Volatility forecasting for the shipping market indexes: an AR-SVR-GARCH approach

Liu, Jiaguo; Li, Zhouzhi; Sun, Hao; Yu, Lean; Gao, Wenlian

doi:10.1080/03088839.2021.1898689

Cited by 15 publications

(7 citation statements)

References 32 publications

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“…The research findings of GARCH modelling have been reported in some studies such by Liu, et al(2021), Luo (2017), De Gaetano (2020), and Fritz & Oertel (2021). The studies document that GARCH model gives a better performance to forecast the volatility measurements.…”

Section: Introductionmentioning

confidence: 70%

The Implementation of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Model on the Index Forecasting of Sharia Stocks in Asian Countries

Sukartini,

Moin

2022

IJEBMR

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The study of forecasting volatility of stocks has been discussed and investigated among scholars. Volatility plays important role in determining stock value as well as portfolio in stock market. This study investigates the use of GARCH model (generalized autoregressive conditional heteroskedasticity) in forecasting Islamic index stock in Asian countries. This study employs data from yahoo. finance including six countries namely India, Singapore, Japan, China, Malaysia, and Indonesia. There are 1304 data observation of daily closing price for the period between January 2016 and December 2020. The results of the study show that GARCH model can be employed as a mediation of forecasting sharia indexed stock. This implies that GARCH model can be used as forecasting steps in Islamic stock in Asian countries. Investors can take into account the model of GARCH in forecasting of Islamic stock market in Asian countries particularly India, Japan, China, Singapore, Malaysia and Indonesia

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Section: Introductionmentioning

confidence: 70%

The Implementation of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Model on the Index Forecasting of Sharia Stocks in Asian Countries

Sukartini,

Moin

2022

IJEBMR

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“…Kilic ¸and Bayar (2014) also use the same tools to explore the association of ER volatilities with tourism volatilities in Turkey. Pastpipatkul et al (2022) and Liu et al (2022), by using GARCH models, find a significant volatility linking between tourism and ER in Thailand. Sharma and Pal (2020) advance the methods used by Kilic ¸and Bayar (2014) and Saayman and Saayman (2013).…”

Section: 31mentioning

confidence: 97%

“…Volatility is hugely related to time-series applications; hence, it comes in handy with financial and economic concepts. Tourism demand in India is a time series; therefore, volatility spillover can be examined using GARCH models to the tourism demand (Chang et al , 2011; Liu et al , 2022). As per the definition of VSE, these effects are events that occur in one context but affect events in another.…”

Section: Introductionmentioning

confidence: 99%

The volatility spillover effect of macroeconomic indicators on inbound tourism in India

Rastogi

Kanoujiya

2023

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Purpose This study aims to determine the mutual association between the volatility of macroeconomic indicators (MIs) and India’s tourism demand. Design/methodology/approach Bivariate generalized autoregressive conditional heteroscedasticity (GARCH) models are applied to estimate the volatility spillover effect (VSE) from one market to another. Compared to the other methods, bivariate GARCH has wide acceptance for estimating the VSE. The monthly MIs and tourism demand data (2012–2021) are gathered for empirical analysis. Findings The evidence of the growth-led tourism (GLT) demand is seen. In the short term, tourism-led growth (TLG) is indicated. However, this TLG does not sustain itself in the long run. There is significant evidence in favour of the VSE from the MIs to the tourism demand ensuring GLT in India. Practical implications The main implication of the current study is to ignore the short-term influence of tourism demand on the economy because it does not sustain itself in the long run. However, the long-term influence of macroeconomic indicators on tourism demand should be seen with caution. Hedging, if possible, may be considered to protect the tourism sector’s interests from adverse economic fallouts. Originality/value There is a lack of studies on the volatility (especially on the VSE) between MIs and tourism demand. Hence, this study fills the research gap and presents a novel and unique contribution to the extent of the knowledge body on the topic and significantly contributes.

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“…Pérez-Cruz et al [ 17 ] showed that when forecasting the return volatility of the stock market, the SVM model was used to estimate the parameters of the GARCH model, and this estimation method had higher forecasting ability than the ordinary ML method. Liu et al [ 3 ] proposed an AR-SVR-GARCH model and an AR-SVR-GJR model. The empirical results show that both models have better volatility forecasting ability for the dry bulk shipping market, crude oil shipping market and shipping stock market.…”

Section: Related Workmentioning

confidence: 99%

“…With the development of shipping market and the continuous growth of world trade volume, a country’s economy and shipping trade demand are getting closer and closer. More and more scholars link shipping index with global economic development trend [ 1 – 3 ], among which BDI and other shipping indices have been used as economic indicators of world trade by various countries [ 4 ]. Many experts and scholars at home and abroad have been working on how to grasp the trend of future changes in the shipping market through the prediction of shipping indices.…”

Section: Introductionmentioning

confidence: 99%

Forecasting shipping index using CEEMD-PSO-BiLSTM model

Wang

et al. 2023

PLoS ONE

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Shipping indices are extremely volatile, non-stationary, unstructured and non-linear, and more difficult to forecast than other common financial time series. Based on the idea of "decomposition-reconstruction-integration", this article puts forward a combined forecasting model CEEMD-PSO-BiLSTM for shipping index, which overcomes the linearity limitation of traditional models. CEEMD is used to decompose the original sequence into several IMF components and RES sequences, and the IMF components are recombined by reconstruction. Each sub-sequence is predicted and analyzed by PSO-BiLSTM neural network, and finally the predicted value of the original sequence is obtained by summing up the predicted values of each sub-sequence. Using six major shipping indices in China’s shipping market such as FDI and BDI as test data, a systematic comparison test is conducted between the CEEMD-PSO-BiLSTM model and other mainstream time-series models in terms of forecasting effects. The results show that the model outperforms other models in all indicators, indicating its universality in different shipping markets. The research results of this article can deepen and improve the understanding of shipping indices, and also have important implications for risk management and decision management in the shipping market.

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Volatility forecasting for the shipping market indexes: an AR-SVR-GARCH approach

Cited by 15 publications

References 32 publications

The Implementation of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Model on the Index Forecasting of Sharia Stocks in Asian Countries

The Implementation of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Model on the Index Forecasting of Sharia Stocks in Asian Countries

The volatility spillover effect of macroeconomic indicators on inbound tourism in India

Forecasting shipping index using CEEMD-PSO-BiLSTM model

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