Analyzing Financial Time Series Data

1. Introduction

The report aims at analyzing and assessing the various types of risks through a financial time series data with the given observations, and also to see the impact of this observation related to financial time series data on different types of risks. Different methodologies like the descriptive techniques and the correlation method will use to analyse the data and to collect the data related to the stock’s returns and these returns will be monthly through a financial website such as yahoo.com. The primary classification of hazard is mainly alluded to as 'unadulterated and specific risk'. There is harm to people related to building like storm, breakdown, water, and subsidence, vibration, where the Contract terms frequently fulfill the authoritative commitment providing protection cover against the dangers. For students who are working on their data analysis dissertation, seeking data analysis dissertation help and guidance is critical.

There are some basic risk factors in the outcome which are such as harm due to atomic contamination as well as internal war, government strategic changes, supersonic stun waves, implementing different aw system and changes in wellbeing and working process. It is important part to protect the individuals by executing different rules and legislative forms that further ensure individual protection and ordinarily accessible. Further speculations risks are there that can be distributed as per agreement gatherings and time. Extraordinarily antagonistic climate further misfortunes time and cash that also hampers the sterling ground condition, unforeseeable deficiency of work, lack of quality materials availability and other comparative issues that hamper the ability to control the activities (Jia, 2020).

2. Literature Review

Value at risk models made and appropriate for fluid and all around created markets that expect typical dissemination are less dependable for capital business sectors in arising economies, like Central and Eastern European Union part and competitor states. Since capital business sectors in European arising economies are profoundly unpredictable, less fluid, and unequivocally reliant upon unforeseen outside shocks, market hazard assessment dependent on ordinariness presumption in CEE nations is dangerous. This spurs us to carry out GARCH-type techniques that include time-differing instability and substantial tails of the observational conveyance of profits. We test the theory that utilizing the supposition of weighty followed circulation it is feasible to gauge market hazard all the more decisively, particularly in the midst of emergency, than under the suspicion of a typical appropriation or utilizing the authentic recreations strategy. They show that GARCH-type models with t-blunder conveyance in most investigated cases give preferable Value at risk assessment over GARCH type models with ordinary mistakes on account of an almost 100% certainty level, while on account of a 95% certainty level it is the inverse. The aftereffects of backtesting examination for the emergency time frame show that GARCH-type models with t-appropriation of residuals furnish better Value at risk gauges contrasted and GARCH-type models with ordinary dispersion, authentic reenactments, and Risk Metrics strategies. The Risk Metrics technique much of the time thinks little of market risk (Miletic, 2015).

In the worldwide monetary emergency, conditions for financial backers are critical to precisely gauge and assign hazard just as to all the more effectively deal with their portfolio. The impact of outrageous occasions on the patterns in monetary business sectors in arising nations is considerably more articulated since it is a market which is described by lower levels of liquidity and fundamentally more modest market capitalisation (Miletic, 2015). Monetary business sectors in arising nations are normally described by various changes and a more prominent probability of inward and outer shocks like expansion, an abrupt devaluation of public monetary forms, changes in credit scores, risk premium change, and so on As this market is described by a more prominent impact of inward exchange and subsequently a more serious level of instability than the business sectors of created nations, the conveyance of profits is fundamentally more misshaped than typical, which makes assessment of Value at risk with standard techniques that accept an ordinary circulation of profits more troublesome. The use of VaR approach, which is essentially planned and created for fluid and created markets, is important to test developing business sectors that are described by outrageous instability, illiquidity, and the shallowness of the market. Execution of the Value at risk technique in the venture cycle is straightforwardly identified with the choice of the suitable strategy for assessment. In choosing the suitable strategy for key significance, it is significant that it precisely decides the probability of misfortunes.

The risk appraisal esteems can be made utilising parametric and non-parametric techniques. The verifiable recreation technique is the most popular non-parametric Value at risk valuation strategy by which a given percentile gauge depends on acknowledged returns. A trait of this technique is that it doesn't accept a particular yield conveyance ordinary, Student's t-dispersion, etc., aside from the perpetual quality of appropriation during the noticed period (Si-li, 2018) The most popular strategy for surveying parametric Value at risk is a variation covariant technique, which expects that profits follow a particular circulation, which works with the assessment of the comparing percentile. In spite of the fact that for the effortlessness of computing Value at risk, scientists and financial backers regularly accept an ordinary conveyance of profits, this supposition that is normally not satisfied practically speaking. The time-series returns, just as most other monetary series, are typically described by the appropriation that has heavier tails than ordinary and by an aggregation of instability. The class of GARCH-type models of contingent heteroscedasticity considers these properties of monetary series and gives a more precise Value at risk gauge (Sharma, 2012).

3. Data collection and Methodology

For analyzing the exploration that is one experimentally, the researchers must deliberately identify the methodological issues and embracing benefit of choosing the right methods. The appropriate research methods are helpful not only for understanding the results itself but also to analyze the measures of the experiments. Exploration methodology hereby portrays and analyzes the techniques, reviewing the impediments and assets, evaluating their presuppositions and results, explaining their constraints and assets, as well as relating their possibilities to a twilight zone in front of all of them in terms of priority of knowledge. Comprehending the suppositions hidden different strategies is hereby mandatory along with choosing specific methods and strategies that will be appropriate to the existing issues. Hereby, it is beneficial for the specialist to plan the techniques and analyze the exploration.

