Global Financial Management

Size: px
Start display at page:

Download "Global Financial Management"

Transcription

1 - - Global Facal Maaeme Dscou ad Prese alue Techques opyrh 999 by Ers Mau. All rhs reserved. No par of hs lecure may be reproduced whou he permsso of he auhor.. Overvew Las Revso: Sepember 9, 999 I hs lecure we wll roduce dscou echques ad eres rae mahemacs. Ths maeral s mpora several respecs. The echques are fudameal for almos ay facal calculao, ra from smple asks lke calcula he repaymes o a morae or rack a loa balace o more comple applcaos. The maeral s also foudaoal for subseque lecures, parcularly bod valuao, sock valuao ad vesme apprasal echques. Fally, he dscusso below offers some basc shs o he ma asks ad fuco of facal markes, whch we are o o deepe dur he remader of he course.. Objecves A he ed of hs u you should be able o: Track a loa balace. Decde wheher you should re-morae your house. Deerme he requred mohly corbuo o your peso pla. Decde wha lump sum you eed o se asde oday order o fud he collee educao of your chldre.

2 - - alue a perpeual bod. Dsush aual perceae raes ad effecve aual raes ad use hem correcly.. Fuure alue: The value omorrow of a dollar oday Suppose you jus receved a bous payme of $,, ad you ca pu o a bak accou a a rae of 6% p. a. (p. a. per year). Pla ahead, you wa o deerme he cosumpo you ca afford oe year from ow. How does hs compare o recev a bous payme of $, oe year from ow? Oe of he mos fudameal prcples of face s ha a dollar oday (or $,, hs case) s worh more oday ha a dollar omorrow. Le s look a hese umbers more deal. You ca ves he $, a 6%, so a he ed of oe year he sum your savs accou has row o $,6: you ow $6 more a year from ow f you receve he $, oday raher ha a year hece. How would hs relaoshp chae, f you compare a $, oday wh a payme of $, wo years from ow? We kow already ha we oba $,6 a he ed of he frs year f we ves hem he savs accou for oe year. A he ed of he secod year we wll have: $,6.6 $,36. We call he umber $,36 he fuure value of $, a 6%, years from ow. We wsh o develop he cocep of a fuure value more eerally. Observe ha he fuure value ca be obaed as follows: fuure value $,.6.6 $,.6.

3 - 3 - We ca erae hs process order o fd ou wha he fuure value of $, oday a 6% s a he ed of hree, fve or e years, ad how would chae f he eres rae were 8%. We use he follow symbols: Fuure value afer years Ial vesme Ieres rae per year The we ca wre dow a eeral epresso for fuure value: ( ). () Suppose $, ad %. The we oba he follow paer of fuure values for ra from o 3. Fuure alues Fuure value $, $8, $6, $4, $, $, $8, $6, $4, $, $ Perod Afer 3 years, our al value of $, has row o a respecable

4 - 4 - $,. 3 $74,494. osder also he follow eample. Eample : Suppose you oba wo paymes, $5, oday ad $5, eacly oe year from ow. You ca pu hese paymes o a savs accou ad ear eres a a rae of 5%. Wha s he balace your savs accou eacly 5 years from ow? ompue he follow able: Year ash flow Ieres Balace $5,. $. $5,. $5,. $5. $,5. $. $5.5 $, $. $538.3 $, $. $565.3 $, $. $593.8 $, A he ed of he frs year you receve $5 eres (5% of $5,), v you a oal balace of $,5 oe year from ow. The you ca compue fuure values for each year as $,5.5, where for he balace wo years from ow, ad 4 for he balace 5 years from ow, so fve years you have a balace of: $,5.5 4 $, You ca easly eed he above eample o oher applcaos order o keep rack of he fuure developme of a vesme ha you make oday. Ths ad he follow calculaos were doe a spreadshee proram. Please refer o he spreadshee. Replcao of he umbers he eamples wh a calculaor may lead o slhly dffere resuls because of roud errors.

5 Prese alue: The value oday of a dollar omorrow Ofe you wll eed o do he reverse operao of he oe we performed above, ad ask he queso: How much do you have o pu o your bak accou oday, so ha oe year from ow, he balace s eacly $,, f I accrue eres a a rae of 6% o he balace. Hece, you wsh o deerme he amou P ha solves: P.6 $, whch ves you mmedaely: $, P.6 $9, Hece, f you pu $9, o your bak accou oday, he hs amou wll row o eacly $, oe year from ow. We call he amou P (here $9,433.96) he prese value of $, oe year from ow a a eres rae of 6%. I s easy o see from equao () above ha f s he fuure value (. e., he ed of perod balace our savs accou), he s he prese value. We ca herefore solve () for he prese value o fd: () ( ) The prese value formula () preses he flp sde of he prcple ha a dollar omorrow s worh less ha a dollar oday. I our case, oe dollar a year from ow s

