Investment Horizon, Diversification, and the Efficiency of Alternative Beta Forecasts

Hawawini, Gabriel; Vora, Ashok

doi:10.1111/j.1475-6803.1982.tb00621.x

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…A number of authors searched for an optimal combination of measurement and prediction periods (see [37], [44], and [45]), while others attempted to modify historical estimates based on regression to the mean or on Bayesian techniques [6], [46]. The usefulness of the techniques is still in dispute [19], [21]. Rosenberg and others (see [38], [39], [40], and [41]) have studied the prediction of beta from underlying fundamental variables.…”

Section: Parameter Stabilitymentioning

confidence: 99%

A Shifting Regimes Approach to the Stationarity of the Market Model Parameters of Individual Securities

Hays¹,

Upton²

1986

The Journal of Financial and Quantitative Analysis

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Section: Parameter Stabilitymentioning

confidence: 99%

A Shifting Regimes Approach to the Stationarity of the Market Model Parameters of Individual Securities

Hays¹,

Upton²

1986

The Journal of Financial and Quantitative Analysis

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“…Their conclusions are supported by the empirical work of Levy (1984), but challenged by empirical works by Hawawini and Vora (1981) and Saniga et al (1981). Hawawini and Vora (1982) calculated betas with several different methodologies and using daily, weekly, biweekly, and monthly returns and compared their calculated prior period betas to daily betas calculated from the subsequent 600 days. Prior period betas calculated based on daily returns were found to have the highest correlation with the subsequent betas.…”

Section: Literature Reviewmentioning

confidence: 92%

Mean‐variance analysis with REITs in mixed asset portfolios

Waggle

Moon

2006

Managerial Finance

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PurposeAims to test to determine whether the selection of the historical return time interval (monthly, quarterly, semiannual, or annual) used for calculating real estate investment trust (REIT) returns has a significant effect on optimal portfolio allocations.Design/methodology/approachUsing a mean‐variance utility function, optimal allocations to portfolios of stocks, bonds, bills, and REITs across different levels of assumed investor risk aversion are calculated. The average historical returns, standard deviations, and correlations (assuming different time intervals) of the various asset classes are used as mean‐variance inputs. Results are also compared using more recent data, since 1988, with, data from the full REIT history, which goes back to 1972.FindingsUsing the more recent REIT datarather than the full dataset results in optimal allocations to REITs that are considerably higher. Likewise, using monthly and quarterly returns tends to understate the variability of REITs and leads to higher portfolio allocations.Research limitations/implicationsThe results of this study are based on the limited historical return data that are currently available for REITs. The results of future time periods may not prove to be consistent with the findings.Practical implicationsNumerous research papers arbitrarily decide to employ monthly or quarterly returns in their analyses to increase the number of REIT observations they have available. These shorter interval returns are generally annualized. This paper addresses the consequences of those decisions.Originality/valueIt has been shown that the decision to use return estimation intervals shorter than a year does have dramatic consequences on the results obtained and, therefore, must be carefully considered and justified.

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