Oil Price and Fijian Tourism Cycle: A Markov Regime-Switching Model

Soh, Ann-Ni; Puah, Chin‐Hong; Arip, Mohammad Affendy; Kuek, Tai-Hock

doi:10.32479/ijeep.8087

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…Instead of using the traditional single-state approach, Markovswitching regression techniques that confirm the validity of crises were applied in this study. A similar approach was applied in different tourism issues by [23][24][25] with constructive findings. The current study tends to establish the links among different crises and tourism demand forecasting, with tourism stock price as the proxy variable.…”

Section: Methodsmentioning

confidence: 99%

The Impact of COVID-19 Pandemic and Commodities Prices on Booking.com Share Price

Jong¹,

Puah²,

Soh³

2021

Adv. sci. technol. eng. syst. j.

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This paper examines the impacts of the COVID-19 pandemic and selected commodity variables on Booking.com share price using the Markov-switching approach. Daily data spans from January 2017 through July 2020 are utilized in this study. Empirical evidence showed that COVID-19, international crude oil price, and gold price affected the Booking.com share price significantly. A positive relationship was detected between international crude oil price and gold price towards stock price whereas COVID-19 showed an inverse impact on stock price. The empirical findings evidenced a 1% increase in COVID-19 cases adversely affecting the share price by -0.27%. Our findings also suggested that the potential of another wave of COVID-19 is relatively higher as the bounce back period was identified as 67 days. The filtered and smoothed probabilities signaled the Booking.com share price chronologically, and transition probabilities were identified. Six cycles were outlined, and the effectiveness of the Markov-switching approach in detecting vulnerable financial forecasting was demonstrated. The adequate dating evolution provided satisfactory input for policymakers, investors, and researchers to design and mitigate volatility in commodities and crises.

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Section: Methodsmentioning

confidence: 99%

The Impact of COVID-19 Pandemic and Commodities Prices on Booking.com Share Price

Jong¹,

Puah²,

Soh³

2021

Adv. sci. technol. eng. syst. j.

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“…Table 1 depicts the results of correlation analysis. Another similar study that had employed this two-regime model was done by Soh et al (2019c) to model and forecast the Fijian tourism demand to capture the expansion and contraction of the tourism cycles. In this study, the model used can be explained using Equation ( 1).…”

Section: Business Management and Strategymentioning

confidence: 99%

“…Puah et al (2019) and Jong et al (2020) employed panel analysis with the application of the gravity model in modelling the Vietnam and Sabah tourism demand, respectively. This study, on the other hand, employed the Markov regime switching regression instead of a conventional single-state approach (Tang & Tan, 2015;Soh et al, 2019c). Hamilton (1989;1990) introduced the switching regression modelling approach to study the contraction and expansion periods of the economic cycle.…”

Section: Introductionmentioning

confidence: 99%

Macroeconomic Determinants of Tourism Demand in Malaysia: A Markov Switching Regression Approach

Soh¹,

Puah²,

Jong³

2022

BMS

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A wave of studies has always surrounded the nexus of tourism development and economic growth in a nation. An assessment is provided in this study to examine the Malaysian tourism market dynamics. A series of macroeconomic variables has been utilised to model tourism demand and examine causal linkages among tourism and economic growth. Spanning from 2000 till 2018 on a monthly basis, the Markov regime switching regression provides an overview of tourism market performance and potential influences during recession and expansion periods of the Malaysian tourism cycle. Notably, the results present a reference chronology of the crises happening over the past two decades, the Granger causality of the variables, and different behavioural changes of the variables as well for both recession and expansion periods. Significant relationships have been revealed in this study that suggest that overall international tourism can drive economic growth and vice versa.

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“…From the regional economies point of view, Assaf et al (2018) proposed the Bayesian global vector autoregressive (BGVAR) model in demonstrating the spill-over regional effect of tourism demand. Moreover, Soh et al (2019) investigated the possible leading indicators in modelling the tourism demand using Markov regime-switching model. Despite the advancement of internet usage, Li et al (2017) adopted the composite search index to be included in the generalized dynamic factor model to forecast the Chinese tourism demand.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Tourism Demand with Composite Indicator Approach for Fiji

Soh

Puah

Arip

2019

BER

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This study attempts to scrutinize the fluctuations of the Fijian tourism market and forecast the early warning signals of tourism market vulnerability using the tourism composite indicator (TCI). The data employed on a monthly basis from 2000M01 to 2017M12 and the indicator construction steps were adopted from the ideology of the National Bureau of Economic Research (NBER). A parsimonious macroeconomic and non-economic fundamental determinant are included for the construction of TCI. Subsequently, the procedure then employed the seasonal adjustment using Census X-12, Christiano-Fitzgerald filtering approach, and Bry-Boschan dating algorithm. Empirical evidence highlighted the signalling attributes against Fijian tourism demand with an average lead time of 2.75 months and around 54 percent of directional accuracy rate, which is significant at 5 percent significance level. Thus, the non-parametric technique can forecast the tourism market outlook and the constructed TCI can provide information content from a macroeconomic perspective for policymakers, tourism market players and investors.

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Oil Price and Fijian Tourism Cycle: A Markov Regime-Switching Model

Cited by 7 publications

References 12 publications

The Impact of COVID-19 Pandemic and Commodities Prices on Booking.com Share Price

The Impact of COVID-19 Pandemic and Commodities Prices on Booking.com Share Price

Macroeconomic Determinants of Tourism Demand in Malaysia: A Markov Switching Regression Approach

Forecasting Tourism Demand with Composite Indicator Approach for Fiji

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