The effect of estimation risk on optimal portfolio choice

Klein, Roger; Bawa, Vijay S.

doi:10.1016/0304-405x(76)90004-0

Cited by 467 publications

(237 citation statements)

References 13 publications

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“…This special case corresponds to the setting in Klein and Bawa (1976), wh;o make the same observation about the irrelevance of estimation risk in computing I· As those authors explain, allowing for estimation risk simply lowers the fraction invested in the tangent portfolio, since the price of risk in (76) can then be rewritten as…”

Section: The Optimization Problemmentioning

confidence: 90%

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Analyzing investments whose histories differ in length

Stambaugh

1997

Journal of Financial Economics

171

114

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This study explores multivariate methods for investment analysis based on return histories that differ in length across assets. The longer histories provide greater information about moments of return, not only for the longer-history assets, but for the shorter-history assets as well. To account for the remaining parameter uncertainty, or 'estimation risk' , portfolio opportunities are characterized by a Bayesian predictive distribution.Examples involving emerging markets demonstrate the value of using the combined sample of histories and accounting for estimation risk, as compared to truncating the sample to produce equal-length histories or ignoring estimation risk by using maximum-likelihood estimates. ABSTRACTThis study explores multivariate methods for investment analysis based on a sample of return histories that differ in length across assets. The longer histories provide greater information about moments of returns, not only for the longer-history assets, but for the shorter-history assets as well.To account for the remaining parameter uncertainty, or "estimation risk," portfolio opportunities are characterized by a Bayesian predictive distribution. Examples involving emerging markets demonstrate the value of using the combined sample of histories and accounting for estimation risk, as compared to truncating the sample to produce equal-length histories or ignoring estimation risk by using maximum-likelihood estimates.

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Section: The Optimization Problemmentioning

confidence: 90%

“…As illustrated by Zellner and Chetty (1965), Klein and Bawa (1976), and others, portfolio opportunities can be assessed in a Bayesian framework, wherein the conditional distribution p(Rr+ 1 /4>r) is obtained using standard Bayesian principles. First consider the case in which s is non-stochastic.…”

Section: The Bayesian Approachmentioning

confidence: 99%

Analyzing investments whose histories differ in length

Stambaugh

1997

Journal of Financial Economics

171

114

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“…26 Here it is reasonable to suppose that the RMSEs and the variances are closely related 27 , allowing us to use the RMSEs as an indication of how the variances of the forecasts evolve. Recall Tables 4 and 5, the non-monotonic RMSEs imply that the variances of the forecasts are also non-monotonic 28 , they increase up until H = 6; 12 months and then decline.…”

Section: E¤ect Of Parameter Uncertaintymentioning

confidence: 99%

Decision‐Based Forecast Evaluation of UK Interest Rate Predictability

Sirichand

Hall

2015

Journal of Forecasting

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This paper illustrates the importance of density forecasting and forecast evaluation in portfolio decision making. The decision‐making environment is fully described for an investor seeking to optimally allocate her portfolio between long and short Treasury bills, over investment horizons of up to 2 years. We examine the impact of parameter uncertainty and predictability in bond returns on the investor's allocation and we describe how the forecasts are computed and used in this context. Both statistical and decision‐based criteria are used to assess the predictability of returns. Our results show sensitivity to the evaluation criterion used and, in the context of investment decision making under an economic value criterion, we find some potential gain for the investor from assuming predictability. Copyright © 2015 John Wiley & Sons, Ltd.

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“…In general, that means to treat µ and Σ themselves as random variables and to derive the predictive return distributions. First introduced by Mao & Särndal (1966) and further contributed to by Kalymon (1971), Brown (1976) and Klein & Bawa (1976), an expanding part of the literature especially attended to the combination of data with prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Multiple tests for the performance of different investment strategies

2011

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractIn the context of modern portfolio theory, we compare the out-of-sample performance of 8 investment strategies which are based on statistical methods with the out-of-sample performance of a family of trivial strategies. A wide range of approaches is considered in this work, including the traditional sample-based approach, several minimum-variance techniques, a shrinkage, and a minimax approach. In contrast to similar studies in the literature, we also consider shortselling constraints and a risk-free asset. We provide a way to extend the concept of minimum-variance strategies in the context of short-selling constraints. A main drawback of most empirical studies on that topic is the use of simple-testing procedures which do not account for the effects of multiple testing. For that reason we conduct several hypothesis tests which are proposed in the multiple-testing literature. We test whether it is possible to beat a trivial strategy by at least one of the non-trivial strategies, whether the trivial strategy is better than every non-trivial strategy, and which of the non-trivial strategies are significantly outperformed by naive diversification. In our empirical study we use monthly US stock returns from the CRSP database, covering the last 4 decades.Keywords: Asset allocation, Certainty equivalent, Investment strategy, Markowitz, Multiple tests, Naive diversification, Out-of-sample performance, Portfolio optimization, Sharpe ratio.JEL classification: C12, G11. AbstractIn the context of modern portfolio theory, we compare the out-of-sample performance of 8 investment strategies which are based on statistical methods with the out-of-sample performance of a family of trivial strategies. A wide range of approaches is considered in this work, including the traditional sample-based approach, several minimum-variance techniques, a shrinkage, and a minimax approach. In contrast to similar studies in the literature, we also consider short-selling constraints and a risk-free asset. We provide a way to extend the concept of minimum-variance strategies in the context of short-selling constraints. A main drawback of most empirical studies on that topic is the use of simple-testing procedures which do not account for the effects of multiple testing. For that reason we conduct several hypothesis tests which are proposed in the multiple-testing literature. We test whether it is possible to beat a trivial strategy by at least one of th...

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The effect of estimation risk on optimal portfolio choice

Cited by 467 publications

References 13 publications

Analyzing investments whose histories differ in length

Analyzing investments whose histories differ in length

Decision‐Based Forecast Evaluation of UK Interest Rate Predictability

Multiple tests for the performance of different investment strategies

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