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Aggregation, Heterogeneous Autoregression And Volatility Of Daily International Tourist Arrivals And Exchange Rates

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  • CHIA-LIN CHANG
  • MICHAEL MCALEER
Abstract
Tourism is a major source of service receipts for many countries, including Taiwan. The two leading tourism countries for Taiwan, comprising a high proportion of world tourist arrivals to Taiwan, are Japan and USA, which are sources of short and long haul tourism, respectively. As it is well known that a strong domestic currency can have adverse effects on international tourist arrivals, daily data from 1 January 1990 to 31 December 2008 are used to model the world price and US$ / New Taiwan $ and Yen/ New Taiwan $ exchange rates, and tourist arrivals from the world, USA and Japan to Taiwan, as well as their associated volatility. The sample period includes the Asian economic and financial crises in 1997, and part of the global financial crisis of 2008-09. Inclusion of the exchange rate allows approximate daily price effects on world, US and Japanese tourist arrivals to Taiwan to be captured. The Heterogeneous Autoregressive (HAR) model does not reproduce the theoretical hyperbolic decay rates associated with fractionally integrated (or long memory) time series models, but it can nevertheless approximate quite accurately and parsimoniously the slowly decaying correlations associated with such models. The HAR model is used to approximate long memory properties in daily exchange rates and international tourist arrivals, to test whether alternative short and long run estimates of conditional volatility are sensitive to the approximate long memory in the conditional mean, to examine asymmetry and leverage in volatility, and to examine the effects of temporal and spatial aggregation. The empirical results show that the conditional volatility estimates are not sensitive to the approximate long memory nature of the conditional mean specifications. The QMLE for the GARCH(1,1), GJR(1,1) and EGARCH(1,1) models for world, US and Japanese tourist arrivals to Taiwan, and the world price and US$ / New Taiwan $ and Yen/ New Taiwan $ exchange rates, are statistically adequate and
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  • Chia-Lin Chang & Michael Mcaleer, 2012. "Aggregation, Heterogeneous Autoregression And Volatility Of Daily International Tourist Arrivals And Exchange Rates," The Japanese Economic Review, Japanese Economic Association, vol. 63(3), pages 397-419, September.
    • Handle: RePEc:bla:jecrev:v:63:y:2012:i:3:p:397-419
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    2. Chang, Chia-Lin, 2015. "Modelling a latent daily Tourism Financial Conditions Index," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 113-126.
    3. Martins, Luís Filipe & Gan, Yi & Ferreira-Lopes, Alexandra, 2017. "An empirical analysis of the influence of macroeconomic determinants on World tourism demand," Tourism Management, Elsevier, vol. 61(C), pages 248-260.
    4. Chang, Chia-Lin & Hsu, Hui-Kuang & McAleer, Michael, 2013. "Is small beautiful? Size effects of volatility spillovers for firm performance and exchange rates in tourism," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 519-534.
    5. Chang, C-L. & Hsu, S.-H. & McAleer, M.J., 2018. "Asymmetric Risk Impacts of Chinese Tourists to Taiwan," Econometric Institute Research Papers EI2018-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Irandoust, Manuchehr, 2019. "On the relation between exchange rates and tourism demand: A nonlinear and asymmetric analysis," The Journal of Economic Asymmetries, Elsevier, vol. 20(C).
    7. Chia-Lin Chang & Thanchanok Khamkaew & Michael McAleer, 2010. "Estimating Price Effects in an Almost Ideal Demand Model of Outbound Thai Tourism to East Asia," CIRJE F-Series CIRJE-F-735, CIRJE, Faculty of Economics, University of Tokyo.
    8. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    9. Pat Obi & Robert L. Martin & Greg Chidi Obi, 2016. "Tourism: the untapped goldmine in the Gold Coast," Tourism and Hospitality Management, University of Rijeka, Faculty of Tourism and Hospitality Management, vol. 22(1), pages 17-28, May.
    10. Manu Sharma & Geetilaxmi Mohapatra & A. K. Giri, 2022. "Examining the macro-determinants of tourist arrivals in India," SN Business & Economics, Springer, vol. 2(8), pages 1-18, August.
    11. Chih-Yuan Lin & Mateus Lee, 2020. "Taiwan’s opening policy to Chinese tourists and cross-strait relations: The impacts on inbound tourism into Taiwan," Tourism Economics, , vol. 26(1), pages 27-44, February.

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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