6 - 6 - worh oly $ Prese values are mpora f you wsh o compare dffere lables. osder he follow eample: Eample : Suppose your dauher Jae jus raduaed from collee ad wshes o ake a posraduae course. Jae has he choce bewee wo uverses of comparable qualy ha offer he wo-year course of her choce. Uversy A chares $8, of uo fees for he frs year ad s epeced o crease hese fees o $, he secod year, whereas uversy B chares $9, he frs year ad $9,5 he secod year. You ve Jae a sum suffce o cover all her uo fees, ad she ca ves hs a a rae of 5.5% p. a. How much do you have o ve o Jae f she aeds he course a eher uversy A or B? Assume all uo fees are always due a he ed of he year. We use a sep-by-sep approach o calculae he amou Jae has o borrow f she aeds he course a A. The prese value of a payme of $8, a 5.5% a he ed of oe year s: $8,/.55$7, The prese value of a payme of $, a he ed of he secod year s: $,/.55 $ Hece, f Jae chooses A, you have o ve her $6, oday o fully cover her uo fees. Smlarly, for B we fd: $9, $9,5 $7, Le s check ha hs acually works. If Jae chooses B ad you had over he sum of $7,66., she pus o her savs accou ad accumulaes a balace of

7 - 7 - $8,4.74$7, a he ed of he year. The she pays $9, o he uversy ad reas $9,4.74 her accou. A he ed of he secod year she has accumulaed $9, $9,5. her savs accou, jus eouh o mee he bll for uo fees a he ed of he secod year. The follow able racks hepaymes ad balaces of jae s accou. Dae Ial Balace Payme Rema Balace $7,66. $. $7,66. $8,4.74 $9,. $9,4.74 $9,5. $9,5. $. The las eample roduces aoher aspec of prese values ha s worh emphasz. We could smply add he prese values of he wo paymes for uo fees. Ths propery s called value addvy ad makes work wh prese values very srahforward. To eeralze hs uo, we use he symbol for he cash flow a he ed of year. I he prevous eample, we have $9, ad $9,5 for uversy B. The we defe prese value of a sream of paymes as:... ( ) ( ) ( ) ( ) ( ) (3) whch shows ha we ca always calculae he prese value of a seres of paymes by add he prese values of each dvdual payme. Noe ha each eleme of he The Greek leer Σ deoes he so-called summao operaor ad reads as follows: Sum a seres of elemes dsplayed o he rh had sde of he leer Σ for all

8 - 8 - seres (3) has a very smple srucure. We mulply he payme perod by he facor ( ) whch depeds oly o he dae of he payme ad he eres rae. Ths facor s called he dscou facor, ad f we eed o be more precse, we refer also as he -perod dscou facor. The follow able ad raph ve he dscou facor for a eres rae of 6% for up o fve perods: Perod Dscou facor / bewee ad. Hece, he rh had sde of equao (3) s a shorhad for he lef had sde of he equao.

9 - 9 - Dscou facor Perods Eample 3: Recosder he uo fee eample, bu assume ha uversy A requres ha half of he uo fee for a parcular year s pad before ad he oher half a he ed of he academc year. Hece, Jae has o make he follow paymes: Year Payme $4, $9,$4,$5, $5, Noe ha he payme a he ed of he frs year covers half of he uo for he frs ad half of he uo for he secod year. Hece, ow he amou of moey she has o pu asde o cover her uo a uversy A s: $9, $5, $ 4, $7, almos as much as he prese value of uo fees for B.

10 - - The ma advaae of prese values s ha hey make payme sreams wh dffere ms comparable. I our eample, he paymes a uversy A are due a mes ad, hose for B a, ad. I order o compare lke wh lke, we eed o epress hese paymes oe commo u, here oday s dollars. However, he prcple s oly o epress paymes erms of dollars of he same year, we could equally well choose he las year. Suppose we apply formula () o he prese value epresso equao (3). We oba: ( ). (4) We derve he seps lead o (4) he apped. Epresso (4) has a uve erpreao as a loa balace. 3 Suppose you eed o make a sequece of paymes order o mee a oblao (lke collee uo fees), ad you have o borrow he moey you eed for hese paymes from he bak because you have o come. The he value s he balace o your loa o he day afer you made he las payme. To see hs, oe ha you compoud eres o he frs payme over years, so afer years you owe ( ) for borrow. O payme you compoud eres oly over - years, so you owe ( ) a he ed of year. ou hs way, afer he -h payme of (o whch you have o accrued ay eres), your loa balace s ve by (4). 3 Effecvely, here s o dfferece wheher you aalyze hs from he po of vew of a borrower as a loa balace, or from he po of vew of a leder as a fud o whch you make perod paymes.

