Resampled Frontiers Versus Diffuse Bayes: An Experiment

Markowitz, Harry M.; Usmen, Nilufer

doi:10.1142/9789812700865_0009

Cited by 41 publications

(38 citation statements)

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“…The Res portfolio turned out to be more financially efficient, diversified, and stable. This is a well-known result in the literature when we compare the traditional Markowitz approach with resampling (Markowitz and Usmen, 2003). As resampling techniques typically generate more diversified portfolios, they tend to reduce turnover and increase financial out-of-sample efficiency.…”

Section: Resultsmentioning

confidence: 80%

“…Resampling techniques (Michaud, 1998) were developed as a way to deal with estimation error. Markowitz recognized that resampling methods could be used to obtain better estimates for the inputs of the mean-variance optimization (Markowitz and Usmen, 2003). Jorion (1991) used the Bayesian approach to overcome the weakness of expected returns estimated solely by sample information.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Several out-of-sample evaluations have shown results in favor of resampling methodology, using different sets of data (see, for instance, Markowitz and Usmen, 2003;Pawley, 2005;Wolf, 2006;Fernandes and Ornelas, 2009). However, these evaluations cannot give definitive conclusions in favor of using resampling, given sampling limitations.…”

Section: Literature Reviewmentioning

confidence: 99%

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Combining equilibrium, resampling, and analyst’s views in portfolio optimization

Fernandes

Ornelas

Cusicanqui³

2012

Journal of Banking & Finance

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Portfolio optimization methodologies play a central role in strategic asset allocation (SAA), where it is desirable to have portfolios that are efficient, diversified, and stable. Since the development of the traditional mean-variance approach of Markowitz (1952), many improvements have been made to overcome problems such as lack of diversification and strong sensitivity of optimal portfolio weights to expected returns.The Black and Litterman (1992) model (BL) is among the most used approaches. The idea behind this model is that expected returns are the result of two important sources of information: the first is market information in the form of equilibrium returns (implicit returns that clear out the outstanding market allocation), and the second is analysts' views, which tilt the market portfolio to another diversified portfolio compatible with investor beliefs. In this fashion, portfolio managers get an intuitive but formal model to generate optimal allocation. However, while the BL model offers a very useful and intuitive approach to deal with asset allocation, the inputs considered for the calculation of equilibrium returns are subject to estimation error, and thus expected returns will also contain estimation error. Michaud (1998) proposed the use of a statistical tool known as resampling to deal with estimation error, which is an important source of lack of diversification in mean-variance portfolios. This technique considers that data come from a stochastic process instead of being a deterministic input as in Markowitz (1952). This paper proposes the use of a portfolio optimization methodology which combines features of both the BL and resampling methodologies. This novel methodology allows the combination of equilibrium and investor's views as in BL, and at same time deals with estimation risk as in Michaud (1998). Thus, it generates robust and diversified optimal allocations which are desirable properties for long-term investors such as central banks and sovereign wealth funds. We empirically test the new methodology using a sample of fixed income and equity indices, achieving very supportive results. We find strong evidence supporting the use of resampling techniques to improve standard models like BL and Markowitz, and this result is more pronounced for medium levels of risk. In general, our proposed methodologies, both with and without views, generated very competitive portfolios compared to the other methodologies, considering the three evaluation dimensions: financial efficiency, diversification, and allocation stability. For medium levels of risk, our methodologies are markedly better than others.

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Section: Resultsmentioning

confidence: 80%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Combining equilibrium, resampling, and analyst’s views in portfolio optimization

Fernandes

Ornelas

Cusicanqui³

2012

Journal of Banking & Finance

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“…Resampling approaches differ in the set of restrictions used to determine the mean-variance frontiers and in the way how the frontiers are averaged to get the definite portfolio weights. Some authors criticize that the unconditionally optimal portfolio does not simply follow from an average over R vectors of conditionally optimal portfolio weights (see, e.g., Scherer [59] or Markowitz and Usmen [50]), others point out that the ad-hoc approach of resampling could be improved by using a Bayesian approach (see, e.g., Scherer [60], or Harvey et al [33,34]). Despite the critique, all those studies appreciate the out-of-sample characteristics of resampled portfolios.…”

Section: Parameter Uncertaintymentioning

confidence: 99%

“…14 Some authors do propose schemes how to generate portfolio decisions from the cross section of the simulation results, see, e.g., Michaud and Michaud [53]. These schemes are, however, criticized by other authors for not being well-founded in decision theory, e.g., Markowitz and Usmen [50] and others mentioned in the text above.…”

Section: Parameter Uncertaintymentioning

confidence: 99%

Risk Control in Asset Management: Motives and Concepts

Dangl

Randl

Zechner

2015

Innovations in Quantitative Risk Management

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In traditional portfolio theory, risk management is limited to the choice of the relative weights of the riskless asset and a diversified basket of risky securities, respectively. Yet in industry, risk management represents a central aspect of asset management, with distinct responsibilities and organizational structures. We identify frictions that lead to increased importance of risk management and describe three major challenges to be met by the risk manager. First, we derive a framework to determine a portfolio position's marginal risk contribution and to decide on optimal portfolio weights of active managers. Second, we survey methods to control downside risk and unwanted risks since investors frequently have nonstandard preferences, which make them seek protection against excessive losses. Third, we point out that quantitative portfolio management usually requires the selection and parametrization of stylized models of financial markets. We, therefore, discuss risk management approaches to deal with parameter uncertainty, such as shrinkage procedures or resampling procedures, and techniques of dealing with model uncertainty via methods of Bayesian model averaging.We thank Victor DeMiguel, Matthias Scherer, Neal Stoughton, Raman Uppal, Arne Westerkamp, Rudi Zagst and an anonymous referee for helpful comments.

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Multiperiod stochastic programming portfolio optimization for diversified funds

Fulton

Bastian

2018

Int. J Fin Econ

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We investigate a multiperiod, stochastic portfolio optimization model for diversified funds choices associated with traditional 401K or 403B plans. This optimization model minimizes the L1‐norm for negative return rate risk (the downside mean absolute deviation) while examining parameters that maintain model feasibility. Important components of the model are the incorporation of appropriate time‐series components and evaluation of scenarios based on investor outlook. A case study experimentation of the model on five potential investment funds using historical data from 2003 to 2013 was conducted, and parameter constraints for diversification and minimum acceptable return rates were manipulated to produce contour plots. The maximum geometric rate of return investment strategy provided by the optimization would have resulted in a 9.7% geometric return rate in 2014 as compared with a 5.0% for a uniform distribution of investment funds across choices.

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Resampled Frontiers Versus Diffuse Bayes: An Experiment

Cited by 41 publications

References 0 publications

Combining equilibrium, resampling, and analyst’s views in portfolio optimization

Combining equilibrium, resampling, and analyst’s views in portfolio optimization

Risk Control in Asset Management: Motives and Concepts

Multiperiod stochastic programming portfolio optimization for diversified funds

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