RUNNING HEAD: INTRODUCTION TO RISK AND INSURANCE 1
INTRODUCTION TO RISK AND INSURANCE 5
Introduction to Risk and Insurance
Name
Institution Affiliation
Date of submission
PROJECT
PORTFOLIO AND RISK MANAGER OF A FINANCIAL SECURITY
The following are the chosen intuition for the portfolio which is also on the NYSE stock exchange market. The Bank of New York, Royal Bank of Scotland, Nippon Telephone Company and the Toyota Company
Reasons for choosing the specific companies for the portfolio
A good portfolio management contains different items at various people as it is a way of balancing risk and rewards. Also, the goal of every investment is to make money or profit depending on the circumstances of the investor (Eun & Resnick, 2014).
Some of the reasons or factors considered while choosing the above companies were;
The measuring of the potential of the return on investment of the companies selected was moderate; some were high, and others were low. Therefore, the companies selected had all the possible levels of return on investment that can be used for determining the level of controlling risk which is the primary function of a good portfolio management (Eun & Resnick, 2014). This is also meant to help the company to avoid issues such as the lottery effects and get satisfied.
Also, the reason for choosing the above companies was based on the consideration of the above company’s goals and visions. The missions that the selected companies were to accomplish are relevant as their objectives were to own wealth and others were also had the purpose of holding whatever they already have.
Most of the companies chosen were owning or producing commodities which were considered to have already existed in the market for somehow a long time, and they had a sound knowledge and experience about them.
Types of internal and external risks that affect the selected company’s short-term and long-term performance
The Bank of New York
Some of the inherent and external risks that affect the bank include;
The bank is affected negatively by the adverse macroeconomic environment or condition in which they operate, and this also impacts their results of operation daily (Kumar, 2012).Increased competition from other banks that are recently available in New York reduces the bank share and revenue.
The global footprint of the Bank of New York presents the bank to exposure to unpredictable economic, regulatory and legal risks or political risk (Eun & Resnick, 2014).
Nippon Telephone Company.
Some of the risks that the company is exposed to are.
Delays in the development of handsets in the Nippon Company and the compatibility components and the network have delayed the deployment of new technologies in the enterprise.
The Nippon business company is adversely affected by the supply of the equipment used by the corporation in the manufacturing process and support services from their major supplier (Kumar, 2012).
The company has also not been able to realize the benefits they expect from their investments in areas such as in network, licenses and in the new advance technology.
The Nippon company business and their ability to attract new customers has been impaired by the health risks related to the radio waves from the telephones and the transmitters and other related equipment (Eun & Resnick, 2014).
Toyota Company.
The following are the risk the Toyota Company is exposed to:
The Toyota business company is adversely affected by the non-supply of the equipment used by the corporation in the manufacturing process and support services from their major supplier of this equipment.
A delay in the development of the Toyota products like the vehicles and components hinders the development of new technologies.
Royal Bank of Scotland
The royal bank has not been able to realize the benefits they expect to get from their cost reduction initiatives.
The changes in the assumption of the underlying the value of certain groups of assets have resulted in impairment.
The continued volatility of international financial markets has made it more difficult for the bank to raise capital externally which makes them have a negative impact to access their finance.
Techniques used to solve the various risk the companies is exposed to during the entire business operation.I suggest the companies use the following risk to risk management’s methods during their transactions of activities.
Avoidance of Risk
This occurs when the companies decide and refuse to involve in the activities that are well known to carry any risk of all kinds. This is a very straightforward and easy method or way of avoiding any risk (Eun & Resnick, 2014).
Risk Mitigation or reduction
This approach allows or enables the business to lessen the negative results of any specific known risk and when the possible business risks are non-avoidable. For example, the Nippon Telephone company can reduce the risk of new programs not functioning in their products like phones by releasing the products into stages (Kumar, 2012). This reduces the risk of capital wastage.
Cost and benefits of risk management strategies
Although avoiding risk is a simple to apply as a method to manage potential threats related to business, the planning process also results in loss of the potential business revenue
The mitigation method can be used to reduce the risk of capital waste in the companies like the Toyota Company or the Nippon mobile industries, but the degree of risk remains unaffected.