11 - - Eample 4: For our eample we ca epress he payme sreams o A ad B erms of her ed of year values by us he fuure value formula: $7, $8,947. Suppose you dd o ve ay moey o Jae, ad she had o ake ou a loa o cover her uo. The she would make he requred paymes as descrbed eample 3. O he frs payme of $4, a he be of he frs year she owes $4,.55$4, a he ed of he frs year. The she makes aoher payme of $9,, add hs amou o her loa balace Over her secod year she has o pay eres o he $4,, so her lably from hs payme creases o $4,.55$4,45.$4,.55. Add o hs he prcpal ad eres from he secod payme, whch amous o $9,.55$9,495. o ve a oal of $3,947. Add he las payme afer year of $5, ves a oal loa balace of $8,947., whch s eacly he fuure value we jus compued. The follow able ves he balaces ad paymes: Year Ial Balace Payme Ieres Balace $. $4,. $. $4,. $4,. $9,. $. $3,. $3,. $5,. $77. $8,947. Hece, we ca summarze ha prese values ad fuure values are useful order o compare payme sreams. The mpora prcple s value addvy: we ca add prese values ad fuure values, provded hey are epressed erms of dollars of he same year. Prese values have he erpreao of moey o be se asde (for a lably), or wealh erms of curre dollars (for a asse). If I have o make paymes

12 - - over he e years, ad I ca accumulae eres a a rae % per year, he he lump sum I have o se asde oday order o mee hese oblaos s he prese value of hese paymes. Smlarly, fuure values keep rack of accou or loa balaces. If I have o make paymes over he e years, ad I ca borrow he moey ad accumulae eres a a rae % per year, he he loa balace afer mak he las payme s he fuure value from hese paymes. Smlarly, f I make paymes o a savs accou ad accumulae eres, he he balace of my savs accou s ve by he fuure value of all paymes o hs accou..4. ompoud Iervals So far we have made oe lm assumpo by maa ha eres s compouded aually. Ths s ulkely, ad dffere facal coracs come wh dffere compoud ervals: morae ad cred card loas ypcally compoud eres mohly, savs accous quarerly, ad bods sem-aually. Hece, we modfy our symbols as follows: m m R R/m r Number of years Number of compoud perods per year Number of compoud perods Nomal or saed eres rae, also called he APR (aual perceae rae) perodc eres rae effecve aual eres rae Eample 5: Suppose you have a 5-year morae wh a saed APR of 9%, where eres s compouded mohly. The 5, m ad he umber of compoud perods

13 - 3 - s 53 mohs. The APR s R9%, ad.75% s he mohly eres rae. Eample 6: You ake ou a loa o face your car a a eres rae of %, wh quarerly paymes. The, m4 ad /45 years. The APR s R%, 3% s he quarerly eres rae. I order o roduce he cocep of a effecve aual rae, we eed o sudy he mpac of dffere compoud ervals a lle more closely. The mos srahforward case here s fuure value. Suppose you ake ou a loa of dollars oday wh a APR of R ad compoud ervals. The formula () above s sll vald, bu s ow he umber of compoud ervals, whch s eerally o he umber of years, ad s he eres rae per compoud erval, o per year. I order o epress hs erms of years ad aual perceae raes, we ca subsue o oba: R ( ) m m (5) Eample 7: Suppose you borrow $, a a APR of % ad repay oe lump sum a he ed of he year. If eres s compouded aually, he you owe $, a he ed of he year. However, f eres s compouded sem-aually, he your eres rae for half a year s 6%, so your loa balace afer s mohs s $,6. Therefore, a he ed of he year you eed o repay $,6.6$,36..

14 - 4 - The addoal $36. represes he compoud eres (6% of he $6 eres added o your loa balace afer 6 mohs). Smlarly, wh quarerly compoud your loa balace would accumulae o $,55.9. The follow able ves your lably for dffere compoud ervals f you repay afer oe or afer wo years oe lump sum: ompoud Perod ompoud Iervals Year $,. $, mohs $,36. $, mohs 3 $,48.64 $,653.9 Quarer 4 $,55.9 $,667.7 mohs 6 $,6.6 $,68.4 Mohs $,68.5 $, Days 365 $,74.75 $,7.99 Hours 876 $,74.96 $,7.47 Secods 556 $,74.97 $,7.49 We ca see mmedaely ha creas he umber of compoud perods also creases he effecve coss of a loa. Hece, f he compoud erval s o oe year, he he APR does o ve us he eres coss of he loa for oe year ay more. Ths leads us o he defo of a effecve eres rae whch we deoe by r, ad whch s dffere from he saed or omal eres rae or APR. To see he dfferece, cosder he case of a mohly compoud perod eample 7 ad he prevous able. The saed eres rae (APR) s %. The eres ha accumulaes o he loa s he same as f we had aual compoud ad a eres rae of.685%, subsaally hher ha he saed rae of %. (see he shaded row he able) Ths eres rae of.685% s our effecve aual rae. I s defed as he rae ha we eed o apply o he oral loa order o oba he oal eres ha accumulaes o he loa oe year. Noe ha hs rae also works for wo or ay umber of years. I our eample, afer wo years we have

