Search engine for discovering works of Art, research articles, and books related to Art and Culture
Javascript must be enabled to continue!
Volatility Analysis of Nepalese Stock Market
View through CrossRef
Modeling and forecasting volatility of capital markets has been important area of inquiry and research in financial economics with the recognition of time-varying volatility, volatility clusturing, and asymmetric response of volatility to market movements. Given the anticipated growth of the Nepalese stock market and increasing interest of investors towards investment in Nepalese stock market, it is important to understand the pattern of stock market volatility. In the paper, the volatility of the Nepalese stock market is modeled using daily return series consisting of 1297 observations from July 2003 to Feb 2009 and different classes of estimators and volatility models. The results indicate that the most appropriate model for volatility modeling in Nepalese market, where no significant asymmetry in the conditional volatility of returns was captured, is GARCH(1,1). The study revealed strong evidence of time-varying volatility, a tendency of the periods of high and low volatility to cluster and a high persistence and predictability of volatility in the Nepalese stock market.Key words: Conditional heteroskedasticity, ARCH, GARCH, volatility clustering, leverage effect, Nepalese Stock MarketThe Journal of Nepalese Business Studies Vol. V, No. 1, 2008, December Page: 76-84
Title: Volatility Analysis of Nepalese Stock Market
Description:
Modeling and forecasting volatility of capital markets has been important area of inquiry and research in financial economics with the recognition of time-varying volatility, volatility clusturing, and asymmetric response of volatility to market movements.
Given the anticipated growth of the Nepalese stock market and increasing interest of investors towards investment in Nepalese stock market, it is important to understand the pattern of stock market volatility.
In the paper, the volatility of the Nepalese stock market is modeled using daily return series consisting of 1297 observations from July 2003 to Feb 2009 and different classes of estimators and volatility models.
The results indicate that the most appropriate model for volatility modeling in Nepalese market, where no significant asymmetry in the conditional volatility of returns was captured, is GARCH(1,1).
The study revealed strong evidence of time-varying volatility, a tendency of the periods of high and low volatility to cluster and a high persistence and predictability of volatility in the Nepalese stock market.
Key words: Conditional heteroskedasticity, ARCH, GARCH, volatility clustering, leverage effect, Nepalese Stock MarketThe Journal of Nepalese Business Studies Vol.
V, No.
1, 2008, December Page: 76-84.
Related Results
The Impact of Interest Rate Volatility on Stock Returns Volatility: Empirical Evidence from Pakistan Stock Exchange
The Impact of Interest Rate Volatility on Stock Returns Volatility: Empirical Evidence from Pakistan Stock Exchange
Apprehension pertaining to Stock return volatility always has been producing the appreciable significance in the various current research works and it has been lucrative to many re...
On Volatility, Outliers, and Uncertainty
On Volatility, Outliers, and Uncertainty
This dissertation is composed of three loosely related chapters, all of which are empirical.In Chapter 1, I examine whether expectations are formed in a systematically different ma...
Forecasting Volatility
Forecasting Volatility
This monograph puts together results from several lines of research that I have pursued over a period of years, on the general topic of volatility forecasting for option pricing ap...
Analyzing Stock Market Trends with Time Series Analysis
Analyzing Stock Market Trends with Time Series Analysis
The stock market is a vital component of modern economies, serving as a mechanism for companies to raise capital and for investors to participate in the growth of those companies. ...
“Investor attention fluctuation and stock market volatility: Evidence from China”
“Investor attention fluctuation and stock market volatility: Evidence from China”
This paper examines the linkage between Chinese stock market volatility and investor attention fluctuation. In Heterogeneous autoregressive (HAR) model, first, we analyzed the link...
VOLATILITY DYNAMICS OF ISLAMIC AND CONVENTIONAL STOCKS IN INDONESIA: EVIDENCE FROM GARCH MODELS
VOLATILITY DYNAMICS OF ISLAMIC AND CONVENTIONAL STOCKS IN INDONESIA: EVIDENCE FROM GARCH MODELS
Understanding stock market volatility is essential for effective risk management and portfolio decision-making, particularly in emerging markets characterized by high uncertainty. ...
Single Stock Futures Trading and Stock Price Volatility: Empirical Analysis
Single Stock Futures Trading and Stock Price Volatility: Empirical Analysis
This study examines impact of the introduction of single stock futures contracts on the return volatility of the SSFs-listed underlying stocks. The study documents a significant de...
Estimating Stochastic Volatility under the Assumption of Stochastic Volatility of Volatility
Estimating Stochastic Volatility under the Assumption of Stochastic Volatility of Volatility
We propose novel nonparametric estimators for stochastic volatility and the volatility of volatility. In doing so, we relax the assumption of a constant volatility of volatility an...