scholarly journals Checking presence of excess volatility in forecasting volatility of a set of market indexes through an empirical comparison of three GARCH models

2014 ◽  
Vol 4 (2) ◽  
pp. 573-579
Author(s):  
Islem Boutabba
Keyword(s):  
Empirical Study ◽  
Efficient Market Hypothesis ◽  
Garch Models ◽  
Leverage Effect ◽  
Empirical Comparison ◽  
Behavioural Finance ◽  
Volatility Clustering ◽  
Excess Volatility ◽  
Market Indexes ◽  
Forecasting Volatility

Classical financial theory is based on Efficient Market Hypothesis (EMH). Several researchers like Schiller (1981) (1990), Le Roy and Porter (1980) have extensively argued for the invalidity of EMH.Volatility excess has been detected and highlighted by many researchers; however it has not been explained very well by EMH. For this reason, we conducted an empirical study to identify the variable characteristics of volatility by comparing three GARCH models (GARCH, E-GARCH and GRJ-GARCH) over five different market indexes to examine prediction of returns volatility. This comparison led us to detect several volatility characteristics like volatility clustering and leverage effect. This change in volatility regime is an irrefutable proof of the presence of volatility excess.Given the inability of classical financial theory in explaining volatility excess, researchers started to focus on behavioural finance (Barret and Saphister (1996)).

scholarly journals Checking presence of excess volatility in forecasting volatility of a set of market indexes through an empirical comparison of three GARCH models

2014 ◽  
Vol 3 (3) ◽  
pp. 385-394
Author(s):  
Islem Ahmed Boutabba
Keyword(s):  
Empirical Study ◽  
Efficient Market Hypothesis ◽  
Garch Models ◽  
Leverage Effect ◽  
Empirical Comparison ◽  
Behavioural Finance ◽  
Volatility Clustering ◽  
Excess Volatility ◽  
Market Indexes ◽  
Forecasting Volatility

Classical financial theory is based on Efficient Market Hypothesis (EMH). Several researchers likeSchiller (1981) (1990), Le Roy and Porter (1980) have extensively argued for the invalidity of EMH. Volatility excess has been detected and highlighted by many researchers; however it has not been explained very well by EMH. For this reason, we conducted an empirical study to identify the variable characteristics of volatility by comparing three GARCH models (GARCH, E-GARCH and GRJ-GARCH) over five different market indexes to examine prediction of returns volatility.This comparison led us to detect several volatility characteristics like volatility clustering and leverage effect. This change in volatility regime is an irrefutable proof of the presence of volatility excess.Given the inability of classical financial theory in explaining volatility excess, researchers started to focus on behavioural finance (Barret and Saphister (1996)).

scholarly journals PERAMALAN VOLATILITAS SAHAM MENGGUNAKAN MODEL EXPONENTIAL GARCH DAN THRESHOLD GARCH

2019 ◽  
Vol 8 (4) ◽  
pp. 309
Author(s):  
SITI RAHAYU NINGSIH ◽  
I WAYAN SUMARJAYA ◽  
KARTIKA SARI
Keyword(s):  
Asset Returns ◽  
Financial Data ◽  
Stock Index ◽  
Garch Models ◽  
Leverage Effect ◽  
Financial Asset ◽  
Asymmetric Volatility ◽  
High Volatility ◽  
Financial Asset Returns ◽  
Forecasting Volatility

In financial data there is asymmetric volatility, which denotes the different movements on conditional volatility of increase and decrease financial asset returns. The exponential GARCH and threshold GARCH models can be used to capture asymmetric volatility, called leverage effect. The aim of this research is to determine the best model between exponential GARCH and threshold GARCH models, and to know the results of forecasting volatility the LQ-45 stock index using the best model. The research showed that the best model to predicting volatility is EGARCH(2,1), because it has the smallest AIC value compared to other models. Then forecasting volatility of the LQ-45 stock index using EGARCH(2,1) showed that volatility increase from the first period until fourteenth period, this means that it has high volatility.

A Study on Unfolding Volatility and Leverage Effect in Indian Stock Market

GIS Business ◽  
1970 ◽  
Vol 13 (2) ◽  
pp. 7-14
Author(s):  
Shabarisha Narayan, ◽  
Madegowda J.
Keyword(s):  
Stock Market ◽  
Random Variable ◽  
Leverage Effect ◽  
Symmetric Model ◽  
Volatility Forecasting ◽  
Volatility Clustering ◽  
Indian Stock Market ◽  
Forecasting Volatility

Return is the major attribute of an investment asset that can be considered as a random variable. The variability in return can be expressed as volatility. Forecasting volatility and modelling are the most prolific areas for the research. Volatility and Leverage effect are the two crucial stipulations to study market contradictions and trends that prevail for a drawn-out period. It is observed that when volatility beams the markets soar and when markets roar the volatility fades away. Leverage has a larger scope in managing volatility when investors tend to shuffle their positions. This literature aims to identify the volatility clustering and leverage effect caused to NSE NIFTY 50 index. The study contrasts volatility clustering using symmetric model of i.e., GARCH (1,1). Leverage effects is studied and compared using TGARCH and EGARCH models.

