Market risk analysis of a five-company portfolio using Value at Risk

Banking Risk Assignment

Summary (300 Words)

The assignment requires analyzing five companies with a one million British pound portfolio from the same sector. This analysis is based on market risk based on a paper by Sollis (2009). According to the author, understanding more concerning Value at Risk and applying related techniques will help compute the risks of exposure to a portfolio of real-world financial securities (Acharya). The discussion will be critically on the measurement of market risk using techniques of Value at Risk. Also, the new developments will help the learner display awareness of the limitations and methods by presenting how the results have been derived clearly (Wanat et al. 21). The portfolio in this section will consist of five real-world companies, with the length of the sample period being less than five years ending on November 30, 2021. Interpretations and comparisons should be provided instead of illustration methods to capture the correct details.

On the other hand, the other section of the assignment requires analyzing three companies someone’s choices. This analysis will be based on the credit risk; this will be on the portfolio of loans of three companies (Dorozik et al.).The portfolio composition includes company names, maturity in terms of years, the repayment value at maturity, and the annual interest. In the report, assumptions will be made concerning the three loans, and any assumptions made on the estimations should be indicated (Varghese et al. 39). The loans are speculated to be senior unsecured debts nominated in the form of US dollars, and the kind of analysis made is conducted on November 30, 2021. The loans repayment is on the date of maturity. Using credit metrics, which should be fully implemented and KVM, the relative VaR and Expected Shortfall with Monte Carlo simulation for the portfolio are conducted. The time horizons are one-year and two-year periods with a confidence interval of 99%. The results are to be interpreted, compared, and discussed critically, a reality check carried out, and the results should be determined according to the expectations. Reasons should accompany this.

Dividends declared per common share $ 2.12 $ 2.08 $ 2.04

Since investors use debt ratios in the analysis of the company finances in terms of purchase of assets and the ability of the company to pay its debts (Asperti et al.), this will be our main focus for Walmart. The ratio of Walmart D/E in October 2020 was 1.87. This figure has been steady, indicating that the company uses more debt to finance assets than equity. However, the management of its debts has not wavered in a long time. Since the company has not used its excess debts even in turbulent periods. The company’s target ratio is 2.8 in the fiscal year that ended in October 2020. VAR= [Rp – (z) (?)] Vp = VAR = [0.1 – (1.87) (0.15)] 20000 = -$3000 (rounded).

The third example of a company is the major IT firm-HP. If an investor wants to calculate the market risk associated with the stock price, the current quotation stands at $1100 because of the expected growth. The calculation of the risk of premium will be as follows.

Particulars

Value

Current stock price

1000

Expected stock price

1100

Time in months

8

The expected rate of returns

10%

Annualized returns

15%

US Treasury Bills

4%

Inflation

3.50 %

Market premium Risk

11%

The advantage is that the financial products sold to the investor community are aggressive marketing. While ignoring the risks and the downfalls, the growth part is represented. This is why products are bought in large numbers in the economic expansion cycles (Mogel et al.). During recession times, through the concept of market risk, financial products can be understood (Pearson). The above illustration helps calculate the real rates of returns while considering the inflation rates. Some disadvantages accompany this; they include the high chances of recession which are prone to changes in the economy. Unlike the credit risk, this affects all the asset classes. It is vital to note that it can be dangerous for an investor to ignore the market risk in this company while building its portfolio.

The fourth example is market risks applied to Dow Jones; the VaR techniques will now be applied to Dow Jones Industrial Average. This will help in identifying if the technique has changed over time. In the forecast of Eco metrics, past observations have to be balanced. The risk management concept regarding Value at Risk help in understanding the Value of side risks in the investment portfolio. The VaR techniques are categorized into two, conditional and unconditional methods. In estimating results, the differences experienced between the actual and expected number of VaR isolations are squared every year. And then, the answer is multiplied by the number of trading days of the year concerning the total days that have been traded in the entire sample. To obtain the efficiency of the calculations, the average VaR helps achieve that. The computations include multiplication of estimated VaR, and the variance methods and historical simulation assist in the tail estimations.

Finally, the fifth company is the Dutch AEX, where the first step is analyzing data to obtain the facts that are stylized to get the stock market returns (Benjasil). Historical simulation is one for the analysis is made. The analysis of this company may prove to be difficult if the portfolio analysis risk is examined using large stock right away (Benard et al.).When the normal distribution is applied in the AEX portfolio and an evaluation sample of 1000 days, the following parameters are obtained in the preceding data.

