Modeling daily price limits |
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| Institution: | 1. Department of Finance, National Central University, Chung Li, Taiwan 320;1. Department of Pharmacology, JSS College of Pharmacy, Ooty, India;2. Department of Neurochemistry, National Institute of Mental Health & Neuro Sciences, Bangalore, India;3. Department of Entomology and Nematology, and Comprehensive Cancer Research Center, University of California, Davis, United States;1. School of Mathematics, Statistics and Computer Science, University of KwaZula-Natal, Private Bag X01, Scottsvile, Pietermaritzburg 3209, South Africa;2. Department of Mathematics, Visva-Bharati University, Santiniketan, West Bengal 731235, India;3. Department of Mathematics, Turku Hansda Lapsa Hemram Mahavidyalay (Burdwan University), West Bengal 731216, India;1. Faculty of Business, Multimedia University, Melaka, Malaysia, Malaysia;2. Faculty of Management, Multimedia University, Cyberjaya, Malaysia, Malaysia;1. Pavlov Institute of Physiology, St. Petersburg 199034, Russia;2. Department of Integrative Biology and Physiology, University of California, Los Angeles, CA 90095, USA;3. Brain Research Institute, University of California, Los Angeles, CA 90095, USA;4. Institute for Information Transmission Problems, Moscow 127994, Russia;5. Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan 420006, Russia |
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| Abstract: | This paper characterizes the behavior of observed asset prices under price limits and proposes the use of two-limit truncated and Tobit regression models to analyze regression models whose dependent variable is subject to price limits. Through a proper arrangement of the sample, these two models, the estimation of which is easy to implement, are applied only to subsets of the sample under study, rather than the full sample. Using the estimation of simple linear regression model as an example, several Monte Carlo experiments are conducted to compare the performance of the maximum likelihood estimators (MLEs) based on these two models and a generalized method of moments (GMM) estimator developed by K. C. John Wei and R. Chiang. The results show that under different price limits and various distributional assumptions for the error terms, the MLEs based on the two-limit Tobit and truncated regression models and the GMM estimator perform reasonably well, while the naive OLS estimator is downward biased. Overall, the MLE based on the two-limit Tobit model outperforms the other estimators. |
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