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 Feedback Effects, Asymmetric Trading, and the Limits to Arbitrage

2015 ◽  
Vol 105 (12) ◽  
pp. 3766-3797 ◽  
Author(s):  
Alex Edmans ◽  
Itay Goldstein ◽  
Wei Jiang
Keyword(s):  
Private Information ◽  
Firm Value ◽  
Bad News ◽  
Good News ◽  
Asymmetric Effect ◽  
Trading Behavior ◽  
Feedback Effects ◽  
Limits To Arbitrage ◽  
Corporate Decisions ◽  
Fundamental Value

We analyze strategic speculators’ incentives to trade on information in a model where firm value is endogenous to trading, due to feedback from the financial market to corporate decisions. Trading reveals private information to managers and improves their real decisions, enhancing fundamental value. This feedback effect has an asymmetric effect on trading behavior: it increases (reduces) the profitability of buying (selling) on good (bad) news. This gives rise to an endogenous limit to arbitrage, whereby investors may refrain from trading on negative information. Thus, bad news is incorporated more slowly into prices than good news, potentially leading to overinvestment. (JEL D83, G12, G14)

scholarly journals Leverage Effect for Volatility with Generalized Laplace Error

2014 ◽  
Vol 29 (2) ◽  
Author(s):  
Farrukh Javed ◽  
Krzysztof Podgórski
Keyword(s):  
Good News ◽  
Leverage Effect ◽  
Economic Time Series ◽  
News Shocks ◽  
Fundamental Properties ◽  
The Past ◽  
Volatility Models ◽  
Asymmetric Power ◽  
Power Models ◽  
Economic Time

AbstractWe propose a new model that accounts for the asymmetric response of volatility to positive (`good news') and negative (`bad news') shocks in economic time series – the so-called leverage effect. In the past, asymmetric powers of errors in the conditionally heteroskedastic models have been used to capture this effect. Our model is using the gamma difference representation of the generalized Laplace distributions that efficiently models the asymmetry. It has one additional natural parameter, the shape, that is used instead of power in the asymmetric power models to capture the strength of a long-lasting effect of shocks. Some fundamental properties of the model are provided including the formula for covariances and an explicit form for the conditional distribution of `bad' and `good' news processes given the past – the property that is important for statistical fitting of the model. Relevant features of volatility models are illustrated using S&P 500 historical data.

scholarly journals Asymmetric Information and Volatility of Stock Returns in Nigeria

2017 ◽  
Vol 9 (3) ◽  
pp. 220 ◽  
Author(s):  
Joshua Odutola Omokehinde ◽  
Matthew Adeolu Abata ◽  
Russell Olukayode Christopher Somoye ◽  
Stephen Oseko Migiro
Keyword(s):  
Asymmetric Information ◽  
Stock Returns ◽  
Global Financial Crisis ◽  
Bad News ◽  
Good News ◽  
Significance Level ◽  
Asymmetric Power ◽  
Long Time ◽  
Best Fit ◽  
Total Impact

This paper investigates the effect of asymmetric information on volatility of stock returns in Nigeria using the best-fit Asymmetric Power Autoregressive Conditional Heteroskedasticity, APARCH (1,1) model, under the Generalized Error Distribution (GED) at 1% significance level from 3 January 2000 to 29 November 2016. The descriptive statistical results showed that the returns were not normally and linearly distributed, with strong evidence of a heteroskedasticity effect. The results of the analysis also confirmed the effect of asymmetric information on the volatility of stock returns in the Nigerian stock market. The asymmetric parameter (γ) was negative at (-1.00), which is statistically significant at 1% level. This confirms that there is an asymmetric or leverage effect where bad news had a more destabilizing effect on the volatility of stock returns than good news. The total impact of bad news on volatility was explosive at 2.0, during the period under review. Also, the volatility persistence which is measured by the sum of ARCH(α) and GARCH(β) stood at 1.695950. This is above unity and suggests that volatility takes a long time to attenuate in Nigeria. This could be largely ascribed to the persistent effect of the 2008 global financial crisis, which probably eroded investors’ confidence in the market.

scholarly journals Asymmetric Information and Volatility of Stock Returns in Nigeria

2017 ◽  
Vol 9 (3(J)) ◽  
pp. 220-231
Author(s):  
Joshua Odutola Omokehinde ◽  
Matthew Adeolu Abata ◽  
Olukayode Russell ◽  
Stephen Oseko Migiro ◽  
Christopher Somoye
Keyword(s):  
Asymmetric Information ◽  
Stock Returns ◽  
Global Financial Crisis ◽  
Bad News ◽  
Good News ◽  
Significance Level ◽  
Asymmetric Power ◽  
Long Time ◽  
Best Fit ◽  
Total Impact

