Academic journal article Journal of Business Economics and Management

Lithuanian Stock Market Analysis Using a Set of Garch Models

Academic journal article Journal of Business Economics and Management

Lithuanian Stock Market Analysis Using a Set of Garch Models

Article excerpt

1. Introduction

Generalized autoregressive conditional heteroskedasticity models (GARCH) are quite popular all over the world. These models can be used for stock, bond, indices, currency, derivative price volatility modelling and forecasting. GARCH models were applied in various areas, so the main point of the author is not to analyse the objects of the research but to find which models are the most popular.

There is not much research which analyses GARCH models in Lithuania. Girdzijauskas and Simutis (informatics) (2002), Danilenko (mathematics) (2007) applied GARCH (1,1) model for financial markets. Girdzijauskas and Simutis I2002) analysed S&P500 index's volatility so there is only one work of Danilenko (2007), in which the volatility of the OMXV index is modelled and forecasted. These researches are not carried out by economists so the main point of the articles is to present the main characteristics of the GARCH model and not the ability of modelling and forecasting volatility. Such authors as Leipus, Norvaisa (2004) and Klivecka (2007) analysed the GARCH models from a mathematical background. So there is no research in which some different GARCH type of models were applied to the Lithuanian stock market. There is a lack of such researches and economic view.

The aim of the research is: after analyzing stock price volatility factors and specifics of generalized autoregressive conditional heteroskedasticity models as a tool of volatility modelling, to create a classification system of stock price volatility factors and also practically to apply a set of "GETIP" models to the Lithuanian stock market. "GETIP" is an acronym for five GARCH type models which are applied to the OMXV index and to all corporations from the Main and Secondary lists. "GETIP" models are GARCH (1,1), EGARCH (1,1), TARCH (1,1), IGARCH (1,1) and PARCH (1,1).

The tasks of the article are:

1. To inspect the reasons of stock return volatility.

2. To suggest a classification system of stock price volatility factors.

3. To apply GARCH models to the Lithuanian stock market: OMXV index and to all corporations from the Main and Secondary lists.

The object of the research is Lithuanian stock market (OMXV index and all stocks of corporations from the Main and Secondary lists).

In this article statistical, mathematical and econometric methods are used, i.e. correlation analysis, static and dynamic prognostication, various unit root tests (ADF, PP), ARCH-LM--heteroskedasticity test, autocorrelation, partial autocorrelation, ARMA (1,1), calculated "LADSH" model suitability selection criterions, various prognostication accuracy estimation parameters, applied set of general autoregressive conditional heteroskedasticity models "GETIP", descriptive statistics, regression analysis, time series. Thus, qualitative and quantitative models are used. For applying the GARCH models Eviews6 software was used.

GARCH models are widely applied for modelling the volatility of various markets, but nobody tried to apply these models to the Lithuanian stock market. In this work for the first time various GARCH models are applied to the Lithuanian stock market and the best ones are chosen for modelling and forecasting. The research is carried out using not only the OMXV index but also all the corporations from the Main and Secondary lists. Such a wide investigation of GARCH models allowed us to find the most suitable GARCH model not only for the market as a whole, but also for every company separately and for different sectors.

2. Factors of stock return volatility

Macroeconomic variables play a key role in asset pricing theories. For this reason, many authors have empirically studied the link between macroeconomic variables and stock market volatility. A number of studies document that a relationship exists between macroeconomic variables and equity market returns. …

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