Modeling Stock Market Volatility in India: A Comparison of Univariate Deterministic Models

ICFAI Journal of Applied Finance, pp. 19-33, October 2003

19 Pages Posted: 23 Sep 2004

See all articles by Saikat Sovan Deb

Saikat Sovan Deb

Institute of Chartered Financial Analysts of India (ICFAI) - The Icfai Institute for Management Teachers (IIMT)

Srivyal Vuyyuri

Independent

Bijan Roy

ICFAI University

Abstract

There are various conflicting evidences in existing literature about the predicting power of different volatility-forecasting models. There are evidences in favor of the simpler regression model (Dimson and Marsh 1990), as well as complex GARCH family models (Akgiray 1989, Pagan and Schwert 1989, and Brailsford and Faff 1996). In this paper, monthly volatility of market indices (Sensex & S&PCNX-Nifty) of Indian capital markets has been modeled using eight different univariate models. Out-of-sample forecasting performance of these models has been evaluated using different symmetric, as well as asymmetric loss functions. The GARCH (1,1) model has been found to be the over all superior model based on most of the symmetric loss functions though ARCH (9) has been found to be better than the other models for investors who are more concerned about under predictions than over predictions.

Keywords: Forecasting, volatility, GARCH

JEL Classification: C52, C53, C59

Suggested Citation

Deb, Saikat Sovan and Vuyyuri, Srivyal and Roy, Bijan, Modeling Stock Market Volatility in India: A Comparison of Univariate Deterministic Models. ICFAI Journal of Applied Finance, pp. 19-33, October 2003, Available at SSRN: https://ssrn.com/abstract=594222

Saikat Sovan Deb (Contact Author)

Institute of Chartered Financial Analysts of India (ICFAI) - The Icfai Institute for Management Teachers (IIMT) ( email )

3rd Floor, Astral Heights
Road No.1, Banjara Hills
Hyderabad, 500 034
India

HOME PAGE: http://www.icfai.org

Srivyal Vuyyuri

Independent ( email )

Bijan Roy

ICFAI University ( email )

Hyderabad, Andhra Pradesh
India

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