Banking and Risk Assignment

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.

Part A (Market Risk)

The Value a Risk (VaR) estimates the monetary loss, which is probably the worst in financial investment in a given period in the future (Abrunhosa & Sofia). The statement of VaR has a confidential level defined by the probability in which the actual monetary loss will not exceed the VaR (Zhang 47). For example, suppose the VaR of a certain one-day investment is one million pounds with a confidential level of 99%. In that case, the probability relating to the actual loss the next day will be 99% worse than one million pounds (Dimopoulou). Therefore, VaR is a technique banks use to model financial risks. In major cases, several approaches are used in the calculation of VaR. the approaches employed are: Historical Simulation (HS) Approach, Variance-covariance (VCV) approach, and Monte Carlo Simulation (MCS) Approach. The market risk compares the VaR methods and helps in testing the VaR approaches.

Using the above methods, therefore, computation of risks exposure faced by a portfolio of real-world financial securities of five companies will be discussed. The first company is Invesco QQQ Trust. The institutional investors of this institution use VaR to evaluate portfolio risk, considering that QQQ is one of the largest popular indexes in the non-financial stock that makes its trade on the Nasqad exchange (Ahmadi & Malihe 230). The three methods of calculating VaR are; Historical method, variance-covariance method, and Monte Carlo simulation (Vasileiouet et al.). The historical method has re-organized historical returns putting them from worst to best (Dionne). From the risk perspective, the assumption is that history repeats itself. For example, in Nasdaq 100 ETF, rich data of 1,400 points are produced to calculate every daily return. When this is presented in a histogram, the highest bar will have more than two hundred and fifty days where daily returns range between 0 and 1% (Halkos et al. 207). At the far right, a single day is represented at 13% within five years when the QQQ daily returns were 12.4%. In another way, 95% confidence is expected for the gains to exceed 4% (Mukalenge). VaR allows an outcome worse than -4% return. An increase in confidence requires a move to the left on the histogram. If $100 is invested, the confidence level is 99%, regarding the highest worst daily loss of $7. Using the Variance- Covariance Method, the estimation is on standard deviation and average, which helps plot a normal distribution curve. The idea is similar to that of the historical method, with the normal curve having the advantage of knowing where the worst 1% and 5% lie in the curve.

Confidence Shift of standard deviation

95% (high) -1.65 multiplied by co-efficient

99% (really high) -2.33 multiplied by co-efficient

The actual daily standard deviation of QQQ, 2.64%, makes the average daily return close to zero. The results of having standard deviation in the formula give the following:

Confidence Shift of co-efficient calculation Equals

95% -1.65 x co-efficient -1.65 x (2.64%) -4.36%

=

99% -2.33x co-efficient -2.33x (2.64%) -6.15%

Using the Monte Carlo Simulation method, the model develops future stock price returns with multiple hypothetical trials running (Chakraborty et al.).A simulation on this company was done using 100 trials; two outcomes were between -15% and -20% and three between -20% and -25%. That would mean the worst 5% were less than -15%. This method concludes that with 95% confidence, a loss of more than 15% is not expected in a given month.

The second example is Walmart Stores, Inc. it is considered a multinational retail corporation that runs large discount warehouses and superstores (Asperti et al.). It was founded by Sam Walton and made sales over $300 billion a year; it is considered valuable in the world. 1.5 million workers around the globe are hired here; hence it is the world’s biggest employer; with more than It has more than five thousand stores globally, 80% of those are in the United States. Its direct competitors are; Home Depot, Kmart, Safeway, Sears, and Kroger, but it makes higher sales than all combined.

The company\'s consolidated income statement is as follows in the years 2018,2019, and 2020.

(Amounts in millions, except per share data) 2020 2019 2018

Revenues:

Net sales $ 519,926 $ 510,329 $ 495,761

Membership and other income 4,038 4,076 4,582

Total revenues 523,964 514,405 500,343

Costs and expenses:

Cost of sales 394,605 385,301 373,396

Operating, selling, general and administrative expenses 108,791 107,147 106,510

Operating income 20,568 21,957 20,437

Interest:

Debt 2,262 1,975 1,978

Finance, capital lease and financing obligations 337 371 352

Interest income (189) (217) (152)

Interest, net 2,410 2,129 2,178

Loss on extinguishment of debt — — 3,136

Other (gains) and losses (1,958) 8,368 —

Income before income taxes 20,116 11,460 15,123

Provision for income taxes 4,915 4,281 4,600

Consolidated net income 15,201 7,179 10,523

Consolidated net income attributable to noncontrolling interest (320) (509) (661)

Consolidated net income attributable to Walmart $ 14,881 $ 6,670 $ 9,862

Net income per common share:

Basic net income per common share attributable to Walmart $ 5.22 $ 2.28 $ 3.29

Diluted net income per common share attributable to Walmart 5.19 2.26 3.28

Weighted-average common shares outstanding:

Basic 2,850 2,929 2,995

Diluted 2,868 2,945 3,010

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).