Develop A Base Model For Risk Assessment Assignment Sample

Introduction: Develop A Base Model For Risk Assessment

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3.0 Calculation of the average return, standard deviation, coefficient of variation, cumulative wealth index and parametric value at risk with compounded returns

3.1 Amcor Plc

Figure 1: Cumulative wealth index and compounded return

(Source: In MS excel self created)

Average (Simple average)	0.1645%
Average (Geometric average)	1.00117314
Standard deviation	0.03032395
Mean	0.1645%
Coefficient of variation	1843.51435
Expected return	0.1645%
Min return	-20.1573%
VaR	-0.0469994

Table 1: Average return, standard deviation, coefficient of variation, cumulative wealth index and parametric value at risk with compounded return

(Source: In MS excel self created)

 

3.2 Abacus property group

Figure 2: Cumulative wealth index and compounded return

(Source: In MS excel self created)

 

Average (Simple average)	0.0833%
Average (Geometric average)	1.000221086
Stadard deviation	0.035294421
Mean	0.0833%
Coefficient of variation	4235.958579
Expected return	0.0833%
Min return	-10.4377%
VaR	-0.057443575

Table 2: Average return, standard deviation, coefficient of variation, cumulative wealth index and parametric value at risk with compounded return

(Source: In MS excel self created)

3.3 Altium Limited

Figure 3: Cumulative wealth index and compounded return

(Source: In MS excel self created)

Average (Simple average)	-0.0498%
Average (Geometric average)	0.997866669
Stadard deviation	0.056811714
Mean	-0.0498%
Coefficient of variation	-11400.27972
Expected return	-0.0498%
Min return	-22.2571%
VaR	-0.082211882

Table 3: Average return, standard deviation, coefficient of variation, cumulative wealth index and parametric value at risk with compounded return

(Source: In MS excel self created)

3.4 Goodman Group

Figure 4: Cumulative wealth index and compounded return

(Source: In MS excel self created)

 

Average (Simple average)	-0.2627%
Average (Geometric average)	0.996653002
Stadard deviation	0.03830966
Mean	-0.2627%
Coefficient of variation	-1458.252776
Expected return	-0.2627%
Min return	-12.6263%
VaR	-0.063044009

Table 4: Average return, standard deviation, coefficient of variation, cumulative wealth index and parametric value at risk with compounded return

(Source: In MS excel self created)

3.5 APA Group

Figure 5: Cumulative wealth index and compounded return

(Source: In MS excel self created)

Average (Simple average)	-0.1233%
Average (Geometric average)	0.998235646
Stadard deviation	0.032652522
Mean	-0.1233%
Coefficient of variation	-2648.519902
Expected return	-0.1233%
Min return	-14.8676%
VaR	-0.050036374

Table 5: average return, standard deviation, coefficient of variation, cumulative wealth index and parametric value at risk with compounded return

(Source: In MS excel self created)

3.6 Transurban group

Figure 6: Cumulative wealth index and compounded return

(Source: In MS excel self created)

Average (Simple average)	-0.0992%
Average (Geometric average)	0.998456144
Stadard deviation	0.033779298
Mean	-0.0992%
Coefficient of variation	-3406.255562
Expected return	-0.0992%
Min return	-11.3176%
VaR	-0.047823398

Table 6: Average return, standard deviation, coefficient of variation, cumulative wealth index and parametric value at risk with compounded return

(Source: In MS excel self created)

3.7 CSL Limited

Figure 7: Cumulative wealth index and compounded return

(Source: In MS excel self created)

Average (Simple average)	-0.1778%
Average (Geometric average)	0.9977
Stadard deviation	0.032462
Mean	-0.1778%
Coefficient of variation	-1825.58
Expected return	-0.1778%
Min return	-8.2174%
VaR	-0.0547

Table 7: Average return, standard deviation, coefficient of variation, cumulative wealth index and parametric value at risk with compounded return

