Forecasting the Sensex and Nifty Indices using ARIMA and GARCH Models

Vol 10 , Issue 1 , January - June 2023 | Pages: 57-75 | Research Paper

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https://doi.org/10.17492/jpi.mudra.v10i1.1012304

Published Online: June 03, 2023

Author Details ( * ) denotes Corresponding author

1. * Tejesh H R, Research Scholar, Commerce, Vijayanagara Sri Krishnadevaraya University Ballari, Karnataka, Ballari, Karnataka, India (hrtejesh@gmail.com)

2. Jeelan Basha V, Professor & Dean, Commerce, Vijayanagara Sri Krishnadevaraya University, Jnana Sagara Campus, Vinayaka Nagar, Cantonment, Ballari-583105, Karnataka, Ballari, Karnataka, India (drjeelanbasha@yahoo.co.in)

This study applies autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH 1, 1) model to forecast daily stock market prices and returns for the BSE 30 (Sensex) and NSE 50 (Nifty) indices. Unit root in the data series is detected through the implementation of ADF, PP and KPSS tests. The findings indicate that the GARCH (1,1) model for both the selected indices provides better predictions than ARIMA models. These research findings will undoubtedly provide valuable insights to the financial institutions, investors, portfolio managers, and other financial market participants.

Keywords

ARCH; ARIMA; GARCH; Stationarity

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