modeling 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 stock markets in bangladesh. results of the estimated ma(1)-garch(1,1) model for dse and garch(1,1) model for cse reveal that the stock markets of bangladesh capture volatility clustering, while volatility is moderately persistent in dse and highly persistent in cse. estimated ma(1)-egarch(1,1) model shows that effect of bad news on stock market volatility is greater than effect induced by good news in dse, while egarch(1,1) model displays  that volatility spill over mechanism is not asymmetric in cse. therefore, it is concluded that return series of dse show evidence of three common events, namely volatility clustering, leptokurtosis and the leverage effect, while return series of cse contains leptokurtosis, volatility clustering and long memory. finally, this study explores that ma(1)-garch(1,1) is the best model for modeling volatility of dhaka stock market returns, while garch models are inadequate for volatility modeling of cse returns.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Modeling 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 کامل

Forecasting Stock Market Volatility Using (Non-Linear) Garch Models

In this papeT we study the performance of the GARCH model and two of its non-linear modifications to forecast weekly stock market volatility. The models are the Quadratic GARCH (Engle and Ng. 1993) and the Glosten. Jagannathan and Runkle (1992) models which have been proposed to describe, for example, the often observed negative skewness in stock market indices. We find that the QGARCH model is...

متن کامل

Analysis of Stock Market Volatility by Continuous-time GARCH Models

The discrete time ARCH/GARCH model of Engle and Bollarslev has been enormously influential and successful in the modelling of financial data. Recently, Klüppelberg, Lindner, andMaller (2004) introduced the so-called “COGARCH”model as a continuoustime analogue to the GARCH model. Many aspects of the COGARCH have been investigated, including various of its theoretical properties, its relations to...

متن کامل

modeling volatility: evidence from tehran stock exchange

the research problem investigated in this paper is modeling volatility and analyzing risk and return’s relationship in tehran stock exchange using garch-family models including garch(1,1), garch(2,2), egarch(1,1), pgarch(1,1), tgarch(1,1), garch(1,1)-m and cgarch(1,1). using the daily returns of tehran stock exchange companies, we focused on two portfolios of all the companies during a 10-year-...

متن کامل

Modeling Stock Return Volatility Using Symmetric and Asymmetric Nonlinear State Space Models: Case of Tehran Stock Market

Volatility is a measure of uncertainty that plays a central role in financial theory, risk management, and pricing authority. Turbulence is the conditional variance of changes in asset prices that is not directly observable and is considered a hidden variable that is indirectly calculated using some approximations. To do this, two general approaches are presented in the literature of financial ...

متن کامل

Predictability of Stock Return Volatility from GARCH Models

This paper focuses on the performance of various GARCH models in terms of their ability of delivering volatility forecasts for stock return data. Volatility forecasts obtained from a variety of mean and variance specifications in GARCH models are compared to a proxy of actual volatility calculated using daily data. In-sample tests suggest that a regression of volatility estimates on actual vola...

متن کامل

منابع من

با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
international journal of business and development studies

جلد ۸، شماره ۱، صفحات ۶۱-۷۶

کلمات کلیدی

میزبانی شده توسط پلتفرم ابری

copyright © 2015-2023