Stable Limit Theory for the Gaussian QMLE in a Non-Stationary Asymmetric GARCH Model

2017 ◽  
Author(s):  
Stelios Arvanitis
Keyword(s):  
Garch Model ◽  
Stable Limit ◽  
Limit Theory ◽  
Asymmetric Garch

Sea clutter modeling using an autoregressive generalized nonlinear-asymmetric GARCH model

2017 ◽  
Vol 62 ◽  
pp. 52-64 ◽  
Author(s):  
Yunjian Zhang ◽  
Zhenmiao Deng ◽  
Jianghong Shi ◽  
Yixiong Zhang ◽  
Hui Liu
Keyword(s):  
Garch Model ◽  
Sea Clutter ◽  
Asymmetric Garch ◽  
Clutter Modeling

Modeling S&P Bombay Stock Exchange BANKEX Index Volatility Patterns Using GARCH Model

2019 ◽  
pp. 259-268
Keyword(s):  
Time Series ◽  
Banking Sector ◽  
Stock Exchange ◽  
Time Lag ◽  
Garch Model ◽  
Recent Past ◽  
Volatility Clustering ◽  
Asymmetric Volatility ◽  
Empirical Results ◽  
Asymmetric Garch

The main objective of this chapter is to estimate volatility patterns in the case of S&P Bombay Stock Exchange (BSE) BANKEX index in India. In recent past, the Indian banking sector was one of the fastest-growing industries and all major banks have been included in S&P BANKEX index as index benchmark constituent companies. The financial econometric framework is based on asymmetric GARCH (1, 1) model which is performed in order to capture asymmetric volatility clustering and leptokurtosis. Data time lag is considered from the first transaction day of January 2002 to last transaction day of June 2014. The empirical results revealed the existence of volatility shocks in the selected time series and also volatility clustering. The volatility impact has generated highly positive clockwise and impacted actual stocks. Moreover, the empirical findings reveal that the BANKEX index grown over 17 times in 12 years and volatility returns have been found present in listed stocks.

scholarly journals Forecasting Inflation Applying ARIMA Model with GARCH Innovation: The Case of Pakistan

2021 ◽  
Vol 7 (2) ◽  
pp. 305-316
Author(s):  
Tahira Bano Qasim ◽  
Hina Ali ◽  
Natasha Malik ◽  
Malika Liaquat
Keyword(s):  
Arima Model ◽  
Consumer Price Index ◽  
Garch Model ◽  
Conditional Variance ◽  
Suitable Model ◽  
Asymmetric Effect ◽  
Variance Model ◽  
Conditional Mean ◽  
Asymmetric Garch ◽  
Adf Test

Purpose: The research aims to build a suitable model for the conditional mean and conditional variance for forecasting the rate of inflation in Pakistan by summarizing the properties of the series and characterizing its salient features. Design/Methodology/Approach: For this purpose, Pakistan’s Inflation Rate is based upon the Consumer Price Index (CPI), ranging from January 1962 to December 2019 has been analyzed. Augmented Dickey Fuller (ADF) test that was used for testing the stationarity of the series. The ARIMA modeling technique is a conditional mean and GARCH model for conditional variance. Models are selected on AIC and BIC model selection criteria. The estimating and forecasting ability of three ARIMA models with the GARCH (2,2) model has been compared to capture the possible nonlinearity present in the data. To depict the possible asymmetric effect in the conditional variance, two asymmetric GARCH models, EGARCH and TGARCH models have been applied. Findings: Based on statistical loss functions, GARCH (2,2) model is the best variance model for this series. The empirical results reveal that the performance of model-2 is best for all the three variance models. However, the GARCH model is the best as the variance model for this series. This shows that the asymmetric effect invariance is not so important for the rate of inflation in Pakistan.  Implications/Originality/Value: The current study was based on the least considered variables and the pioneer in testing the complex relationship through the ARIMA model with GARCH innovation.

scholarly journals SKEW AND IMPLIED LEVERAGE EFFECT: SMILE DYNAMICS REVISITED

2015 ◽  
Vol 18 (04) ◽  
pp. 1550022 ◽  
Author(s):  
VINCENT VARGAS ◽  
TUNG-LAM DAO ◽  
JEAN-PHILIPPE BOUCHAUD
Keyword(s):  
Linear Models ◽  
Garch Model ◽  
Leverage Effect ◽  
Option Markets ◽  
Empirical Results ◽  
Price Series ◽  
Non Linear ◽  
Asymmetric Garch ◽  
Linear Effects ◽  
Using Data

We revisit the "Smile Dynamics" problem, which consists in relating the implied leverage (i.e. the correlation of the at-the-money volatility with the returns of the underlying) and the skew of the option smile. The ratio between these two quantities, called "Skew-Stickiness Ratio" (SSR) by Bergomi (2009), saturates to the value 2 for linear models in the limit of small maturities, and converges to 1 for long maturities. We show that for more general, non-linear models (such as the asymmetric GARCH model), Bergomi's result must be modified, and can be larger than 2 for small maturities. The discrepancy comes from the fact that the volatility skew is, in general, different from the skewness of the underlying. We compare our theory with empirical results, using data both from option markets and from the underlying price series, for the S&P 500 and the DAX. We find, among other things, that although both the implied leverage and the skew appear to be too strong on option markets, their ratio is well explained by the theory. We observe that the SSR indeed becomes larger than 2 for small maturities, signalling the presence of non-linear effects.

scholarly journals A dynamic markov regime-switching asymmetric GARCH model and its cumulative impulse response function

2021 ◽  
Vol 16 (1) ◽  
pp. 2537-2559
Author(s):  
Gado SEMA ◽  
Mamadou Abdoulaye Konté ◽  
Abdou Kâ Diongue
Keyword(s):  
Impulse Response ◽  
Financial Market ◽  
Regime Switching ◽  
Impulse Response Function ◽  
Garch Model ◽  
Market Volatility ◽  
Markov Regime Switching ◽  
Proposed Model ◽  
Asymmetric Garch ◽  
Better Than

In this paper, we consider the Markov regime-switching GJR-GARCH(1,1) model to capture both the cumulative impulse response and the asymmetry of the dynamic behavior of financial market volatility in stationary and explosive states. The model can capture regime shifts in volatility between two regimes as well as the asymmetric response to negative and positive shocks. A Monte Carlo simulation is conducted to validate the main theory and find that the regime-switching GJR-GARCH model performs better than the standard GJR-GARCH model. Applications to Brazilian stock market data show that the proposed model performs well in terms of cumulative impulse response.

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