15 - 5 - accumulaed $,.685 $, The effecve aual rae s wha we eed order o compue he effecve eres coss of he loa. Hece, mus sasfy: R ( r) m m (6) whch ves us mmedaely ha: r R m m (7) whch s derved he apped. Eample 8: For he umbers eample 7 we oba for he effecve aual rae: ompoud Perod ompoud Iervals Effecve aual eres rae Year.% 6 mohs.36% 4 mohs % Quarer 4.559% mohs 6.66% Mohs.685% Days % Hours % Secods % Oe observao s mmedae from hs able: he effecve eres rae creases wh he umber of compoud ervals, bu does so a a ever smaller rae. As he umber of compoud ervals becomes fely lare (or, equvalely, as he leh of oe compoud erval becomes fesmally small), we ca fd a very covee epresso for he effecve aual rae as:

16 - 6 - r e R (8) where e represes Euler s umber (e.788). 4 Noe ha you have o epress he eres rae decmal form here, e.., % as.. I our eample we oba: r e Ths s he same umber we oba wh compoud every secod up o 6 decmal places. I markes where we have o work wh daly ervals (e.. fuures ad opos markes) couous compoud s mos of he me easer ha compu effecve aual raes from (8). Eample 9: You have o make a payme o a loa wh a curre balace of $, ha maures 5 days from ow. Ieres accumulaes daly o hs loa a a rae of 6% p. a. Wha s he effecve aual rae o hs loa? Wha error do you make your calculao f you assume ha eres s compouded couously? If eres accumulaes daly, he he effecve aual rae s:.6 r or 6.83%. Ths ves a loa balace 5 days from ow of 5 5 $ $, 365.6, 365 ( r ) $, Noe ha e s also he base of he aural loarhm, commoly deoed by l: l(e).

17 - 7 - Wh couous compoud we oba: r e.6 $, e $,98.39 a dfferece of 6 ces o a loa balace of $,. Eample : Suppose he effecve aual eres rae s 9%. Whch APR do you have o use f eres o hs accou accumulaes mohly? ouously? Ths s a more advaced queso. We eed a ukow eres rae R such ha: R.9 R (.9 ). 865 or 8.65%. The operao for couous compoud volves ak los: e R (.9) R l or 8.6%. Noe ha you have o be careful wh epress he eres rae ad caledar me here. R s a aual rae, hece me has o be epressed fracos of oe year. 5 Eample : Suppose you accrue eres o your cred card couously a a rae of.5% per moh. Wha s he effecve mohly rae? Wha s he effecve aual rae? 5 If we had epressed he eres rae for aoher compoud erval, e.., as a mohly rae, he we would have o epress me erms of mohs. Ths s smply a requreme o kep he us of measureme cosse.

18 - 8 - Wha would be f eres compouded mohly? Hece, how much eres do you accrue o a balace of $, f you repay afer 6 weeks (4 days)? ompue.5 he effecve mohly rae frs as e. 5. ompoud over mohs ves or 9.7%. If eres compouded mohly he effecve aual rae would be oly , or 9.56%. To compue your loa balace afer 4 days wh couous compoud, use: $, $, Dscou wh a fe me horzo Equaos (3) ad (4) sum up all he coceps reard prese values ad fuure values. However, some cases s possble o smplfy hese epressos f he sream of paymes has a cera paer. Surprsly, he eases formula obas he case where paymes () are cosa, ad () coue defely o he fuure. Ths paer s kow from so-called osols. These are perpeual bods ("cosoldao bods") ssued by he Brsh overme he 9 h ceury ha have a cosa coupo ad are ever repad. 6 Aoher applcao s compay valuao, where we cao assume ha dvded paymes sop a a defe po me We wll have more o say abou hese he lecure o bod valuao. We wll dscuss hs more deal he lecure o sock valuao.

