An Analysis of the Effects of Tourism Demand, Yield Curve, and Stock Returns on Economic Growth of Thailand: A Comparison Between the Bayesian DCC-GARCH and Bayesian Change-Point Methods

Pastpipatkul, Pathairat; Piboonrungroj, Pairach; Subsai, Panicha

doi:10.1007/978-3-030-97273-8_49

Cited by 1 publication

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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%

“…Brida et al (2016) opine in favour of TLG in an extensive literature review of more than 100 studies. Pastpipatkul et al (2022) also advocate the TLG in their research. However, Song and Wu (2022) advocate in their critique paper on the TLG and raise several questions on the research design adopted by research papers supporting TLG.…”

Section: Tourism and Economic Growth Of The Countrymentioning

confidence: 99%

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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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