Financial Risk Management - Desklib

This article discusses Financial Risk Management and covers topics such as reinvestment risk, extension risk, investment risk, diversification, covariance, R2 and volatility. It includes three case studies with calculations and analysis. The subject is relevant for finance and risk management courses in colleges and universities.

Financial Risk Management

TABLE OF CONTENTS
CASE STUDY 1..............................................................................................................................3
A..................................................................................................................................................3
B..................................................................................................................................................4
C..................................................................................................................................................5
D .................................................................................................................................................6
CASE STUDY 2..............................................................................................................................6
(c)................................................................................................................................................6
(d)................................................................................................................................................8
CASE STUDY 3..............................................................................................................................8
a)..................................................................................................................................................8
b).................................................................................................................................................9
c)................................................................................................................................................10
d)...............................................................................................................................................11
REFERENCES..............................................................................................................................13

CASE STUDY 1
A
Expected return
Expected return ∑(probability*return)
economic state X Y Z
Boom 0.03 -0.0075 0.030
Moderate growth 0.0825 0.011 0.149
slow growth 0.016 0.014 0.046
recession -0.010 0.014 0.003
Expected return 0.12 0.03 0.23
Standard deviation
standard
deviation √∑ (return - expected return)^2 * probability
standard deviation of X
economic state X
return -
expected return
(return -
expected
return)^2
(return - expected
return)^2*
probability
Boom 20% 0.0815 0.00664 0.0009963
Moderate growth 15% 0.03 0.00099 0.0005457
slow growth 8% -0.039 0.00148 0.0002965
recession -10% -0.22 0.04774 0.0047742
∑(return - expected return)^2* probability 0.0066128
√∑(return - expected return)^2* probability 0.0813188
standard deviation of Y
economic state Y
return - expected
return
(return -
expected
return)^2
(return - expected
return)^2*
probability
Boom -5% -0.0815 0.00664 0.0009963
Moderate growth 2% -0.0115 0.00013 7.274E-05
slow growth 7% 0.039 0.00148 0.0002965
recession 14% 0.1085 0.01177 0.0011772

∑ (return - expected return)^2* probability 0.0025428
√∑(return - expected return)^2* probability 0.0504257
standard deviation of Z
economic state Y
return - expected
return
(return -
expected
return)^2
(return - expected
return)^2*
probability
Boom 20% -0.0275 0.00076 0.0001134
Moderate growth 27% 0.0425 0.00181 0.0009934
slow growth 23% 0.002 4E-06 0.0000008
recession 3% -0.2 0.04 0.004
∑(return - expected return)^2* probability 0.0051077
√∑(return - expected return)^2* probability 0.071468
B
Covariance
economic
state Probability X Y Z
Boom 0.15 20% -5% 20%
Moderate
growth 0.55 15% 2% 27%
slow
growth 0.2 8% 7% 23%
recession 0.1 -10% 14% 3%
Covariance
of X and Y -0.0076
Covariance
of Y and Z -0.0043
Covariance
of Z and X 0.00892
Correlation
economic
state Probability X Y Z
Boom 0.15 20% -5% 20%

Moderate
growth 0.55 15% 2% 27%
slow
growth 0.2 8% 7% 23%
recession 0.1 -10% 14% 3%
Correlation
of X and Y -0.9579
Correlation
of Y and Z -0.6747
Correlation
of Z and X 0.85776
C
expected return
economic
state Probability X Y Z
Boom 0.15 40% 25% 35%
expected
return 0.06 0.0375 0.0525
expected
return 0.15
standard deviation
economic
state Y
return -
expected
return
(return -
expected
return)^2
(return -
expected
return)^2*
probability
Boom 40% 0.25 0.0625 0.009375
25% 0.10 0.01 0.0015
35% 0.20 0.04 0.006
∑(return - expected return)^2* probability 0.016875
√∑(return - expected return)^2* probability 0.1299038

