scholarly journals PERBANDINGAN MODEL GARCH SIMETRIS DAN ASIMETRIS PADA DATA KURS HARIAN

2020 ◽  
Vol 4 (4) ◽  
pp. 627-637
Author(s):  
Isna Shofia Mubarokah ◽  
Anwar Fitrianto ◽  
Farit M Affendi
Keyword(s):  
Exchange Rates ◽  
Garch Model ◽  
Financial Data ◽  
Garch Models ◽  
Data Series ◽  
Asymmetric Effect ◽  
Daily Return ◽  
Volatility Model ◽  
Asymmetric Garch ◽  
The Garch Model

ARCH and GARCH models are widely used in financial data to describe its volatility pattern. The models assume the positive and negative return residual gives the same or symmetric influence on its volatility. However, in reality, this assumption is frequently violated, which is called heteroscedasticity. Therefore, to deal with heteroscedasticity and asymmetric data, the asymmetric GARCH models, which are EGARCH and GJR-GARCH models are used. This research aims to compare the models between symmetric and asymmetric GARCH to make financial data modeling. It uses daily data on three foreign exchange rates for IDR including IDR/CNY, IDR/JPY, and IDR/USD. The data series to be used here are from January 4, 2016, to January 20, 2020. This research method is started by selecting the best mean model for each data. Based on the best mean model, then modeling the mean and variance function are simultaneously conducted using the GARCH model. To test whether there was an asymmetric effect on the data, a Lagrange multiplier test was applied on the residuals of the GARCH model. The results show that the asymmetric effect was found in the IDR/CNY and IDR/JPY exchange rates. To overcome this asymmetric effect, EGARCH and GJR-GARCH model were applied to the two exchange rates. Then the two models are compared to find out which volatility model is better. Using AIC and BIC we find EGARCH as the best model for IDR/CNY exchange rates daily return and GJR-GARCH as the best model for IDR/JPY exchange rates daily return.

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 The Application of the Multivariate GARCH Models on the BRICS Exchange Rates

2020 ◽  
Vol 9 (4) ◽  
pp. 23
Author(s):  
Lebotsa Daniel Metsileng ◽  
Ntebogang Dinah Moroke ◽  
Johannes Tshepiso Tsoku
Keyword(s):  
South Africa ◽  
Exchange Rates ◽  
Time Series Data ◽  
Garch Model ◽  
Multivariate Garch ◽  
Series Data ◽  
Garch Models ◽  
Conditional Volatility ◽  
Volatility Persistence ◽  
Multivariate Garch Models

The study investigated the BRICS exchange rate volatility using the Multivariate GARCH models. The study used the monthly time series data for the period January 2008 to January 2018. The BEKK-GARCH model revealed that all the variables were found to be statistically significant. The diagonal parameters estimates showed that only Russia and South Africa were statistically significant. This implied that the conditional variance of Russia and South Africa’s exchange rates are affected by their own past conditional volatility and other BRICS exchange rates past conditional volatility. The BEKK-GARCH model also revealed that there is a bidirectional volatility transmission between Russia and South Africa. The results from the DCC-GARCH model revealed that Brazil, China, Russia and South Africa had the highest volatility persistence and India has the least volatility persistence. All the BRICS exchange rates show that the fitted residuals are not normally distributed except for Russia. The recommendations for future studies were articulated.

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.

scholarly journals Detection and Modeling of Asymmetric GARCH Effects in a Discrete-Time Series

2017 ◽  
Vol 6 (6) ◽  
pp. 111
Author(s):  
Emmanuel Alphonsus Akpan ◽  
Imoh Udo Moffat
Keyword(s):  
Time Series ◽  
Lagrange Multiplier ◽  
Discrete Time ◽  
Share Price ◽  
Asymmetric Effect ◽  
Lagrange Multiplier Test ◽  
Residual Series ◽  
Asymmetric Garch ◽  
Size Bias ◽  
The Garch Model

