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 Optimal Option Portfolio Strategies: Deepening the Puzzle of Index Option Mispricing

2017 ◽  
Vol 52 (1) ◽  
pp. 277-303 ◽  
Author(s):  
José Afonso Faias ◽  
Pedro Santa-Clara
Keyword(s):  
Asset Allocation ◽  
Risk Premia ◽  
Volatility Risk ◽  
Short Sample ◽  
Option Returns ◽  
Portfolio Strategies ◽  
Portfolio Strategy ◽  
Out Of Sample ◽  
Index Option ◽  
Mean Variance

Traditional methods of asset allocation (such as mean–variance optimization) are not adequate for option portfolios because the distribution of returns is non-normal and the short sample of option returns available makes it difficult to estimate their distribution. We propose a method to optimize a portfolio of European options, held to maturity, with a myopic objective function that overcomes these limitations. In an out-of-sample exercise incorporating realistic transaction costs, the portfolio strategy delivers a Sharpe ratio of 0.82 with positive skewness. This performance is mostly obtained by exploiting mispricing between options and not by loading on jump or volatility risk premia.

Optimal Portfolio Choice with Estimation Risk: No Risk-Free Asset Case

2021 ◽  
Author(s):  
Raymond Kan ◽  
Xiaolu Wang ◽  
Guofu Zhou
Keyword(s):  
Portfolio Choice ◽  
Exact Distribution ◽  
Optimal Portfolio ◽  
Portfolio Construction ◽  
Estimation Risk ◽  
Choice Problem ◽  
Out Of Sample ◽  
Mean Variance Portfolio ◽  
Mean Variance ◽  
Explicit Expressions

We propose an optimal combining strategy to mitigate estimation risk for the popular mean-variance portfolio choice problem in the case without a risk-free asset. We find that our strategy performs well in general, and it can be applied to known estimated rules and the resulting new rules outperform the original ones. We further obtain the exact distribution of the out-of-sample returns and explicit expressions of the expected out-of-sample utilities of the combining strategy, providing not only a fast and accurate way of evaluating the performance, but also analytical insights into the portfolio construction. This paper was accepted by Tyler Shumway, finance.

Out-of-sample performance of the Black-Litterman model

2021 ◽  
pp. 29-51
Author(s):  
Frieder Meyer-Bullerdiek
Keyword(s):  
Portfolio Optimization ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Jel Classification ◽  
Stock Index ◽  
Out Of Sample ◽  
Weighted Portfolio ◽  
German Stock ◽  
Mean Variance Portfolio ◽  
Mean Variance

The aim of this paper is to test the out-of-sample performance of the Black Litterman (BL) model for a German stock portfolio compared to the traditional mean-variance optimized (MV) portfolio, the German stock index DAX, a reference portfolio, and an equally weighted portfolio. The BL model was developed as an alternative approach to portfolio optimization many years ago and has gained attention in practical portfolio management. However, in the literature, there are not many studies that analyze the out-of-sample performance of the model in comparison to other asset allocation strategies. The BL model combines implied returns and subjective return forecasts. In this study, for each stock, sample means of historical returns are employed as subjective return forecasts. The empirical analysis shows that the BL portfolio performs significantly better than the DAX, the reference portfolio and the equally weighted portfolio. However, overall, it is slightly outperformed by the MV portfolio. Nevertheless, the BL portfolio may be of greater interest to investors because -according to this study, where the subjective return forecasts are based on historical returns of a rather long past period of time-it could lead in most cases to lower absolute (normalized) values for the stock weights and for all stocks to smaller fluctuations in the (normalized) weights compared to the MV portfolio. JEL classification numbers: C61, G11. Keywords: Black-Litterman, Mean-variance, Portfolio optimization, Performance.

scholarly journals Mean-Variance Optimization Is a Good Choice, But for Other Reasons than You Might Think

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 29 ◽  
Author(s):  
Andrea Rigamonti
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Risk Measures ◽  
Traditional Approach ◽  
Real Data ◽  
Downside Risk ◽  
Good Choice ◽  
Mean Variance Portfolio ◽  
Mean Variance ◽  
Allocation Decisions

Mean-variance portfolio optimization is more popular than optimization procedures that employ downside risk measures such as the semivariance, despite the latter being more in line with the preferences of a rational investor. We describe strengths and weaknesses of semivariance and how to minimize it for asset allocation decisions. We then apply this approach to a variety of simulated and real data and show that the traditional approach based on the variance generally outperforms it. The results hold even if the CVaR is used, because all downside risk measures are difficult to estimate. The popularity of variance as a measure of risk appears therefore to be rationally justified.

Humans, Econs and Portfolio Choice

2016 ◽  
Vol 07 (02) ◽  
pp. 1750001 ◽  
Author(s):  
Michael J. Best ◽  
Robert R. Grauer
Keyword(s):  
Prospect Theory ◽  
Portfolio Choice ◽  
Loss Aversion ◽  
Asset Allocation ◽  
Power Utility ◽  
Numerical Example ◽  
Portfolio Choices ◽  
Out Of Sample ◽  
Finance Theory ◽  
Mean Variance

We compare the portfolio choices of Humans — prospect theory investors — to the portfolio choices of Econs — power utility and mean-variance (MV) investors. In a numerical example, prospect theory portfolios are decidedly unreasonable. In an in-sample asset allocation setting, the prospect theory results are consistent with myopic loss aversion. However, the portfolios are extremely unstable. The power utility and MV results are consistent with traditional finance theory, where the portfolios are stable across decision horizons. In an out-of-sample asset allocation setting, the power utility and portfolios outperform the prospect theory portfolios. Nonetheless the prospect theory portfolios with loss aversion coefficients of 2.25 and 2 perform well.

scholarly journals SCALED AND STABLE MEAN-VARIANCE-EVAR PORTFOLIO SELECTION STRATEGY WITH PROPORTIONAL TRANSACTION COSTS

2017 ◽  
Vol 18 (4) ◽  
pp. 561-584 ◽  
Author(s):  
Ebenezer Fiifi Emire ATTA MILLS ◽  
Bo YU ◽  
Jie YU
Keyword(s):  
Transaction Costs ◽  
Portfolio Selection ◽  
Downside Risk ◽  
Estimation Risk ◽  
Expected Return ◽  
Proportional Transaction Costs ◽  
Portfolio Returns ◽  
L2 Norm ◽  
The Mean ◽  
Mean Variance

This paper studies a portfolio optimization problem with variance and Entropic Value-at-Risk (evar) as risk measures. As the variance measures the deviation around the expected return, the introduction of evar in the mean-variance framework helps to control the downside risk of portfolio returns. This study utilized the squared l2-norm to alleviate estimation risk problems arising from the mean estimate of random returns. To adequately represent the variance-evar risk measure of the resulting portfolio, this study pursues rescaling by the capital accessible after payment of transaction costs. The results of this paper extend the classical Markowitz model to the case of proportional transaction costs and enhance the efficiency of portfolio selection by alleviating estimation risk and controlling the downside risk of portfolio returns. The model seeks to meet the requirements of regulators and fund managers as it represents a balance between short tails and variance. The practical implications of the findings of this study are that the model when applied, will increase the amount of capital for investment, lower transaction cost and minimize risk associated with the deviation around the expected return at the expense of a small additional risk in short tails.

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