In this study, we predict the daily volatility of the S&P CNX NIFTY market index of India using the basic "heterogeneous autoregressive" (HAR) and its variant. In doing so, we estimated several HAR and Log form of HAR models using different regressor. The different regressors were obtained by...

In this study, we predict the daily volatility of the S&P CNX NIFTY market index of India using the basic "heterogeneous autoregressive" (HAR) and its variant. In doing so, we estimated several HAR and Log form of HAR models using different regressor. The different regressors were obtained by...

In this study, a vector autoregression (VAR) model with time-varying parameters (TVP) to predict the daily Indian rupee (INR)/US dollar (USD) exchange rates for the Indian economy is developed. The method is based on characterization of the TVP as an optimal control problem. The methodology is a...

The purpose of this paper is to develop and identify the best hybrid model to predict stock index returns. We develop three different hybrid models combining linear ARIMA and non-linear models such as support vector machines (SVM), artificial neural network (ANN) and random forest (RF) models to...

This study attempts to forecast the next day’s returns of two time series in the Hang Seng Index (HSI) and Standard & Poor’s (S&P) 500 indices using Artificial Neural Networks (ANN) with past returns as input variables. Results from ANN are compared with those from the...