Does beta react to market conditions? Estimates of ‘bull’ and ‘bear’ betas using a nonlinear market model with an endogenous threshold parameter

Woodward, George; Anderson, Heather M.

doi:10.1080/14697680802595643

Cited by 45 publications

(35 citation statements)

References 31 publications

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“…However, recent empirical studies develop and apply such models to the risk premiums of stocks. For example, Chen, Lin, and Yu () and Woodward and Anderson () apply threshold‐type CAPM frameworks to investigate regime‐switching in stock returns, particularly driven by time‐varying β s. Gulen, Xing, and Zhang () and Ozoguz () utilize two‐state Markov‐switching models to investigate CRSP value‐weighted portfolios. Their frameworks assume that the same unobserved state variable governs risk‐adjusted stock returns ( α ), the volatility of excess returns, and the factor coefficients.…”

Section: Literature Reviewmentioning

confidence: 99%

New sources of housing market risk: Asset pricing for the US state‐level housing markets

Huang

2019

International Finance

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This study develops three novel housing risk factors to explore the sources of risk in US state‐level housing markets. The asset‐pricing models enrich our investigations into housing markets from three perspectives: housing boom–bust, volatile–calm, and factors’ time‐varying impact regimes. The findings indicate that state‐level housing returns all show significant exposure to systemic risk of housing markets, but the demographic and economic factors’ explanatory powers for housing returns differ across markets. Although the liquidity factor is significant under all three frameworks, the inventory and credit factors show significant impacts only in the impact‐switching specification. There are differences in the responses of housing returns to various risk factors and shifts in housing regimes across state housing markets, depending on whether the states exhibited housing bubbles. The results suggest that an effective housing stabilization mechanism must be capable of tracking shifts in the relations between housing markets and risk factors.

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Section: Literature Reviewmentioning

confidence: 99%

New sources of housing market risk: Asset pricing for the US state‐level housing markets

Huang

2019

International Finance

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“…In down market, prices are failing and going down, which shows a lack of confidence. Recently, Mitchell and Pulvino () and Woodward and Anderson () ended with the same conclusion that there is a correlation between the security's Beta and the market states.…”

Section: Random Betamentioning

confidence: 99%

A multiple objective stochastic portfolio selection problem with random Beta

Abdelaziz¹,

Masmoudi

2013

Int Trans Operational Res

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When selecting a portfolio, we need to consider, in general, the portfolio return and portfolio risk. Many risk measures have been used in portfolio selection problems as the Beta risk measure, introduced by the capital asset pricing model. Most of the existing research papers suppose that security's Beta has a deterministic value. Recently, many researchers argued that in selecting the optimal portfolio, securities’ Beta should be considered as an uncertain parameter. In this paper, we set up fundamentals to model the portfolio's Beta as a random variable and propose a multiple objective stochastic portfolio selection model with random Beta. To solve the proposed model, we apply a stochastic goal programming approach. A numerical example from the US stock exchange market is reported.

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“…Clinebell et al (1993) show that observed differences of beta coefficients between "bull" and "bear" market conditions are significant. More recently, Woodward and Anderson (2009) applying a logistic smooth transition market model for Australian industry portfolios report that "bull" and "bear" betas are significantly different for most industries while the transition between "bull" and "bear" states is rather abrupt. The variation on asset returns (Rosenberg and Guy, 1976a, b;Bos and Newbold, 1984) has been attributed to the influence of microeconomic factors such as operational changes in the company or macroeconomic factors such as the rate of inflation and expectations about relevant future events.…”

Section: Introductionmentioning

confidence: 99%

Forecasting time-varying daily betas: a new nonlinear approach

Messis

Zapranis²

2016

Managerial Finance

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Purpose -The purpose of this paper is to examine the predictive ability of different well-known models for capturing time variation in betas against a novel approach where the beta coefficient is treated as a function of market return. Design/methodology/approach -Different GARCH models, the Kalman filter algorithm and the Schwert and Seguin model are used against our novel approach. The mean square error, the mean absolute error and the Diebold and Mariano test statistic constitute the measures of forecast accuracy. All models are tested over nine consecutive years and three different samples. Findings -The results show substantial differences in predictive accuracy among the samples. The new approach of modelling the systematic risk overwhelms the rest of the models in longer samples. In the smallest sample, the Kalman filter random walk model prevails. The examination of parameters between two groups of stocks with best and worst accuracy results depicts significant variations. For these stocks, the iid assumption of return is rejected and large differences exist on diagnostic tests. Originality/value -This study contributes to the literature with different ways. First, it examines the predictive accuracy of betas with different well-known models and introduces a novel approach. Second, after constructing betas from the estimated models' parameters, they are used for out-of-sample instead of in-sample forecasts over nine consecutive years and three different samples. Third, a more closely examination of the models' parameters could signal at an early stage the candidate models with the expected lowest forecasting errors. Finally, the study carries out some diagnostic tests for examining whether the existence of iid normal returns is accompanied by better performance.

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Does beta react to market conditions? Estimates of ‘bull’ and ‘bear’ betas using a nonlinear market model with an endogenous threshold parameter

Cited by 45 publications

References 31 publications

New sources of housing market risk: Asset pricing for the US state‐level housing markets

New sources of housing market risk: Asset pricing for the US state‐level housing markets

A multiple objective stochastic portfolio selection problem with random Beta

Forecasting time-varying daily betas: a new nonlinear approach

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