scholarly journals Optimal omega-ratio portfolio performance constrained by tracking error

2020 ◽  
Vol 17 (3) ◽  
pp. 263-280
Author(s):  
Wade Gunning ◽  
Gary van Vuuren
Keyword(s):  
Recent Work ◽  
Tracking Error ◽  
Sharpe Ratio ◽  
Main Axis ◽  
Portfolio Performance ◽  
Optimal Portfolios ◽  
Investment Style ◽  
The Mean ◽  
Omega Ratio ◽  
Mean Variance

The mean-variance framework coupled with the Sharpe ratio identifies optimal portfolios under the passive investment style. Optimal portfolio identification under active investment approaches, where performance is measured relative to a benchmark, is less well-known. Active portfolios subject to tracking error (TE) constraints lie on distorted elliptical frontiers in return/risk space. Identifying optimal active portfolios, however defined, have only recently begun to be explored. The Ω – ratio considers both down and upside portfolio potential. Recent work has established a technique to determine optimal Ω – ratio portfolios under the passive investment approach. The authors apply the identification of optimal Ω – ratio portfolios to the active arena (i.e., to portfolios constrained by a TE) and find that while passive managers should always invest in maximum Ω – ratio portfolios, active managers should first establish market conditions (which determine the sign of the main axis slope of the constant TE frontier). Maximum Sharpe ratio portfolios should be engaged when this slope is > 0 and maximum Ω – ratios when < 0.

scholarly journals Mean-Variance Portfolio Selection with Tracking Error Penalization

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1915
Author(s):  
William Lefebvre ◽  
Grégoire Loeper ◽  
Huyên Pham
Keyword(s):  
Portfolio Selection ◽  
Tracking Error ◽  
Efficient Frontier ◽  
Minimum Variance ◽  
Sharpe Ratio ◽  
Portfolio Strategy ◽  
The Mean ◽  
Mean Variance Portfolio ◽  
Mean Variance ◽  
Variance Criterion

This paper studies a variation of the continuous-time mean-variance portfolio selection where a tracking-error penalization is added to the mean-variance criterion. The tracking error term penalizes the distance between the allocation controls and a reference portfolio with same wealth and fixed weights. Such consideration is motivated as follows: (i) On the one hand, it is a way to robustify the mean-variance allocation in the case of misspecified parameters, by “fitting" it to a reference portfolio that can be agnostic to market parameters; (ii) On the other hand, it is a procedure to track a benchmark and improve the Sharpe ratio of the resulting portfolio by considering a mean-variance criterion in the objective function. This problem is formulated as a McKean–Vlasov control problem. We provide explicit solutions for the optimal portfolio strategy and asymptotic expansions of the portfolio strategy and efficient frontier for small values of the tracking error parameter. Finally, we compare the Sharpe ratios obtained by the standard mean-variance allocation and the penalized one for four different reference portfolios: equal-weights, minimum-variance, equal risk contributions and shrinking portfolio. This comparison is done on a simulated misspecified model, and on a backtest performed with historical data. Our results show that in most cases, the penalized portfolio outperforms in terms of Sharpe ratio both the standard mean-variance and the reference portfolio.

scholarly journals Estratégia Long-Short, Neutra ao Mercado, e Index Tracking Baseadas em Portfólios Cointegrados

2010 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
João Frois Caldeira ◽  
Marcelo Savino Portugal
Keyword(s):  
Ad Hoc ◽  
Tracking Error ◽  
Covariance Matrices ◽  
Return Level ◽  
Index Tracking ◽  
Optimal Portfolios ◽  
The Mean ◽  
Efficient Allocations ◽  
Mean Variance ◽  
Tracking Portfolios

The traditional models to optimize portfolios based on mean-variance analysis aim to determine the portfolio weights that minimize the variance for a certain return level. The covariance matrices used to optimize are difficult to estimate and ad hoc methods often need to be applied to limit or smooth the mean-variance efficient allocations recommended by the model. Although the method is efficient, the tracking error isn’t certainly stationary, so the portfolio can get distant from the benchmark, requiring frequent re-balancements. This work uses cointegration methodology to devise two quantitative strategies: index tracking and long-short market neutral. We aim to design optimal portfolios acquiring the asset prices’ co-movements. The results show that the devise of index tracking portfolios using cointegration generates goods results, replicating the benchmark’s return and volatility. The long-short strategy generated stable returns under several market circumstances, presenting low volatility.

