Empirical test of the efficiency of the UK covered warrants market: Stochastic dominance and likelihood ratio test approach

Chan, Chia Ying; Peretti, Christian de; Qiao, Zhuo; Wong, Wing‐Keung

doi:10.1016/j.jempfin.2011.09.001

Cited by 60 publications

(24 citation statements)

References 58 publications

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“…Further research could also apply the DD tests to explore the short-run underreaction and long-run overreaction phenomena. In addition, the theory of SD has been widely used to explain well-known economic and financial phenomena or financial anomalies and used to test for market efficiency (Lean, et al, 2010;Chan, et al, 2012). It could also be used to assist investors for decision making.…”

Section: Discussionmentioning

confidence: 99%

Stochastic dominance statistics for risk averters and risk seekers: an analysis of stock preferences for USA and China

Bai

McAleer

et al. 2014

Quantitative Finance

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We derive the limiting process of the stochastic dominance statistics for risk averters as well as for risk seekers when the underlying processes might be dependent or independent. We take account of the dependency of the partitions and propose a bootstrap method to decide the critical point. In addition, we illustrate the applicability of the stochastic dominance statistics for both risk averters and risk seekers to analyze the dominance relationship between the Chinese and US stock markets in the entire period as well as the sub-periods before and after the financial crises, including the internet bubble and the recent sub-prime crisis. The findings could be used to draw inferences on the preferences of risk averters and risk seekers in investing in the Chinese and US stock markets. The results also enable us to examine whether there is any arbitrage opportunity in these markets and whether these markets are efficient and investors are rational.

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Section: Discussionmentioning

confidence: 99%

Stochastic dominance statistics for risk averters and risk seekers: an analysis of stock preferences for USA and China

Bai

McAleer

et al. 2014

Quantitative Finance

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“…The MML with asymmetric innovations would be an interesting issue as an extension. Further extensions include the applicability of the MML for linear models to other prominent economics or finance models (as in, for example, Fong et al, 2005Fong et al, , 2008Chan et al, 2012). Another possible area for further research is to compare the beta coefficients estimated above with the equity cost of capital for each portfolio (Wong and Chan, 2004).…”

Section: Discussionmentioning

confidence: 99%

Robust Estimation and Forecasting of the Capital Asset Pricing Model

2013

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In this paper, we develop a modified maximum likelihood (MML) estimator for the multiple linear regression model with underlying student t distribution. We obtain the closed form of the estimators, derive the asymptotic properties, and demonstrate that the MML estimator is more appropriate for estimating the parameters of the Capital Asset Pricing Model by comparing its performance with least squares estimators (LSE) on the monthly returns of US portfolios. The empirical results reveal that the MML estimators are more efficient than LSE in terms of the relative efficiency of one-step-ahead forecast mean square error in small samples.JEL classifications: C1, C2, G1.

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“…Academics and practitioners can apply stochastic dominance tests in many different areas for big data, medium data, and small data. The extant literature for applying stochastic dominance tests includes Lean (2005), Fong, Lean, and, Gasbarro, Wong, and Zumwalt (2007), Lean, Smyth, and Wong (2007), Qiao, Qiao, and Wong (2010), Wong (2010, 2015), Lean, Phoon, and Wong (2012), Chan, de Peretti, Qiao, and Wong (2012), Qiao, Clark, and Wong (2012), Qiao, Wong, and Fung (2013), Qiao and Wong (2015), Hoang, Lean, and Wong (2015), Hoang, Wong, and Zhu (2015), among many others.…”

Section: Stochastic Dominancementioning

confidence: 99%

Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections

2018

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This paper provides a review of some connecting literature in Decision Sciences, Economics, Finance, Business, Computing, and Big Data. We then discuss some research that is related to the six cognate disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models. Moreover, they could then conduct simulations to examine whether the estimators or statistics in the new theories on estimation and hypothesis have small size and high power. Thereafter, academics and practitioners could then apply their theories to analyze interesting problems and issues in the six disciplines and other cognate areas. KeywordsDecision sciences, economics, finance, business, computing, and big data, theoretical models, econometric and statistical models, applications. JEL Classification AbstractThis paper provides a review of some connecting literature in Decision Sciences, Economics, Finance, Business, Computing, and Big Data. We then discuss some research that is related to the six cognate disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models. Moreover, they could then conduct simulations to examine whether the estimators or statistics in the new theories on estimation and hypothesis have small size and high power. Thereafter, academics and practitioners could then apply their theories to analyze interesting problems and issues in the six disciplines and other cognate areas.

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Empirical test of the efficiency of the UK covered warrants market: Stochastic dominance and likelihood ratio test approach

Cited by 60 publications

References 58 publications

Stochastic dominance statistics for risk averters and risk seekers: an analysis of stock preferences for USA and China

Stochastic dominance statistics for risk averters and risk seekers: an analysis of stock preferences for USA and China

Robust Estimation and Forecasting of the Capital Asset Pricing Model

Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections

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