(Source: In MS excel self created)

3.8 Ansell limited

Figure 8: Cumulative wealth index and compounded return

(Source: In MS excel self created)

Average (Simple average)	0.0259%
Average (Geometric average)	0.999460692
Stadard deviation	0.04039658
Mean	0.0259%
Coefficient of variation	15622.37043
Expected return	0.0259%
Min return	-20.5409%
VaR	-0.047419713

Table 8: Average return, standard deviation, coefficient of variation, cumulative wealth index and parametric value at risk with compounded return

(Source: In MS excel self created)

3.9 Adbri Limited

Figure 9: Cumulative wealth index and compounded return

(Source: In MS excel self created)

Average (Simple average)	0.6707%
Average (Geometric average)	1.004965466
Stadard deviation	0.060600504
Mean	0.6707%
Coefficient of variation	903.4849989
Expected return	0.6707%
Min return	-21.6138%
VaR	-0.077880933

Table 9: Average return, standard deviation, coefficient of variation, cumulative wealth index and parametric value at risk with compounded return

(Source: In MS excel self created)

3.10 Block Inc

Figure 10: Cumulative wealth index and compounded return

(Source: In MS excel self created)

Average (Simple average)	0.3734%
Average (Geometric average)	0.999692664
Standard deviation	0.091145214
Mean	0.3734%
Coefficient of variation	2440.95179
Expected return	0.3734%
Min return	-28.5902%
VaR	-0.118004468

Table 10: Average return, standard deviation, coefficient of variation, cumulative wealth index and parametric value at risk with compounded return

(Source: In MS excel self created)

4.0 Comment on the estimated statistics

The statistics that are provided above from those statistics it has come to know that these statistics for a rationale investor are very much useful for the investment opportunity evaluation. The expected return and average return provide major insights regarding each investment's potential probability. The risk level that is involved it is determined in this regard through the “coefficient of variation”, and through the “standard deviation” The major downside of the risk information is provided through the VaR, and minimum return. In this regard, it is important to note that depending on the historical data these statistics are based, and future performance production is not required in this regard. The past trends picture is provided through this, including the volatility (Birkel et al. 2019). But in this regard change is noticed regarding the company-related factors and conditions of the market. That is the reason additional information and analysis is needed usually to make correct decisions regarding investment.

For a rational investor, it will be very much beneficial that other factors must be considered, and those factors are the financial health of a company, trends of the industry, the landscape that is competitive, expertise in management, and major catalysts or risk. By conducting thorough analysis and research the portfolio investment diversification, and long-term horizon of investment consideration are recommended practices that are general for rational investors.

Covariance matrix

5.0 Covariance matrix

	Amcor Plc	Abacus Property Group	Altium Limited	Goodman Group	APA Group	Transurban Group	CSL Limited	Ansell Limited	Adbri Limited	Block Inc
Amcor Plc	0.09%	0.00%	0.00%	0.00%	0.00%	0.00%	0.01%	0.01%	0.01%	0.03%
Abacus Property Group	0.00%	0.12%	0.04%	0.08%	0.05%	0.06%	0.03%	0.03%	0.09%	0.10%
Altium Limited	0.00%	0.04%	0.32%	0.08%	0.04%	0.06%	0.07%	0.07%	0.04%	0.18%
Goodman Group	0.00%	0.08%	0.08%	0.15%	0.05%	0.07%	0.06%	0.06%	0.07%	0.14%
APA Group	0.00%	0.05%	0.04%	0.05%	0.11%	0.05%	0.03%	0.03%	0.04%	0.06%
Transurban Group	0.00%	0.06%	0.06%	0.07%	0.05%	0.11%	0.05%	0.04%	0.06%	0.14%
CSL Limited	0.01%	0.03%	0.07%	0.06%	0.03%	0.05%	0.10%	0.05%	0.04%	0.11%
Ansell Limited	0.01%	0.03%	0.07%	0.06%	0.03%	0.04%	0.05%	0.16%	0.04%	0.13%
Adbri Limited	0.01%	0.09%	0.04%	0.07%	0.04%	0.06%	0.04%	0.04%	0.37%	0.16%
Block Inc	0.03%	0.10%	0.18%	0.14%	0.06%	0.14%	0.11%	0.13%	0.16%	0.83%

