Abstract
The modeling of strategies for buying and selling in Stock Market Investment has been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Markov Switching models for forecasting the time series observations based on stock prices. The semi-parametric estimators for these models are a class of popular methods that have been used extensively by researchers to increase the accuracy of estimation. The main part of these estimators is based on kernel functions. Despite the existence of many kernel functions that are capable in applications for forecasting the stock prices, there is a widely use of Gaussian kernel in these estimators. But there is a question if other types of kernel function can be used in these estimators. This paper tries to introduce the other kernel functions that can be a good replacement for this kernel function to increase the ability of Markov Switching models. We first test six popular kernel functions to find the best one based on simulation studies and then offer the new strategy of buying and selling stocks by the best kernel function selection on real data.
Highlights
The many investigations in economic and financial mathematics focused on what makes an investor profitable in the stock market
We considered six Semi-parametric Markov Switching models (called MS-SEMI-K (i), i=1,...,6) based on six kernel functions and two regimes (M=2)
We have offered the strategy of buying and selling stock in financial markets by a special class of Markov switching models based on the joint conditional probability matrix
Summary
The many investigations in economic and financial mathematics focused on what makes an investor profitable in the stock market. We first focus on selecting the best kernel function in a special class of Markov switching models called semi-parametric Markov switching offered by Nademi & Farnoosh (2014) for modeling the time series data and offer the new strategy of buying and selling stocks by the best-selected kernel function of this model on real data. We introduce the Markov switching model introduced by Nademi & Farnoosh (2014) and in the second subsection, their algorithm for estimating the parameters will be reviewed Note that, their semi-parametric algorithm is a part of a more general algorithm as EM algorithm that applies to the class of Markov switching models
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