Forecasting stock market volatility in India - Using linear and non - Linear models
subject: BSE Sensex; Emerging markets; Forecasting; GARCH model; Volatility
description: Volatility models and their forecasting performance attracted the interest of many economic agents, especially for financial risk management. The role of economic agents is to decide which one will be best model for forecasting volatility. This paper examines the modeling and forecasting performance of BSE Sensex daily stock market returns over the period from 1 July 1997 to 31 October 2008, by using simple Random Walk, GARCH, EGARCH and TGARCH models. The out-of-sample forecasts are evaluated by using MAE, RMSE, MAPE and Theil - U Statistics. The result suggests the standardized residual of white noise series strongly rejects the null hypothesis for GARCH model and capture the serial dependence and inherent nonlinearity series. Moreover, Random walk model dominates the forecasting performance and it is considered as the best model followed by the TGARCH model. International Economic Society.
creator: Srinivasan K.; Deo M.
source: International Journal of Economic Perspectives, Vol-4, No. 4, pp. 617-622.
date: 2010-01-01
rights: Restricted Access
relation: ISSN: 13071637
format: Online
language: English
type: Article
coverage: Srinivasan K., Department of Management Studies, Christ University, Bangalore, Karnataka - 560 029, India; Deo M., Department of Commerce (SOM), Pondicherry Central University, Kalapet, Puducherry, 605 014, India