Table 11: Covariance matrix

(Source: In MS excel self created)

	Amcor Plc	Abacus Property Group	Altium Limited	Goodman Group	APA Group	Transurban Group	CSL Limited	Ansell Limited	Adbri Limited	Block Inc
Amcor Plc	0.09%
Abacus Property Group	0.00%	0.12%
Altium Limited	0.00%	0.04%	0.32%
Goodman Group	0.00%	0.08%	0.08%	0.15%
APA Group	0.00%	0.05%	0.04%	0.05%	0.11%
Transurban Group	0.00%	0.06%	0.06%	0.07%	0.05%	0.11%
CSL Limited	0.01%	0.03%	0.07%	0.06%	0.03%	0.05%	0.10%
Ansell Limited	0.01%	0.03%	0.07%	0.06%	0.03%	0.04%	0.05%	0.16%
Adbri Limited	0.01%	0.09%	0.04%	0.07%	0.04%	0.06%	0.04%	0.04%	0.37%
Block Inc	0.03%	0.10%	0.18%	0.14%	0.06%	0.14%	0.11%	0.13%	0.16%	0.83%

Table 12: Covariance matrix

(Source: In MS excel self created)

6.0 Correlation matrix

	Amcor Plc	Abacus Property Group	Altium Limited	Goodman Group	APA Group	Transurban Group	CSL Limited	Ansell Limited	Adbri Limited	Block Inc
Amcor Plc	1
Abacus Property Group	-0.020613415	1
Altium Limited	-0.000954857	0.194378522	1
Goodman Group	-0.039235604	0.602661914	0.368300072	1
APA Group	0.020617365	0.436470416	0.222744393	0.414364624	1
Transurban Group	0.000678409	0.542910021	0.304878139	0.549163932	0.458014071	1
CSL Limited	0.089710919	0.299020882	0.354240841	0.470058489	0.265002063	0.445305581	1
Ansell Limited	0.050696024	0.228191556	0.285561422	0.406190705	0.205070899	0.283050789	0.418884312	1
Adbri Limited	0.02816294	0.407501235	0.112699312	0.289335275	0.186488991	0.308867035	0.20462848	0.162968196	1
Block Inc	0.093680501	0.301859824	0.345237592	0.412613632	0.201870703	0.459332948	0.36029368	0.361692895	0.281840351	1

Table 13: Correlation matrix

(Source: In MS excel self created)

7.0 portfolio return and standard deviation of the minimum variance portfolio and the optimum portfolio

The value of the portfolio return is present 0.16%, 0.08%, -0.05%, -0.26%, -0.12%, -0.10%, -0.18%, 0.03%, 0.67%, and 0.37%. The value of the “standard deviation” present in this regard is 6.05%. [Referred to appendix 1]

8.0 If 2% of the portfolio return forged and invest some of your money in risk-free assets

If 2% of the portfolio return is forgone and in risk-free assets, if money investment is done then the value of the “standard deviation” is 324724999.08%. The value of the portfolio return is present 0.16%, 0.08%, -0.05%, -0.26%, -0.12%, -0.10%, -0.18%, 0.03%, 0.67%, and 0.37%. [Referred to appendix 2]

a) The way analysis is helpful to a client in making an investment decision

Figure 11: The way analysis is helpful to a client in making an investment decision

(Source: In MS Word self created)

