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472 Corporate Finance BRILLIANT’S
commonly used measure of risk. Variance VarH$m h¡Ÿ& d¡[aEÝg, EŠgnoŠQ>oS> H¡$e-âbmo go àË`oH$ g§^m{dV
measures the deviation about expected cash H¡$e-âbmo Ho$ S>o{dEeZ H$mo _mnVm h¡Ÿ& ñQ>¡ÊS>S>© S>o{dEeZ,
flows of each of the possible cash flows. d¡[aEÝg H$m ñH$d¡`a ê$Q> hmoVm h¡Ÿ&
Standard deviation is the square root of
variance.
n 2
S.D. CF EVCF P
Symbolically, t t
t 1
(b) Coefficient of Variation: Coefficient of (b) H$mo{\${eEÝQ> Am°\$ d¡[aEeZ: H$mo{\${eEÝQ> Am°\$
variation is a relative measure of risk. It is d¡[aEeZ, [añH$ H$m [abo{Q>d _oOa h¡ Bgo àmo~o{~{bQ>r
defined as the standard deviation of {S>ñQ´>rã`yeZ Ho$ ñQ>¡ÊS>S>© S>o{dEeZ Ho$ ê$n _| n[a^m{fV
probability distribution divided by its {H$`m OmVm h¡ {Ogo EŠgnoŠQ>oS> d¡ë`y go {S>dmBS> H$a {X`m
expected value.
J`m h¡Ÿ&
S.D.
C.V. =
Expected value
The coefficient of variation is a useful H$mo{\${eEÝQ> Am°\$ d¡[aEeZ [añH$ H$mo _mnZo H$m Cn`moJr
measure of risk when we are comparing the VarH$m h¡ O~ h_ Eogo àmoOoŠQ²>g H$mo H$ånoAa H$a aho hm|
projects which have:
{OZH$m…
(a) Same standard deviation but different (a) ñQ>¡ÊS>S>© S>o{dEeZ g_mZ hmo naÝVw EŠgnoŠQ>oS> d¡ë`yO
expected values, or AbJ-AbJ hmo `m
(b) Different standard deviations but same (b) ñQ>¡ÊS>S>© S>o{dEeZ AbJ-AbJ hmo naÝVw EŠgnoŠQ>oS>
expected values, or g_mZ hm| `m
(c) Different standard deviation and different (c) ñQ>¡ÊS>S>© S>o{dEeÝg ^r AbJ-AbJ hmo VWm
expected values. EŠgnoŠQ>oS> d¡ë`yO ^r AbJ hm|Ÿ&
Note: While comparing projects on the ZmoQ>… [añH$ Ed§ [aQ>Z© Ho$ AmYma na àm°OoŠQ²>g H$mo
basis of risk and return, which project should H$ånoAa H$aVo g_` {H$g àmoOoŠQ> H$mo àmW{_H$Vm Xr OmEJr
be preferred depends upon the attitude of `h [añH$ Ho$ à{V BÝdoñQ>a Ho$ EQ>rQ²>`yS> na {Z^©a H$aoJmŸ&
investor towards risk. If he wants to obtain a `{X dh A{YH$ [aQ>Z© MmhVm h¡ Vmo Cgo A{YH$ [añH$ dmbo
higher return, he may prefer a project with
higher risk. If he is risk-averse, he should prefer àmoOoŠQ> H$mo àmW{_H$Vm XoZr hmoJrŸ& `{X dh [añH$ boZo _| é{M
less risky project. Zht aIVm Vmo Cgo H$_ [añH$s àmoOoŠQ> H$mo AnZmZm hmoJmŸ&
2. Simulation Analysis 2. {gå`wboeZ EZm{b{gg
Simulation is another statistical technique {gå`wboeZ, A{ZpíMVVm H$s pñW{V`m| _| {ZU©` boZo
to deal with uncertainty. It is also based on the H$s EH$ AÝ` gm§p»`H$s` nÕ{V h¡Ÿ& `h ^r àmo~o{~{bQ>r Ho$
concept of probability. The meaning of ‘simu- H$m°ÝgoßQ> na AmYm[aV h¡Ÿ& '{gå`wboeZ' H$m AW© h¡ dmñVd
lation’ is ‘creation of an appearance without _| hþE {~Zm {H$gr KQ>Zm H$m Am^mg H$aZmŸ& {gå`wboeZ
reality’. In simulation, the appearance seems _|, H$moB© KQ>Zm dmñV{dH$ bJVr h¡ naÝVw dmñV{dH$ hmoVr
to be true but it is not real. The information Zht h¡Ÿ& {gå`wboeZ EZm{b{gg go àmßV gyMZmAm| H$m Cn`moJ