ABSTRACT

The main goal of this article is to investigate empirically the Kalman approach to estimate the time-varying beta parameter as a systematic investment risk market in Poland, Czech Republic, and Hungary. In our research, we investigate the assessments of beta on the basis of seven specifications of time-varying beta for the 12 largest companies listed on the Warsaw Stock Exchange (Poland), 7 on Prague Stock Exchange (Czech Republic), and 11 on Budapest Stock Exchange (Hungary). The obtained results are compared with the estimates received on the basis of Sharpe’s linear model. Estimations are made using the maximum likelihood method for monthly data in the period 2005–2017. We are presenting the ranking of the used specifications according to three criteria of goodness of fit and the matrix of correlation coefficients between the results of these specifications. The results show that the Kalman filter estimators outperform the others.

摘要

本文的主要目标是对波兰、捷克共和国和匈牙利的系统投资风险市场使用卡尔曼方法来估计时变贝塔参数进行实证研究。在我们的研究中，我们根据在华沙证券交易所（波兰）上市的 12 家最大的公司、在布拉格证券交易所（捷克共和国）上市的 7 家公司和在布达佩斯上市的 11 家公司的 7 种时变贝塔系数规范，调查了贝塔系数的评估证券交易所（匈牙利）。将获得的结果与根据夏普线性模型收到的估计值进行比较。使用最大似然法对 2005-2017 年期间的月度数据进行估计。我们根据三个拟合优度标准和这些规范结果之间的相关系数矩阵来展示所使用规范的排名。结果表明卡尔曼滤波器估计器优于其他估计器。