This study traced the patterns of discrete time series over time with respect to GARCH effect and asymmetric GARCH effect. Particularly, we paid attention to the weakness of the GARCH model in modeling the asymmetry of GARCH effect. In order to handle this weakness, we applied the sign and size bias test which comprises sign bias test, negative size bias test, positive size bias test, and Lagrange Multiplier test in order to identify the asymmetric effect in the residual series of the GARCH model. Where the asymmetric effect is present and significant, we fit the asymmetric GARCH models. Exploring the share price returns of Zenith bank plc obtained from the Nigerian Stock Exchange from January 4, 2006 to May 26, 2015, our findings indicated the presence of GARCH effect and was adequately captured by GARCH(0,1) model. Also, the sign and size bias test for asymmetric GARCH effect on the residual series of GARCH(0,1) model showed a joint significance as indicated by the Lagrange Multiplier test. Moreover, the asymmetric GARCH effect was adequately captured by EGARCH(0,1) and TGARCH(0,1) models. In addition, the significance of the size bias test indicated that the size of negative and positive returns has an impact on the predicted heteroscedasticity. Hence, we concluded that GARCH(0,1) model adequately predicted the GARCH effect but failed to capture the asymmetric effect in the share price returns of the discrete series. However, this was complemented by both EGARCH(0,1) and TGARCH(0,1) models with the size of both the negative and positive effects taken into consideration.

scholarly journals Aplikasi GARCH dalam Mengatasi Volatilitas Pada Data Keuangan

2017 ◽  
Vol 7 (2) ◽  
pp. 107
Author(s):  
, Hartati ◽  
Imelda Saluza
Keyword(s):  
Time Series ◽  
Garch Model ◽  
Practical Experience ◽  
Financial Data ◽  
Demand And Supply ◽  
Volatility Clustering ◽  
Wide Range ◽  
One Year ◽  
The Garch Model

The financial market is a place or means convergence between demand and supply of a wide range of financial instruments Long-term (over one year). Activities that occur in the financial markets in the long term will form a series of data is often called a time series that contains a set of information from time to time. Practical experience shows that many time series exhibit their periods with great volatility. The greater the volatility, the greater the chance to experience a gain or loss. Important properties are often owned by the data time series in finance, especially to return data that the probability distribution of returns are fat tails (tail fat) and volatility clustering or often referred to as a case heteroskedastisitas. Not all models are able to capture the nature of heteroscedasticity, one of the models that are able to do is Generalized Autoregressive Heteroskedasticity Condition (GARCH). So the purpose of this study was to determine the GARCH model in dealing with the volatility that occurred in the financial data. The results showed that the GARCH model is best suited to see volatility in the financial data.

scholarly journals Quasi-maximum likelihood estimator of Laplace (1, 1) for GARCH models

2017 ◽  
Vol 15 (1) ◽  
pp. 1539-1548
Author(s):  
Haiyan Xuan ◽  
Lixin Song ◽  
Muhammad Amin ◽  
Yongxia Shi
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Strong Consistency ◽  
Garch Model ◽  
Garch Models ◽  
Parameter Vector ◽  
Likelihood Estimator ◽  
Comparison Results ◽  
Quasi Maximum Likelihood ◽  
The Garch Model

Abstract This paper studies the quasi-maximum likelihood estimator (QMLE) for the generalized autoregressive conditional heteroscedastic (GARCH) model based on the Laplace (1,1) residuals. The QMLE is proposed to the parameter vector of the GARCH model with the Laplace (1,1) firstly. Under some certain conditions, the strong consistency and asymptotic normality of QMLE are then established. In what follows, a real example with Laplace and normal distribution is analyzed to evaluate the performance of the QMLE and some comparison results on the performance are given. In the end the proofs of some theorem are presented.

scholarly journals PERAMALAN VOLATILITAS RETURN SAHAM MENGGUNAKAN METODE ASYMMETRIC POWER ARCH (APARCH)