This paper investigates the effect of asymmetric information on volatility of stock returns in Nigeria using the best-fit Asymmetric Power Autoregressive Conditional Heteroskedasticity, APARCH (1,1) model, under the Generalized Error Distribution (GED) at 1% significance level from 3 January 2000 to 29 November 2016. The descriptive statistical results showed that the returns were not normally and linearly distributed, with strong evidence of a heteroskedasticity effect. The results of the analysis also confirmed the effect of asymmetric information on the volatility of stock returns in the Nigerian stock market. The asymmetric parameter (γ) was negative at (-1.00), which is statistically significant at 1% level. This confirms that there is an asymmetric or leverage effect where bad news had a more destabilizing effect on the volatility of stock returns than good news. The total impact of bad news on volatility was explosive at 2.0, during the period under review. Also, the volatility persistence which is measured by the sum of ARCH(α) and GARCH(β) stood at 1.695950. This is above unity and suggests that volatility takes a long time to attenuate in Nigeria. This could be largely ascribed to the persistent effect of the 2008 global financial crisis, which probably eroded investors’ confidence in the market.

scholarly journals Volatility of Regional Sharia Compliance Returns and US News Impact

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.

scholarly journals Modeling Price Volatility for Selected Agricultural Commodities in Ethiopia: The Application of GARCH Models

2021 ◽  
Author(s):  
Tirngo
Keyword(s):  
Price Volatility ◽  
Bad News ◽  
Good News ◽  
Red Pepper ◽  
Garch Models ◽  
Conditional Heteroskedasticity ◽  
Return Volatility ◽  
Agricultural Commodities ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Commodities Prices

Abstract The purpose of this study was to model and forecast volatility of returns for selected agricultural commodities prices using generalized autoregressive conditional heteroskedasticity (GARCH) models in Ethiopia. GARCH family models, specifically GARCH, threshold generalized autoregressive conditional heteroskedasticity (TGARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) were employed to analyze the time varying volatility of selected agricultural commodities prices from 2011to 2021. The data analysis results revealed that, out of the GARCH specifications, TGARCH model with Normal distributional assumption of residuals was a better fit model for the price volatility of Teff and Red Pepper in which their return series reacted differently to the good and the bad news. The study indicated the presence of leverage effect which implied that the bad news could have a larger effect on volatility than the good news of the same magnitude, and the asymmetric term was found to be significant. Also, TGARCH model was found to be the accurate model for forecasting price return volatility of the same commodities, namely Teff and Red Pepper. In short, the study concludes that TGARCH was to be the best fit to model and forecast price return volatility of Teff and Red Pepper in the Ethiopian context.

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 The Impact Of Political Instability Driven By The Tunisian Revolution On Stock Market Volatility: Evidence From Sectorial Indices

2018 ◽  
Vol 34 (2) ◽  
pp. 339-354 ◽  
Author(s):  
Salma Zaiane
Keyword(s):  
Stock Exchange ◽  
Bad News ◽  
Good News ◽  
Stock Market Volatility ◽  
Asymmetric Effect ◽  
Political News ◽  
Stock Indices ◽  
Terrorist Acts ◽  
Tunisian Revolution ◽  
The Impact

The aim of this paper is to study the impact of political uncertainty, driven by the Tunisian Revolution, on return and volatility of major sectorial stock indices in the Tunisian Stock Exchange. We specifically use EGARCH (1.1) model from 01/12/2010 to 31/08/2016. This model is applied to the daily returns relevant to ten sectorial stock indices and to the Tunisian benchmark index (TUNINDEX). To test the impact of political news on returns and volatility, we divided them into two groups (good and bad news). Our results show that both of good and bad news have increased the volatility of major selected indices, including the TUNINDEX. However, the return of all indices are not affected by the political news. We then examined the impact of terrorism on the behavior of indices return and volatility. Results show that the Tunisian market responds significantly to terrorist acts. Hence, the return declines and the volatility increase the day of terrorist attacks. Furthermore, results confirm that bad news have stronger effect on the volatility than good news, which reveal the asymmetric effect of volatility.

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

2020 ◽  
Vol 2 (1) ◽  
pp. 35-43
Author(s):  
Rismawan Ridha ◽  
Ananto Wibowo
Keyword(s):  
Bad News ◽  
Good News ◽  
Leverage Effect ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Conditional Heteroscedasticity

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.

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

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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