Stock market volatility on shipping stock prices: GARCH models approach

Ruslan, Siti Marsila Mhd; Mokhtar, Kasypi

doi:10.1016/j.jeca.2021.e00232

Cited by 13 publications

(8 citation statements)

References 53 publications

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“…Most earlier studies employed stock return data as the observed variable in the GARCH model (Azakia et al, 2020;Irfan et al, 2021;Mhd Ruslan & Mokhtar, 2021;Nurdany et al, 2021). To calculate the return value of each stock index, the stock price data must be transformed into a natural logarithm using a first-order differential equation (Aliyev et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…Several previous studies that analyzed stock volatility also used the GARCH model, includingAliyev et al (2020),Azakia et al (2020), Mhd Ruslan & Mokhtar (2021,Naik et al (2020), andNurdany et al (2021).To test using the GARCH model, the data must first go through the stationarity test process. The stationarity test can be done by using unit roots and correlograms.…”

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confidence: 99%

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Islamic and Conventional Stocks’ Volatility: Evidence from the Three Waves of Covid-19 Pandemic

Alghifary¹,

Kadji²,

Hafizah³

2023

IJIEF

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This study aims to compare the performance of Islamic and conventional stocks’ performance amid a crisis. The performance is measured by analyzing the volatility of the Indonesian Sharia Stock Index (ISSI) and the Composite Stock Price Index (IHSG) IHSG during the Covid-19 pandemic. Based on the results of the different tests using the paired t-test and Wilcoxon rank test methods, it was concluded that the ISSI and IHSG experienced significant changes before and after discovering the first case of Covid-19 in Indonesia. Significant changes in both values are also found when the Delta variance spreads. Meanwhile, when the third wave occurred due to the presence of the Omicron variant, ISSI and IHSG were able to move more stable and did not experience significant shocks. Then, the estimation results of the GARCH model conclude that both Islamic stocks and conventional stocks have an immense volatility power with an identical value of 0.94 or close to 1. The volatility is also significantly influenced by the previous volatility and the squared error representing other previous events outside the model. The volatility that occurs in Islamic and conventional stocks is not much different, even though both stocks have different characters in the ratio of debt and income. Fundamental factors also cause this high volatility in the form of shocks in several macroeconomic variables, including the rupiah exchange rate, gold prices, and world oil prices. In addition, the contagion effect that occurred during the Covid-19 crisis also contributed to the spread of systemic risk in global stock indexes on stock volatility in Indonesia

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Islamic and Conventional Stocks’ Volatility: Evidence from the Three Waves of Covid-19 Pandemic

Alghifary¹,

Kadji²,

Hafizah³

2023

IJIEF

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“…The volatility of time series data and the market's price volatility risk can be captured by examining the variance of the residual series generated through autoregressive models (Geng et al, 2021; Umar et al, 2021). Previous studies have demonstrated the efficacy of GARCH (1, 1) models in forecasting and extracting volatility from financial time return series (Ma et al, 2019; Mhd Ruslan & Mokhtar, 2021; Wang et al, 2020). Therefore, this study employs the GARCH (1, 1) model to extract the volatility of the coal and metal markets.…”

Section: Methodsmentioning

confidence: 99%

The time‐varying volatility spillover effects between China's coal and metal market

Lin,

Lan

2024

Journal of Futures Markets

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This study employs a time‐varying parameter vector autoregression methodology with the Diebold and Yilmaz spillover index to scrutinize the temporal fluctuations in volatility spillovers between the Chinese coal and metal markets. The analysis is conducted from the dual perspectives of security indices and futures prices. The findings reveal a robust correlation between the coal and metal markets, with the coal market serving as a primary conduit for volatility spillover into the metal market. Furthermore, this study investigates the time‐specific impacts of coal decommissioning policies, the COVID‐19 pandemic, and the coal supply crisis on the coal–metal market volatility spillovers. The findings indicate that these three unique shocks significantly increase the overall risk spillover index between the coal and metal markets. Moreover, during these exceptional events, the extent or role of risk spillover in the coal–metal market undergoes varying degrees of change. On the basis of these findings, this article presents pertinent policy recommendations.

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“…The generic GARCH model or GARCH (1,1) was used in this study. Many previous studies used the GARCH (1,1) model with the copula model to ascertain the dependence of the financial data, such as stock index and exchange rate returns (Patton, 2001(Patton, , 2006Wu et al, 2012;Aloui et al, 2013;Puarattanaarunkorn et al, 2016;Marsila et al, 2021). Conditional variance equation…”

Section: Garch (11) Modelmentioning

confidence: 99%

An analysis of dependency of stock markets after unlimited QE announcements during COVID-19 pandemic

Puarattanaarunkorn,

Autchariyapanitkul,

Kiatmanaroch

2023

AJEB

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PurposeUnlimited quantitative easing (QE) is one of the monetary policies used to stimulate the economy during the coronavirus disease 2019 (COVID-19) pandemic. This policy has affected the financial markets worldwide. This empirical research aims at studying the dependence among stock markets before and after unlimited QE announcements.Design/methodology/approachThe copula-based GARCH (1,1) and minimum spanning tree models are used in this study to analyze 14 series of stock market data, on 6 ASEAN and 8 other countries outside the region. The data are divided into two periods to compare the differences in dependence.FindingsThe findings show changes in dependence among the volatility of daily returns in 14 stock markets during each period. After the unlimited QE announcement, the upper tail dependence became more apparent, while the role of the lower tail dependence was reduced. The minimum spanning tree can show the close relationships between stock markets, indicating changes in the connection network after the announcement.Originality/valueThis study allows the dependency to be compared between stock market volatility before and after the announcement of unlimited QE during the COVID-19 pandemic. Moreover, the study fills the literature gap by combining the copula-based GARCH and the minimum spanning tree models to analyze and reveal the systemic network of the relationships.

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Stock market volatility on shipping stock prices: GARCH models approach

Cited by 13 publications

References 53 publications

Islamic and Conventional Stocks’ Volatility: Evidence from the Three Waves of Covid-19 Pandemic

Islamic and Conventional Stocks’ Volatility: Evidence from the Three Waves of Covid-19 Pandemic

The time‐varying volatility spillover effects between China's coal and metal market

An analysis of dependency of stock markets after unlimited QE announcements during COVID-19 pandemic

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