Optimal Portfolio Choice with Parameter Uncertainty

Kan, Raymond; Zhou, Guofu

doi:10.1017/s0022109000004129

Cited by 590 publications

(439 citation statements)

References 31 publications

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“…Regarding Tu and Zhou (2011), we consider the optimal combination of the 1/N rule and the sample tangency portfolio, and the optimal combination of the 1/N rule and the portfolio in Kan and Zhou (2007), because they are the only ones analytically tractable and considered in more recent literature such as Moorman (2014). We also exclude portfolios rules that do not require optimization and covariance matrix inversion, the two most notable characteristics of the mean-variance portfolios, such as the ones in Kirby and Ostdiek (2012).…”

Section: Portfolio Rulesmentioning

confidence: 99%

“…Kan and Zhou's (2007) Three-Fund Rule ("Kan-Zhou") If the estimation errors of two risky portfolios are not perfectly correlated, one can reduce the estimation errors by combining them. Kan and Zhou (2007) propose using the sample global minimum-variance portfolio to reduce estimation risk for the sample tangency portfolio.…”

Section: Mixture Of Minimum-variance and The 1/n Portfolio ("Ew-min")mentioning

confidence: 99%

“…Kan and Zhou (2007) call their portfolio the three-fund rule since the investor should allocation his wealth into three funds: The sample global minimum-variance portfolio, the sample tangency portfolio, and the risk-free asset. As the number of assets relative to time (N /T ) grows, a larger fraction of wealth is allocated in the global minimum-variance portfolio as the tangency portfolio parameters become increasingly difficult to estimate.…”

Section: Mixture Of Minimum-variance and The 1/n Portfolio ("Ew-min")mentioning

confidence: 99%

“…Tu and Zhou (2011) also combine the 1/N rule with the three-fund rule proposed by Kan and Zhou (2007). This portfolio has weights: (25) andη,π 2 , c 1 , andθ 2 are given in (28).…”

Section: Combination Of 1/n With the Three-fund Portfolio ("Ckz")mentioning

confidence: 99%

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Mean-Variance versus Naave Diversification: The Role of Mispricing

Qiao,

Yan,

Zhang

2016

SSRN Journal

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Section: Portfolio Rulesmentioning

confidence: 99%

Section: Mixture Of Minimum-variance and The 1/n Portfolio ("Ew-min")mentioning

confidence: 99%

Section: Mixture Of Minimum-variance and The 1/n Portfolio ("Ew-min")mentioning

confidence: 99%

“…Tu and Zhou (2011) also combine the 1/N rule with the three-fund rule proposed by Kan and Zhou (2007). This portfolio has weights: (25) andη,π 2 , c 1 , andθ 2 are given in (28).…”

Section: Combination Of 1/n With the Three-fund Portfolio ("Ckz")mentioning

confidence: 99%

See 2 more Smart Citations

Mean-Variance versus Naave Diversification: The Role of Mispricing

Qiao,

Yan,

Zhang

2016

SSRN Journal

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“…Especially, in contrast to other studies (DeMiguel et al, 2009a,b) we take the risk-free asset into consideration. This is extremely important since Tobin's two-fund separation theorem breaks down in the presence of estimation risk (Kan & Zhou, 2007). 4.…”

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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Modeling, Pricing, and Risk Management of Assets and Derivatives in Energy and Shipping

Sclavounos

2012

Encyclopedia of Financial Models

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Derivatives are financial contingent claims designed for the pricing, transfer and management of risk embedded in underlying securities in the fixed income, equity and foreign exchange markets. Their rapid growth spurred their introduction to the energy commodity and shipping markets where the underlying assets are real commodities, crude oil, refined products, natural gas, electricity and shipping tonnage. Risk neutral pricing and stochastic models developed for financial derivatives have been extended to energy derivatives for the modeling of correlated commodity and shipping forward curves and for the pricing of their contingent claims. This has enabled the valuation and risk management of a wide range of assets and derivatives in the energy and shipping markets. They include storage for natural gas, floating storage of crude oil, products and Liquefied Natural Gas in tankers, refineries, power plants and utility scale wind farms, shipping structured securities, cargo vessels and shipping derivatives portfolios.

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Optimal Portfolio Choice with Parameter Uncertainty

Cited by 590 publications

References 31 publications

Mean-Variance versus Naave Diversification: The Role of Mispricing

Mean-Variance versus Naave Diversification: The Role of Mispricing

Multiple tests for the performance of different investment strategies

Modeling, Pricing, and Risk Management of Assets and Derivatives in Energy and Shipping

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