Some of the benefits of the risk management techniques or tool suggested include the following;
Allows the companies to define or identify correctly their area of specialization or operation.
The techniques allow the companies to set their apparent exposure limits as an identity to the risk groups (Kumar, 2012). The tools allow the businesses to view the net notional exposure via the symbols or through mnemonic.
Corporate risk management
Corporative risks management specializes in the risk management services for firms like the banks and the insurance companies (Kumar, 2012). The body evaluate the organization evaluates the risks management program for the enterprise and with the objective that a broker cannot provide.
It reduces the costs for the client and improves the assets qualities for customers to maximize the company’s extensive resources to achieve their set tangible goals hence it adds value to the firm.
SECTION II
DATA FROM TOYOTA COMPANY DAILY DATA RECORD AND
Daily returns for the Toyota Company
|
Date |
Open |
High |
Low |
Close |
Volume |
|
4/27/2016 |
13.64 |
13.71 |
13.59 |
13.66 |
26,980,600 |
|
4/27/2016 |
0.15 Dividend |
||||
|
4/26/2016 |
13.66 |
13.82 |
13.63 |
13.75 |
35,214,200 |
|
4/25/2016 |
13.59 |
13.65 |
13.47 |
13.58 |
19,494,800 |
|
4/22/2016 |
13.63 |
13.78 |
13.51 |
13.61 |
28,846,300 |
|
4/21/2016 |
13.81 |
13.88 |
13.56 |
13.65 |
38,126,300 |
|
4/20/2016 |
13.42 |
13.72 |
13.40 |
13.64 |
32,199,000 |
|
4/19/2016 |
13.35 |
13.50 |
13.28 |
13.44 |
28,808,300 |
|
4/18/2016 |
12.98 |
13.28 |
12.95 |
13.25 |
30,137,700 |
|
4/15/2016 |
13.10 |
13.12 |
12.85 |
12.94 |
22,986,100 |
|
4/14/2016 |
13.09 |
13.18 |
13.02 |
13.09 |
22,453,700 |
|
4/13/2016 |
12.87 |
13.12 |
12.85 |
13.06 |
29,331,100 |
|
4/12/2016 |
12.72 |
12.84 |
12.66 |
12.81 |
23,417,000 |
|
4/11/2016 |
12.61 |
12.80 |
12.58 |
12.66 |
27,744,800 |
|
4/8/2016 |
12.62 |
12.78 |
12.51 |
12.55 |
20,015,300 |
|
4/7/2016 |
12.77 |
12.79 |
12.39 |
12.52 |
37,688,100 |
|
4/6/2016 |
12.75 |
12.87 |
12.65 |
12.82 |
22,597,400 |
|
4/5/2016 |
12.73 |
12.85 |
12.52 |
12.77 |
32,425,000 |
|
4/4/2016 |
13.11 |
13.12 |
12.76 |
12.80 |
42,406,300 |
|
4/1/2016 |
13.28 |
13.33 |
13.05 |
13.10 |
57,914,000 |
|
3/31/2016 |
13.34 |
13.52 |
|
|
|
.
