臺灣博碩士論文加值系統

為了改善中國旅客來台旅遊人數減少之問題，我國政府推出「新南向政策」藉以強化我國與南亞、東南亞地區國家、紐西蘭及澳洲之合作，並持續促進我國經濟發展。隨著政策發展，新南向國家旅客對台灣觀光產業之重要性與日俱增。因此，本研究主要探討新南向國家旅客至台灣旅遊人次變動率的短期與長期持續衝擊及旅遊需求風險特性(不對稱效果和槓桿效果)，並期望研究結果能對公、私部門制訂旅遊相關政策與行銷企劃時有所參考價值。研究結果發現，除了馬來西亞與印度，其他新南向國家來台旅遊人次變動率對衝擊具有長期與短期持續性影響，其中，馬來西亞旅客之來台旅遊需求僅具有短期衝擊效果，而印度旅客僅反映長期衝擊效果。此外，除了印度旅客，其他新南向政策國旅客來台旅遊人次變動率的波動具有不對稱效果，表示這些國家之來台旅遊需求受負面事件影響大於正面事件影響。另外，本研究亦在新加坡、菲律賓、泰國、紐西蘭的旅客來台旅遊人次變動率的波動中捕捉到槓桿效果，顯示這些國家的旅客對於小事件衝擊的反應較為敏感。

To mitigate the loss of inbound Chinese tourists, Taiwan government has launched the ‘New Southbound Policy’ and promoted a series of programs to intensify the collaboration with the New Southbound countries, including countries in southeastern Asia, as well as New Zealand and Australia, in hopes of boosting the economic development of Taiwan. As a result of the policy, the New Southbound countries have gradually become the main source of inbound international tourists for Taiwan, and the importance of the New Southbound countries is on the rise. Hence, the paper aims at investigating the tourism demand of these countries and further analyze the characteristics (symmetric, asymmetric and leverage effect) for the volatility. Also, the paper detects the persistence of shocks on the volatility of the change rate of daily international inbound tourists from the New Southbound countries. The paper adopts the daily data from 1 January 2014 to 17 March 2020 with three econometric models, which are GARCH(1,1), GJR(1,1), and EGARCH(1,1) models for empirical analysis. The findings show that for most New Southbound countries, the impact of shocks on tourism demand would persist in both long- and short-run. For Malaysia, impact of shocks only last for a short period of time, while for India, the impact on the tourism demand caused by shocks would only be shown in the long run. In addition, asymmetric effects are captured in the volatility of international inbound tourists from all studied countries, except for India. It means that tourists, Indian tourists excluded, from the New Southbound countries are more sensitive to negative shocks as the volatility of the international inbound tourists would react greatly to negative events comparing to the positive ones. Furthermore, the leverage effects are captured in half of the studied countries, including Singapore, Vietnam, Philippines, Thailand, and New Zealand, which means that small-scale incidents rather than big ones are more likely to bring volatility to their tourism demand. Overall, to constantly increase the number of international inbound tourists from the new Southbound countries, it is suggested that the Taiwan authority and the private tourism agencies reduce the negative events that would damage the image of Taiwan to the least possible level, as well as stimulate the tourism demand by launching programs which entail ongoing preferential policy and short-term promotions.

Table of Contents
Chapter 1 Introduction 1
Chapter 2 Literature Review 9
Chapter 3 Methodology 14
3.1 Fundamental Tourism Finance Equation 14
3.2. Univariate Conditional Volatility Models 15
3.2.1. GARCH model 15
3.2.2. GJR model 17
3.2.3. EGARCH model 18
3.3. Data and Variables 20
Chapter 4 Empirical Results 27
4.1. Malaysia 27
4.2. Singapore 30
4.3. Indonesia 32
4.4. Vietnam 34
4.5. Philippines 36
4.6. Thailand 38
4.7. India 40
4.8. Australia 42
4.9. New Zealand 44
4.10. Potential markets 46
Chapter 5 Discussion 48
5.1. Key source markets—Malaysia, Singapore, Vietnam and Thailand 49
5.2. Growing markets – Indonesia, Philippines, and India 50
5.3. Australia and New Zealand 51
5.4. Potential markets (Myanmar, Cambodia, Laos, Bhutan, Brunei, and Sri Lanka) 51
Chapter 6 Conclusion 53
References 55

List of Figures
Figure 1. The total number of inbound visits and the visitor expenditures (foreign exchange earnings from tourism industry) (Taiwan Tourism Bureau, 2022). 2
Figure 2. The annual total number of international inbound tourists from New Southbound countries (by region) (Taiwan Tourism Bureau, 2022). 4
Figure 3. Changes in the number of international inbound tourists from New Southbound countries (by residence) (Taiwan Tourism Bureau, 2022). 5
Figure 4. The total number of tourists from China and New Southbound countries (Taiwan Tourism Bureau, 2022). 6
Figure 5. The total number of Daily International Inbound Tourists from 1 January 2014 to 17 March 2020. 22
Figure 6. The Change Rate of Daily International Inbound Tourists from 1 January 2014 to 17 March 2020. 23
 
