Modelling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH Model

2008 ◽  
Author(s):  
Annastiina Silvennoinen ◽  
Timo Terasvirta
Keyword(s):  
Garch Model ◽  
Smooth Transition ◽  
Conditional Heteroskedasticity ◽  
Conditional Correlation ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Smooth Transition Conditional Correlation ◽  
Multivariate Autoregressive

scholarly journals Analysing volatility spillovers between grain and freight markets

Pomorstvo ◽  
2020 ◽  
Vol 34 (2) ◽  
pp. 428-437
Author(s):  
Totakura Bangar Raju ◽  
Ayush Bavise ◽  
Pradeep Chauhan ◽  
Bhavana Venkata Ramalingeswar Rao
Keyword(s):  
Supply And Demand ◽  
Conditional Heteroskedasticity ◽  
Conditional Correlation ◽  
Market Prices ◽  
Volatility Spillovers ◽  
Market Index ◽  
Research Article ◽  
Autoregressive Conditional Heteroskedasticity ◽  
The Subject ◽  
Freight Market

The International Grain Council (IGC) circulates two price indices which are the Grain and Oilseeds Index (GOI) and the Grain and Oilseeds Freight Market Index (GOFI). These two indices indicate the respective market prices. The GOI markets are affected by various factors like supply and demand, weather, freight markets, etc. This research article attempts to explore and analyse volatility in GOI and GOFI markets using various GARCH family models, that is Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) analysis. The multivariate Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity model (DCC GARCH) is used to find the spillovers between the two markets and thereby explore the effect of GOFI on GOI markets from the year 2013. The research article consists of four sections after introducing the subject namely a literature review, research methodology and models, analysis and conclusions of the study.

scholarly journals Model ARIMA-GARCH Pada Peramalan Harga Saham PT. Jasa Marga (Persero)

2021 ◽  
Vol 3 (3) ◽  
pp. 164-170
Author(s):  
Fransisca Trisnani Ardikha Putri ◽  
Etik Zukhronah ◽  
Hasih Pratiwi
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Garch Model ◽  
Conditional Heteroskedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Conditional Heteroskedasticity

Abstract– PT Jasa Marga is a great reputation company, the leader in comparable businesses, has a steady income, and paying dividends consistently. This paper aims to find the best model to forecast stock price of PT Jasa Marga using ARIMA-GARCH. The data used is daily stock price of PT Jasa Marga from March 2020 to March 2021. Autoregressive Integrated Moving Average (ARIMA) is a method that can be used to forecast stock prices. However, an economical data tend to have heteroscedasticity problems, one of the methods used to overcome them is Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Future stock price of PT Jasa Marga is forecasted with ARIMA-GARCH model.  The data is modeled with ARIMA first, if there is heteroscedasticity, combine the model with GARCH model. The result of this study indicated that ARIMA (1, 1, 1) – GARCH (2, 2) is the best model, with MAPE 1,5647 Abstrak– PT Jasa Marga adalah perusahaan yang reputasinya baik, terdepan di perusahaan-perusahaan sejenis, stabil pendapatannya, dan pembayaran devidennya konsisten. Paper ini bertujuan untuk mencari model terbaik dalam meramalkan harga saham PT Jasa Marga menggunakan ARIMA-GARCH. Data harga saham yang diolah yaitu data sekunder dari PT Jasa Marga pada Maret 2020 hingga Maret 2021. Autoregressive Integrated Moving Average (ARIMA) sebagai metode yang dapat dimanfaatkan guna meramalkan harga saham. Akan tetapi, data tentang ekonomi cenderung memiliki masalah heteroskedastisitas, metode yang umum dipakai untuk mengatasinya adalah Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Harga saham PT Jasa Marga diramalkan dengan model ARIMA-GARCH.  Data terlebih dahulu dimodelkan dengan ARIMA, jika didapati adanya heteroskedastisitas, maka model tersebut dikombinasikan dengan GARCH. Penelitian ini menghasilkan ARIMA (1,1,1)-GARCH(2,2) sebagai model terbaik dengan MAPE 1,5647.