19 - 9 - Therefore, assume ha for all perods sar a (. e., sar a he ed of he curre perod) for all perods o he fuure, ad also assume ha s he approprae dscou rae. The urs ou ha he prese value of hs sream of perpeual paymes s: ( ) ( ). (9) We derve hs epresso he apped us a sadard formula for he summao of eomerc seres. Eample : Suppose you are offered a perpeual bod ha ves you oe aual payme of $5 a he ed of each year, ad he e payme s eacly oe year from oday. The approprae dscou rae s 4% p. a. How much are you wll o pay for hs bod? Apply equao (9) ves: $5 $,5..4 You may fd equao (9) more uve by epress as:. Ths ca be ve he follow erpreao. Suppose you borrow, ad you pay he eres due o hs loa a he ed of each perod, bu you ever make a repayme of prcpal. The s your eres payme a he ed of each year. Of course, f you ever repay ay prcpal, he you have o keep mak eres paymes a he ed of each year defely.

20 - - Eample 3: Suppose you ake ou a loa of $, a a rae of 7.5% wh aual compoud, ad do o repay ay prcpal ad make aual eres paymes a he ed of he year. The you pay he leder $5.75$, a he ed of each year, ad you always owe he prcpal..6 Aues ad Moraes osa perpeual paymes are easy o aalyze, bu hey are o very commo. A more commo payme paer s a so-called auy, where paymes are also cosa, bu eed over a fe perod of me. The mos freque eample for hs s a morae loa, where he borrower repays he leder a loa a specfed umber of equal salmes. Eample 4: You ake ou a loa of $5, o your house a a morae rae (APR) of 6% over 3 years. Ths meas ha you repay he bak by mak 36 mohly paymes. Wha s he mohly repayme o hs loa? Noe ha.6/.5%, so, clearly, he aswer s more ha $75 (.5% of $5,), sce $75 would oly repay he eres, bu o repay ay prcpal. We eed o deerme how much more. Our objecve s ow o value a sream of cosa paymes. Apply (3) oce more ves: ( ). ()

21 - - Aa, we ve a rorous demosrao of () he apped. However, () ca also be demosraed very uvely. I s easy o see ha a auy s smply a dfferece bewee wo perpeues. To see hs, cosder he follow able: Tme - Perpeuy P Perpeuy P Auy A P-P We ca ow epress he paymes o he auy as: ash Flow ( A) ash Flow( P) ash Flow( P). Hece, apply he prcple of value addvy, ( A) ( P ) ( ) P. We have already esablshed he prevous seco ha he value of P a me zero s /. Moreover, by he same prcple he value of P a me s also /. Hece, he prese value of P s: ( P) ( ). Noe ha he paymes for P sar a me. However, he prese value of P a me s /, so we eed o dscou / over perods, o perods. Hece:

22 ( A) ( P ) ( P) ( ). - - ( ) By remember how o epress a auy as a dfferece bewee wo perpeues, all you eed o remember s he prese value formula (), ad he formula for perpeues (9), ad you wll always kow how o derve () ad wha umber o use he epoe for. Eample 5: Sar Moraes cosders buy a morae from Moo Bak. The morae was orally a hry-year fed rae morae ad sll has eacly wey years of mohly paymes. The morae rae areed o he morae s 9%, ad he mohly paymes are $,5 per moh. How much s Sar Moraes wll o pay whe hey purchase he morae f he curre -year morae rae s 6%? The frs mpora observao here s ha he oral morae rae of 9% s compleely rreleva here. Sce eres raes have falle, Moo s dscou fuure paymes a 6%. Hece, he perodc eres rae s.6/.5 or.5%, ad he umber of perods s 4 mohs. Hece, we use () as follows: $, $9,37 Hece, Sar s wll o pay $9,37 for hs morae. If you ake ou a morae you are probably more eresed a dffere queso: ve he eres rae ad he amou you wsh o borrow, wha s your mohly repayme? Noe ha () has a very smply srucure. You smply mulply he cosa perodc

23 - 3 - payme by a facor ha oly depeds o he eres rae ad he umber of perods. Ths facor s called he auy facor. We use he symbol A () for hs facor, ha s defed as: A () ( ) () We ca herefore epress () as: A () Now s smple o solve for : A () () Equao () has a mpora erpreao. Suppose we wsh o ake ou a loa wh a amou, he s he cosa perodc payme we eed o make order o repay he loa over perods f he perodc eres rae s. Eample 6: You wa o ake ou a morae of $, o your house, ad you are offered a eres rae (APR) of 6% o a 5-year morae. Ieres s compouded mohly. Wha s your mohly repayme? Smply apply () ad () above wh 6%/.5% ad 58 o e: $, $,687.7

