Comparison of Modeling Volatility of Indonesia Banks Using ARCH, GARCH, TARCH and EGARCH

Hasanah, Eneng Nur

doi:10.29313/performa.v0i0.4422

Cited by 2 publications

(4 citation statements)

References 7 publications

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“…This model captures the leverage effect, where negative returns increase volatility more than positive returns of the same magnitude (Eraker & Wu, 2017;Sichigea et al, 2020). Similarly, the TARCH (Threshold GARCH) model differentiates between positive and negative shocks, offering insights into market reactions to different news types (Elek & Markus, 2010;Goncalves et al, 2009;Hasanah, 2019).…”

Section: Methodological Approaches To Analysing Volatilitymentioning

confidence: 99%

“…The GARCH (1,1) and EGARCH (1,1) models were instrumental in elucidating the intricate volatility dynamics of the cryptocurrency market (Caporale et al, 2015;Q. H. Chen et al, 2023;Hasanah, 2019;Naimy et al, 2021;Zhou, 2021). The GARCH model, emblematic of symmetric volatility patterns, revealed a significant constant volatility component with a coefficient of 0.001385 (p=0.0109).…”

Section: Commerce and Business Researchermentioning

confidence: 99%

“…By employing sophisticated econometric models such as GARCH (1,1), EGARCH (1,1), and FIGARCH (1,D,1), we endeavor to quantify the impact of news on the volatility of the CMC 200 Index. These models, renowned for their ability to capture volatility clustering and leverage effects, provide a robust framework for our analysis (Goncalves et al, 2009;Hasanah, 2019;Segnon et al, 2023;. Furthermore, the study incorporates the News Impact Curve and other tools to offer a nuanced understanding of how different types of news can influence market dynamics differently.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Beyond the Hype: Evaluating the Real Impact of News on Cryptocurrency Market Volatility

A.,

Prakash,

et al. 2023

CBR

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This study used the CMC 200 Index as a cryptocurrency market benchmark to examine complex volatility patterns of cryptocurrencies. The growing interest in cryptocurrencies and the necessity to analyse their market dynamics, especially in the face of external inputs like news, prompted the study. The study examined market responses and causes to diverse stimuli using rigorous analytical models including GARCH, EGARCH, FIGARCH, and News Impact Curve. The asymmetricvolatility or “leverage effect” showed that negative events or news have a greater impact on market volatility than positive developments of similar magnitude. Symmetric volatility indicated large price shifts regardless of news direction. The left-skewed news effect curve emphasises this asymmetric volatility, demonstrating that negative news has a greater impact on market dynamics. The curve’s leftward skew shows the market’s increased susceptibility to pessimism. This suggests that negative news might undermine investor confidence in the crypto market more than favourable news. Beyond these initial reactions, the research revealed a “long memory” in market volatility, suggesting that prior shocks continue to affect its volatility over time. These studies emphasise the importance of investor sentiment in crypto market. Investors in this volatile market need honest communication and strong risk management due to the leverage impact and prior experience.

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Section: Methodological Approaches To Analysing Volatilitymentioning

confidence: 99%

Section: Commerce and Business Researchermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Beyond the Hype: Evaluating the Real Impact of News on Cryptocurrency Market Volatility

A.,

Prakash,

et al. 2023

CBR

View full text Add to dashboard Cite

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“…Next we will test for the ARCH effect of the data to ensure the ARCH and GARCH models are appropriate for the volatility modeling. Testing the ARCH effect will require the following equation (Nurhasanah, 2018): e t 2 = γ 0 + γ 1 e t-1 2 + v t…”

Section: Methodsmentioning

confidence: 99%

Modeling the returns volatility of Indonesian stock indices: The case of SRI-KEHATI and LQ45

Ponziani

2022

JEM

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The purpose of this research is to model the volatility of Stock Indices in Indonesian capital market. This research focuses on two stock indices namely SRI-KEHATI and LQ45. SRI_KEHATI is a stock index that consists of companies whose operations are sustainable and environmentally friendly. This stock index is also known as â€œgreen indexâ€ due to its environment and sustainability concern. This is the novelty of this research that fills in the gap in the literature in which not much known regarding this green index. As the comparison, LQ45 stock index was modeled. The data used in this model were daily returns data of both index. The research period extended from 2 January 2019 to 1 November 2021. The research employed four models i.e. ARCH (1), ARCH (2), GARCH (1,1) and GJR-GARCH (1,1) for both indices returns. The ARCH and GARCH model were employed to capture the conditional variance of the indices return, while GJR-GARCH was specifically chosen to investigate whether there exists asymmetric effect in which return reacts more to bad news than good news. Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC) were chosen as the parameters for choosing the best models. Data analysis showed that GJR-GARCH was the best model for modeling the returns volatility of SRI-KEHATI and LQ45. This model was able to capture the essential property of asymmetric effect present in both models. The second best model was ARCH (2). Apparently, returns variance of Indonesian stock indices are affected more by lagged residuals. The limitation of this research lies in its research period that covered both pre-pandemic and post-pandemic period. Stock market behavior might be very different between these two periods. Future research may endeavor to investigate how the volatility of stock differs between pre-pandemic and post-pandemic period.

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Comparison of Modeling Volatility of Indonesia Banks Using ARCH, GARCH, TARCH and EGARCH

Cited by 2 publications

References 7 publications

Beyond the Hype: Evaluating the Real Impact of News on Cryptocurrency Market Volatility

Beyond the Hype: Evaluating the Real Impact of News on Cryptocurrency Market Volatility

Modeling the returns volatility of Indonesian stock indices: The case of SRI-KEHATI and LQ45

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