An investment analysis includes various metrics, which are simple average, geometric average, “standard deviation”, “coefficient of variation”, “mean”, “minimum return”,” expected return”, compounded return, and VaR or value at risk (Tinanoff et al. 2019). This can provide significant insights to the clients for investment decision-making. Comprehensive understanding is offered through these metrics of the significant rewards, and risks that with the investment is associated. This is helping clients for making investments (Wang et al. 2020).  A quick overview is provided by the simple average regarding the historical performance of an investment. It indicates that over a particular period, a return is earned. The risk or volatility of an investment is measured through the standard deviation. A greater fluctuation in price and major losses is implied through the “standard deviation” which is higher. The “standard deviation is compared with the mean with the help of the coefficient of variation. It provides risk measures that are relative More stable options regarding investment are suggested through the coefficient that is lower. From the historical data, the average future return an investment generates possibly is estimated through the expected return. In the worst-case scenario or regarding the investment the lowest return is represented through minimum return (Zhang et al. 2020). The major downside is understandable through this metric, and capital loss risk is also assessed through this. With a specific frame of time and level of confidence, the major loss of an investment experience is quantified through the VaR (Ivanov & Dolgui, 2021). Over time by considering the reinvestment of the profits the investment growth is reflected through the compounded returns.

Through the consideration of these investment analysis metrics, a comprehensive understanding can be gained by the client of the historical performance of an investment, with the major returns, the profile of risk, and risks downside (Boldog et al. 2020). With this, by staying armed the informed investment can be made by the clients that with their financial goals, the time horizon of investment, and tolerance of risk is aligned.

In this way, the analysis is useful to the client for making decisions regarding investment.

b) Types of Risk, and their identification, and justification of modeling the way modeling mitigates against those risks

Figure 12: Types of Risk, and their identification, and justification of modeling the way modeling militates against those risks

(Source: In MS Word self created)

The data that have been analyzed from that various risks can be identified, and those risks related to the market risk, systematic risk, specific risk, financial risk, and volatility risk. In terms of mitigation of these risks the data that is only at first analyzed is regarding the historical price, without any systematic risk, or techniques of modeling that are mentioned (Gurtu & Johny, 2021). The mitigation of the systematic risks and mitigation risks will typically involve diversification across various classes of assets, management of risk, and strategies of hedging. An example of it is futures and options contracts.

In the form of risk, the presence of market risk is there of losses that from market conditions changes arise. An example regarding it is factors of the economy and volatility of the market. All investments get affected through it in the market by it (Baryannis et al. 2019). In the form of market risk, the presence of systematic risk is present. It cannot be diversified, and the entire market is affected by it. It includes geopolitical events, inflation, and changes in rates. In the form of unsystematic risk, the presence of specific risks s noticed. To a particular company’s factors, it arises, and it includes competitive positioning, decisions of management, and legal issues (Krewski et al. 2020). With the financial structure of the company, the association of financial risk is noticed. An example regarding it is solvency, liquidity, and levels of debt. For a rapid, and large swing in the price of security, the major aspect is the volatility.

c) The use of financial modeling for quantifying, assessing, and interpreting of the risks that the ASX stocks are associated

With the data from the stock market the 10 ASX stocks, or company is discussed it includes, “adjusted closing price, closing price, low price, high price, opening price, date, and cumulative wealth index is presented (Willumsen et al. 2019). Different dates are represented through each row, and corresponding data for that date is contained in the columns.

Regarding this, the stock price movement is happening for every company that is present in an unstable way. 

The date in this regard represents the date for which data on the stock market is recorded. The open is represented in this regard is the stock’s opening price, on a specific date. On a particular date when the price of the stock reaches a higher that is represented in the form of a high, and the time when the price of stocks gets lower at that time it is mentioned as low (Tang et al. 2019). With the accounts for the factor, the presence of an adjusted closing price is noticed. It includes splits of stock and dividends.