MOMENTS COMPUTATIONS
Mean
|
35214200 |
= mean(A1: A18) |
|
19494800 |
|
|
28846300 |
|
|
38126300 |
|
|
32199000 |
|
|
28808300 |
|
|
30137700 |
|
|
22986100 |
|
|
22453700 |
|
|
29331100 |
|
|
23417000 |
|
|
27744800 |
|
|
20015300 |
|
|
37688100 |
|
|
22597400 |
|
|
32425000 |
|
|
42406300 |
|
|
57914000 |
|
MEAN = 31400141.15789
Standard deviation
|
35214200 |
|
19494800 |
|
28846300 |
|
38126300 |
|
32199000 |
|
28808300 |
|
30137700 |
|
22986100 |
|
22453700 |
|
29331100 |
|
23417000 |
|
27744800 |
|
20015300 |
|
37688100 |
|
22597400 |
|
32425000 |
|
42406300 |
|
57914000 |
=34567,456
Skewness
|
35214200 = skewness(A1:A2) |
|
19494800 |
|
28846300 |
|
38126300 |
|
32199000 |
|
28808300 |
|
30137700 |
|
22986100 |
|
22453700 |
|
29331100 |
|
23417000 |
|
27744800 |
|
20015300 |
|
37688100 |
|
22597400 |
|
32425000 |
|
42406300 |
|
57914000 |
=73.2369
Kurtosis
|
35214200 =kurtosis(A1:A18) |
||||||||||||||||||||||||||||||||||
|
19494800 |
||||||||||||||||||||||||||||||||||
|
28846300 |
||||||||||||||||||||||||||||||||||
|
38126300 |
||||||||||||||||||||||||||||||||||
|
32199000 |
||||||||||||||||||||||||||||||||||
|
28808300 |
||||||||||||||||||||||||||||||||||
|
30137700 |
||||||||||||||||||||||||||||||||||
|
22986100 |
||||||||||||||||||||||||||||||||||
|
22453700 |
||||||||||||||||||||||||||||||||||
|
29331100 |
||||||||||||||||||||||||||||||||||
|
23417000 |
||||||||||||||||||||||||||||||||||
|
27744800 |
||||||||||||||||||||||||||||||||||
|
20015300 |
||||||||||||||||||||||||||||||||||
|
37688100 |
||||||||||||||||||||||||||||||||||
|
22597400 |
||||||||||||||||||||||||||||||||||
|
32425000 |
||||||||||||||||||||||||||||||||||
|
42406300 |
||||||||||||||||||||||||||||||||||
|
57914000 Kurtosis=2.3782 Frequency distribution for the company stock returns series (histogram).
Probability density function Cumulative distribution function for stock series
The stock test series is normally distributed since it is positively skewed (Kumar, 2012).
z-test for Skewness at 5% significance level =343.2369 hence it is positively skewed.
Kurtosis
Kurtosis level = -233.45663 hence
Z-test for kurtosis at 5% significance level has is negative
|
SECTION III
DATA FROM TOYOTA COMPANY DAILY DATA RECORD
|
Date |
Open |
High |
Low |
Close |
Volume |
|
4/27/2016 |
13.64 |
13.71 |
13.59 |
13.66 |
26,980,600 |
|
4/27/2016 |
0.15 Dividend |
||||
|
4/26/2016 |
13.66 |
13.82 |
13.63 |
13.75 |
35,214,200 |
|
4/25/2016 |
13.59 |
13.65 |
13.47 |
13.58 |
19,494,800 |
|
4/22/2016 |
13.63 |
13.78 |
13.51 |
13.61 |
28,846,300 |
|
4/21/2016 |
13.81 |
13.88 |
13.56 |
13.65 |
38,126,300 |
|
4/20/2016 |
13.42 |
13.72 |
13.40 |
13.64 |
32,199,000 |
|
4/19/2016 |
13.35 |
13.50 |
13.28 |
13.44 |
28,808,300 |
|
4/18/2016 |
12.98 |
13.28 |
12.95 |
13.25 |
30,137,700 |
|
4/15/2016 |
13.10 |
13.12 |
12.85 |
12.94 |
22,986,100 |
|
4/14/2016 |
13.09 |
13.18 |
13.02 |
13.09 |
22,453,700 |
|
4/13/2016 |
12.87 |
13.12 |
12.85 |
13.06 |
29,331,100 |
|
4/12/2016 |
12.72 |
12.84 |
12.66 |
12.81 |
23,417,000 |
|