D
value at risk
expected weighted return on portfolio
weight return
X 9.5 0.7037 6.69
Y 4 0.2963 1.19
Portfolio value 13.5
average return 3.94
standard deviation 2.75
Portfolio standard deviation 0.38136
confidence level 0.95
min return with 95 % -0.5882
value of portfolio 5.55981
value at risk 7.94019
With the help of the above calculation it is clear that the value at risk is the outline the possible
financial loss of the portfolio. With help of the above calculation it is clear that the loss of the
portfolio is approximately 7.94019.
CASE STUDY 2
(c)
Calculation of mean, variance and standard deviation:
Gold Price Mean:
Gold price = 1922 + 1901 + 1867 + 1859 + 1867 + 1809 + 1719 + 1761 + 1851 + 1835 / 10
= 1839.1
Gold Price Variance and Standard deviation
Gold
price Deviation from the mean Squared deviation from the mean
1922 82.9 6872.41
1901 61.9 3831.61
1867 27.9 778.41

1859 19.9 396.01
1867 27.9 778.41
1809 -30.1 906.01
1719 -120.1 14424.01
1761 -78.1 6099.61
1851 11.9 141.61
1835 -4.1 16.81
18391 Sum = 34244.9
Variance = 34244.9 / (10 – 1) = 3804.989
Standard deviation = square root of 3804.989
= 61.68
Silver Price Mean:
Sliver price = 26 + 25 + 25 + 25 + 26 + 28 + 26 + 26 + 28 + 27 / 10
= 26.2
silver
price Deviation from the mean Squared deviation from the mean
26 -0.2 0.04
25 -1.2 1.44
25 -1.2 1.44
25 -1.2 1.44
26 -0.2 0.04
28 1.8 3.24
26 -0.2 0.04
26 -0.2 0.04
28 1.8 3.24
27 0.8 0.64
262 Sum = 11.6
Variance = 11.6 / (10 – 1) = 1.28889
Standard deviation = square root of 1.28889
= 1.135
The covariance and correlation between gold and silver price is as follows:
Covariance = -15.02
Correlation = -0.23831

Calculation of annualized standard deviation of return:
Formula = Standard deviation of monthly return * square root of 12 months
Gold Price = 61.68 * 3.46 = 213.4128
Silver Price = 1.135 * 3.46 = 3.9271
(d)
Yes, in order to manage the risk attached with the price of the commodity such as metal
mining, the company should hedge their sales revenue. This is because the price of the industrial
products get changes over the span of time. So, hedging the sales revenue of the commodity
helps the industrial companies in limits the loses to the great extent. It is also good for the
company to increase liquidity as it facilitates investors to invest in various assets classes (Peng
and et.al., 2018). Thus, it is advisable to the companies that they have to use hedge technique to
protect their sales revenue by setting the lower margin outlay. This will help the company
especially industrial in protecting their sales revenue from the changes taken place in external
environment.
CASE STUDY 3
a)
Reinvestment risk refers to inability of investor to get the cash flow from the investment for
further investment. In the case of UK banking chances that cash flow received from the
investment will be less than the amount invested. The one of the crucial vulnerable to reinvest is
found to be in callable bonds. In the banking system reinvestment risk refers to declining rate
which leads to affect the liquidity and position of the banking institution. Extension risk is
mostly refers to having differ repayments due to market condition. It is mostly concerned with
the secondary credit market.
In the UK banking system there is rise in prices of products which is leading to result in
inflation situation system (Gonçalves, 2021). The mentioned kinds of risk is resulting in having
bank to pay higher level of focus on rate of risk which can decrease the prevailing threats. The
structure of UK banking institutions are formulated in such manner which can incline its ability
to manage and monitor the prevailing situation in turn higher competitive advantages can be
derived. The impact of reinvestment risk incline in banks for bonds with longer maturities &