Improving Portfolio Selection Using Option-Implied Volatility and Skewness

2013 ◽  
Vol 48 (6) ◽  
pp. 1813-1845 ◽  
Author(s):  
Victor DeMiguel ◽  
Yuliya Plyakha ◽  
Raman Uppal ◽  
Grigory Vilkov
Keyword(s):  
Risk Premium ◽  
Implied Volatility ◽  
Substantial Improvement ◽  
Sharpe Ratio ◽  
Expected Returns ◽  
Portfolio Performance ◽  
Short Sales ◽  
Volatility Risk ◽  
Out Of Sample ◽  
Mean Variance

AbstractOur objective in this paper is to examine whether one can use option-implied information to improve the selection of mean-variance portfolios with a large number of stocks, and to document which aspects of option-implied information are most useful to improve their out-of-sample performance. Portfolio performance is measured in terms of volatility, Sharpe ratio, and turnover. Our empirical evidence shows that using option-implied volatility helps to reduce portfolio volatility. Using option-implied correlation does not improve any of the metrics. Using option-implied volatility, risk premium, and skewness to adjust expected returns leads to a substantial improvement in the Sharpe ratio, even after prohibiting short sales and accounting for transaction costs.

The Impact of Financial Crises on the Asset Allocation: Classical Theory Versus Behavioral Theory

2019 ◽  
Vol 32 (2) ◽  
pp. 218-236
Author(s):  
Amen Aissi Harzallah ◽  
Mouna Boujelbene Abbes
Keyword(s):  
Financial Crisis ◽  
Risk Aversion ◽  
Asset Allocation ◽  
Global Financial Crisis ◽  
Optimal Portfolio ◽  
Optimal Portfolios ◽  
The Mean ◽  
Degree Of Risk ◽  
The Impact ◽  
Mean Variance

The aim of this article is to compare the portfolio optimization generated by the behavioral portfolio theory (BPT) and the mean variance theory (MVT) by investigating the impact of the global financial crisis on the asset allocation. We use data from the Canadian Stock Exchange over the 2002–2015 period. By comparing both approaches, we show that for any level of aspiration and admissible failure, the BPT optimal portfolio will always contain a part of the mean–variance frontier. Thus, in the case of higher degree of risk aversion induced by typical BPT investors, the security set is located on the upper right of the Markowitz frontier. However, even if the optimal portfolios of MVT and BPT may coincide, MVT investors associated with an extremely low degree of risk aversion will not systematically choose BPT optimal portfolios. Our results also indicate the period of financial crisis generate huge losses in MVT portfolio values that implies a lower expected return and a higher level of risk. Furthermore, we point out the absence of the BPT optimal portfolio when potential losses are higher during the 2008 global financial crisis. JEL: G11, G17, G40

scholarly journals Seleção de Carteiras: Escolha entre modelos baseada em persistência de performance

2020 ◽  
Vol 18 (1) ◽  
pp. 91
Author(s):  
Ricardo De Souza Tavares ◽  
João Frois Caldeira
Keyword(s):  
Portfolio Selection ◽  
Selection Procedure ◽  
Sharpe Ratio ◽  
Suggested Approach ◽  
Average Return ◽  
The Mean ◽  
Investment Portfolios ◽  
Mean Variance

<p>This essay presents an alternative to the problem of choosing between strategies for building investment portfolios. We propose a new portfolio selection procedure, dividing the sample into three equal parts (for estimations initiations, training, and evaluation outside the sample) in which, at each point of time, the strategy with the best performance is chosen in a window of p recent observations for a given criterion. We considered as criteria the mean, variance, and Sharpe ratio, aiming to construct sequences of allocation choices that best adapted to the different contexts and databases analyzed. Results indicate that the suggested approach was capable of generating allocation sequences with good performance in terms of average return and Sharpe ratio.</p>

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