VAR= [Rp – (z) (?)] Vp = VAR = [0.1 – (1.65) (0.15)] 20000 => -$3000 (rounded). This gives 15% of the Portfolio.

Where,

Rp = portfolio return.

Z= Z value for a 5% confidence level in a one-tailed test.

?= portfolio’s standard deviation.

Vp= Value of the portfolio

Estimate Standard Error

0.0470% 0.02106%

1.1608% 0.00035%

The analysis does not need assumptions concerning distributions (Einhorn et al. 20). It is because the analysis uses the distribution of portfolio returns. In our AEX portfolio, the evaluation period, in this case, will be a thousand trading days. The historical simulation approach accurately predicts the VaR for more conservative left tail probabilities. Therefore, this approach is unnecessary in predicting extreme risk cases if an analyst is unwilling to employ the substantial length window size.

Part B (Credit Risk)

Additionally, this describes the introduction to credit metrics, transition matrices, the Expected Value of loans, VaR computation, and Monte Carlo Simulation of Returns. Credit metrics’ intended goal is to create a benchmark in improving and comparing the overall credit risk understanding in the market to ensure the regulatory capital by financial institutions are the true economic risks in the market (Arabi et al.).The use of transition matrices includes its acquisition and calculation of expected values of loans portfolio. The third stage is the Monte Carlo simulation and VaR computation using credit metrics. This involves the key issues and considerations facing this section: correlation coefficient, simulation time, relative VaR, and distributed returns.

The following is a report of three companies, stating their portfolios by filling the table. The three are l real-world companies.

Loan

Name of company

Number of maturity years

Repayment value at maturity in US dollars

Annual interest in percentage

1

Tesla Company

5

12

6

2

General Council of Notaries

4

10

7

3

United Airlines Holdings Inc.

3

8

5

The first company is Tesla Company, whose capital structure has been a major concern for investors and analysts. When one looks at the company’s financial status, one may think that the company is in serious trouble (Sharpe 280). Tesla has financed operations in development, production, administration by sales income, stock offering, and sale of bonds. In May 2013, Tesla raised US$1.02 billion, which is equivalent to US$660 million from the sale of bonds to help in repayment of Energy partially; the Department for received loans from the ATVM loan program after their first quarter, which is profitable. In February 2014, Tesla was able to raiseUS$2 billion from bonds to build the first Giga Factory. In August 2015, Tesla continued to raise US$738 million in stock, which helped in building the Model X. In May 2016, Tesla raised US$1.46 billion to make the Model 3. By 2016, Tesla had raised over US$4.5 billion since its 2010 IPO.

Tesla ventured as an Inter-brand and became among the top100 Best Global Brands in 2016 in position 100 with US$4 billion as its brand valuation (Sharpe 280). In the year 2020, Tesla’s brand was worth US$11.35 billion according to the ratings by Kantra; it was close behind Toyota, Mercedes, and BMW, but ahead of all other automakers, and the only automotive brand whose Value increased since the previous year. On October 26, 2016, Tesla posted a profitable quarter in the first eight quarters, making it a defying industry according to expectations (Sharpe 280). In September 2018, the company had its lowest stock in that year. As of December 31, 2019, ownership by Musk was 38,658,670 Tesla shares or 20.8% of Tesla. On January 10, 2020, Tesla became the most valuable American automaker in existence with a capitalization of US$86 billion in its market. On July 1, 2020, Tesla reached a market capitalization of US$206 billion, and this was greater than Toyota’s US$202 billion. It was, therefore, the best automaker in the world according to market capitalization. Tesla issued US$2 billion of new shares on February 18, 2020. From July 2019 to June 2020, four profitable quarters followed each other for the first time, hence its eligibility for inclusion in the S&P 500. In August 2020, Tesla announced a 5-for-1 stock split, which will take effect on August 31, 2020. For investors to look into the rock solids in terms of financing this company, the long-term debt was $2 billion. A 9.4$ billion was also among the long-term debt already been included. The only way this company could be funded was through increment of the share equity in the long-term debt raises.

The table below shows Tesla’s timeline of its production and sales. Its sales worldwide passed 250,000 units in September 2017, and Tesla produced its 300,000th vehicle in February 2018 (Sharpe 280). The global sales of this company achieved the 500,000 unit milestone in December 2018. The increase in its sales was 50%, from 245,240 units in 2018 to 367,849 units in 2019. On March 9, 2020, Tesla produced its one-millionth electric car.

