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Tiêu đề Examining The Co-Movement Between Cryptocurrency Market And Major Stock Markets In The World
Tác giả Nguyen Thi Thu Hien
Người hướng dẫn PhD Le Hong Thai
Trường học Vietnam National University
Chuyên ngành Finance and Banking
Thể loại graduation project
Năm xuất bản 2023
Thành phố Hanoi
Định dạng
Số trang 63
Dung lượng 43,69 MB

Cấu trúc

  • 2.1. Theoretical baSÌS.............................. - - - -- 1n nh TH TH TT TH HH HT HH HH 13 1. Factors affecting the price of financial assets and the price of a stock.....................-...ô---ôô- 13 (13)
    • 2.1.2. CrYPtOCUITENCY ..................... -. - - 5G ST nọ 18 2.1.3. h‹u3 áo nh... ............. 45 (0)
  • 2.2. Current status of virtual currencies in some countries around the world (32)
  • Z.2.1. USA ^^ - (0)
    • 2.3. Literature T@Vẽ@W ......................... HH HH Ho Ho. TT Họ Tp 34 2.4, RES@arch Sap ỪỪ:.:.:.:'.::Ẽ (0)
    • 2.5. Chapter SUMIMALY ........................... - G1111 HT HH Họ TT vn 37 (37)
  • Chapter 3: Research Methodology ..............................- - - - - co HH te 38 3.1. Research Methods ................................. - - -- <1 11H TH HH Họ nọ tk 38 3.2. Research data ..............................- LH TH TH TH Họ HT TT tr 39 (38)
  • Chapter 4: Research results ...........................- -- - - - G1111 119 v1 SH SH nọ HH net 41 4.1. Descriptive Statistics .......................... cọ HH 41 4.2. Empirical research results .............................. -..-- --- 6 + 1E k* TH TT TH HH 49 4.2.1. The comovement between the prices of cryptocurrencies and the world stock market ¡6 07077 ----:1 (41)
    • 4.2.2. The comovement between the trading volume of cryptocurrencies and the world stock (53)
  • Chapter 5: COmclusion 0886866 (0)
    • 5.1. Summary of research results ............................ -- - 2 <1 1E HH HH HH tk 58 5.2. Policy 4ì)(()0))0 0c) 00 (58)

Nội dung

VIETNAM NATIONAL UNIVERSITYUNIVERSITY OF ECONOMICS AND BUSINESS FACULITY OF FINANCE AND BANKING EXAMINING THE CO-MOVEMENT BETWEEN CRYPOCURRENCY MARKET AND MAJOR STOCK MARKETS IN THE WORL

Theoretical baSÌS - - - 1n nh TH TH TT TH HH HT HH HH 13 1 Factors affecting the price of financial assets and the price of a stock - ô -ôô- 13

Current status of virtual currencies in some countries around the world

Countries worldwide exhibit varied responses to virtual currency development, with 107 out of 251 nations permitting Bitcoin and other cryptocurrencies, as reported by Coin.dance In nations like the US, Canada, Russia, Japan, and Australia, virtual currency activities are deemed legal Conversely, countries such as Vietnam, Afghanistan, Bangladesh, and Bolivia classify the use of virtual currency for transactions as illegal Additionally, China, India, Saudi Arabia, Egypt, Zambia, and Indonesia have enacted laws that prohibit all crypto-related activities Below, we outline specific regulations from various countries regarding virtual currencies.

The United States leads the world in regulations concerning virtual currencies, with varying responses from its 50 states California has established the Bitcoin Foundation, which does not recognize Bitcoin as legal tender and has halted all commercial transactions involving it Conversely, several states are moving towards approving Bitcoin and blockchain technology, with Arizona, Vermont, and Delaware already enacting supportive laws.

The US Securities and Exchange Commission (SEC) has cautioned investors regarding the high risks associated with virtual currency investments, emphasizing the necessity for more stringent regulations in this sector.

The Commodity Futures Trading Commission (CFTC) became the first US agency to allow crypto derivatives to be publicly traded.

The IRS classifies Bitcoin as a virtual asset, subjecting it to property tax rules rather than treating it as fiat currency All Bitcoin transactions must be reported to the IRS, and US taxpayers receiving Bitcoin in exchange for goods must include its value in their annual income tax return, calculated at the exchange rate at the time of receipt Taxpayers holding Bitcoin as an investment must report any gains or losses, with taxes applied similarly to other investment assets Bitcoin miners are also liable for taxes on the value of mined Bitcoins, assessed at the market rate on the mining date, and any remuneration in Bitcoin is taxed like traditional income Failure to comply with these tax obligations can result in penalties.

US law The US Internal Revenue Service requires Bitcoin-related transactions to be recorded for tax administration.

Japan actively regulates virtual currencies, having recognized Bitcoin and other cryptocurrencies as official payment methods since April 1, 2017 On September 30, 2017, the Financial Conduct Authority (FSA) of Japan issued operating licenses to the first 11 virtual currency exchanges and mandated that all exchanges register with the agency.

