2016;Ruiz-Torrubiano and Suárez, 2015;Xue et al, 2006) [11][12][13], such constraints are often embedded in the MV model and/or its extensions.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Further examination of the 1/N portfolio rule: a comparison against Sharpe-optimal portfolios under varying constraints

Nor¹,

Islam²

2017

EA-XXI

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“…Two recent examples of this are Gârleanu and Pedersen (2013) and Mei et al (2016), who formulate dynamic mean-variance models with transaction costs in discrete time that correspond to the LQR problem.…”

Section: Optimal Controlmentioning

confidence: 99%

Decision Making under Uncertainty in Financial Markets : Improving Decisions with Stochastic Optimization

Ekblom

2018

Linköping Studies in Science and Technology. Dissertations

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This thesis addresses the topic of decision making under uncertainty, with particular focus on financial markets. The aim of this research is to support improved decisions in practice, and related to this, to advance our understanding of financial markets. Stochastic optimization provides the tools to determine optimal decisions in uncertain environments, and the optimality conditions of these models produce insights into how financial markets work. To be more concrete, a great deal of financial theory is based on optimality conditions derived from stochastic optimization models. Therefore, an important part of the development of financial theory is to study stochastic optimization models that step-by-step better capture the essence of reality. This is the motivation behind the focus of this thesis, which is to study methods that in relation to prevailing models that underlie financial theory allow additional real-world complexities to be properly modeled.The overall purpose of this thesis is to develop and evaluate stochastic optimization models that support improved decisions under uncertainty on financial markets. The research into stochastic optimization in financial literature has traditionally focused on problem formulations that allow closed-form or 'exact' numerical solutions; typically through the application of dynamic programming or optimal control. The focus in this thesis is on two other optimization methods, namely stochastic programming and approximate dynamic programming, which open up opportunities to study new classes of financial problems. More specifically, these optimization methods allow additional and important aspects of many real-world problems to be captured. This thesis contributes with several insights that are relevant for both financial and stochastic optimization literature. First, we show that the modeling of several real-world aspects traditionally not considered in the literature are important components in a model which supports corporate hedging decisions. Specifically, we document the importance of modeling term premia, a rich asset universe and transaction costs. Secondly, we provide two methodological contributions to the stochastic programming literature by: (i) highlighting the challenges of realizing improved decisions through more stages in stochastic programming models; and (ii) developing an importance sampling method that i Decision Making under Uncertainty in Financial Markets can be used to produce high solution quality with few scenarios. Finally, we design an approximate dynamic programming model that gives close to optimal solutions to the classic, and thus far unsolved, portfolio choice problem with constant relative risk aversion preferences and transaction costs, given many risky assets and a large number of time periods.

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“…Mei, X., DeMiguel, V., Nogales, F. J. (Mei, DeMiguel, Nogales, 2016) [12] analyzed the optimal portfolio policy for a multi-period mean-varia nce investor facing multiple risky assets in the presence of general transaction costs. For proportional transaction costs, they gave a closedform expression for a no-trade region, shaped as a multi-dimensional parallelogram, and showed how the optimal portfolio policy can be efficiently computed for many risky assets by solving a single quadratic program.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Portfolio optimization using the GO-GARCH model: evidence from Ukrainian Stock Exchange

Lakshina

Zoriana²,

Deari³

2016

EA-XXI

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Abstract. This paper provides an experimental study on optimal portfolio composition. Data on seven stocks, included in Ukrainian Exchange Index, for the period from January to December 2015 are considered. In total, seven big industrial, electric and military companies are selected from the Ukrainian Exchange: Avdiivka Coke Plant, PJSC; Azovstal Iron and Steel Works, PJSC; Raiffeisen Bank Aval, JSC; Centerenergo, PJSC; Donbasenergo, PJSC; Motor Sich, JSC; and Ukrnafta, OPJC. The sample amounts to 226 observations. The analysis covers descriptive statistics, correlation, and, finally, optimal investment weights, which are calculated using Sharpe ratio. Covariance matrix of returns is estimated by means of generalized orthogonal GARCH model with Gaussian and normalinverse Gaussian distributions for errors. Selected stocks during the considered period have on average negative rates of returns. At the same time, these stocks in most of cases are positively correlated with each other, leading hence to a fewer room for the efficient diversification. Both Gaussian and normal-inverse Gaussian portfolios preclude that on average investment weights one should be focused mainly on Centerenergo and Motor Sich stocks. Based on these results, the investor should buy 46% of Centerenergo's stocks and 34% of Motor Sich's stocks. Selected stocks during the considered period have on average negative rates of returns. At the same time, these stocks in most of cases are positively correlated with each other, leading hence to a fewer room for the efficient diversification. Despite this, our results denoted that implementation of multivariate GARCH together with normal-inverse Gaussian distribution for errors enables to reduce the portfolio risk substantially. Comparing optimal GO-GARCH portfolios with naїve portfolio with all weights equal and Ukrainian Exchange Index we demonstrate that the former provide smaller portfolio variance and better VaR than naїve portfolio and the Index. Keywords: Portfolio; GO-GARCH Model; Return; Risk; Optimizations; Stock Exchange; Portfolio JEL Classіfіcatіon: G11; C58 Acknowledgement. This paper has been prepared under financial support of Ukrainian Stock Exchange. DOI: https://doi.org/10.21003/ea.V160-23 Мацук З. А. кандидат економічних наук, доцент, Івано-Франківський національний технічний університет нафти і газу, Івано-Франківськ, Україна Деарі Ф. кандидат економічних наук, доцент, Північно-Східний європейський університет, Тетово, Республіка Македонія Лакшина В. В. аспірант, старший викладач, Національний дослідний університет «Вища школа економіки», Москва, Росія Оптимізація інвестиційного портфеля за допомогою моделі GO-GARCH (на матеріалах Української фондової біржі) Анотація. У статті представлено емпіричне дослідження процесу формування оптимального портфеля на основі даних семи акцій, які включено до індексу Української біржі, за період з січня по грудень 2015 року. Аналіз включає в себе описову статистику, кореляцію та розрахунок оптимальних ваг у портфелі. Варіаційно-коваріаційна матри...

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Multiperiod portfolio optimization with multiple risky assets and general transaction costs

Cited by 52 publications

References 20 publications

Further examination of the 1/N portfolio rule: a comparison against Sharpe-optimal portfolios under varying constraints

Further examination of the 1/N portfolio rule: a comparison against Sharpe-optimal portfolios under varying constraints

Decision Making under Uncertainty in Financial Markets : Improving Decisions with Stochastic Optimization

Portfolio optimization using the GO-GARCH model: evidence from Ukrainian Stock Exchange

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