The quantitative research method's limitations have improper representation of the objective populace, which may block the analyst from accomplishing its ideal points and destinations. Regardless of applying a fitting inspecting plan portrayal of the subjects, it relies on appropriating noticed information. This may prompt a miscount of likelihood dispersion and lead to misrepresentation in the suggestion. The quantitative research approach typically requires an enormous sample size. In any case, because of the absence of any asset in massive research, research gets inconceivable. In many non-industrial nations, invested individuals may come up short on the information and particularly the assets that are expected to direct intensive quantitative examination. In some cases, scientists deal with issues to control the climate where the respondents answer the review's inquiries. Reactions frequently rely upon a specific time that is reliant on the conditions happening during that particular period.

For analyzing quantitative data, the researchers should tackle the deal of the day and it is costly for them to investigate n conduct in depth research. Total randomization and proper control gatherings are ensured and the analysis is arranged cautiously to guarantee the randomization and effective control. The population ample is also managed well by information assortment in order to develop quantitative exploration techniques which are against the subjective methods of conducting exploration.

It is required to develop board measurable investigation or quantitative examination and it can be performed by involving non-factual foundation. The measurable assessment are based on the logical orders and it is hard or the non-mathematicians to perform such critical analysis. The prerequisites for the effective factual affirmation are also analyzed efficiently to identify the outcomes through quantitative analysis. Speculation is also difficult to be measured proficiently that raises further uncertainty in the outcome. The results are finally tested and refined for unambiguous outcome which requires additional time, assets and venture to complete such test.

The restrictions in the blended strategies research are promptly self-evident, in which the individuals are commonly more tedious and complex. Subjective and quantitative stages require being led simultaneously concurrent triangulation, in which a group is mandatory with serious asset. The working information is required by the foremost specialists on quantitative and subjective standards by knowing how to join them to guarantee excellent results. Another impediment for blended technique research is not with the technique but on how the more extensive examination network reviews it and how it requires affirming its place inside (Kothari, 2004).

4. Analysis

The monthly close share price data of 2019 through this critical analysis, where the share price of the following return tends to be decrease in the initial month and then also there is decease in the next month, hence there is a mix trend. Closing returns can be computed by implementing the formula and as per the analysis, the percentages of the closing returns on monthly basis shows the negative trend, it means have negative change from one month to the preceding month, and in few months shows 0%, means no change. But, there are positive changes in the last two months November and December.

The descriptive statistics of this financial time series data shows the diverse variables like the range its 3 and it has been computed by taking the difference between the minimum and the maximum value of the column monthly closing returns. Other variable like mean, median, and mode also shows the computation in different way with the different formula and have the averages and rearrange the data in a moderate form.

Variance and standard deviation also shows the risk with these closing returns and the most suitable type for this investment is the risk adverse type from the investor’s point of view. In contributing, the risk is utilized as a pointer of market unpredictability and in this manner of hazard. Reach bound protections, or those that don't wander a long way from their means, are not viewed as an extraordinary danger. That is on the grounds that it very well may be accepted with relative sureness that they keep on acting similarly. The factors like propensity to spike and extremely enormous exchanging range are considered as a bigger misfortune. In any case, the danger is not something awful in the speculation world. The less secure the security, the more noteworthy potential it has for payout.

The standard deviation is resulted as high with the costs swing up or down altogether, which further means that, there is high instability. Additionally, when the standard deviation is low, it means there is a tight spread between exchanging ranges, which refers to the fact that instability is low. While standard deviation is a significant proportion of speculation hazard, it isn't the one to focus on. There are numerous measures of financial backers that can utilize to decide if a resource is excessively dangerous for them or not unsafe enough.

There is a non-linear relationship factor between the two variables in the context of correlation coefficient. But it all depends on the relationship between the two variables of the dataset. The strength of correlation coefficient between the two variables depends upon the data nature. Correlation coefficient is 0.12 between the CPI index and closing price which further indicates that there is positive interlink between these two variables. On the other hand, another analysis also shows a moderate positive correlation between GDP and the closing prices, where the coefficient is 0.25. Hence, for measuring short run and long run impacts, both the correlation coefficient and standard deviations are effective to be analyzed well. The aim of the company in short run is to maximize return on investment and it means that the organization is taking higher risk to increase their return. However, in long run, there I no such high risk and the company is successful to ensure moderate return on their investment with positive correlation coefficient.

5. Conclusion

This report it’s all about the different approaches to risk analysis and also define it methodology as well. The time series financial data of the particular stocks returns also shows different results in terms of different techniques of descriptive techniques, such as mean median, variance, co-variance, and correlation coefficient. The different risk analysis model such as GARCH and other volatility methods like VaR also have direct impact on the stock returns in a particular time period. So overall, all the risk analysis models and methods somehow have direct and indirect impact on the monthly stock return’s data. Through conducting the quantitative data analysis, the researchers can consider quantitative data and perform the methods efficiently where in this study the standard deviations, correlation coefficient analysis and linear regression method are utilized to perform the data analysis efficiently.

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