Quarter

Cumulative

production

Total

production

Model S

sales

Model X

sales

Model 3

sales

Model Y

sales

Total

sales

In transit

Q3 2012

N/A

350

250+

250+

Q4 2012

N/A

2,750+

2,400

2,400

Q1 2013

N/A

5,000+

4,900

4,900

Q2 2013

N/A

N/A

5,150

5,150

Q3 2013

N/A

N/A

5,500+

5,500+

Q4 2013

~34,851

6,587

6,892

6,892

Q1 2014

~41,438

7,535

6,457

6,457

Q2 2014

~48,973

8,763

7,579

7,579

Q3 2014

~57,736

~7,075

7,785

7,785

Q4 2014

64,811

11,627

9,834

9,834

Q1 2015

76,438

11,160

10,045

10,045

Q2 2015

89,245

12,807

11,532

11,532

Q3 2015

102,336

13,091

11,597

6

11,603

Q4 2015

116,373

14,037

17,272

206

17,478

Q1 2016

131,883

15,510

12,420

2,400

14,820

2,615

Q2 2016

150,228

18,345

9,764

4,638

14,402

5,150

Q3 2016

175,413

25,185

16,047

8,774

24,821

5,065

Q4 2016[d]

200,295

24,882

12,700

9,500

22,254

6,450

Q1 2017

225,713

25,418

~13,450

~11,550

25,051

~4,650

Q2 2017

251,421

25,708

~12,000

~10,000

22,026

~3,500

Q3 2017

276,757

25,336

14,065

11,865

222

26,137

4,820

Q4 2017

301,322

24,565

~15,200

~13,120

1,542

29,967

3,380

Q1 2018

335,816

34,494

11,730

10,070

8,182

29,997

6,100

Q2 2018

389,155

53,339

10,930

11,370

18,440

40,740

15,058

Q3 2018

469,297

80,142

14,470

13,190

56,065

83,725

11,824

Q4 2018

555,852

86,555

13,500

14,050

63,359

90,700

2,907

Q1 2019

632,952

77,100

12,100

50,900

63,000

10,600

Q2 2019

720,000

87,048

17,650

77,550

95,200

7,400

Q3 2019

816,155

96,155

17,483

79,703

97,186

Q4 2019

921,046

104,891

19,475

92,620

112,095

Q1 2020

1,023,718

102,672

12,230

76,266

88,496

Q2 2020

1,105,990

82,272

10,614

80,277

90,891

Q3 2020

1,251,026

145,036

15,275

124,318

139,593

Q4 2020

1,430,783

179,757

18,966

161,701

180,667

Q1 2021

1,611,121

180,338

2,030[e]

182,847

184,877

Q2 2021

1,817,542

206,421

1,895

199,409

201,304

Q3 2021

2,055,365

237,823

9,289

232,102

241,391

In terms of the credit risk, in the Monte Carlo Simulation and VaR computation using credit metrics, the values are based on the standard normal distribution of the Tesla Company (Khraibani 47). These values are obtained in two ways before they are transformed in various measures (Dhankar 284).In the evaluation of VaR computation, the results are distributed to imply forward loan values; the approach takes place in three stages (Angelidis et al.).The first stage is the default for each rating category that involves the probabilities (Naimy 145). The second stage calculates the expected values for both the default and the non-default conditions. Analytical VaR can be explained using a self-explanatory graph below.

Additionally, the third step involves the calculation of standard deviations of the loans by use of recovery rates, probability of default, and expected time (Baek et al.).This approach helps in benchmarking, which involves comparing and contrasting the credit risks. It helps the Tesla Company in particular in terms of loans. The assumption, in this scenario, is that the loan is a senior unsecured debt that is nominated in the form of United States Dollars. And upon the maturity date of this company, the loan will be repaid. The details concerning this are tabulated above.

The second company is the General Council of Notaries, a Spanish National Statistic Institute (Colucci). The period of its coverage is from 2007 (Chen et al. 304). The valuation of this particular real estate market risk exposure has been hard from the traditional time. This is because of inadequate data, lack of flow in the returns of the normal distribution, and lack of methodology (RADIVOJEVI? et al. 42). However, certain regulations like Solvency II, Base II, and III help make it possible to assess the real estate using the credit risk through the Value at risk (Al Janabi). This particular study develops a procedure in the provision of an internal model which helps in the valuation of real market risks (Brown 40). The calculations of Monte Carlo simulations help in the valuation of the risks. In the preceding years, there have been changes in how insurance sectors measure their risks based on the risks measurements or the provision of risk-based systems (Al Janabi 135). This is a result of Solvency II and the Solvency Test Regulation of Swiss and the accounting standards development. In the Insurance world, the regulation solvency of the Europeans helps in harmonizing and modernizing the insurance sectors, which is regulated by two main objectives (Bakri). This specific section helps evaluate property using the methodologies for modeling the risks. The unexpected losses are calculated, and the presentation is through the Monte Carlo Simulation Approach (McCullagh). This is also incorporated in the VaR parametric to help in quantifying (Beebe).

