Volatility Modeling for Forecasting Stock Index with Fixed Parameter Distributional Assumption
نویسندگان
چکیده
The aim of this paper is to empirically investigate the in sample and out of sample forecasting performance of several GARCH-type models such as GARCH, EGARCH and APARCH model with Gaussian, student-t, Generalized error distribution (GED), student-t with fixed DOF 10 and GED with fixed parameter 1.5 distributional assumption in case of Colombo Stock Exchange (CSE), Sri Lanka. The daily All Share Price Index (ASPI) of CSE from January 02, 1998 to December 29, 2006 for a total number of 2150 observations is used for empirical analysis. We consider first 1950 observations for in sample estimation and last 200 observations for out of sample forecasting evaluation. Our empirical study showed that fixed DOF 10 of student-t density and fixed parameter 1.5 of GED density fail to improve the in sample estimation performance compared to student-t and GED distributional assumption. Among all of these models, APARCH model with student-t density give better in sample estimation results. In case of out-of-sample forecasting performance we found that APARCH model with all distributional assumption give lower value of Mean Squared Error (MSE) and Mean Absolute Error (MAE). According to the densities student-t distribution with fixed DOF 10, student-t and Gaussian distributional assumptions give better results in case of GARCH, EGARCH and APARCH model respectively. The estimation results of SPA test suggest that APARCH model with Gaussian distributional assumption give better forecasting performance in case of all share price index of CSE, Sri Lanka. 1 Assistant Professor, Department of Statistics, Statistics and Mathematics School, Yunnan University of Finance and Economics, Kunming-650221, P.R. China e-mail: [email protected] 2 Lecturer in Statistics, Biraldah College, Rajshahi Education Board, Rajshahi, Bangladesh 3 Lecturer in Statistics, Kafuria Degree College, Bangladesh National University, Bangladesh Article Info: Received : October 2, 2012. Revised : October 30, 2012. Published online : January 15, 2013 110 Md. Mostafizur Rahman et al. JEL classification numbers: C52, C53, H54, G15.
منابع مشابه
A Fuzzy Random Walk Technique to Forecasting Volatility of Iran Stock Exchange Index
Study of volatility has been considered by the academics and decision makers dur-ing two last decades. First since the volatility has been a risk criterion it has been used by many decision makers and activists in capital market. Over the years it has been of more importance because of the effect of volatility on economy and capital markets stability for stocks, bonds, and foreign exchange mark...
متن کاملModeling Stock Market Volatility Using Univariate GARCH Models: Evidence from Bangladesh
This paper investigates the nature of volatility characteristics of stock returns in the Bangladesh stock markets employing daily all share price index return data of Dhaka Stock Exchange (DSE) and Chittagong Stock Exchange (CSE) from 02 January 1993 to 27 January 2013 and 01 January 2004 to 20 August 2015 respectively. Furthermore, the study explores the adequate volatility model for the stoc...
متن کاملThe role in index jumps and cojumps in forecasting stock index volatility: Evidence from the Dow Jones index
Modeling and forecasting realized volatility is of paramount importance. Previous studies have examined the role of both the continuous and jump components of volatility in forecasting. This paper considers how to use index level jumps and cojumps across index constituents for forecasting index level volatility. In combination with the magnitude of past index jumps, the intensity of both index ...
متن کاملInvestigating the Asymmetry in Volatility for the Iranian Stock Market
This paper investigates the asymmetry in volatility of returns for the Iranian stock market using the daily closing values of the Tehran exchange price index (TEPIX) covering the period from March 25, 2001 to July 25, 2012, with a total of 2743 observations. To this end, two sets of tests have been employed: the first set is based on the residuals derived from a symmetric GARCH (1,1) model. The...
متن کاملFORECASTING FINANCIAL VOLATILITY: EVIDENCE FROM CHINESE STOCK MARKET by
Volatility models and their forecasts are of interest to many types of economic agents, especially for financial risk management. Since 1982 when Engle proposed the Autoregressive Conditionally Heteroscedastic (ARCH) model, there have emerged numerous models for forecasting volatility. Given the vast number of models available, agents must decide which one to use. This paper explores a number o...
متن کامل