24 - 4 - Noe ha hs s a he ross payme for eres ad prcpal, ad does o ake o accou a deducos or escrow paymes coeced wh hs loa. I s srucve o rewre () by subsu for he auy facor: The umeraor of hs epresso s already famlar from our dscusso of perpeues. If we do o make ay repaymes of prcpal, he we mus make mohly eres paymes equal o. The we would owe he full amou (or par amou) of he loa a he ed of he perod. The epresso he deomaor s clearly smaller ha oe, hece creases he payme o accou for he fac ha we make repaymes of prcpal as well as eres paymes. Eample 4 (co.) I eample 4 we already esablshed ha he mohly payme has o eceed $75. We ca ow calculae as: $5, $ Noe, however, ha he composo of he mohly payme bewee eres ad prcpal chaes over he lfeme of he loa. I he early saes of repay a loa, eres accous for mos of he mohly paymes. However, as you repay he prcpal, he loa balace decreases ad so does he eres compoe, ad a he ed he mohly

25 - 5 - paymes are almos erely repaymes of prcpal. The mechacs of hs are he subjec of a repayme schedule, whch s bes demosraed by way of a eample. Eample 7: Recosder eample 6 ad cosder he frs mohly payme of $, Afer oe moh, he eres owed o $, s eacly $,, or.5% of $,. Hece, he rema $687.7 s a repayme of prcpal, ad a he ed of he frs moh, you owe he bak he rema $,-$687.7$99,3.9. Hece, for he secod moh you have o pay eres oly o hs slhly reduced amou, whch s $996.56, so ha your prcpal payme he secod moh mus be $687.7-$996.56$69.5. The follow able shows he frs ad las few mohs of he repayme schedule: 8 Moh Ial Balace Payme Ieres Prcpal Ed Balace $,. $,687.7,. $687.7 $99,3.9 $99,3.9 $, $69.5 $98,6.3 3 $98,6.3 $, $694.6 $97, $97,96.53 $, $698.8 $97, $5,.93 $, $,66.65 $3, $3,35.8 $, $,67.96 $, $,679.3 $, $,679.3 $. The fure dsplayes he me-seres paers of he oal payme, ad s decomposo o prcpal ad eres.

26 - 6 - $, Repayme of a Morae Payme $,5 $, $5 $ Payme Ieres Prcpal Moh So far we have aalyzed he prese value of aues. Ofe we are also eresed he fuure value of a auy, for eample, f you wa o deerme he value you accumulae a peso pla f you make cosa paymes over a cera perod of me. Us () ad apply () ves: ( ) ) ( ) ( ). (3) Eample 8: Today s your 35 h brhday, ad you recko you ca pu asde $,4 a quarer o a peso pla where your moey accumulaes a a rae of 5% p. a., 8 See he spreadshee for he calculao of he complee repayme schedule.

27 - 7 - compouded quarerly. How much wll you have accumulaed he pla afer you made he las payme o your 65 h brhday? Effecvely, you make 34 quarerly paymes of $,4 over 3 years ha are compouded a.5% per quarer. Us (3): (.5 ) $66,5. 94 $, Grow Perpeues ad Grow Aues Our aalyss of perpeues ad aues above was lmed by he assumpo ha paymes say cosa over me. I s acually srahforward o aalyze a more eeral case, where paymes are allowed o row a a cosa rae over me. Hece, we posulae:... 3 ( ) ( ) ( ) ( )... ( ) Eample 9: The US overme wshes o ssue perpeual bods. I order o provde vesors wh a more aracve vesme, he reasury deparme decdes ha he aual coupo o oe bod s $ he frs year, ad row a a rae of 3% per year afer ha. The he aual coupo s $3 he secod year of he bod, $6.9 he hrd year, ad by he eh year has become a remarkable $3.48. Evdely, he mechacs are he same as he fuure value calculao from () above, where he rowh rae 3% akes he place of he eres rae. I urs ou ha such a row perpeuy s jus as srahforward o value as a cosa perpeuy. We ca adap (9) o ve:

28 ( ) ( ) ( ) (4) Equao (4) s derved he apped. Noe ha hs resul s oly vald f >: ca ever become eave f s posve. Eample : Recosder eample 9, ad suppose ha he aual rowh rae of coupos s 3%, ad he eres rae s 5%. Apply (4) o ve: $.5.3 $5, Noe ha (9) s a specal case of (4), hece we eed o memorze oly (4): If we se (4) we oba (9) aa. We ca apply he same loc o row aues, where he umber of paymes s fe. Smlarly, a fe sequece of row paymes s row auy. The parallel epresso for () s: (5) Eample : Recosder he eample from your peso pla eample 8. However, suppose you epec your corbuos o row a a rae of.5% per quarer, so your frs quarer s corbuos are sll o o be $,4 as before, bu he secod quarer you ow corbue $,4. Wha s he ed balace your fud afer you made he las payme o your 65 h brhday ow? Wha corbuo would you have o sar wh f you waed o accumulae $,,