Reference list

Journal

• Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179-2202, retrieved from,    https://eprints.keele.ac.uk/id/eprint/8142/1/2018_IJPR.pdf [Retrieved on 14.4.2023]
• Birkel, H. S., Veile, J. W., Müller, J. M., Hartmann, E., & Voigt, K. I. (2019). Development of a risk framework for Industry 4.0 in the context of sustainability for established manufacturers. Sustainability, 11(2), 384, retrieved from,   https://www.mdpi.com/2071-1050/11/2/384/pdf [Retrieved on 20.4.2023]
• Boldog, P., Tekeli, T., Vizi, Z., Dénes, A., Bartha, F. A., & Röst, G. (2020). Risk assessment of novel coronavirus COVID-19 outbreaks outside China. Journal of clinical medicine, 9(2), 571, retrieved from,    https://www.mdpi.com/2077-0383/9/2/571/pdf [Retrieved on 15.4.2023]
• Gurtu, A., & Johny, J. (2021). Supply chain risk management: Literature review. Risks, 9(1), 16, retrieved from,    https://www.mdpi.com/2227-9091/9/1/16/pdf [Retrieved on 12.4.2023]
• Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775-788, retrieved from,    https://www.tandfonline.com/doi/pdf/10.1080/09537287.2020.1768450 [Retrieved on 17.4.2023]
• Krewski, D., Andersen, M. E., Tyshenko, M. G., Krishnan, K., Hartung, T., Boekelheide, K., ... & Cote, I. (2020). Toxicity testing in the 21st century: progress in the past decade and future perspectives. Archives of toxicology, 94, 1-58, retrieved from,    https://www.academia.edu/download/61554850/2019_Krewski_et_al_AOT_TT21C_Update_FINAL_December_1420191218-1739-1xoj1tx.pdf [Retrieved on 18.4.2023]
• Tang, S., Shelden, D. R., Eastman, C. M., Pishdad-Bozorgi, P., & Gao, X. (2019). A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends. Automation in Construction, 101, 127-139, retrieved from,   https://www.arataumodular.com/app/wp-content/uploads/2022/08/A-Review-Of-Building-Information-Modeling-BIM-And-The-Internet-Of-Things-IOT-Devices-Integration-Present-Status-And-Future-Trends.pdf [Retrieved on 19.4.2023]
• Tinanoff, N., Baez, R. J., Diaz Guillory, C., Donly, K. J., Feldens, C. A., McGrath, C., ... & Twetman, S. (2019). Early childhood caries epidemiology, aetiology, risk assessment, societal burden, management, education, and policy: Global perspective. International journal of paediatric dentistry, 29(3), 238-248, retrieved from,    https://iapdworld.org/wp-content/uploads/2020/02/Tinanoff_et_al-2019-International_Journal_of_Paediatric_Dentistry.pdf [Retrieved on 16.4.2023]
• Wang, C., Cheng, Z., Yue, X. G., & McAleer, M. (2020). Risk management of COVID-19 by universities in China. Journal of Risk and Financial Management, 13(2), 36, retrieved from,   https://www.mdpi.com/1911-8074/13/2/36/pdf [Retrieved on 10.4.2023]
• Willumsen, P., Oehmen, J., Stingl, V., & Geraldi, J. (2019). Value creation through project risk management. International Journal of Project Management, 37(5), 731-749, retrieved from,    https://fardapaper.ir/mohavaha/uploads/2019/06/Fardapaper-Value-creation-through-project-risk-management.pdf [Retrieved on 13.4.2023]
• Zhang, C., Tian, G., Fathollahi-Fard, A. M., Wang, W., Wu, P., & Li, Z. (2020). Interval-valued intuitionistic uncertain linguistic cloud petri net and its application to risk assessment for subway fire accident. IEEE transactions on automation science and engineering, 19(1), 163-177, retrieved from,  https://drive.google.com/file/d/1N1cEAZm5ssqn9-QVFgN0vSdrEMjhrywg/view [Retrieved on 11.4.2023]