Understanding Portfolio Efficiency with Conditioning Information

AbstractI develop two new types of portfolio efficiency when returns are predictable. The first type maximizes the unconditional Sharpe ratio of excess returns and differs from unconditional efficiency unless the safe asset return is constant over time. The second type maximizes conditional mean-variance preferences and differs from unconditional efficiency unless, additionally, the maximum conditional Sharpe ratio is constant. Using stock data, I quantify and test their performance differences with respect to unconditionally and fixed-weight efficient returns. I also show the relevance of the two new portfolio strategies to test conditional asset pricing models.

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Linear Beta Pricing with Inefficient Benchmarks

Quarterly Journal of Finance ◽

10.1142/s2010139213500043 ◽

2013 ◽

Vol 03 (01) ◽

pp. 1350004 ◽

Cited By ~ 4

Author(s):

George Diacogiannis ◽

David Feldman

Keyword(s):

Asset Pricing ◽

Market Risk ◽

Expected Returns ◽

Pricing Model ◽

Pricing Models ◽

Expected Return ◽

Risk Premiums ◽

Asset Pricing Models ◽

Current Asset ◽

Mean Variance

Current asset pricing models require mean-variance efficient benchmarks, which are generally unavailable because of partial securitization and free float restrictions. We provide a pricing model that uses inefficient benchmarks, a two-beta model, one induced by the benchmark and one adjusting for its inefficiency. While efficient benchmarks induce zero-beta portfolios of the same expected return, any inefficient benchmark induces infinitely many zero-beta portfolios at all expected returns. These make market risk premiums empirically unidentifiable and explain empirically found dead betas and negative market risk premiums. We characterize other misspecifications that arise when using inefficient benchmarks with models that require efficient ones. We provide a space geometry description and analysis of the specifications and misspecifications. We enhance Roll (1980), Roll and Ross's (1994), and Kandel and Stambaugh's (1995) results by offering a "Two Fund Theorem," and by showing the existence of strict theoretical "zero relations" everywhere inside the portfolio frontier.

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Correction: A Mean-Variance Framework for Tests of Asset Pricing Models

Review of Financial Studies ◽

10.1093/rfs/7.4.803 ◽

1994 ◽

Vol 7 (4) ◽

pp. 803-804 ◽

Cited By ~ 1

Author(s):

Shmuel Kandel ◽

Robert F. Stambaugh

Keyword(s):

Asset Pricing ◽

Pricing Models ◽

Asset Pricing Models ◽

Mean Variance

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ON THE ROLE OF SKEWNESS, KURTOSIS, AND THE LOCATION AND SCALE CONDITION IN A SHARPE RATIO PERFORMANCE EVALUATION SETTING

International Journal of Theoretical and Applied Finance ◽

10.1142/s0219024915500375 ◽

2015 ◽

Vol 18 (06) ◽

pp. 1550037 ◽

Cited By ~ 4

Author(s):

BENJAMIN R. AUER

Keyword(s):

Performance Measures ◽

Fund Performance ◽

Sharpe Ratio ◽

Investment Fund ◽

Return Distribution ◽

Cross Sectional ◽

Asset Return ◽

Mean Variance ◽

Skewness And Kurtosis

In recent years, researchers and practitioners have invested considerable effort in the development of new investment fund performance measures that account for mean, variance and the higher moments of the return distribution. To justify the application and necessity of the new performance measures in decision-making, some authors argue that the theoretical conditions required to use the Sharpe ratio are violated by high skewness and kurtosis in empirical asset return data. In this note, we highlight that high levels of skewness and kurtosis and even cross-sectional variations in skewness and kurtosis do not allow a decision-theoretic rejection of the Sharpe ratio. However, we also point out that while it is hard to discard the measure on decision-theoretic grounds, it can be challenged on technical grounds because it has several undesirable properties.

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