29 - 9 - by your 65 h brhday? We compue hs wo seps. The frs sep compues he prese value as of your 35 h brhday. Ths s from (5): $, $88,878.6 The secod sep s o cover he prese value o a fuure value us (), whch ves us $88, $838,66.8. I order o accumulae $,, you eed o crease your paymes by a facor of $,,/$ , whch raslaes o a corbuo he al quarer of $,86.7. Noe, however, ha you are commed o crease hs by.5% per year, so he fal quarer s corbuo s o o be $5,8.67, almos double of wha you corbue oday! ocluso The ma purpose of hs oe s o llusrae dscou echques ad her applcaos. These echques are fudameal for all facal calculaos subseque lecures. I s easy o e los he maze of dffere formulas. I s ofe easer o memorze hese by udersad he relaoshps bewee he ma coceps. Prese values ad fuure values are bascally flp sdes of he same co, smply reverse he dreco me. You oly eed o remember he eerc formula () ad how relaes o (). All oher fuure value formulas are specal applcaos (so (4) follows from (3), (3) follows from ()). The eerc prese value formula s (3). I ecompasses all subseque formulas as specal cases. The ma specal cases are perpeues ad aues.

30 - 3 - Thk abou dscou raes as apply o perods. Perods are ypcally shorer ha oe year. osa aues ad cosa perpeues are specal cases of row aues ad row perpeues respecvely: Oly lear (4) ad (5) whch are more eeral. (9) s a specal case of (4), () s a specal case of (5). Hece, lear (), (), (3), (4) ad (5) s suffce, ad everyh else falls o place.

31 - 3 - Apped Dervao of equao (4): ombe () ad (3) as follows: from () ( ) ( ) ( ) ( ) ( )... ( ) ( ) ( ) ( )... ( ) ves (4) ( ) from (3) ( ) Dervao of equao (7): Dvd boh sdes of equao (6) by we e: R m ( r) m Now ake he -h roo o boh sdes. (Apply he epoe / o boh sdes, remember / ha ( ) ): ( r) R m m Subrac o boh sdes ves (7). Dervao of equao (9): We make he follow subsuo:

32 - 3 - ad observe ha < <. The we ca rewre he frs equaly (9) as: ( )... 3 Mulply boh sdes by - o oba: ( ) ( )( ) { } ( ) { } ( ) { } ( ) where he las equaly obas from cacel equvale epressos ad observ ha for <, coveres o zero as becomes lare. Fally, solv for ad subsu for ves: whch proves (9). Dervao of equao (): We use eacly he same sraey as he dervao of equao (9). Frsly: ( )... 3 he, afer mulply wh -:

33 ( ) ( )( ) { } ( ) { } ( ) { } ( ) { } ( ) ( ) ( ) The we solve for as before: ( ) afer subsu for, whch ves (). Dervao of equao (4): We proceed as he dervao of equao (9) above, ecep ha we ow defe: The we ca rewre he lef had sde of (4) as: The, us parallel seps o he dervao of (9): Noe ha he sum coveres oly f <, whch s equvale o <. Oherwse

34 we would have ha he sum dveres o fy. Dervao of equao (5): Us he same procedure as for equaos () ad (4), we oba: ( ) ( )

35 Impora Termoloy auy auy facor 3 APR 4 compoud eres 4 osols 8 dscou facor 8 effecve eres rae 4 fuure value row auy 8 row perpeuy 7 omal eres rae 4 par amou 4 prese value 5 value addvy 7

36 Impora Formulae Fuure value: Prese value: ( ) () () ( )... ( ) ( ) ( ) ( ) ( ) (3) Dscou facor: ( ) Auy: ( ) ( ) A () Grow Perpeuy: (4) Grow auy: (5)

(1) Cov(, ) E[( E( ))( E( ))]

(1) Cov(, ) E[( E( ))( E( ))] Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )

More information

Midterm Exam. Tuesday, September hour, 15 minutes

Midterm Exam. Tuesday, September hour, 15 minutes Ecoomcs of Growh, ECON560 Sa Fracsco Sae Uvers Mchael Bar Fall 203 Mderm Exam Tuesda, Sepember 24 hour, 5 mues Name: Isrucos. Ths s closed boo, closed oes exam. 2. No calculaors of a d are allowed. 3.

More information

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo

More information

Synopsis of Various Rates of Return

Synopsis of Various Rates of Return Syopss of Varous Raes of Reur (Noe: Much of hs s ake from Cuhberso) I he world of face here are may dffere ypes of asses. Whe aalysg hese, a ecoomc sese, we aemp o characerse hem by reducg hem o some of

More information

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period.

