Asset Pricing in a Generalized Mean-Lower Partial Moment Framework: Theory and Evidence

Harlow, W. V.; Rao, Ramesh P.

doi:10.2307/2330813

Cited by 385 publications

(241 citation statements)

References 36 publications

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“…We choose a mean-variance utility function, which we extend to incorporate downside risk following Harlow and Rao (1989) and Fishburn (1977). This functional form allows us to focus on the "usual" fluctuations around the mean and keeps our results comparable to theoretical papers that use mean variance utility functions.…”

Section: Estimation Inference and Forecastsmentioning

confidence: 99%

Bubbles and Investment Horizons

Guenster

Kole

2009

SSRN Journal

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The current theoretical literature makes contradicting predictions regarding the impact of an investor's horizon on his optimal trading strategy in the presence of bubbles. We analyze this relation empirically using a Regime Switching Model to identify bubbles and crashes. We base our analysis on industry returns and find high positive returns after bubbles at the one-month horizon. At intermediate horizons of 2-4 months our findings are mixed, but thereafter, for horizons up to five years, returns following a bubble are again more positive than returns in the absence of a bubble. We compare a mean-variance as well as a downside-risk averse investor's portfolio allocation in the presence and absence of a bubble. The weight allocated to the bubbly asset is higher for horizons up to 5 years. These findings suggest that even for a rather unsophisticated trader who does not follow daily market news, riding bubbles is a more profitable strategy than refraining from investing in the bubbly asset. Given the broad range of horizons during which riding bubbles is the optimal strategy, our results question the idea that bubbles are zero-sum games.

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Section: Estimation Inference and Forecastsmentioning

confidence: 99%

Bubbles and Investment Horizons

Guenster

Kole

2009

SSRN Journal

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“…Hung (2008) represented Mean-semi variance models for fuzzy portfolio selection using downside risk value and measures which only gauges the negative deviations from a reference return level, to replace variance. In the second direction also we have some wealthy researches for example: initially Harlow and Rao (1989) proposed a framework for asset pricing based on generalized mean-lower partial moment. Simaan (1997) estimated risk value in portfolio selection as absolute deviation instead of variance and introduced a mean-absolute deviation model for portfolio selection problems.…”

Section: Introductionmentioning

confidence: 99%

A multi period portfolio selection using chance constrained programming

Hassanlou¹

2017

10.5267/j.dsl

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This paper considers a portfolio selection problem with normally distributed returns and different rates for borrowing and lending. The primary concern is to determine the amount of investment in different planning horizons when the rate of borrowing is greater than the rate of lending. Chance constrained programming as an appropriate tool for addressing intrinsic uncertainty in portfolio selection problem is used. To solve this nonlinear programming, Genetic Algorithm is utilized. Numerical experiments are performed and the results are analyzed to present the performance of the proposed methodology.Growing Science Ltd. All rights reserved. 7

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“…Recognizing this, many studies have used some measures of downside risk to explain expected returns. Harlow and Rao (1989), for example, use asymmetric β's in empirical tests of mean-semivariance models, a special case of the mean-lower partial moment equilibrium framework developed by Bawa and Lindenberg (1977).…”

Section: Introductionmentioning

confidence: 99%

No-arbitrage conditions and expected returns when assets have different β’s in up and down markets

Xu¹,

Pettit²

2014

J Asset Manag

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We present a model of expected returns when assets have different β's in up and down markets. Using no-arbitrage argument, we show that upside and downside β's are priced separately, and their risk premiums can be expressed in terms of the price and expected payoff of a call and a put option, respectively, on the market index. For the upside β, the higher the price of the call option relative to its expected payoff, the smaller the risk premium; but for the downside β, the higher the price of the put option relative to its expected payoff, the larger the risk premium. Our model provides a useful perspective on what systematic risks are and how they are priced. Empirical evidence shows that contemporaneous stock returns are strongly correlated with downside β's, but weakly correlated with upside β's.

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Asset Pricing in a Generalized Mean-Lower Partial Moment Framework: Theory and Evidence

Cited by 385 publications

References 36 publications

Bubbles and Investment Horizons

Bubbles and Investment Horizons

A multi period portfolio selection using chance constrained programming

No-arbitrage conditions and expected returns when assets have different β’s in up and down markets

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