1. INTRODUCTION
The way financial institutions conduct business has changed dramatically as a result of new technology. Financial institutions are better able to provide their customers with a wider selection of goods and services thanks to the development of Internet technologies. This gives customers a leg up because they have easier access to the transactions. In the banking and financial industry, where brickandmortar banks have been being replaced by networked and digital banking services over the internet, the adoption of developing technology provides a competitive edge. As a result, various financial technology products and services had to be created in order to accommodate the entry of new technologies. A digital currency called bitcoin is one of the most recent and cuttingedge innovations to hit the market today (Gopane, 2018), All across the world, anyone can use the bitcoin cryptocurrency and digital payment system (Liu and Serletis, 2019). In a P2P network, transactions are carried out directly between parties without the need for third parties to intervene. A network of nodes checks the details of each transaction and records them in a blockchain, which is a public distributed ledger. The blockchain is a public record of all Bitcoin transactions. The new approach does this by giving the people full authority over all decisions (Abakah et al., 2020;Kadhim et al., 2020). Each Bitcoin node has software installed to keep track of transactions on the blockchain (Kalyani, 2016). Transactions in which payee Z pays payee X bitcoins using widely accessible software are broadcast to the network while transactions in which payee Z pays payee X bitcoins are broadcast using widely available software (Katsiampa, 2019;Niknezhad et al., 2020).
In a blockchain, transactions between two parties are recorded in an open, distributed ledger that is verifiable and efficient at the same time (Beneki et al., 2019). The mining process is verified in order to build trust in the bitcoin networks. Trust is built when the majority of miners have a financial motive to keep the network running. Like precious metals, most cryptocurrencies, such as Bitcoin, are designed to gradually reduce the amount of currency in circulation before capping the total amount of currency ever issued (Katsiampa et al., 2019). Bitcoin's price rose from $6,415.28 to $6,415.28 when it was first created. Transaction confirmation is extremely important in the bitcoin network because of this. Users of the bitcoin cryptocurrency network have to worry about escalating speculations as a result of this condition. Speculating is a term used to describe the extent to which financial positions are established based on firm assumptions of future market values (Fruehwirt et al., 2021). Additionally, recent hightech innovation allows for the creation of several digital goods, such as bitcoin. However, because of the volatility of the bitcoin currency and the rapid advancement of technology, the majority of speculators are trying to predict its value. For this reason, we must first assess the volatility condition in order to accurately anticipate the bitcoin exchange rate and return. This research fills in several previously unanswered questions about cryptocurrency (Bitcoin) exchange rate and return volatility estimates that have never been addressed before. Globalization and technological advances have led to a fundamental shift in how the financial system operates (Ftiti et al., 2021;Hasan et al., 2021). There are many crucial issues that worldwide exchanges have to deal with notwithstanding new technology's impact on investing selections (Ma et al., 2020). The bitcoin cryptocurrency is one of the most recent digital currencies to gain widespread adoption. This transaction has a major impact on the amount of digital currency exchanged. Decentralized, private, and irreversible payment network Bitcoin is built on opensource software and protocols.
The protocol makes it easy to send money across borders for any amount of goods, big or small. Even while users can request privacy in the bitcoin transactional system, some experts describe it as anonymous. The blockchain is the public record of all bitcoin transactions (Zhang and Li, 2020). Cryptocurrencies allow for transactions to be carried out anonymously, so anyone can use them, no matter where they live or what they do with their personal information. A peertopeer (P2P) network has been referred to as Bitcoin by Yaya (2021). If a large number of users all use their own computers to run the necessary software and communicate with one another, P2P networks are likely to arise. A public company like Google has its own staff and computer servers that are used to store users' data, unlike some other online companies that have a centralized location for management and servers. Bitcoin currency is a form of electronic cash that can be used to make payments to and from parties other than the company that issued it. There is no difference between the value of a physical money and a digital currency in terms of units of account.
