scholarly journals Bitcoin İçin Volatilite Tahmini: Simetrik ve Asimetrik Garch Modelleri İçin Ampirik Bir Uygulama (Volatility Forecast For Bitcoin: An Empirical Application for Symmetric And Asymmetric Garch Models)

2021 ◽  
Vol 4 (13) ◽  
pp. 3346-3359
Author(s):  
Ahmet Bülent Atasoy ◽  
Gülfen Tuna
Keyword(s):  
Garch Models ◽  
Empirical Application ◽  
Asymmetric Garch ◽  
Volatility Forecast

Learning for infinitely divisible GARCH models in option pricing

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Fumin Zhu ◽  
Michele Leonardo Bianchi ◽  
Young Shin Kim ◽  
Frank J. Fabozzi ◽  
Hengyu Wu
Keyword(s):  
Option Pricing ◽  
Stock Returns ◽  
Bayesian Learning ◽  
Space Structure ◽  
Estimation Methods ◽  
Garch Models ◽  
Learning Approaches ◽  
Infinitely Divisible ◽  
Asymmetric Garch ◽  
Non Gaussian

AbstractThis paper studies the option valuation problem of non-Gaussian and asymmetric GARCH models from a state-space structure perspective. Assuming innovations following an infinitely divisible distribution, we apply different estimation methods including filtering and learning approaches. We then investigate the performance in pricing S&P 500 index short-term options after obtaining a proper change of measure. We find that the sequential Bayesian learning approach (SBLA) significantly and robustly decreases the option pricing errors. Our theoretical and empirical findings also suggest that, when stock returns are non-Gaussian distributed, their innovations under the risk-neutral measure may present more non-normality, exhibit higher volatility, and have a stronger leverage effect than under the physical measure.

Re-Examining the Nifty Returns after the First Decade of Derivative Trading in Indian Capital Market Using Non-Linear Asymmetric GARCH Models

2012 ◽  
Vol 3 (4) ◽  
pp. 29-52 ◽  
Author(s):  
Sunita Narang
Keyword(s):  
Capital Market ◽  
Garch Models ◽  
Conditional Heteroskedasticity ◽  
Indian Market ◽  
Total Period ◽  
Dummy Variable ◽  
Asymmetric Garch ◽  
Indian Stock Market ◽  
Surrogate Index ◽  
The Impact

This article examines the Indian stock market for conditional volatility using symmetric and asymmetric GARCH (Generalized Autoregressive Conditional Heteroskedasticity) variants with reference to a comprehensive period of 20 years from July 3, 1990 to November 30, 2010 using S&P CNX Nifty. The impact of future trading on Nifty return and volatility is assessed using dummy variable in total period and using Log (Open Interest of Nifty futures) in post-derivative period. Along with the period of two decades the analysis has also been done on a sub-period of a decade from 1995 to 2005 with NiftyJunior as surrogate index as it had no derivatives during this period. The results show that the PGARCH model is best suited to Indian market conditions.

scholarly journals QML INFERENCE FOR VOLATILITY MODELS WITH COVARIATES

2018 ◽  
Vol 35 (1) ◽  
pp. 37-72 ◽  
Author(s):  
Christian Francq ◽  
Le Quyen Thieu
Keyword(s):  
Realized Volatility ◽  
Garch Models ◽  
Likelihood Estimator ◽  
Asymptotic Results ◽  
Monte Carlo Experiments ◽  
True Value ◽  
Volatility Models ◽  
Information Sets ◽  
Asymmetric Garch ◽  
Quasi Maximum Likelihood

The asymptotic distribution of the Gaussian quasi-maximum likelihood estimator (QMLE) is obtained for a wide class of asymmetric GARCH models with exogenous covariates. The true value of the parameter is not restricted to belong to the interior of the parameter space, which allows us to derive tests for the significance of the parameters. In particular, the relevance of the exogenous variables can be assessed. The results are obtained without assuming that the innovations are independent, which allows conditioning on different information sets. Monte Carlo experiments and applications to financial series illustrate the asymptotic results. In particular, an empirical study demonstrates that the realized volatility can be a helpful covariate for predicting squared returns.

Modelling Malaysian gold using symmetric and asymmetric GARCH models

2014 ◽  
Vol 8 ◽  
pp. 817-822 ◽  
Author(s):  
Maizah Hura Ahmad ◽  
Pung Yean Ping
Keyword(s):  
Garch Models ◽  
Asymmetric Garch

scholarly journals Estimating stock market volatility using asymmetric GARCH models

2008 ◽  
Vol 18 (15) ◽  
pp. 1201-1208 ◽  
Author(s):  
Dima Alberg ◽  
Haim Shalit ◽  
Rami Yosef
Keyword(s):  
Stock Market ◽  
Market Volatility ◽  
Garch Models ◽  
Stock Market Volatility ◽  
Asymmetric Garch

scholarly journals Using Models of the GARCH Family to Estimate the Level of Food and Non-Food Inflation in Ethiopia

2021 ◽  
Vol 20 (4) ◽  
pp. 726-749
Author(s):  
T.H. Abebe ◽  
Keyword(s):  
Financial Time Series ◽  
Garch Models ◽  
Suitable Model ◽  
Volatility Clustering ◽  
Inflation Volatility ◽  
Volatility Model ◽  
Asymmetric Garch ◽  
Alternative Hypotheses ◽  
Food Inflation ◽  
Family Models

An increase in inflation volatility implies higher uncertainty about future prices. As a result, producers and consumers can be affected by the increased inflation volatility, because it increases the uncertainty and the risk in the market. Thus, inflation volatility attracts the attention of researchers to find a suitable model which can predict the future conditions of the market. This study aims to fit appropriate ARMA-GARCH family models for food and non-food inflation rate of from the period January 1971 through June 2020. Since the main objective of the study is identifying an appropriate model for inflation series, the null and alternative hypotheses are defined in comparison of the two types of models. H0: The symmetric GARCH models better capture inflation volatility of Ethiopia. H1: The asymmetric GARCH models better capture inflation volatility of Ethiopia. The ARMA-GARCH family models were applied to capture the stylized facts of financial time series such us leptokurtic, volatility clustering and leverage effects. The mean model results show that, an ARMA (1, 2) and ARIMA (0, 1, 1) models are identified as the best fitted model for food and non-food inflation, respectively. From the estimation results of volatility model, an asymmetric TGARCH (1, 1) model with Student's t- distributional assumptions of the residual is the best model for non-food inflation. Thus, modeling of information, news of events is very significant determinants of volatility and GARCH family models are appropriate for the given series (monthly food-inflation volatility) of Ethiopia under the study period considered.

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