Robust Estimation and Forecasting of the Capital Asset Pricing Model

Bian, Guorui; McAleer, Michael; Wong, Wing‐Keung

doi:10.1142/s2010495213500073

Cited by 20 publications

(8 citation statements)

References 86 publications

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“…They find that their proposed estimator is adaptive and uniformly better than the LS estimators. Bian et al (2013) apply the model developed by Bian and Wong (1997) to revisit the CAPM on the returns of 12 US portfolios. They show that the g-prior estimators obtained by using the g-prior Bayesian regression model are more efficient than and outperform the LS estimator uniformly on all the 12 US portfolios.…”

Section: Robust Estimationmentioning

confidence: 99%

Review on behavioral economics and behavioral finance

Wong

2020

SEF

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Purpose This paper aims to give a brief review on behavioral economics and behavioral finance and discusses some of the previous research on agents' utility functions, applicable risk measures, diversification strategies and portfolio optimization. Design/methodology/approach The authors also cover related disciplines such as trading rules, contagion and various econometric aspects. Findings While scholars could first develop theoretical models in behavioral economics and behavioral finance, they subsequently may develop corresponding statistical and econometric models, this finally includes simulation studies to examine whether the estimators or statistics have good power and size. This all helps us to better understand financial and economic decision-making from a descriptive standpoint. Originality/value The research paper is original.

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Section: Robust Estimationmentioning

confidence: 99%

Review on behavioral economics and behavioral finance

Wong

2020

SEF

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“…In the empirical study, we find that the robust Bayesian estimate is uniformly more efficient than the least squares estimate in terms of the relative efficiency of one-step ahead forecast mean square error, especially for small samples. Bian, McAleer, and Wong (2013) develop a modified maximum likelihood (MML) estimator for the multiple linear regression model with underlying student t distribution.…”

Section: Other Econometric Models/testsmentioning

confidence: 99%

Management Information, Decision Sciences, and Financial Economics: A Connection

2018

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The paper provides a brief review of the connecting literature in management information, decision sciences, and financial economics, and discusses some research that is related to the three cognate disciplines. Academics could develop theoretical models and subsequent econometric models to estimate the parameters in the associated models, and analyze some interesting issues in the three related disciplines.

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“…Bian et al (2013) develop a modified maximum likelihood (MML) estimator for the multiple linear regression model with underlying Student's t-distribution.…”

Section: Other Econometrics Models and Testsmentioning

confidence: 99%

Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections

Chang

McAleer

Wong

2018

JRFM

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Abstract:The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology, and discusses research issues that are related to the various disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models, as well as conduct simulation to examine whether the estimators in their theories on estimation and hypothesis testing have good size and high power. Thereafter, academics and practitioners could apply theory to analyse some interesting issues in the seven disciplines and cognate areas.

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Robust Estimation and Forecasting of the Capital Asset Pricing Model

Cited by 20 publications

References 86 publications

Review on behavioral economics and behavioral finance

Review on behavioral economics and behavioral finance

Management Information, Decision Sciences, and Financial Economics: A Connection

Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections

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