There has been a surge in demand and speculation for bitcoin as a result of the growing number of people using the currency. This was a really volatile situation. The spread between the expected and actual return on an investment is what is used to calculate volatility. Market participants can bet on future volatility if a precise forecast of future volatility is available (Lahmiri and Bekiros, 2019). Many academic fields analyze the volatility of the market, the price of housing, and other aspects of a country's economy. A stochastic volatility model is compared to implied volatility in South Korean data in this paper (Warlina and Alkhadad, 2019). Market volatility or a lower return can help both implied volatility forecasts. House price return volatility is timevarying, as evidenced by the timevarying volatility patterns in city and countrylevel price return volatility statistics. Throughout the research period, it appears that there were periods of high and stable volatility. Third, the volatility of a country or city might be affected by a significant economic event. Because the volatility of house price returns varies by location, you may see some correlation in the volatility series. In this study it has been noticed that discovered that there are significant disparities in the herding times between the portfolios. Stocks with higher degrees of herding or adverse herding will also have more volatility, so herding can be considered an extra risk factor in this case.
2. RESEARCH METHODOLOGY
2.1 Research Model
2.1.1 Model of the Unexpected Shocks (MUS)
Suppose a shock to the market is unforeseeable. The shock's arrival time is presumed to be absolutely random. If the shock is exogenous in nature, its effects are only shortlived. The effects of an endogenous shock, on the other hand, can last for a significantly longer period of time. Initial drift decreases by the amount 0 and volatility increases by the amount 2 at time zero after the shock. The market recovers at random time t0 – the drift grows by 0 and volatility reduces by 2 – because an arbitrage opportunity has to be eliminated. Assume that time t0 corresponds to the point at which the market begins to recover (t). In this instance, h'(t)N0, since a market rebound becomes more and more likely as time goes on, since the effect of an exogenous shock is temporary.
2.1.2 Detrended Fluctuation Analysis (DFA)
Ethereum, Bitcoin, Litecoin, and Ripple are four wellknown cryptocurrencies for which we’ve applied the Hurst exponent metric in search of longrange dependence. There are numerous methods for determining the Hurst exponent for financial time series. Detrended Fluctuation Analysis employs two distinct methodologies for analyzing data: (DFA). Assume r(t) is the logarithmic return determined by taking the timevarying hourly bitcoin price, X(t).
3.2 Calculation of Volatility using Logarithmic Return Standard Deviations
This section explains how to calculate Bitcoin's exchange rate volatility using a mathematical formula. Violability is determined by standard deviation in this study. Statisticians use the standard deviation to measure data variability or dispersion, which is a statistical measure. As you can see, a high standard deviation suggests a larger range of possible values, whereas a low standard deviation shows that data points are more likely to be in close proximity to their mean (also referred to as the expected value). As a result, the following procedure was used to determine standard deviation’s (also known as volatility) The first step is to determine what you want to accomplish. To begin, the return on investment for each period was determined using a continuously compounded formula. The logarithmic return computation is shown in Equation (3).
where:
Logarithmic return for observation period I Bitcoin exchange rate closing price at observation period I Bitcoin exchange rate closing price at observation period i1 The next step is to: Equation is used to compute the average logarithmic return (5).
Standard deviation or volatility is derived using Equation (5).
3.5 High Volatility Detection using Statistical Process Control
Studying how a process evolves over time can be accomplished with the aid of a control chart. Graphs are arranged according to time intervals. For every control chart, there is a central line that represents the average, a lower line that represents the lower limit, and an upper line that represents the top limit. These graphs were generated using data that has been collected over time. Upper and lower limits for statistical quality control charts are determined using 3sigma limits (3sigma limits). Control charts are used to set limits for a statistically controlled manufacturing or business process. Most data will fall inside the first three standard deviations, according to an empirical guideline.
