In this paper we propose further advancements in the Markov chain stock model. First, we provide a formula for the second order moment of the fundamental price process with transversality conditions that avoid the presence of speculative bubbles. Second, we assume that the process of the dividend growth is governed by a finite state discrete time Markov chain and, under this hypothesis, we are able to compute the moments of the price process. We impose assumptions on the dividend growth process that guarantee finiteness of price and risk and the fulfilment of the transversality conditions. Subsequently, we develop non parametric statistical techniques for the inferential analysis of the model. We propose estimators of price, risk and forecasted prices and for each estimator we demonstrate that they are strongly consistent and that properly centralized and normalized they converge in distribution to normal random variables, then we give also the interval estimators. An application that demonstrate the practical implementation of methods and results to real dividend data concludes the paper.

Novel advancements in the Markov chain stock model: analysis and inference

D'AMICO, Guglielmo;DE BLASIS, RICCARDO
2017-01-01

Abstract

In this paper we propose further advancements in the Markov chain stock model. First, we provide a formula for the second order moment of the fundamental price process with transversality conditions that avoid the presence of speculative bubbles. Second, we assume that the process of the dividend growth is governed by a finite state discrete time Markov chain and, under this hypothesis, we are able to compute the moments of the price process. We impose assumptions on the dividend growth process that guarantee finiteness of price and risk and the fulfilment of the transversality conditions. Subsequently, we develop non parametric statistical techniques for the inferential analysis of the model. We propose estimators of price, risk and forecasted prices and for each estimator we demonstrate that they are strongly consistent and that properly centralized and normalized they converge in distribution to normal random variables, then we give also the interval estimators. An application that demonstrate the practical implementation of methods and results to real dividend data concludes the paper.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/669925
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