https://orcid.org/0000-0002-1358-5592
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ABSTRACT
This study aims to identify the sources of spillovers affecting tourism tokens and classify the type of assets to which they correspond. Using daily data for different asset classes from June 2018 through November 2022, we employ a TVP-VAR methodology to test the connectedness between two tourism tokens, two leading travel equity indices, and the two dominant cryptocurrencies, namely, Bitcoin and Ethereum. The findings show that tourism tokens are relatively independent of fluctuations in the traditional sources affecting the travel and leisure sector, such as the U.S. dollar, the price of oil, or travel equity indices. These results hint that tourism tokens are more closely related to cryptocurrencies rather than pure travel goods. The results may help decision-makers in the travel and hospitality industries considering the use of tourism tokens identify the potential forces impacting them.
Disclosure statement
No potential conflict of interest was reported by the authors.
Declaration of interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests.
Notes
1 For a discussion of the definitions and the use of blockchain in the tourism industry, see Nam et al. (Citation2021) and Rashideh (Citation2020). Rana et al. (Citation2022) also provides a literature review of the topic.
2 Most studies use the two terms, coins, and tokens, interchangeably but there is a major difference between the two. Unlike coins, tokens do not have their own blockchain and are issued on top of existing networks.
3 In essence, the selection of assets for our study is determined by the research objective. Specifically, we aim to investigate whether ‘tourism tokens’, a type of crypto asset focused on the tourism industry, are linked to factors that influence tourism demand, represented by such as the US dollar index, oil prices, and prominent tourism indices, or they are more connected to crypto assets such as Bitcoin and Ethereum. While we have a relatively large set of assets within our system, it is still in line with related studies. For example, a related study, Yousaf, Abrar et al. (Citation2023) uses travel & tourism tokens, as well as travel and tourism stock index, oil prices, Bitcoin, bonds, gold and US dollar. Different from Yousaf, Abrar et al. (Citation2023), we also include the European tourism stock index and Ethereum within our system. Our system includes two tourism tokens: AVA (Travala.com) and LOC (Locktrip). These two tokens have the longest periods with complete data and have been listed and traded on the leading crypto exchange markets. While we do not include other tokens with incomplete data, we originate a tourism token index composed of six tokens. Replicating our analyses with the inclusion of this index, we provide additional evidence.
4 Time-varying parameter vector autoregressive.
5 BIC is the Bayesian information criterion.
6 All figures and connectedness measures are generated with the help of the online platform: https://sites.google.com/view/davidgabauer/econometric-code
7 We follow a long stream of previous studies, highlighting the benefits and merits of using the TVP-VAR approach (e.g., Aharon & Demir, Citation2022; Aharon & Qadan, Citation2022; Antonakakis et al., Citation2018). In a nutshell, TVP-VAR is an additional state-of-the-art setting which has several advantages over the traditional Diebold and Yilmaz (Citation2012) approach. TVP-VAR monitors more precisely the variations in the parameters, avoids losing observations, and is therefore more efficient, particularly in small size samples (Antonakakis et al., Citation2018). In addition, TVP-VAR is less sensitive to outliers given the use of the underlying Kalman filter and does not involve any arbitrary selection of window size. Finally, the approach is relative more successful in providing strong estimates in the presence of structural breaks (Antonakakis et al., Citation2020).
8 Appendix B presents the FROM and TO dynamic measures separately. Appendix C shows the net pairwise directional connectedness and the dynamic pairwise connectedness.
9 Our main analysis using tourism token index is conducted by forecast horizon H = 10, in line with the analyses in Section 5. We repeat the estimations using different forecast horizons, including H = 4, H = 6, and H = 12. The results for all alternatives are qualitatively same as the original results obtained using the assumption of H = 10. We do not report the results for brevity and they are available upon request.
10 The definition of before and during COVID-19 is determined by the declaration of COVID-19 as a pandemic by the WHO on March 11, 2020. Therefore, the period before COVID-19 is June 1, 2018 – March 10, 2020. The COVID-19 period begins on March 11, 2020. For the official declaration please see: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020