Robust Bayesian Portfolio Choices

Anderson, Evan W.; Cheng, Ai-ru

doi:10.1093/rfs/hhw001

Cited by 27 publications

(16 citation statements)

References 38 publications

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“…2 Numerous other combination and related Bayesian model averaging and learning approaches have also been proposed in the literature to help improve on the standard portfolio allocation procedures; see, e.g., Tu and Zhou (2011) and Anderson and Cheng (2016) and the many additional references therein.…”

Section: Introductionmentioning

confidence: 99%

Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions

Bollerslev

Patton

Quaedvlieg

2018

Journal of Econometrics

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We propose a new framework for modeling and forecasting common financial risks based on (un)reliable realized covariance measures constructed from high-frequency intraday data. Our new approach explicitly incorporates the effect of measurement errors and time-varying attenuation biases into the covariance forecasts, by allowing the ex-ante predictions to respond more (less) aggressively to changes in the ex-post realized covariance measures when they are more (less) reliable. Applying the new procedures in the construction of minimum variance and minimum tracking error portfolios results in reduced turnover and statistically superior positions compared to existing procedures. Translating these statistical improvements into economic gains, we find that under empirically realistic assumptions a risk-averse investor would be willing to pay up to 170 basis points per year to shift to using the new class of forecasting models.

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Section: Introductionmentioning

confidence: 99%

Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions

Bollerslev

Patton

Quaedvlieg

2018

Journal of Econometrics

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“…The robust formulation of the mean-variance model focuses on an investor that chooses the best portfolio under the worst possible scenario. The worst prior is chosen from an uncertainty set, which can be related to expected returns, covariances or some prior rule, for example (Anderson andCheng, 2016, p.1362;Goldfarb and Iyengar, 2003, p.5;Lorenzo et al, 2007, p.42-43). This is based on the multiple-priors and ambiguity-aversion literature coined by Gilboa and Schmeidler (1989) and Epstein and Schneider (2003).…”

Section: Moment Restrictionsmentioning

confidence: 99%

“…By Bayesian-averaging or optimizing combination weights for a mean-variance investor, past research documents out-of-sample performance gains through combining high bias with high variance strategies (Anderson and Cheng, 2016;Tu and Zhou, 2011;DeMiguel et al, 2009b;Kan and Zhou, 2007).…”

Section: Moments Forecastingmentioning

confidence: 99%

“…Following Kirby and Ostdiek (2012), I consider 1 = 1 and 2 = 2. Anderson andCheng (2016, p.1349) suggest robust versions of the ̂, (ac-mv), ̂, (acbs) and ̂− , (ac-mv-min) portfolios, which allow uncertainty about future expectations.…”

Section: Reward-to-risk Timing Portfoliomentioning

confidence: 99%

“…2. Following Anderson andCheng (2016, p.1351) I will consider a fixed model uncertainty parameter of = 4. This is because, they show that their dynamic method is inferior to a fixed in the case of monthly data (Anderson andCheng, 2016, p.1365).…”

Section: Robust Portfoliosmentioning

confidence: 99%

See 2 more Smart Citations

An Updated Review of (Sub-)Optimal Diversification Models

Bock

2018

SSRN Journal

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In the past decade many researchers have proposed new optimal portfolio selection strategies to show that sophisticated diversification can outperform the naïve 1/N strategy in out-ofsample benchmarks. Providing an updated review of these models since DeMiguel et al. (2009b), I test sixteen strategies across six empirical datasets to see if indeed progress has been made. However, I find that none of the recently suggested strategies consistently outperforms the 1/N or minimum-variance approach in terms of Sharpe ratio, certaintyequivalent return or turnover. This suggests that simple diversification rules are not in fact inefficient, and gains promised by optimal portfolio choice remain unattainable out-of-sample due to large estimation errors in expected returns. Therefore, further research effort should be devoted to both improving estimation of expected returns, and possibly exploring diversification rules that do not require the estimation of expected returns directly, but also use other available information about the stock characteristics.

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Bayesian learning in dynamic portfolio selection under a minimax rule

Cai,

2024

OR Spectrum

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Robust Bayesian Portfolio Choices

Cited by 27 publications

References 38 publications

Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions

Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions

An Updated Review of (Sub-)Optimal Diversification Models

Bayesian learning in dynamic portfolio selection under a minimax rule

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