List of Tables
Table 1. Descriptive Statistics 26
Table 2. Estimation Results for International Inbound tourists from Malaysia. 29
Table 3. Estimation Results for International Inbound tourists from Singapore 31
Table 4. Estimation Results for International Inbound tourists from Indonesia 33
Table 5. Estimation Results for International Inbound tourists from Vietnam 35
Table 6. Estimation Results for International Inbound tourists from Philippines 37
Table 7. Estimation Results for International Inbound tourists from Thailand 39
Table 8. Estimation Results for International Inbound tourists from India 41
Table 9. Estimation Results for International Inbound tourists from Australia 43
Table 10. Estimation Results for International Inbound tourists from New Zealand 45
Table 11. Estimation Results for International Inbound tourists from Potential Markets 47
Table 12. Summary of the risk volatility attributes to the change rate of daily inbound tourists from New Southbound countries 48

1.Balli, H. O., Tsui, W. H., & Balli, F. (2019). Modelling the volatility of international visitor arrivals to New Zealand. Journal of Air Transport Management, 75, 204-214. doi:10.1016/j.jairtraman.2018.10.002
2.Black, F. (1976). Studies of stock price volatility changes, in: Proceedings of the 1976 Meeting of the Business and Economic Statistics Section, American Statistical Association, Washington DC., 177-181.
3.Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. doi:10.1016/0304-4076(86)90063-1
4.Chan, F., Lim, C., & McAleer, M. (2005). Modelling multivariate international tourism demand and volatility. Tourism Management, 26(3), 459-471. doi:10.1016/j.tourman.2004.02.013
5.Chang, C., & McAleer, M. (2009). Daily inbound tourists, exchange rates and volatility for Korea and Taiwan. SSRN Electronic Journal. doi:10.2139/ssrn.1504651
6.Chang, C., & McAleer, M. (2012). Aggregation, heterogeneous autoregression and volatility of Daily International Inbound tourists and exchange rates*. Japanese Economic Review, 63(3), 397-419. doi:10.1111/j.1468-5876.2011.00563.x
7.Chang, C., & McAleer, M. (2017). The correct regularity condition and interpretation of asymmetry in EGARCH. Economics Letters, 161, 52-55. doi:10.1016/j.econlet.2017.09.017
8.Chang, C., Hsu, S., & McAleer, M. (2019). Asymmetric risk impacts of Chinese tourists to Taiwan. International Journal of Tourism Research, 21(5), 718-734. doi:10.1002/jtr.2299
9.Chang, C., McAleer, M., & Lim, C. (2009). Modelling short and long haul volatility in Japanese inbound tourists to New Zealand and Taiwan. SSRN Electronic Journal. doi:10.2139/ssrn.1356567
10.Chang, C., McAleer, M., & Lim, C. (2012). Modeling the volatility in short and long haul Japanese inbound tourists to New Zealand and Taiwan. International Journal of Tourism Sciences, 12(1), 1-24. doi:10.1080/15980634.2012.11434650
11.Chhorn, T., & Chaiboonsri, C. (2018). Modelling and forecasting inbound tourists to Cambodia: An application of Arima-GARCH approach. Journal of Management, Economics and Industrial Organization, 1-19. doi:10.31039/jomeino.2018.2.2.1
12.Chikobvu, D., & Makoni, T. (2019). Statistical modelling of Zimbabwe’s international inbound tourists using both symmetric and asymmetric volatility models. Journal of Economic and Financial Sciences, 12(1). doi:10.4102/jef.v12i1.426
13.Choi, K., & Kim, I. (2021). Co-movement between inbound tourists of inbound tourism markets in South Korea: Applying the dynamic copula method using secondary time series data. Sustainability, 13(3), 1283. doi:10.3390/su13031283
14.Coshall, J. T. (2009). Combining volatility and smoothing forecasts of UK demand for international tourism. Tourism Management, 30(4), 495-511. doi:10.1016/j.tourman.2008.10.010
15.Dacorogna, M. M., Müller, U. A., Pictet, O. V., & Olsen, R. B. (1997). Modelling short-term volatility with GARCH and Harch Models. SSRN Electronic Journal. doi:10.2139/ssrn.36960
16.Divino, J. A., & McAleer, M. (2010). Modelling and forecasting daily international mass tourism to Peru. Tourism Management, 31(6), 846-854. doi:10.1016/j.tourman.2009.09.002
17.Tourism Statistics Database of the Taiwan Tourism Bureau (2020). Retrieved November 2, 2022, from https://admin.taiwan.net.tw/English/ActivitiesDetailE001100.aspx?Cond=300191b9-68c4-4a72-96a7-bce1f0c5d0d9
18.Dutta, A., Mishra, T., Uddin, G. S., & Yang, Y. (2020). Brexit uncertainty and volatility persistence in tourism demand. Current Issues in Tourism, 24(16), 2225-2232. doi:10.1080/13683500.2020.1822300
19.Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987. doi:10.2307/1912773
