Abstract
This paper aims to compare the volatility forecasting performance of linear and nonlinear models for ISE-30 future index which is traded in Turkish Derivatives Exchangefor the period between 04.02.2005-17.06.2011. As a result of analyses, we conclude that ANN model has better forecasting performance than traditional ARCH-GARCH models. This result is important in many fields of finance such as investment decisions, asset pricing, portfolio allocation and risk management
Highlights
IntroductionAs a barometer of the market risk, volatility is important for investment decisions, asset pricing, portfolio allocation and risk management in finance
Volatility is defined as the fluctations in security prices
In volatility forecasting, in addition to Engle’s (1982) Autoregressive Conditional Heteroscedasticity Model ( ARCH) and Bollerslev’s (1986) Generalized Autoregressive Heteroscedasticity Model ( GARCH), Artifical Neural Network ( ANN)model is being used in the literature.ANN model which mimics the human brain function is successful in the estimation of the stock price behaviour due to its feature of learning from tha data
Summary
As a barometer of the market risk, volatility is important for investment decisions, asset pricing, portfolio allocation and risk management in finance. In this respect, it is crucial to forecast volatility accurately in finance literature. Associated with the increasing importance of volatility, different volatility models come into use in the finance literature. The paper aims to compare the performance of linear and nonlinear models in forecasting the volatility of ISE-30 future contracts which is traded in Turkish Derivatives Exchange. The papers mostly focus on comparing forecasting performance of volatility models in spot markets. Different from the existing literature, this paper focus on forecasting volatility in future markets.
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