Forecasting Financial Market  Case Study: Tehran Stock Market<br/>

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Forecasting Financial Market Case Study: Tehran Stock Market

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 42816 (01)
University: Sharif University of Technology
Department: Industrial Engineering
Advisor(s): Modarres Yazdi, Mohammad
Abstract:
In this thesis, we examine different forecasting methods to predict volatility in financial markets. Tehran Exchange Price Index (TEPIX) is adapted to forecast in short and long term periods. TEPIX is the most important index in Tehran Stock Market which is officially reported daily. Autoregressive Integrated Moveing Average models (ARIMA), Generalaized Autoregressive Heteroskedastic models (GARCH) and Artificial Neural Networks (ANN) are used for forecasting TEPIX. Spectral Analysis is also regarded as a completely new approach in financial mathematics to forecast TEPIX in short and long term periods. We consider different criteria to compare the performance of different methods of forecasting. The best method is proposed for forecasting TEPIX in short term and long term periods measured by these criteria. Then, we can propose a heuristic hybrid method by combining the best model with other models of forecasting. At the end, a statistical test is used to test the null hypothesis of equality of accuracy of different methods of forecasting
Keywords:
Spectral Analysis ; Neural Networks ; General Autoregressive Conditional Heteroskedastic (GARCH) ; Volatility ; Forecasting ; Stock Market ; Tehran Stock Exchange ; Autoregressive Integrated Moving Average (ARIMA)

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