论文标题
股息折扣模型的评论：从确定性到随机模型
A review of the Dividend Discount Model: from deterministic to stochastic models
论文作者
论文摘要
本章介绍了从基本模型（Williams 1938，Gordon和Shapiro 1956）开始的股息折扣模型的综述，再到更近的复杂模型（Ghezzi and Piccardi 2003，Barbu etal。2017，D'Amico和de Blasis 2018），将其重点放在股票过程的模型上，而不是折叠式因素，而不是折现型的模型。本章从介绍基本估值模型，并在执行计算时需要考虑一些一般方面。然后，第1.3节介绍了戈登增长模型（Gordon 1962）的一些扩展（Malkiel 1963，Fuller和Hsia 1984，Molodovsky等，1965年，Brooks and Helms 1990，Barsky和De Long 1993），并报告了一些经验证据。可以在Kamstra（2003）和Damodaran（2012）中找到对Gordon股票评估模型及其扩展的扩展评论。在第1.4节中，重点是最新的进步，这些进步使我们成为马尔可夫链以建模股息过程（Hurley and Johnson 1994，Yao 1997，Hurley and Johnson 1998，Ghezzi and Piccardi 2003，Barbu等人，Barbu等人，2017年，D'Amico and de Blasis and de Blasis 2018）。这些模型的优点是获得取决于股息系列状态的不同估值，从而使模型更接近现实。此外，这些模型允许获得单股票或股票投资组合的风险的度量。
This chapter presents a review of the dividend discount models starting from the basic models (Williams 1938, Gordon and Shapiro 1956) to more recent and complex models (Ghezzi and Piccardi 2003, Barbu et al. 2017, D'Amico and De Blasis 2018) with a focus on the modelling of the dividend process rather than the discounting factor, that is assumed constant in most of the models. The Chapter starts with an introduction of the basic valuation model with some general aspects to consider when performing the computation. Then, Section 1.3 presents the Gordon growth model (Gordon 1962) with some of its extensions (Malkiel 1963, Fuller and Hsia 1984, Molodovsky et al. 1965, Brooks and Helms 1990, Barsky and De Long 1993), and reports some empirical evidence. Extended reviews of the Gordon stock valuation model and its extensions can be found in Kamstra (2003) and Damodaran (2012). In Section 1.4, the focus is directed to more recent advancements which make us of the Markov chain to model the dividend process (Hurley and Johnson 1994, Yao 1997, Hurley and Johnson 1998, Ghezzi and Piccardi 2003, Barbu et al. 2017, D'Amico and De Blasis 2018). The advantage of these models is the possibility to obtain a different valuation that depends on the state of the dividend series, allowing the model to be closer to reality. In addition, these models permit to obtain a measure of the risk of the single stock or a portfolio of stocks.