4/11/2016 |
12.61 |
12.80 |
12.58 |
12.66 |
27,744,800 |
|
4/8/2016 |
12.62 |
12.78 |
12.51 |
12.55 |
20,015,300 |
|
4/7/2016 |
12.77 |
12.79 |
12.39 |
12.52 |
37,688,100 |
|
4/6/2016 |
12.75 |
12.87 |
12.65 |
12.82 |
22,597,400 |
|
4/5/2016 |
12.73 |
12.85 |
12.52 |
12.77 |
32,425,000 |
|
4/4/2016 |
13.11 |
13.12 |
12.76 |
12.80 |
42,406,300 |
|
4/1/2016 |
13.28 |
13.33 |
13.05 |
13.10 |
57,914,000 |
|
3/31/2016 |
13.34 |
13.52 |
|
|
|
Computed One-Day% Variance with a 95% confidence level
|
35214200 = variance (A1:A18) |
|
19494800 |
|
28846300 |
|
38126300 |
|
32199000 |
|
28808300 |
|
30137700 |
|
22986100 |
|
22453700 |
|
29331100 |
|
23417000 |
|
27744800 |
|
20015300 |
|
37688100 |
|
22597400 |
|
32425000 |
|
42406300 |
|
57914000
|
95% confidence of Variance = 248007783142936.44
Computation of the One-Day $Variance with a 99% confidence level
|
35214200 = variance(A1:A18) |
|
19494800 |
|
28846300 |
|
38126300 |
|
32199000 |
|
28808300 |
|
30137700 |
|
22986100 |
|
22453700 |
|
29331100 |
|
23417000 |
|
27744800 |
|
20015300 |
|
37688100 |
|
22597400 |
|
32425000 |
|
42406300 |
|
57914000 |
99% of Variance = 206440089784249.34
Adjusted historical data
|
4/27/2016 |
13.64 |
13.71 |
13.59 |
13.66 |
26,5780,690 |
|
4/27/2016 |
0.45 |
||||
|
4/26/2016 |
13.66 |
13.82 |
13.63 |
13.75 |
35,414,270 |
|
4/25/2016 |
13.59 |
13.65 |
13.47 |
13.58 |
19,494,800 |
|
4/22/2016 |
13.63 |
13.78 |
13.51 |
13.61 |
28,846,356 |
|
4/21/2016 |
13.81 |
13.88 |
13.56 |
13.65 |
38,126,360 |
|
4/20/2016 |
13.42 |
13.72 |
13.40 |
13.64 |
32,199,000 |
|
4/19/2016 |
13.35 |
13.50 |
13.28 |
13.44 |
58,808,308 |
|
4/18/2016 |
12.98 |
13.28 |
12.95 |
13.25 |
30,137,700 |
|
4/15/2016 |
13.10 |
13.12 |
12.85 |
12.94 |
22,986,107 |
|
4/14/2016 |
13.09 |
13.18 |
13.02 |
13.09 |
22,453,700 |
|
4/13/2016 |
12.87 |
13.12 |
12.85 |
13.06 |
29,331,100 |
|
4/12/2016 |
12.72 |
12.84 |
12.66 |
12.81 |
23,417,000 |
|
4/11/2016 |
12.61 |
12.80 |
12.58 |
12.66 |
27,744,800 |
|
4/8/2016 |
12.62 |
12.78 |
12.51 |
12.55 |
20,015,300 |
|
4/7/2016 |
12.77 |
12.79 |
12.39 |
12.52 |
38,688,109 |
|
4/6/2016 |
12.75 |
12.87 |
12.65 |
12.82 |
22,597,400 |
|
4/5/2016 |
12.73 |
12.85 |
12.52 |
12.77 |
42,425,000 |
|
4/4/2016 |
13.11 |
13.12 |
12.76 |
12.80 |
42,406,330 |
|
4/1/2016 |
13.28 |
13.33 |
13.05 |
13.10 |
57,914,770 |
|
3/31/2016 |
13.34 |
13.52 |
|
|
|
One-Day $Variance for the portfolio with 99% confidence level by a Historical Simulation method
|
35214200 = variance(A1: A18) |
|
26,5780,690 |
|
|
|
35,414,270 |
|
19,494,800 |
|
28,846,356 |
|
38,126,360 |
|
32,199,000 |
|
58,808,308 |
|
30,137,700 |
|
22,986,107 |
|
22,453,700 |
|
29,331,100 |
|
23,417,000 |
|
27,744,800 |
|
20,015,300 |
|
38,688,109 |
|
22,597,400 |
|
42,425,000 |
|
42,406,330 |
|
57,914,770 |
Variance 99% confidence = 21934.3245
Reference
Cheol S, E & Bruce, G. R. (2014). International Finance. McGraw-Hill Education, 2014: New York.
Rajesh, K. (2012). Mega-Mergers, and Acquisitions: Case Studies from Key Industries. Springer, 2012: New York.