Realized volatility transmission within Islamic stock markets: A multivariate HAR-GARCH-type with nearest neighbor truncation estimator

Ng, Sew Lai; Cheong, Chin Wen; Chong, Lee Lee

doi:10.1016/j.bir.2020.10.001

Cited by 11 publications

(6 citation statements)

References 55 publications

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“…According to Ng, Chin, and Chong [13], the Islamic Finance Development Indicator (IFDI) in 2018 confirmed the Islamic financial markets' (IFMs) long-term viability by demonstrating the outstanding growth, as seen by an annual growth rate of roughly 11%, making it enticing to investors. Notwithstanding, the prevailing crisis in global financial markets, which is championed by the COVID-19 pandemic, has not only impacted conventional markets; IFMs have had their "fair" share [14] to the extent that recognised rating agencies like S&P projected that the IFM would decline in growth in 2020, followed by a slight rebound in 2021 [15].…”

Section: Introductionmentioning

confidence: 99%

Information Flow from COVID‐19 Pandemic to Islamic and Conventional Equities: An ICEEMDAN‐Induced Transfer Entropy Analysis

Bossman

2021

Complexity

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With the steady growth in the data set on the COVID-19 pandemic, empirical works that employ novel and yet appropriate statistical techniques to corroborate previous findings of the pandemic and its consequences on financial markets are necessary. This paper examined the impact of COVID-19 information flow on the Islamic and conventional equities within the short-, mid-, and long-term horizons to assess possible diversification prospects in the era of the pandemic. To the studied equities markets, a novel technique based on a denoised frequency-domain entropy paradigm was applied. The operability of entrenched market dynamics in the long-term horizon of the COVID-19 pandemic period is reinforced by the results. The findings divulge diversification opportunities between Islamic and conventional equities in the short- and mid-term periods of the COVID-19 pandemic. The risks on equities from Japan or Bahrain could be diversified by equities from Jordan in the short-term, while in the intermediate-term stocks from Japan could diversify with the UAE and USA equities. The results imply that it is imperative for investors and fund managers to employ portfolio management techniques that show how to use benefits together with risk prevention and management across distinct time scales.

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Section: Introductionmentioning

confidence: 99%

Information Flow from COVID‐19 Pandemic to Islamic and Conventional Equities: An ICEEMDAN‐Induced Transfer Entropy Analysis

Bossman

2021

Complexity

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“…Research related to stock volatility has been conducted by Ng, Chin, & Chong (2020), investigates the transmission of realized volatility between the Malaysian Islamic stock market and various global sectoral Islamic stock markets. The results show that the volatility of the Malaysian Islamic stock market equities depends significantly on its equity in the short, medium, and long term.…”

Section: Literature Reviewmentioning

confidence: 99%

ARCH and GARCH Models on the Indonesian Sharia Stock Index

Budiandru

2021

jakis

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Investments in Islamic stocks are in demand because of the profit-sharing system so that the company is more stable in facing uncertain global economic conditions. This study aims to analyze the volatility of the Indonesian Sharia Stock Index and the Indonesian Sharia Stock Index's potential in the future. We use daily data from 2012 to 2020 and the Autoregressive Conditionally Heteroscedasticity-Generalized Autoregressive Conditional Heteroskedasticity (ARCH-GARCH) method. The results show that the Indonesian Sharia Stock Index's volatility is influenced by the risk of the two previous periods and the return volatility in the previous period. Potential Indonesian Sharia Stock Index tends to fluctuate in return by an average of 3 percent.

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“…The model can accurately reflect the regular fluctuations in financial data volatility [24]. Previous study by [25] showed the heteroskedasticity component of stock market returns can be employed to forecast future market value. Factors such as political events [26] and general economic conditions [27] could affect the stock price movements and these factors only can be measures through news and bulletin.…”

Section: Introductionmentioning

confidence: 99%

Heteroscedasticity effects as component to future stock market predictions using RNN-based models

Sadon,

Ismail,

Khamis

et al. 2024

PLoS ONE

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Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long Short-Term Memory (LSTM). This proposed model is referred to as GARCH-LSTM model. The proposed model is expected to improve prediction accuracy by considering heteroscedasticity elements. First, the GARCH model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded a p-value less than 0.05 indicating there is valuable information remaining in the residual, known as heteroscedasticity element. Next, the dataset with heteroscedasticity is then modelled using an LSTM-based RNN algorithm. Experimental results revealed that hybrid GARCH-LSTM had the lowest MAE (7.961), RMSE (10.466), MAPE (0.516) and HMAE (0.005) values compared with a single LSTM. The accuracy of forecasting was also significantly improved by 15% and 13% with hybrid GARCH-LSTM in comparison to single LSTMs. Furthermore, the results reveal that hybrid GARCH-LSTM fully exploits the heteroscedasticity element, which is not captured by the GARCH model estimation, outperforming GARCH models on their own. This finding from this study confirmed that hybrid GARCH-LSTM models are effective forecasting tools for predicting stock price movements. In addition, the proposed model can assist investors in making informed decisions regarding stock prices since it is capable of closely predicting and imitating the observed pattern and trend of KLSE stock prices.

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Realized volatility transmission within Islamic stock markets: A multivariate HAR-GARCH-type with nearest neighbor truncation estimator

Cited by 11 publications

References 55 publications

Information Flow from COVID‐19 Pandemic to Islamic and Conventional Equities: An ICEEMDAN‐Induced Transfer Entropy Analysis

Information Flow from COVID‐19 Pandemic to Islamic and Conventional Equities: An ICEEMDAN‐Induced Transfer Entropy Analysis

ARCH and GARCH Models on the Indonesian Sharia Stock Index

Heteroscedasticity effects as component to future stock market predictions using RNN-based models

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