2020 ◽  
Vol 9 (3) ◽  
pp. 157
Author(s):  
JUITA HARYATI SIDADADOLOG ◽  
I WAYAN SUMARJAYA ◽  
NI KETUT TARI TASTRAWATI
Keyword(s):  
Stock Return ◽  
Bad News ◽  
Good News ◽  
Garch Models ◽  
Leverage Effect ◽  
Asymmetric Effect ◽  
Asymmetric Power ◽  
Asymmetric Garch ◽  
Effect Condition

Model APARCH is one of the asymmetric GARCH models. These models are able to capture the incidence of good news and bad news in the volatility. The APARCH model has an asymmetric coefficient to cope with leverage effect by modeling a leverage that has heteroscedasticity and asymmetric effect condition. The results of this research were obtained by the appropriate APARCH model. The model is the APARCH(1,2) model because all parameters are significant. Thus, proceeds from the volatility of stock return for the next 14 days with the model volatility APARCH(1,2) increased from period one to period fourteen.

Modelling Nigerian Exchange Rates with Asymmetric GARCH Models

2020 ◽  
Vol 39 (1) ◽  
Author(s):  
Ojo O. Oluwadare ◽  
Adedayo A. Adepoju ◽  
Olaoluwa S. Yaya
Keyword(s):  
Exchange Rates ◽  
Serial Correlation ◽  
Garch Models ◽  
Asymmetric Volatility ◽  
First Order ◽  
Volatility Models ◽  
Asymmetric Power ◽  
Asymmetric Garch ◽  
Current Returns ◽  
Better Than

This work consider the estimation of some naira exchange rate returns by volatility models which include the asymmetric variants, with estimation performed under normally distributed assumption of Generalized Autoregressive Conditional Heteroscedastic (GARCH). The symmetric versions are Riskmetrics, ARCH and GARCH models. Initially, first order serial correlation was observed in the returns series, implying the dependencies of current returns on the immediate past. Of the asymmetric volatility models, the Exponential GARCH (EGARCH) and Asymmetric Power ARCH (APARCH) posed to perform better than the other symmetric forms in the predicting the volatility of naira exchange returns.

scholarly journals Modelling Nigerian Exchange Rates with Asymmetric GARCH Models

2021 ◽  
Vol 39 (2) ◽  
Author(s):  
Ojo O. Oluwadare ◽  
Adedayo A. Adepoju ◽  
Olaoluwa S. Yaya
Keyword(s):  
Exchange Rates ◽  
Serial Correlation ◽  
Garch Models ◽  
Asymmetric Volatility ◽  
First Order ◽  
Volatility Models ◽  
Asymmetric Power ◽  
Asymmetric Garch ◽  
Current Returns ◽  
Better Than

This work consider the estimation of some naira exchange rate returns by volatility models which include the asymmetric variants, with estimation performed under normally distributed assumption of Generalized Autoregressive Conditional Heteroscedastic (GARCH). The symmetric versions are Riskmetrics, ARCH and GARCH models. Initially, first order serial correlation was observed in the returns series, implying the dependencies of current returns on the immediate past. Of the asymmetric volatility models, the Exponential GARCH (EGARCH) and Asymmetric Power ARCH (APARCH) posed to perform better than the other symmetric forms in the predicting the volatility of naira exchange returns.

scholarly journals Randomised Pseudolikelihood Ratio Change Point Estimator in Garch Models

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
George Awiakye-Marfo ◽  
Joseph Mung’atu ◽  
Patrick O. Weke
Keyword(s):  
Change Point ◽  
Garch Model ◽  
Garch Models ◽  
Variance Change ◽  
Point Estimator ◽  
Ratio Change ◽  
Simulation Results ◽  
The Garch Model

In this paper, a randomised pseudolikelihood ratio change point estimator for GARCH model is presented. Derivation of a randomised change point estimator for the GARCH model and its consistency are given. Simulation results that support the validity of the estimator are also presented. It was observed that the randomised estimator outperforms the ordinary CUSUM of squares test, and it is optimal with large variance change ratios.

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