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 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.

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.

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 Modeling Stock Return Data using Asymmetric Volatility Models : A Performance Comparison based on the Akaike Information Criterion and Schwarz Criterion

2019 ◽  
Vol 1 (1) ◽  
pp. 40
Author(s):  
E Setiawan ◽  
N Herawati ◽  
K Nisa
Keyword(s):  
Stock Return ◽  
Information Criterion ◽  
Performance Comparison ◽  
Garch Models ◽  
Forecasting Model ◽  
Asymmetric Volatility ◽  
Volatility Models ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Schwarz Criterion ◽  
Return Data

The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) modelhas been widely used in time series forecasting especially with asymmetricvolatility data. As the generalization of autoregressive conditionalheteroscedasticity model, GARCH is known to be more flexible to lag structures.Some enhancements of GARCH models were introduced in literatures, among themare Exponential GARCH (EGARCH), Threshold GARCH (TGARCH) andAsymmetric Power GARCH (APGARCH) models. This paper aims to compare theperformance of the three enhancements of the asymmetric volatility models bymeans of applying the three models to estimate real daily stock return volatilitydata. The presence of leverage effects in empirical series is investigated. Based onthe value of Akaike information and Schwarz criterions, the result showed that thebest forecasting model for daily stock return data is the APARCH model.Keywords: Volatility, GARCH, TGARCH, EGARCH, APARCH, AIC and SC.

scholarly journals Modelling and Forecasting Volatility of Crude Oil Returns in Nigeria based on Six Error Innovations

2021 ◽  
Vol 3 (1) ◽  
pp. 78-93
Author(s):  
Yunusa Adavi Ojirobe ◽  
Abdulsalam Hussein Ahmad ◽  
Ikwuoche John David
Keyword(s):  
Crude Oil ◽  
Price Volatility ◽  
Oil Price Volatility ◽  
Volatility Models ◽  
Evaluation Phase ◽  
Asymmetric Power ◽  
Asymmetric Garch ◽  
And Performance ◽  
Forecasting Volatility ◽  
T Distribution

Modeling price volatility of crude oil (PVCO) is pertinent because of the overbearing impact on any oil-producing economy. This study aimed at evaluating the performance of some volatility models in modeling and forecasting crude oil returns. Utilizing daily returns data from October 23, 2009, to March 23, 2020, this study attempted to capture the dynamics of crude oil price volatility in Nigeria using a symmetric and asymmetric GARCH models. In our research, we considered the generalized autoregressive conditional heteroscedastic model (GARCH), Exponential (E-GARCH), Glosten, Jagannathan and Runkle (GJR-GARCH) and Asymmetric Power (AP-ARCH) under six error innovations that include the skewed variant of the student-t, generalized error and normal distribution. From the results obtained, it was discovered that the AP-ARCH (1, 1) model performed better in the fitting and performance evaluation phase. The skew Student’s t-distribution (SStD) was also reported to be the best performing error innovation in most of the models. Based upon these results, we conclude that the AP-ARCH (1, 1)-SStD model is the best model for capturing the dynamics of crude oil returns in Nigeria.

scholarly journals Evaluating the Forecasting Performance of GARCH Models Using White’s Reality Check

2005 ◽  
Vol 25 (1) ◽  
pp. 43
Author(s):  
Leonardo Souza ◽  
Alvaro Veiga ◽  
Marcelo C. Medeiros
Keyword(s):  
Performance Measures ◽  
Gaussian White Noise ◽  
Garch Models ◽  
Parametric Test ◽  
Back Testing ◽  
Reality Check ◽  
Volatility Models ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Better Than

The important issue of forecasting volatilities brings the difficult task of back-testing the forecasting performance. As volatility cannot be observed directly, one has to use an observable proxy for volatility or a utility function to assess the prediction quality. This kind of procedure can easily lead to poor assessment. The goal of this paper is to compare different volatility models and different performance measures using White’s Reality Check. The Reality Check consists of a non-parametric test that checks if any of a number of concurrent methods yields forecasts significantly better than a given benchmark method. For this purpose, a Monte Carlo simulation is carried out with four different processes, one of them a Gaussian white noise and the others following GARCH specifications. Two benchmark methods are used: the naive (predicting the out-of-sample volatility by in-sample variance) and the Riskmetrics method

scholarly journals Modeling Stock Return Data Using Asymmetric Volatility Models: A Performance Comparison Based On the Akaike Information Criterion and Schwarz Criterion

INSIST ◽  
2018 ◽  
Vol 3 (2) ◽  
pp. 160
Author(s):  
Eri Setiawan ◽  
Netti Herawati ◽  
Khoirin Nisa
Keyword(s):  
Stock Return ◽  
Information Criterion ◽  
Performance Comparison ◽  
Conditional Heteroscedasticity ◽  
Stock Return Volatility ◽  
Asymmetric Volatility ◽  
Volatility Models ◽  
Asymmetric Power ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Return Data

The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model has been widely used in time series forecasting especially with asymmetric volatility data. As the generalization of autoregressive conditional heteroscedasticity model, GARCH is known to be more flexible to lag structures. Some enhancements of GARCH models were introduced in literatures, among them are Exponential GARCH (EGARCH), Threshold GARCH (TGARCH) and Asymmetric Power GARCH (APGARCH) models. This paper aims to compare the performance of the three enhancements of the asymmetric volatility models by means of applying the three models to estimate real daily stock return volatility data. The presence of leverage effects in empirical series is investigated. Based on the value of Akaike information and Schwarz criterions, the result showed that the best forecasting model for our daily stock return data is the APARCH model.

scholarly journals Pemodelan Pengukuran Luas Panen Padi Nasional Menggunakan Generalized Autoregressive Conditional Heteroscedastic Model (GARCH)

2014 ◽  
Vol 33 (1) ◽  
pp. 17
Author(s):  
Teuku Achmad Iqbal ◽  
Kusman Sadik ◽  
I Made Sumertajaya
Keyword(s):  
Time Series Data ◽  
Garch Model ◽  
Series Data ◽  
Percentage Error ◽  
Garch Models ◽  
Absolute Deviation ◽  
Significant Coefficient ◽  
Asymmetric Garch ◽  
Heteroscedastic Model ◽  
Better Than

This study was aimed to build a model for the estimation of national harvested area of rice by incorporating element of variant heterogeneity and the influence of asymmetry factors on time series data using five types of GARCH models, namely: symmetric GARCH, exponential asymmetric GARCH, quadratic asymmetric GARCH, Threshold GARCH, and non-linear asymmetric GARCH. Those models were compared and evaluated, and then the best model was used to predict the accuracy of the national rice harvested area. The results showed that two types of GARCH had significant coefficient, indicating the validity of the model. Those models were symmetric GARCH and quadratic GARCH models. Based on the value of mean absolute percentage error (MAPE) for the twelve month periods ahead, quadratic GARCH model was better than the symmetric GARCH model. Furthermore, based on the value of mean absolute deviation (MAD) and mean square error (MSE), quadratic GARCH model also seemed to be a better model than symmetric GARCH model. The best model can be used to predict the harvested area in the subsequent year.

Sign in / Sign up

Export Citation Format

Share Document