Moreover, the probability of the risk occurring is determined in addition to the loss (Culp et al. 50).In determining the risks, the contemplates of index data have been proposed and should be followed so that the earlier data can have a chance to be computed. The produced variations are temporary every month, which provides the amount of variation and the frequency of the variations about the invested unit. Therefore, it is concluded that the statistical distribution that reflects the intensity of a change can thus be obtained. Also, another distribution that reflects the frequency of the change can as well be obtained. The distribution in terms of statistics can be determined using Monte Carlo Simulation, The VaR of the real estate portfolio, and the credit metrics in the computations.

The other company is United Airlines Holdings Inc. The company works under the operations of a holding company, and through its subsidiaries, air transport services are provided (Kuhn et al. 880). It manages and owns airlines that help transport people and cargoes (Syahirah). The services it provides are experienced worldwide. In the fiscal year in 2017, the earnings that were reported were US$2.131 billion, and the total annual revenue was US$37.736 billion. This increased 3.2% over the previous year (Syahirah). The shares that the company traded and its market capitalization was valued to be US$23.1 billion. This company was ranked be number eighty-one in 2018 in the fortune list of the United States.

Year

Revenue

in mil. USD$

Net income

in mil. USD$

Total Assets

in mil. USD$

Price per Share

in USD$

Employees

2011

37,110

837

37,988

21.59

87,000

2012

37,152

?723

37,628

21.21

88,000

2013

38,279

571

36,812

31.65

87,000

2014

38,901

1,132

36,595

47.51

84,000

2015

37,864

7,340

40,861

59.86

84,000

2016

36,556

2,263

40,140

53.02

88,000

2017

37,736

2,131

42,326

69.17

89,800

2018

41,303

2,122

49,024

77.25

92,000

2019

43,259

3,009

52,611

86.82

9

The expected shortfall is also called the conditioned Value at Risk, which helps in the risk assessment to measure the quantity amount of tail risk (Samuelson 523). This depends on the investment portfolio that a company has. The assumptions concern the stochastic probability that affects the car. In its calculation, the average values that are beyond VaR are considered.

P(x) dx represents the probability density of getting x.

C represents the cut-off point of the VaR breakpoint sets of distribution.

VaR represents the level of VaR that has been agreed upon.

The Conditioned Value at Risk and the profile of investments in rare cases exceeds the investment-grade bonds of the US stocks (Sample). The more volatile classes of assets, for example, the small-cap United States stock, the derivatives, and the emergence of market stocks exhibit many CVaRs, which are much greater than the VaRs (Thinh et al. 101). The financial investments most of the time lean on VaR because of the outlier data in the models.

Conclusion

In conclusion, although VaR has been seen to have weaknesses, there is a lack of a clear picture of how VaR is employed since some use hyperbole in elaborating on VaR computations. However, VaR can be calculated by assuming that portfolio returns are distributed normally. The calculations can also be done based on assumptions that portfolio returns are not distributed normally. In the above analysis involving the calculation of VaR using various methods and approaches, it is correct to indicate that VaR does not claim to prevent financial crises. Therefore, the VaR approach of measuring risks does not claim to capture the overall effect of the rare events relating to financial investments. However, it does not necessarily mean that VaR should continue to be used in the way it has been used over time. Due to the financial regulation and risk management worldwide, banks have not fully covered their losses, especially those associated with the collapse of securitized assets.

Using the above argument, it is fit to conclude that VaR has not failed since the rare events are one percent out of the ninety-nine percent events that VaR does not claim to cover. When the matter is looked into in a deep perspective, it is revealed that VaR has been consistently underestimated across the financial sector for quite some time. The current financial crisis may be due to the failure of recognizing flaws and reluctance in acting upon the VaR methods being employed. Therefore, the key methodologies of VaR are seriously flawed. The results that have been obtained are according to my expectations because by use of the various methods, the various companies have almost the same outcomes since I have obtained them from the same sector. This means that the financial crisis facing these companies will save the entire business and economic world if dealt with.