Estimating and Forecasting Volatility Using Leverage Effect

PERAMALAN VOLATILITAS SAHAM MENGGUNAKAN MODEL EXPONENTIAL GARCH DAN THRESHOLD GARCH

E-Jurnal Matematika ◽

10.24843/mtk.2019.v08.i04.p270 ◽

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.

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

GIS Business ◽

10.26643/gis.v13i2.3295 ◽

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.

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

JOURNAL OF SOCIAL SCIENCE RESEARCH ◽

10.24297/jssr.v4i2.3152 ◽

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)).

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

JOURNAL OF SOCIAL SCIENCE RESEARCH ◽

10.24297/jssr.v3i3.3266 ◽

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)).

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