GARCH AND VOLUME EFFECTS IN THE AUSTRALIAN STOCK MARKETS

2009 ◽  
Vol 05 (01) ◽  
pp. 0950005 ◽  
Author(s):  
JINGLIANG XIAO ◽  
ROBERT D BROOKS ◽  
WING-KEUNG WONG
Keyword(s):  
Stock Returns ◽  
Firm Size ◽  
Trading Volume ◽  
Explanatory Power ◽  
Garch Model ◽  
Conditional Heteroskedasticity ◽  
Autoregressive Conditional Heteroskedasticity ◽  
The Impact ◽  
Daily Returns ◽  
Volume Effects

This paper explores the relationship between volume and volatility in the Australian Stock Market in the context of a generalized autoregressive conditional heteroskedasticity (GARCH) model. In contrast to other studies who only examine the interaction of GARCH and volume effects on a small number of stocks, we examine these effects on the entire available data for the Australian All Ordinaries Index. We also emphasize on the impact of firm size and trading volume. Our results indicate that GARCH model testing and estimation is impacted by firm size and trading volume. Specifically, our analysis produces the following major findings. First, generally, daily trading volume, used as a proxy for information arrival time, is shown to have significant explanatory power regarding the variance of daily returns. Second, the actively traded stocks which may have a larger number of information arrivals per day have a larger impact of volume on the variance of daily returns. Third, we find that low trading volume and small firm lead to a higher persistence of GARCH effects in the estimated models. Fourth, unlike the elimination effect for the top most active stocks, in general, the elimination of both autoregressive conditional heteroskedasticity (ARCH) and GARCH effects by introducing the volume variable on all other stocks on average is not as much as that for the top most active stocks. Fifth, the elimination of both ARCH and GARCH effects by introducing the volume variable is higher for stocks in the largest volume and/or the largest market capitalization quartile group. Our findings imply that the earlier findings in the literature were not a statistical fluke and that, unlike most anomalies, the volume effect on volatility is not likely to be eliminated after its discovery. In addition, our findings reject the pure random walk hypothesis for stock returns.

scholarly journals PENERAPAN METODEEXPECTED SHORTFALLPADA PENGUKURAN RISIKO INVESTASI SAHAM DENGAN VOLATILITAS MODEL GARCH

2019 ◽  
Vol 8 (1) ◽  
pp. 184-193
Author(s):  
Nurul Fitria Fitria Rizani ◽  
Mustafid Mustafid ◽  
Suparti Suparti
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Price ◽  
Garch Model ◽  
Expected Shortfall ◽  
Conditional Heteroskedasticity ◽  
Investment Risk ◽  
Stock Investment ◽  
Autoregressive Conditional Heteroskedasticity ◽  
High Volatility

One of the methods that can be used to measure stock investment risk is Expected Shortfall (ES). ES is an expectation of risk size which value is greater than Value at Risk (VaR), ES has characteristics of sub-additive and convex. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to model stock data that has high volatility. Calculating ES is done with data that shows deviations from normality using Cornish-Fisher's expansion. This researchapplies the ES at the closing stock price of PT Astra International Tbk. (ASII), PT Bank Negara Indonesia (Persero) Tbk. (BBNI), and PT Indocement Tunggal Prakarsa Tbk. (INTP) for the period of 11 February 2013 - 31 March 2019. Based on the volatility of GARCH (1,1) analysis, we find ES calculation for each stock by 95% level  confidence. The ES for ASII shares is 4.1%, greater than the VaR value which isonly 2.64%.The ES for BBNI shares is 4.38%, greater than it’s VaR value which is only 2,86%. The ES for INTP shares is 6.22%, which is also greater than it’s VaR value which is only3,99%. The greather of VaR then Thegreather of ES obtained.Keywords: Expected Shortfall, Value at Risk, GARCH

STUDI SPILLOVER EFEK EXCHANGE-TRADED FUNDS (ETFs) DI ASEAN

2019 ◽  
Vol 4 (2) ◽  
pp. 245-256
Author(s):  
Ahmad Juliana ◽  
Apriliani Mutoharo
Keyword(s):  
Unit Root ◽  
Unit Root Test ◽  
Spillover Effect ◽  
Garch Model ◽  
Conditional Heteroskedasticity ◽  
Financial Asset ◽  
Financial Security ◽  
Exchange Traded Funds ◽  
Arch Effect ◽  
Autoregressive Conditional Heteroskedasticity

The volatility of financial security make an investor difficult and inaccurate to predict the value of targeted investation. The failure for predicting the value of financial asset will mitigate for either succeed or not an investation. That condition will not happen if an investor has knowledge for predicting the volatility financial asset. There for, we need study for forecasting the spillover effect of financial asset using ARCH-GARCH model. The novelty of this study is, we compare the three of ASEAN ETFs that still rarely investigate, are:  Indonesia, Malaysia and Singapore using 5 samples of ETFs. We applied Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) for predicting the spillover efect. The result of unit root test shown that the data not stationer at level, however stationer at first difference. The result of GARCH for JK-LQ45, EWS and EIDO are not significant and it mean there is not ARCH effect. In contract the result are significant for ETF EWM and FXSG. We also found the best AIC are from ETF EWS and ETF FXSG.

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