As of July 1, 2017, trading virtual currency in Japan is tax-free; however, the Japanese National Tax Agency clarified in September and December 2017 that profits from virtual currency investments are subject to income tax These profits are determined through a method known as "tax aggregation."

In Japan, 33 virtual currencies are combined with other income sources like salaries and business earnings, subjecting them to taxation rates ranging from 5% to 45% However, there is currently no official legislation governing this tax on virtual currencies.

China previously dominated the global Bitcoin trading landscape, holding a staggering 90% of the market share However, following the government's strict ban on cryptocurrencies, traders in China have increasingly resorted to underground trading methods or shifted their transactions to overseas markets, particularly in Japan.

On September 4, 2017, China banned all companies and individuals from raising capital through ICOs, and ICOs are considered illegal activities in the country On February 5,

In 2018, the People's Bank of China implemented a ban on all domestic and foreign virtual currency-related services for Chinese citizens, utilizing the Great Firewall to restrict access to unwanted foreign websites.

Mlambo and Maredza (2007) utilized descriptive statistics, the ARCH test, and the single root test alongside the GARCH estimation technique to demonstrate that the null hypothesis is not supported Their findings indicate that distortions in exchange rate markets negatively impact efficient capital allocation within South Africa's stock market Additionally, the study highlights that various macroeconomic factors, such as interest rates, total mining output, money supply, and US interest rates, also influence the stock market's performance.

Lee (2009) re-evaluated the relationship between stock returns and inflation, focusing on the inflationary illusion hypothesis using an extended dataset from the U.S economy While the hypothesis effectively explains the postwar relationship, it fails to account for certain aspects of the prewar period, which exhibited overvaluation amid high inflation The analysis revealed two distinct types of relationships between stock returns and inflation in developed countries, identifying negative correlations that align with the varying significance of different inflation shocks.

Boako et al (2019) utilized random copulas to analyze the tail distribution and risk-sharing dynamics between stock market returns and gold Their findings indicate that during times of crisis, there are fluctuations in the correlation between these assets, challenging the traditional perception of gold as a safe haven and a means of diversification.

Park et al (2020) explored the relationship between green bonds and the stock market, utilizing a multivariate GARCH model to analyze volatility spillovers Their findings indicated that the spillover effect between green bonds and the stock market is not significant.

In a 2022 study by Van Outvorst, the effects of interest rate changes on traditional investors and the electronic market were examined Participants were categorized into traditional and crypto-asset groups, tasked with making portfolio allocation decisions involving varying interest rates and a mix of savings, risk assets, Bitcoin, and AEX stocks The findings revealed that fluctuations in interest rates did not significantly alter the investment behaviors of either traditional investors or cryptocurrency holders, indicating that both groups respond similarly to changes in interest rates.

Haq et al (2022) demonstrate a moderate positive co-movement between green bonds and sustainable cryptocurrencies, such as Bitcoin, in the short term, while showing a negative correlation with the long-term sustainable emerging market index (DJSWI) Their study utilizes a partial wavelet matching framework to effectively capture the two-way covariate trend.

USA ^^ -

Chapter SUMIMALY - G1111 HT HH Họ TT vn 37

Chapter 2 presents an overview of stock pricing models, financial asset prices and some basic theories about cryptocurrencies and the world stock market Besides, chapter 2 also presents the basic factors affecting the price of financial assets Chapter 2, then presents an overview of studies on positive and negative impacts on stock markets in countries around the world From there, it is possible to detect gaps in the literature as well as potential contributions of this study.

Research Methodology - - - - - co HH te 38 3.1 Research Methods - - <1 11H TH HH Họ nọ tk 38 3.2 Research data - LH TH TH TH Họ HT TT tr 39

The wavelet coherence approach effectively identifies the time and frequency bands necessary to quantify the co-movement between two related time series variables (Liu, 1994) According to Torrence and Compo (1998), the wavelet coherency of two time series is defined through smoothing in both time and frequency domains For the two time series x(t) and y(t), their cross-wavelet transforms are represented as W1(u,s) and W2(u,s), with the cross-wavelet transform detailed in Equation (9).

The continuous wavelet transforms, W(u,s) and W*(u,s), represent two time series variables, x and y, respectively, where 's' denotes the scale and 'u' indicates the position index The use of the asterisk (*) signifies the complex conjugate, allowing the wavelet transform to effectively capture the local covariance between the two time series variables.

The wavelet coherence method developed by Torrence and Compo (1998) effectively calculates cross-wavelet power, highlighting regions with increased covariance between time series variables across various scales.

Wavelet coherence is a valuable tool for identifying periods of co-movement in time series variables, even when wavelet power is low This analysis follows the methodology established by Torrence and Webster (1999), which builds upon the work of Torrence and Compo (1998) The squared wavelet coherence coefficient is defined in Equation (10).

Where 5’ is a smoothing operator over time and space, R7(u,s) is localized squared correlation in time and frequency, and the squared wavelet coefficient is in the range of 0) signify an in-phase relationship or positive correlation, while those pointing left (

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