20.Fernando, S., Bandara, J. S., Liyanaarachch, S., Jayathilaka, R., & Smith, C. (2013). Political violence and volatility in International Inbound tourists: The case of sri lanka. Tourism Analysis, 18(5), 575-586. doi:10.3727/108354213x13782245307876
21.Gencer Celik, G. (2020). Volatility modelling for Tourism Sector Stocks in Borsa Istanbul. International Journal of Economics and Financial Issues, 10(3), 158-165. doi:10.32479/ijefi.9811
22.Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779-1801. doi:10.1111/j.1540-6261.1993.tb05128.x
23.Kim, S. S., & Wong, K. K. (2006). Effects of news shock on inbound tourist demand volatility in Korea. Journal of Travel Research, 44(4), 457-466. doi:10.1177/0047287505282946
24.Ling, S., & McAleer, M. (2002). Stationarity and the existence of moments of a family of Garch Processes. Journal of Econometrics, 106(1), 109-117. doi:10.1016/s0304-4076(01)00090-2
25.Ling, S., & McAleer, M. (2003). Asymptotic theory for a vector arma-GARCH model. Econometric Theory, 19(02). doi:10.1017/s0266466603192092
26.Lorde, T., & Moore, W. (2008). Modeling and forecasting the volatility of long-stay inbound tourists. Tourism Analysis, 13(1), 43-51. doi:10.3727/108354208784548742
27.Makoni, T., & Chikobvu, D. (2018). Modelling tourism demand volatility using a seasonal autoregressive integrated moving average autoregressive conditional heteroscedasticity model for Victoria Falls Rainforest Arrivals in Zimbabwe. Journal of Economic and Financial Sciences, 11(1). doi:10.4102/jef.v11i1.167
28.Makoni, T., & Chikobvu, D. (2021). Modelling international inbound tourists volatility in Zimbabwe using a GARCH process. April 2021, (Volume 10(2)), 639-653. doi:10.46222/ajhtl.19770720-123
29.McAleer, M. (2014). Asymmetry and leverage in conditional volatility models. Econometrics, 2(3), 145-150. doi:10.3390/econometrics2030145
30.McAleer, M., & Hafner, C. M. (2014). A one line derivation of EGARCH. Econometrics, 2(2), 92-97. doi:10.3390/econometrics2020092
31.McAleer, M. (2015). The fundamental equation in tourism finance. Journal of Risk and Financial Management, 8(4), 369-374. doi:10.3390/jrfm8040369
32.Nelson, D. B. (1990). Arch models as diffusion approximations. Journal of Econometrics, 45(1-2), 7-38. doi:10.1016/0304-4076(90)90092-8
33.Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347. doi:10.2307/2938260
34.Shareef, R., & McAleer, M. (2005). Modelling international tourism demand and volatility in small island tourism economies. International Journal of Tourism Research, 7(6), 313-333. doi:10.1002/jtr.538
35.Shareef, R., & McAleer, M. (2007). Modelling the uncertainty in monthly international inbound tourists to the Maldives. Tourism Management, 28(1), 23-45. doi:10.1016/j.tourman.2005.07.018
36.Shephard, N. (2020). Statistical aspects of ARCH and stochastic volatility. In Time series models (pp. 1-67). Chapman and Hall/CRC.
37.Taiwan Centers for Disease Control (2022). COVID-19 FAQs. Retrieved October 17, 2022, from https://www.cdc.gov.tw/En/Category/QAPage/SbkmnM5v0OwdDMjJ2tI_xw
38.Taiwan Tourism Bureau. (2020). In order to cooperate with the epidemic prevention measures, Taiwan is continuing to ban the travel industry from organizing tours for Taiwanese travelers to visit foreign countries and receiving foreign tour groups to visit Taiwan. Retrieved January 23, 2022, from https://admin.taiwan.net.tw/English/ActivitiesDetailE001100.aspx?Cond=89353d41-1ca3-43eb-8e4b-13ee3af07534
39.Taiwan Tourism Bureau. (2022). Inbound visitors. Tourism Statistics Database of the Taiwan Tourism Bureau. Retrieved August 31, 2022, from https://stat.taiwan.net.tw/inboundSearch
40.Taiwan Tourism Bureau. (2022). Visitor's expenditure. Tourism Statistics Database of the Taiwan Tourism Bureau. Retrieved October 6, 2022, from https://stat.taiwan.net.tw/statistics/year/inbound/expenditure
41.Tendai, M., & Chikobvu, D. (2017). Modelling international inbound tourists and volatility to the Victoria Falls Rainforest, Zimbabwe: Application of the GARCH family of models. African Journal of Hospitality, Tourism and Leisure, 6(4), 1-16.
42.Tsay, R. S. (1983). Conditional Heteroscedastic Time Series Models. Journal of the American Statistical Association, 82(398), 590–604. https://doi.org/10.1002/0471264105.ch3
43.Uzuner, G., & Ghosh, S. (2020). Do pandemics have an asymmetric effect on tourism in Italy? Quality & Quantity, 55(5), 1561-1579. doi:10.1007/s11135-020-01074-7
44.World Travel & Tourism Council (WTTC) (2022). China, Taiwan 2022 Annual Research: Key Highlights. Retrieved November 2, 2022, from https://wttc.org/DesktopModules/MVC/FactSheets/pdf/704/242_20220613171216_Taiwan,%20China2022_pdf