Is the volatility of international tourism revenues affected by tourism source market structure? An empirical analysis of Turkey

2021 ◽  
pp. 135481662110460
Author(s):  
Seymur Ağazade ◽  
Egemen Güneş Tükenmez ◽  
Merve Uzun
Keyword(s):  
Market Structure ◽  
Empirical Analysis ◽  
Relative Entropy ◽  
International Tourism ◽  
Conditional Heteroskedasticity ◽  
Entropy Index ◽  
Autoregressive Conditional Heteroskedasticity

This study examines the effect of tourism source market structure on the volatility of tourism revenues in Turkey, using the number of tourists according to nationality and the data on international tourism revenues. The tourism source market structure was measured using the normalized Herfindahl–Hirschman index and the relative entropy index, which is based on the number of tourists visiting Turkey from 107 source markets. The volatility of tourism revenues and the effect of tourism source market structure on this volatility were assessed using the autoregressive conditional heteroskedasticity (ARCH) method. The results show that both variables measuring tourism source market structure affect the volatility of tourism revenues. Accordingly, the concentration of the tourism source market increases the volatility of tourism revenues, whereas source market diversification decreases it.

Asymmetric statistical features of the Chinese domestic and international gold price fluctuation

2015 ◽  
Vol 29 (17) ◽  
pp. 1550113 ◽  
Author(s):  
Guangxi Cao ◽  
Yingchao Zhao ◽  
Yan Han
Keyword(s):  
Empirical Analysis ◽  
Debt Crisis ◽  
Statistical Tests ◽  
Good News ◽  
Fluctuation Analysis ◽  
Conditional Heteroskedasticity ◽  
Statistical Features ◽  
Gold Market ◽  
Autoregressive Conditional Heteroskedasticity ◽  
The Empirical Analysis

Analyzing the statistical features of fluctuation is remarkably significant for financial risk identification and measurement. In this study, the asymmetric detrended fluctuation analysis (A-DFA) method was applied to evaluate asymmetric multifractal scaling behaviors in the Shanghai and New York gold markets. Our findings showed that the multifractal features of the Chinese and international gold spot markets were asymmetric. The gold return series persisted longer in an increasing trend than in a decreasing trend. Moreover, the asymmetric degree of multifractals in the Chinese and international gold markets decreased with the increase in fluctuation range. In addition, the empirical analysis using sliding window technology indicated that multifractal asymmetry in the Chinese and international gold markets was characterized by its time-varying feature. However, the Shanghai and international gold markets basically shared a similar asymmetric degree evolution pattern. The American subprime mortgage crisis (2008) and the European debt crisis (2010) enhanced the asymmetric degree of the multifractal features of the Chinese and international gold markets. Furthermore, we also make statistical tests for the results of multifractatity and asymmetry, and discuss the origin of them. Finally, results of the empirical analysis using the threshold autoregressive conditional heteroskedasticity (TARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models exhibited that good news had a more significant effect on the cyclical fluctuation of the gold market than bad news. Moreover, good news exerted a more significant effect on the Chinese gold market than on the international gold market.

scholarly journals Has COVID-19 Changed the Hedge Effectiveness of Bitcoin?

2021 ◽  
Vol 9 ◽  
Author(s):  
Yinpeng Zhang ◽  
Panpan Zhu ◽  
Yingying Xu
Keyword(s):  
Futures Markets ◽  
Vector Autoregression ◽  
Spot Market ◽  
Futures Market ◽  
Conditional Heteroskedasticity ◽  
Volatility Spillover ◽  
Empirical Results ◽  
Dynamic Correlation ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Gold Futures

