Forecasting Higher Moments Of Returns For Emerging Markets
The stock market is one of the most attractive options for investors, but selecting the right market for investment is not an easy task. Investors forecast to find the best market that gives them higher returns. Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification. There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision-making. Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices. However, there have been very few studies of groups of stock markets or indices. The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets and there is no one single perfect technique that forecasts accurately. In this context, this study aimed to examine the predictive performance of linear, nonlinear, artificial intelligence, frequency domain, and hybrid models to find an appropriate model to forecast the higher moments of returns emerging markets in order to predict the volatility of markets in the future. In this study, I’m using daily returns obtained from www.msci.com, for analysis. Python is used for applying Self-exciting threshold autoregressive (SETAR), Autoregressive integrated moving average (ARIMA), Artificial neural networks (ANN), Singular Spectrum analysis (SSA), Hybrid model (HM) on data set, for the best possible results.
Keywords: Higher moments, regression,Python,Forecasting,Model Training,Daily Stock Returns,Machine Learning,ARIMA
Tools: Python,Juypter Notebook
Department: Department of Business Studies
Project Team Members
| Name |
Email |
|
Ameer Bushra
|
bushra2018@namal.edu.pk |
Project Poster