higher coupon payment that can decrease h with shorter maturities. In addition to this, it can be
specified that Barclays is one of the larger bank that highly contribute in economic growth h &
development. In order to manage reinvestment risk the much emphasis is provided on application
of different kinds of practices. It includes using non callable bonds, zero coupon instruments,
bond ladders, activity managed funds and long term securities.
Extension risk has adverse impact on the processing of banking function. In addition to this, the
major impact that can arise due to presence of this particular risk includes lowering the
secondary market value via incline in rate (Grundke and Kühn, 2020). In addition to this, it can
be specified that the banking institutions in UK get highly affect which influence its ability to
manage liquidity & efficiency this is basically monitored by these firm through focusing on
involving avoidance, loss prevention, separation, duplication & diversification. This can
contribute in overcoming situation which has adverse impact on the processing of specified
sector. On the basis of this, it can be interpreted that controlling this both kinds of risk in turn
smooth functioning can be derived.
b)
Investment risk is associated with possibilities of losses which can occur due to changes in
expected return within particular period. Investors largely pay attention on measuring investment
risk through giving focus on distinct elements which can lead in arising of risk. There are
different kinds of approaches which can be utilized by investors for mitigating or eliminating
related risk with particular investment.
Diversification refers to making portfolio of investment by involving distinct feature
comprising which can decline risk. It is the practice of spreading investment around so that
exposure to nay particular type of asset is kept to be limited (Duan and et.al., 2018). In order to
mitigate the risk it is highly crucial for an individual to adopt the technique of diversification in
turn having appropriate level of balance in declining possibilities of loss can be done. It aids in
managing volatility of possessing portfolio so that inclining opportunity of deriving profitability
can be achieved.
Covariance measures the e directional relationship between tow variables. In case of
investment risk, covariance is helpful in measuring that directional relationship between two
assets in which positive indicate assets turnover move together. In case of negative be covariance
which indicates inverse relationship. It is basically a statistical tool that contribute in ascertaining

the mentioned kind of relationship so that estimating particular level of risk which lead to profit
or loss can be identified.
R2 is taken into practice as the bench marking for measuring the percentage of an asset.
It is one of the crucial function that provides assistance in interpreting relevant information
regarding movements of security. The combination of diversification, covariance and R2 can be
utilized by organization for having depth insights in turn having relevant ability to mitigate
prevailing level of risk can become possible (Taghizadeh-Hesary and Yoshino, 2019). To get
significant mixture can assist in having proper extent of ability to identify directional movement,
possibilities of losses, performance of assets as compared to standard bench marking. From the
evaluation it can be articulated that having appropriate extent of capabilities to manage the e
prevailing circumstances' int turn avoiding situation that can lead to loss. On the basis of this, it
can be interpreted that investment risk can be properly managed by applying diversification,
covariance and R2.
c)
Volatility refers to rapid change in rate due to which prices of stock increases or declines over
particular duration. In case of higher stock prices there is availability of greater risk which are
required to be taken to be consideration. This contributes in estimating higher level of ability to
understand the prevailing situation. Understanding fluctuation through tending to rapid &
extreme alteration which results in incing or declining stock price which can be measured by
beta coefficient. The volatility is helpful in decreasing security experience period of
unpredictable so that decision regarding investment can be done. There are different kinds of
method available which can taken into consideration for estimating volatility. The one of the
significant approach that is widely used for ascertaining fluctuation involves calculation of
standard deviation. It is highly used method as provides concise details of fluctuation that is easy
to interpret in turn crucial evaluation regarding investment can be done. The specified method
gives emphasis on having relevant measure of market volatility which can be appropriately
analyzed SD.

