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 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 Investment Risk Analysis On Bitcoin With Applied of VaR-APARCH Model

2021 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Irwan Kasse ◽  
Andi Mariani ◽  
Serly Utari ◽  
Didiharyono D.
Keyword(s):  
Risk Analysis ◽  
Value At Risk ◽  
Time Series Data ◽  
Information Criterion ◽  
Series Data ◽  
Investment Risk ◽  
Conditional Heteroscedasticity ◽  
Additional Information ◽  
Asymmetric Power ◽  
Autoregressive Conditional Heteroscedasticity

Investment can be defined as an activity to postpone consumption at the present time with the aim to obtain maximum profits in the future. However, the greater the benefits, the greater the risk. For that we need a way to predict how much the risk will be borne. Modelling data that experiences heteroscedasticity and asymmetricity can use the Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. This research discusses the time series data risk analysis using the Value at Risk-Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCH) model using the daily closing price data of Bitcoin USD period January 1 2019 to 31 December 2019. The best APARCH model was chosen based on the value of Akaike's Information Criterion (AIC). From the analysis results obtained the best model, namely ARIMA (6,1,1) and APARCH (1,1) with the risk of loss in the initial investment of IDR 100,000,000 in the next day IDR 26,617,000. The results of this study can be used as additional information and apply knowledge about the risk of investing in Bitcoin with the VaR-APARCH model.

scholarly journals PERAMALAN VOLATILITAS SAHAM MENGGUNAKAN MODEL THRESHOLD GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Ervina, Dadan Kusnandar, Nurfitri Imro’ah
Keyword(s):  
Akaike Information Criterion ◽  
Information Criterion ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Schwarz Criterion

Model Threshold Generalized Autoregressive Conditional Heteroscedasticity (TGARCH) merupakan model yang digunakan untuk memodelkan volatilitas yang memiliki efek asimetris. Tujuan penelitian ini adalah memodelkan dan meramalkan volatilitas IHSG menggunakan model TGARCH untuk sepuluh periode ke depan. Data yang digunakan adalah data return IHSG penutupan mingguan dari tanggal 8 Februari 2009 sampai dengan 10 Februari 2019. Penelitian ini diawali dengan pembentukan model Box Jenkins. Residual model Box Jenkins terbaik digunakan untuk mendeteksi heteroskedastisitas menggunakan uji ARCH-LM. Data residual yang memiliki heteroskedastisitas dimodelkan ke dalam model GARCH. Residual model GARCH dan residual model Box Jenkins digunakan untuk memeriksa pengaruh asimetris, yaitu dengan melakukan korelasi silang pada kedua residual model tersebut. Berdasarkan hasil korelasi silang yang dilakukan didapatkan adanya pengaruh asimetris terhadap volatilitas, sehingga digunakan model TGARCH untuk mengatasinya. Model TGARCH terbaik dalam penelitian ini adalah TGARCH(1,1) berdasarkan nilai Akaike Information Criterion (AIC) dan Schwarz Criterion (SC) terkecil. Model TGARCH(1,1) digunakan untuk meramalkan volatilitas IHSG. Hasil peramalan volatilitas yang diperoleh untuk sepuluh periode ke depan mengalami peningkatan sebesar 0,000015 sampai dengan 0,000029.Kata Kunci: Asimetris, GARCH, TGARCH

scholarly journals ESTIMASI VALUE AT RISK PORTOFOLIO SAHAM MENGGUNAKAN METODE GARCH-COPULA (Studi Kasus : Harga Penutupan Saham Harian Unilever Indonesia dan Kimia Farma Periode 1 Januari 2013- 31 Desember 2016)

2018 ◽  
Vol 7 (4) ◽  
pp. 397-407
Author(s):  
Lingga Bayu Prasetya ◽  
Dwi Ispriyanti ◽  
Alan Prahutama
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Tail Dependence ◽  
Conditional Heteroscedasticity ◽  
Mutual Correlation ◽  
Copula Method ◽  
Stock Portfolio ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Return Value ◽  
Return Data

Any investment in the stock market will earn returns accompanied by risks. Return and risk has a mutual correlation that equilibrium. The formation of a portfolio is intended to provide a lower risk or with the same risk but provide a higher return. Value at Risk (VaR) is a instrument to analyze risk management. Time series model used in stock return data that it has not normal distribution and heteroscedastisicity is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). GARCH-Copula is a combined method of GARCH and Copula. The Copula method is used in joint distribution modeling because it does not require the assumption of normality of the data and can capture tail dependence between each variable. This research uses return data from stock closing prices of Unilever Indonesia and Kimia Farma period January 1, 2013 until December 31, 2016. Copula model is selected based on the highest likelihood log value is Copula Clayton. Value at Risk estimates of Unilever Indonesia and Kimia Farma's stock portfolio on the same weight were performed using Monte Carlo simulation with backtesting of 30 days period data at 95% confidence level. Keywords : Stock, Risk, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Copula, Value at Risk

scholarly journals PEMODELAN VOLATILITAS SAHAM MENGGUNAKAN MODEL ASYMMETRIC POWER AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Merista Dominika Br Pandia, Naomi Nessyana Debataraja, Shantika Martha
Keyword(s):  
Central Asia ◽  
Arch Model ◽  
Conditional Heteroscedasticity ◽  
Asymmetric Power ◽  
Autoregressive Conditional Heteroscedasticity

