马尔可夫预测模型对股票价格波动的预测分析

Markov Prediction Models and Its Applications in the Analysis on the Volatility of Stock Prices

摘要: 马尔可夫过程最初由俄罗斯数学家马尔可夫(Markov)于1906年研究而得名，它是一类典型的具有马氏性的随机过程。马氏性是指现在发生的事情与过去曾发生的事件无关。简言之，在已知“现在”的条件下，“将来”与“过去”是独立的。马尔可夫过程理论已发展得较为成熟，在自然科学、工程技术及经济金融管理等领域都有广泛的应用。本文以马尔可夫模型的基本理论为出发点，将马尔可夫模型运用在经济金融领域，使用马尔可夫预测模型和加权马尔可夫预测模型对股票价格的波动情况进行预测分析，并以浦发银行的股票价格数据为例进行实证分析。

Abstract: Markov process was originally named after the research of Russian mathematician Markov in 1906. It is a typical stochastic process with Markov property. Markov property refers to the fact that what is happening now is unrelated to past events. In short, given the known “present”, the “future” and “past” are independent. The theory of Markov process has been developed maturely, and it has been widely used in natural science, engineering technology, economic and financial management and other fields. This article takes the basic theory of Markov models as the starting point, applies Markov models to the field of economics and finance, uses Markov prediction models and weighted Markov prediction models to predict and analyze the volatility of stock prices, and conducts experiments using the stock price data of Shanghai Pudong Development Bank as an example.