3.3 The ShapiroWilk Test for Statistical Normality
The ShapiroWilk normality test's null hypothesis is that the population is distributed normally. To test the null hypothesis, the pvalue must fall below a predetermined alpha threshold. If it does, then the data cannot be assumed to be from a normally distributed population. A data set from a normally distributed population cannot be ruled out if the pvalue is less than or equal to the chosen alpha level. Using the ShapiroWilk test, one may determine whether or not a sample is drawn from a normally distributed population. The test returns a value of W. If the W value is greater than the alpha value (0.05), then the data distribution is assumed to be normal. If the G values in your data are low, this indicates that your sample is not normal. The G value formula is as follows:
where:

x is the value of independent variables,

y is the values of the dependent variables, and a is a constant
3. RESULTS AND DISCUSSION
3.1 Assumption Test
3.1.1 Normality test (ShapiroWilk test)
In this research, International variables and adaptation to meet the challenges and expected risks that may result from them on the other hand. Based on the output shown in the table 1, it is shown that Ftest is 23,262 with pvalue (sig) 0,000..we get Ftable 4.2242. Because Ftest > Ftable (23,262 > 4.2242), it led to accept the proposed assumption period 20052019.
3.1.2 KolmogorovSmirnov Test
Normality Test is examined to find whether the residual data is normally distributed or not,. This test has been implemented on employed models (MCA, RMM and REMM). R squares, standard errors and Values of Durbin Watson have been consider to investigate whether assumption is suitable for current analysis or not as shown Table 2.
3.1.3 Heteroskedascity Test
Heteroscedasticity Test is used to find whether the data is homogeny/contain the same variant. Heteroscedasticity Test has been carried out for the selected models to investigate the validity of chosen assumption
3.2 Implementation of Models
According to Table 3, risk assessment and return are relatively similar, with the difference between them being slightly smaller for minimization. Each problem has a unique portfolio composition, which is expected because the variables are highly valued and the problems can be solved in an unlimited number of ways. This enables for the selection of percentages for each stock and for the same returns and risks to be achieved.
3.3 Detection of the High volatility
This section explains how to use a box plot and a statistical process control chart to detect volatility. The use of a boxwhisker plot to identify outliers in Bitcoin return data. The data, it appears, has some outliers. Because of the large number of outliers, the return data on Bitcoin is extremely volatile. Then, the statistical method was carried out in this investigation. data on the return on bitcoin investment (RoI). a single value exceeds the upper control limit (UCL). Next, there are two data points that fall below the lower limit of the control (LCL). Also shown in Table 4 is the variation in data based on the G value The characteristics of data dispersion show that the distribution of Bitcoin return data is quite erratic
3.4 Circulating Supply
A oneday outlier with a loss of 0.065 percent or less. The drop in the total bitcoin market capitalization from $211.5 billion to $137.1 billion in the two weeks following the event. Tether’s value has dropped by only 0.45 percent compared to the 33 percent and 53 percent losses suffered by the ten most valuable cryptocurrencies. Table 5 lists the ten most popular cryptocurrencies, which we'll get into now. As a measure of cryptocurrency liquidity, market capitalization is used in this table, together with the amount of circulating supply and 24hour trading volume. Among our remaining sample, Bitcoin, which had a market valuation of about $241 billion and a 24hour trading volume of more than $22.5 billion, was the most liquid and largest. While Tezos’ market cap is the smallest, its 24hour trading volume is more than $227 million, and its market capitalization is below $4.2 billion, making it the least liquid cryptocurrency. Cryptocurrency market value peaked on September 15th, 2020, at $249 billion, according to data from
4. CONCLUSION
Finally, the goal of this investigation is to determine the degree of volatility associated with cryptocurrency data. Ten cryptocurrencies will be the focus of this study's data collection in 2020. Checks are made to ensure that cryptocurrency exchange rates and returns are normal. Then, a boxwhisker plot and statistical process control are used to examine the volatility dynamic behavior of cryptocurrency returns. During the time of COVID 19, the major findings of a study on cryptocurrency rates were drawn to a close. ShapiroWilk test has been carried out to investigate the rate of the currency exchange accordingly. As a result, the pvalue is displayed as 0.000. Because of this, the distribution of the data is not normal. A descriptive statistical study for Bitcoin returns shows mean 0.006 and standard deviation 0.04458 standard deviation. With a standard error of 4.458 percent, we can estimate the volatility of Bitcoin. As a result, this number is viewed as having a high level of volatility. Investing in bitcoin comes with a significant level of risk, as seen by the relatively high value of volatility. In order to examine the distribution of cryptocurrency returns, a numerical ShapiroWilk normality test is used. Probability (pvalue) is equal to zero.