Abstract
Prior information about a financial market is very essential for investor to invest money on parches share from the stock market which can strengthen the economy. The study examines the relative ability of various models to forecast daily stock indexes future volatility. The forecasting models that employed from simple to relatively complex ARCH-class models. It is found that among linear models of stock indexes volatility, the moving average model ranks first using root mean square error, mean absolute percent error, Theil-U and Linex loss function criteria. We also examine five nonlinear models. These models are ARCH, GARCH, EGARCH, TGARCH and restricted GARCH models. We find that nonlinear models failed to dominate linear models utilizing different error measurement criteria and moving average model appears to be the best. Then we forecast the next two months future stock index price volatility by the best (moving average) model.
Highlights
Volatility in stock market has been one of the most analyzed issues in the past decades
The purpose of this paper is to examine the relative ability of various models to forecast daily stock index future volatility on the basis of error measurement and find the best forecasting model which is suitable for Bangladesh
The purpose of this paper is to examine the relative ability of various models to forecast daily stock index futures volatility
Summary
Volatility in stock market has been one of the most analyzed issues in the past decades. Financial market volatility has a wider impact on financial regulation, monetary policy and macro economy. A high volatility in a stock market creates a bad impact for the country’s economy. For this reason the volatility is an important issue that concerns government policy making, market analysis, corporate and financial managers. To make the market be efficient and make reliable to the investor, many businessmen try to forecast the volatility because the stock market is one of the sources for the industry to raise money
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