ANALISIS VOLATILITAS RETURN INDEKS SAHAM SEKTOR BARANG KONSUMSI DI INDONESIA: APLIKASI METODE TRESHOLD-GARCH (TGARCH)

Sektor barang konsumsi merupakan sektor yang penting dalam perekonomian karena volatilitasnya yang cukup tinggi dan menjadi penopang bursa saham dalam negeri. Saat iklim investasi sedang optimal, nilai indeks saham barang konsumsi cenderung meningkat dan volatilitasnya cukup stabil. Namun, saat terjadi guncangan, indeks saham barang konsumsi menunjukkan penurunan nilai dan sangat tidak stabil dengan volatilitas yang tinggi. Penelitian ini bertujuan untuk menguji kemungkinan terjadinya pola asimetris pada data return indeks saham barang konsumsi serta mengetahui apakah goncangan negatif (bad news) dan positif (good news) berbeda pengaruhnya terhadap volatilitas atau leverage effect. Sumber data berasal dari harian indeks saham dengan alat analisis yang digunakan adalah Treshold Generalize Autoregressive Conditional Heteroscedasticity (TGARCH). Hasil penelitian menunjukkan bahwa volatilitas return indeks saham sektor barang konsumsi signifikan dipengaruhi oleh penyebaran (varians) return dan residual satu periode sebelumnya. Selain itu, tidak ada perbedaan yang signifikan antara bad news dan good news terhadap volatilitas indeks saham sektor barang konsumsi.

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Volatility of Regional Sharia Compliance Returns and US News Impact

Global Regional Review ◽

10.31703/grr.2018(iii-i).21 ◽

2018 ◽

Vol III (I) ◽

pp. 294-307

Author(s):

Kashif Hamid ◽

Rana Shahid Imdad Akash ◽

Muhammad Mudasar Ghafoor

Keyword(s):

Bad News ◽

Portfolio Diversification ◽

Good News ◽

Stock Index ◽

Leverage Effect ◽

Pricing Models ◽

Stock Indices ◽

Long Run ◽

Daily Data ◽

The Impact

Investigation of the impact of US News proxy on the returns of regional sharia compliance indices and volatility is the primary aim of this study. The daily data of Dow Jones Islamic index (DJII), Jakarata Islamic Index (JKII), Karachi Meezan Islamic Index (KMI) and Standard & Poor 500 stock index has been taken for the period of July 01, 2013 to June 30, 2018. GARCH (1,1) is extended with US News proxy for KMI, DJII and JKII. US news proxy identifies that leverage effect reveal the long run persistency in volatility. EGARCH (1,1) model indicates that higher volatility has bee also increased by bad news than good news due to leverage effect in sharia compliance returns. This study leads to extend various assets pricing models by modeling the volatility and will also inform the international and regional investors about the new trends of investment in Islamic stock indices and portfolio diversification.

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

E-Jurnal Matematika ◽

10.24843/mtk.2020.v09.i03.p293 ◽

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.

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Risk Affection and Transmission of News of Conditional Volatility from the Non-Life to Life Insurance Sector

10.47260/bae/725 ◽

2020 ◽

pp. 77-86

Author(s):

Apostolos Kiohos

Keyword(s):

Life Insurance ◽

Bad News ◽

Good News ◽

Insurance Companies ◽

Insurance Markets ◽

Leverage Effect ◽

Conditional Volatility ◽

Risk Transfer ◽

Insurance Sector ◽

Management Procedure

Non- Life and Life Insurance companies are the main expedients of risk transfer and risk management procedure in the economy and the society. This paper examines, in eight worldwide advanced insurance markets, whether there are transmissions of news of conditional volatility from the non-life to life insurance sector. The reason is that, regularly, non-life insurance risks have higher volatility and they are less predictable than life insurance risks. A GJR - GARCH model is used to test these relationships for the period January 1st 1990 to June 28th 2019 using daily trading observations for each listed insurance index. The results suggest that the French and the Australian non-life insurance sectors influence their life insurance sectors to a greater extent than the other countries insurance indices under study. There is also evidence that the leverage effect indicates that bad news concerning the non-life insurance index shows a more intense impact on the volatility of the life insurance index than the good news in the majority of the countries under study. However, bad and good news are symmetrical in French and Australian insurance markets.

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A behavioral analysis of the volatility of interbank interest rates in developed and emerging countries

Journal of Economics Finance and Administrative Science ◽

10.1108/jefas-02-2017-0033 ◽

2017 ◽

Vol 22 (42) ◽

pp. 99-128 ◽

Cited By ~ 1

Author(s):

Nara Rossetti ◽

Marcelo Seido Nagano ◽

Jorge Luis Faria Meirelles

Keyword(s):

Interest Rates ◽

Moving Average ◽

Good News ◽

Macroeconomic Policies ◽

Conditional Heteroscedasticity ◽

Content Type ◽

Factors Affecting ◽

Asymmetric Garch ◽

Auto Regressive ◽

Autoregressive Conditional Heteroscedasticity

Purpose This paper aims to analyse the volatility of the fixed income market from 11 countries (Brazil, Russia, India, China, South Africa, Argentina, Chile, Mexico, USA, Germany and Japan) from January 2000 to December 2011 by examining the interbank interest rates from each market. Design/methodology/approach To the volatility of interest rates returns, the study used models of auto-regressive conditional heteroscedasticity, autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), exponential generalized autoregressive conditional heteroscedasticity (EGARCH), threshold generalized autoregressive conditional heteroscedasticity (TGARCH) and periodic generalized autoregressive conditional heteroscedasticity (PGARCH), and a combination of these with autoregressive integrated moving average (ARIMA) models, checking which of these processes were more efficient in capturing volatility of interest rates of each of the sample countries. Findings The results suggest that for most markets, studied volatility is best modelled by asymmetric GARCH processes – in this case the EGARCH – demonstrating that bad news leads to a higher increase in the volatility of these markets than good news. In addition, the causes of increased volatility seem to be more associated with events occurring internally in each country, as changes in macroeconomic policies, than the overall external events. Originality/value It is expected that this study has contributed to a better understanding of the volatility of interest rates and the main factors affecting this market.

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