Model Generalized Autoregressive Conditional Heteroscedasticity (GARCH) merupakan generalisasi dari model Autoregressive Conditional Heteroscedasticity (ARCH). Model GARCH digunakan untuk memodelkan volatilitas pada return saham yang memiliki heteroskedastisitas. Namun model GARCH mengabaikan efek asimetris pada volatilitas sehingga ditemukan model Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH). Model APARCH digunakan untuk memodelkan volatilitas yang memiliki efek asimetris. Efek asimetris dapat dilihat dari cross correlogram dengan melakukan korelasi silang residual kuadrat model Box-Jenkins dan residual model GARCH. Tujuan penelitian ini adalah untuk menentukan model APARCH return saham Bank Central Asia (BCA) pada 4 Juni 2015 sampai dengan 28 Maret 2018. Hasil penelitian menunjukkan model terbaik Box-Jenkins adalah model AR(3). Residual kuadrat model AR(3) digunakan untuk melakukan uji heteroskedastisitas sedangkan residual model GARCH(1,1) digunakan untuk uji efek asimetris. Model APARCH terbaik yang diperoleh adalah APARCH (1,1). Kata Kunci: Asimetris, GARCH, APARCH

Metode Peramalan Deret Waktu Menggunakan Model Asymmetric Power Autoregressive Conditional Heteroskedastic (APARCH)

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Dian Kurniasari ◽  
Hana Ayu Masha ◽  
Mustofa Usman
Keyword(s):  
Confidence Interval ◽  
Data Model ◽  
Stock Price ◽  
Economic Systems ◽  
Average Model ◽  
Conditional Heteroscedasticity ◽  
Conditional Average ◽  
Asymmetric Power ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Schwarz Criterion

Era globalisasi menyebabkan banyak perubahan dalam pengembangan sistem ekonomi, salah satunya adalah data keuangan. Tujuan dari penelitian ini ialah untuk mendapatkan model terbaik dalam menganalisis dan memprediksi data penutupan harga saham mingguan untuk PT Adhi Karya (Persero) Tbk dari September 1990 hingga Januari 2016 yang berjumlah 1314 data. Model yang digunakan adalah model Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH). Model terbaik dipilih berdasarkan Akaike Info Criterion (AIC) dan Schwarz Criterion (SC). Dari hasil analisis diperoleh model terbaik yaitu APARCH (1,1) dengan ARIMA (1,1,1) sebagai model rerata bersyarat. Hasil peramalan untuk 7 periode berikutnya menunjukkan bahwa perkiraan tersebut dalam interval kepercayaan 95% yang berarti bahwa hasil peramalan menggunakan model ini dapat dipercaya dalam kisaran 95%.   The era of globalization led to many changes in the development of economic systems, one of which is the data that is financially. The purpose of this study is to get the best model in analyzing and predicting weekly stock price closing data for PT Adhi Karya (Persero) Tbk from September 1990 to January 2016 which amounted to 1314 data. The model used is the Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. The best models are selected based on Akaike Info Criterion (AIC) and Schwarz Criterion (SC). From the analysis result obtained the best model that is APARCH (1,1) with ARIMA (1,1,1) as conditional average model. The result of forecasting for the next 7 periods shows that the forecast is within a 95% confidence interval which means that the forecasting result using this model can be trusted in the 95% range.    

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 Do We Need Stochastic Volatility and Generalised Autoregressive Conditional Heteroscedasticity? Comparing Squared End-Of-Day Returns on FTSE

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 12 ◽  
Author(s):  
David E. Allen ◽  
Michael McAleer
Keyword(s):  
Stochastic Volatility ◽  
Ordinary Least Squares ◽  
Conditional Heteroscedasticity ◽  
Daily Data ◽  
Volatility Models ◽  
Tail Behaviour ◽  
Comparable Performance ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Daily Returns ◽  
Better Than

The paper examines the relative performance of Stochastic Volatility (SV) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) models fitted to ten years of daily data for FTSE. As a benchmark, we used the realized volatility (RV) of FTSE sampled at 5 min intervals taken from the Oxford Man Realised Library. Both models demonstrated comparable performance and were correlated to a similar extent with RV estimates when measured by ordinary least squares (OLS). However, a crude variant of Corsi’s (2009) Heterogeneous Autoregressive (HAR) model, applied to squared demeaned daily returns on FTSE, appeared to predict the daily RV of FTSE better than either of the two models. Quantile regressions suggest that all three methods capture tail behaviour similarly and adequately. This leads to the question of whether we need either of the two standard volatility models if the simple expedient of using lagged squared demeaned daily returns provides a better RV predictor, at least in the context of the sample.

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