scholarly journals Modelling and Estimation of Volatility Using ARCH/GARCH Models in Jordan’s Stock Market

2016 ◽  
Vol 8 (1) ◽  
pp. 152 ◽  
Author(s):  
Dana Mohammad AL-Najjar
Keyword(s):  
Stock Returns ◽  
Portfolio Management ◽  
Stock Exchange ◽  
Garch Models ◽  
Leverage Effect ◽  
Direct Role ◽  
Volatility Clustering ◽  
Financial Decisions ◽  
Stock Return Volatility ◽  
Amman Stock Exchange

<p>Financials have been concerned constantly with factors that have impact on both taking and assessing various financial decisions in firms. Hence modelling volatility in financial markets is one of the factors that have direct role and effect on pricing, risk and portfolio management. Therefore, this study aims to examine the volatility characteristics on Jordan’s capital market that include; clustering volatility, leptokurtosis, and leverage effect. This objective can be accomplished by selecting symmetric and asymmetric models from GARCH family models. This study applies; ARCH, GARCH, and EGARCH to investigate the behavior of stock return volatility for Amman Stock Exchange (ASE) covering the period from Jan. 1 2005 through Dec.31 2014. The main findings suggest that the symmetric ARCH /GARCH models can capture characteristics of ASE, and provide more evidence for both volatility clustering and leptokurtic, whereas EGARCH output reveals no support for the existence of leverage effect in the stock returns at Amman Stock Exchange.</p>

scholarly journals Modeling Stock Market Monthly Returns Volatility Using GARCH Models Under Different Distributions

2020 ◽  
Vol 5 (1) ◽  
pp. 42-50
Author(s):  
Rama Krishna Yelamanchili
Keyword(s):  
Stock Market ◽  
Gaussian Distribution ◽  
Garch Model ◽  
Information Criterion ◽  
Conditional Variance ◽  
Garch Models ◽  
Leverage Effect ◽  
Market Returns ◽  
Stock Market Returns ◽  
Volatility Clustering

This papers aims to uncover stylized facts of monthly stock market returns and identify adequate GARCH model with appropriate distribution density that captures conditional variance in monthly stock market returns. We obtain monthly close values of Bombay Stock Exchange’s (BSE) Sensex over the period January 1991 to December 2019 (348 monthly observations). To model the conditional variance, volatility clustering, asymmetry, and leverage effect we apply four conventional GARCH models under three different distribution densities. We use two information criterions to choose best fit model. Results reveal positive Skewness, weaker excess kurtosis, no autocorrelations in relative returns and log returns. On the other side presence of autocorrelation in squared log returns indicates volatility clustering. All the four GARCH models have better information criterion values under Gaussian distribution compared to t-distribution and Generalized Error Distribution. Furthermore, results indicate that conventional GARCH model is adequate to measure the conditional volatility. GJR-GARCH model under Gaussian distribution exhibit leverage effect but statistically not significant at any standard significance levels. Other asymmetric models do not exhibit leverage effect. Among the 12 models modeled in present paper, GARCH model has superior information criterion values, log likelihood value, and lowest standard error values for all the coefficients in the model.        

Estimating and Forecasting Volatility Using Leverage Effect

2017 ◽  
Author(s):  
Christina Dan Wang ◽  
Per A. Mykland ◽  
Lan Zhang
Keyword(s):  
Leverage Effect ◽  
Forecasting Volatility

scholarly journals Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

2017 ◽  
Vol 28 (75) ◽  
pp. 361-376 ◽  
Author(s):  
Leandro dos Santos Maciel ◽  
Rosangela Ballini
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Garch Models ◽  
Time Interval ◽  
Risk Modeling ◽  
Additional Information ◽  
Volatility Models ◽  
Modeling And Forecasting ◽  
The Difference ◽  
Market Indexes

ABSTRACT This article considers range-based volatility modeling for identifying and forecasting conditional volatility models based on returns. It suggests the inclusion of range measuring, defined as the difference between the maximum and minimum price of an asset within a time interval, as an exogenous variable in generalized autoregressive conditional heteroscedasticity (GARCH) models. The motivation is evaluating whether range provides additional information to the volatility process (intraday variability) and improves forecasting, when compared to GARCH-type approaches and the conditional autoregressive range (CARR) model. The empirical analysis uses data from the main stock market indexes for the U.S. and Brazilian economies, i.e. S&P 500 and IBOVESPA, respectively, within the period from January 2004 to December 2014. Performance is compared in terms of accuracy, by means of value-at-risk (VaR) modeling and forecasting. The out-of-sample results indicate that range-based volatility models provide more accurate VaR forecasts than GARCH models.

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