The Bitcoin market has become a research hotspot after the outbreak of Covid-19. In this paper, we focus on the relationships between the Bitcoin spot and futures. Specifically, we adopt the vector autoregression-dynamic correlation coefficient-generalized autoregressive conditional heteroskedasticity (VAR-DCC-GARCH) model and vector autoregression-Baba, Engle, Kraft, and Kroner-generalized autoregressive conditional heteroskedasticity (VAR-BEKK-GARCH) models and calculate the hedging effectiveness (HE) value to investigate the dynamic correlation and volatility spillover and assess the risk reduction of the Bitcoin futures to spot. The empirical results show that the Bitcoin spot and futures markets are highly connected; second, there exists a bi-directional volatility spillover between the spot and futures market; third, the HE value is equal to 0.6446, which indicates that Bitcoin futures can indeed hedge the risks in the Bitcoin spot market. Furthermore, we update the data to the post-Covid-19 period to do the robustness checks. The results do not change our conclusion that Bitcoin futures can hedge the risks in the Bitcoin spot market, and besides, the post-Covid-19 results indicate that the hedging ability of Bitcoin futures increased. Finally, we test whether the gold futures can be used as a Bitcoin spot market hedge, and we further control other cryptocurrencies to illustrate the hedging ability of the Bitcoin futures to the Bitcoin spot. Overall, the empirical results in this paper will surely benefit the related investors in the Bitcoin market.

Quantitative Modeling of Science and Technology Finance Supporting Industrial Innovation Development Based on Internet of Things Technology

2021 ◽  
Vol 7 (5) ◽  
pp. 2055-2072
Author(s):  
Sai Tang ◽  
Zhihui Wang ◽  
Jiahao Zhou ◽  
Xin Zhang
Keyword(s):  
Internet Of Things ◽  
Financial Support ◽  
Science And Technology ◽  
High Tech ◽  
Conditional Heteroskedasticity ◽  
Finance Industry ◽  
Actual Effect ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Internet Of Things Technology ◽  
Innovation And Development

Objectives: In recent years, science and technology financial support industries are actively supporting the innovation and development of high-tech industries. In order to test the actual effect of S&T financial support industry support plan, a GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model is designed by using K-means (K-means clustering) algorithm and GM (1,1) (grey prediction) algorithm, which can quantitatively display the development of S&T financial industry to promote high-tech. The GARCH model is used to quantify the degree of innovation and development of science and technology finance industry in the Internet of Things (loT) technology. Finally, according to the quantitative data obtained by GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model, the actual effect of science and technology finance industry promoting innovation and development of high-tech is evaluated by FAHP (Fuzzy Analytic Hierarchy Process) model. The results show that science and technology finance industry plays a positive role in promoting the innovation and development of loT technology.

Impact of the COVID-19 Outbreak on the Currency Exchanges of Selected Countries

2022 ◽  
Vol 14 (2) ◽  
pp. 73-91
Author(s):  
Sudhi Sharma ◽  
Miklesh Prasad Yadav ◽  
Babita Jha
Keyword(s):  
Growth Rate ◽  
Negative Impact ◽  
Annual Growth ◽  
Conditional Heteroskedasticity ◽  
Annual Growth Rate ◽  
Dummy Variable ◽  
Currency Market ◽  
Autoregressive Conditional Heteroskedasticity ◽  
The Impact

The paper aims to analyse the impact of the COVID outbreak on the currency market. The study considers spot rates of seven major currencies (i.e., EUR/USD, USD/JPY, GBP/USD, AUD/USD, USD/CAD, USD/CHF, and CHF/JPY). To capture the impact of the outbreak on returns and the volatility of returns of seven currencies during pandemic, the study has segregated in two window periods (i.e., pre- [1st Jan 2019 to 31st Dec, 2019] and post-outbreak of COVID-19 [1st Jan, 2020 to 22nd Dec, 2020]). The study has applied various methods and models (i.e., econometric-based compounded annual growth rate [CAGR], dummy variable regression, and generalized autoregressive conditional heteroskedasticity [GARCH]). The result of the study captures the negative impact of the COVID-19 pandemic on three currencies—USD/JPY, AUD/USD, and USD/CHF—and positive significant impact on EUR/USD, GBP/USD, USD/CAD, and CHF/JPY. Investors can take short position in these while having long position in other currencies. The inferences drawn from the analysis are providing insight to investors and hedgers.

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