Maximum downward is another approach which contribute in estimating relevant
information regarding price fluctuations. It aids in estimating that how much it has fell or
inclined in turn formulating appropriate course of action for making decision can become
possible. In addition to this it is used by large number of investors, analyst, etc so that
speculating assets locators and growth investors for the purpose to limit losses (Peng and et.al.,
2018). It aids in predicting the pattern of losses so that formulation of relevant functioning in
turn higher productive result providing outcome can be achieved.
Beta is included as one of the crucial method that can be included to have to measure
stock market. It provides assistance in evaluating relevant risk of stocks In addition to this,
determining diversification of assets through enabling organization to have measure fluctuations
in turn making appropriate decision can become possible. Beta has the values of less than 1
reflecting their lower volatility and vice versa. It gives proper specification regarding extent of
fluctuation in turn making relevant and appropriate decision to avoiding loss occurring situation.
With help of such kinds of method it becomes possible to estimate distinct types of volatility
which involves historical, implied and future realized which larger contribute in making
appropriate decision.
d)
Interest rate risk is considered with having potential change in the overall interest rates which
will decline the value of bond or other fixed rate investment. The rise in interest rate risk decline
the price value of bind and other fixed rate risk. The main cause behind interest rate risk is
associated with incline in equity fall which can paying out more interest. It leads to impact the
credit risk of company which is forces the lender to raise interest rates for potential borrowings.
Organization faces higher interest rate when the proportion of debt in the capital structure. The
different kinds of interest risk involve repricing, yield curve, basis and optionality. It is highly
essential to pay attention on having information regarding the main sources of interest risk which
involve difference in risk of default & over dues, liquidity of debt, term to maturity and leaders
cost of serving loans.
There are several causes which are required to be taken into considerations as it leads to rise in
arriving of interest rate risk. The one of the main source which result in arriving of interest risk
which is re pricing. The re pricing is concerned with changes of interest rate at the time of
financial contract rate is reset it leads to result in arising risk if the interest rates are settled on

liabilities for the period which differs from those on offsetting assets (Kiley and Roberts, 2017).
It occurs when company retires employee stock option that has lower strike price. In addition to
this, company can effectively replace when keep top managers.
Yield curve risk is another source which aids in experiencing an adverse shift in market h
interest aster associated with investing in fixed income instrument. The performance of fixed rate
possessing instrument get affected from this (Brunnermeier and Koby, 2018). The shorter term
changes in this yield may lead to have higher level of impact on prevailing circumstances and
results in change of rate.
Basis risk arise in case of mismatch of the position of hedged. In addition to this, interest rate
risk come into existence due to imperfect hedge which results in losses in investment. It is one of
the significant source of risk which majorly affect the investment by inclining rate of interest.
Optionality risk is considered to be one of the crucial threat for the investors. It arises due to
change in time or scope of monetary instruments that brings cash flow sdue to alteration in the
interest. The main reason for their occurrence is having options embedded in corporate assets,
liabilities, etc. on the basis of the provided information it can be specified that these are main
sources of interest rate risk.

REFERENCES
Books and Journals
Brunnermeier, M.K. and Koby, Y., 2018. The reversal interest rate (No. w25406). National
Bureau of Economic Research.
Duan, F. and et.al., 2018. Energy investment risk assessment for nations along China’s Belt &
Road Initiative. Journal of Cleaner Production. 170. pp.535-547.
Gonçalves, A.S., 2021. Reinvestment risk and the equity term structure. The Journal of Finance.
Grundke, P. and Kühn, A., 2020. The impact of the Basel III liquidity ratios on banks: Evidence
from a simulation study. The Quarterly Review of Economics and Finance. 75. pp.167-
190.
Kiley, M.T. and Roberts, J.M., 2017. Monetary policy in a low interest rate world. Brookings
Papers on Economic Activity. 2017(1). pp.317-396.
Peng, Y. and et.al., 2018. The best of two worlds: Forecasting high frequency volatility for
cryptocurrencies and traditional currencies with Support Vector Regression. Expert
Systems with Applications . 97. pp.177-192.
Taghizadeh-Hesary, F. and Yoshino, N., 2019. The way to induce private participation in green
finance and investment. Finance Research Letters. 31. pp.98-103.