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period. ublc Affars 974 Meze D. Ch Fall Socal Sceces 748 Uversy of Wscos-Madso Sock rces, News ad he Effce Markes Hypohess (rev d //) The rese Value Model Approach o Asse rcg The exbook expresses he sock prce

More information

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period.

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period. coomcs 435 Meze. Ch Fall 07 Socal Sceces 748 Uversy of Wscos-Madso Sock rces, News ad he ffce Markes Hypohess The rese Value Model Approach o Asse rcg The exbook expresses he sock prce as he prese dscoued

More information

The Poisson Process Properties of the Poisson Process

The Poisson Process Properties of the Poisson Process Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad

More information

14. Poisson Processes

14. Poisson Processes 4. Posso Processes I Lecure 4 we roduced Posso arrvals as he lmg behavor of Bomal radom varables. Refer o Posso approxmao of Bomal radom varables. From he dscusso here see 4-6-4-8 Lecure 4 " arrvals occur

More information

The ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3.

The ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3. C. Trael me cures for mulple reflecors The ray pahs ad rael mes for mulple layers ca be compued usg ray-racg, as demosraed Lab. MATLAB scrp reflec_layers_.m performs smple ray racg. (m) ref(ms) ref(ms)

More information

Partial Molar Properties of solutions

Partial Molar Properties of solutions Paral Molar Properes of soluos A soluo s a homogeeous mxure; ha s, a soluo s a oephase sysem wh more ha oe compoe. A homogeeous mxures of wo or more compoes he gas, lqud or sold phase The properes of a

More information

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state)

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state) Pro. O. B. Wrgh, Auum Quaum Mechacs II Lecure Tme-depede perurbao heory Tme-depede perurbao heory (degeerae or o-degeerae sarg sae) Cosder a sgle parcle whch, s uperurbed codo wh Hamloa H, ca exs a superposo

More information

Key words: Fractional difference equation, oscillatory solutions,

Key words: Fractional difference equation, oscillatory solutions, OSCILLATION PROPERTIES OF SOLUTIONS OF FRACTIONAL DIFFERENCE EQUATIONS Musafa BAYRAM * ad Ayd SECER * Deparme of Compuer Egeerg, Isabul Gelsm Uversy Deparme of Mahemacal Egeerg, Yldz Techcal Uversy * Correspodg

More information

Cyclone. Anti-cyclone

Cyclone. Anti-cyclone Adveco Cycloe A-cycloe Lorez (963) Low dmesoal aracors. Uclear f hey are a good aalogy o he rue clmae sysem, bu hey have some appealg characerscs. Dscusso Is he al codo balaced? Is here a al adjusme

More information

Quantitative Portfolio Theory & Performance Analysis

Quantitative Portfolio Theory & Performance Analysis 550.447 Quaave Porfolo heory & Performace Aalyss Week February 4 203 Coceps. Assgme For February 4 (hs Week) ead: A&L Chaper Iroduco & Chaper (PF Maageme Evrome) Chaper 2 ( Coceps) Seco (Basc eur Calculaos)

More information

4. THE DENSITY MATRIX

4. THE DENSITY MATRIX 4. THE DENSTY MATRX The desy marx or desy operaor s a alerae represeao of he sae of a quaum sysem for whch we have prevously used he wavefuco. Alhough descrbg a quaum sysem wh he desy marx s equvale o

More information

Continuous Time Markov Chains

Continuous Time Markov Chains Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,

More information

QR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA

QR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA QR facorzao Ay x real marx ca be wre as AQR, where Q s orhogoal ad R s upper ragular. To oba Q ad R, we use he Householder rasformao as follows: Le P, P, P -, be marces such ha P P... PPA ( R s upper ragular.

More information

Pure Math 30: Explained!

Pure Math 30: Explained! ure Mah : Explaied! www.puremah.com 6 Logarihms Lesso ar Basic Expoeial Applicaios Expoeial Growh & Decay: Siuaios followig his ype of chage ca be modeled usig he formula: (b) A = Fuure Amou A o = iial

More information

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction refeed Soluos for R&D o Desg Deermao of oe Equao arameers Soluos for R&D o Desg December 4, 0 refeed orporao Yosho Kumagae refeed Iroduco hyscal propery daa s exremely mpora for performg process desg ad

More information

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae

More information

For the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body.

For the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body. The kecs of rgd bodes reas he relaoshps bewee he exeral forces acg o a body ad he correspodg raslaoal ad roaoal moos of he body. he kecs of he parcle, we foud ha wo force equaos of moo were requred o defe

More information

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting Appled Mahemacs 4 5 466-477 Publshed Ole February 4 (hp//wwwscrporg/oural/am hp//dxdoorg/436/am45346 The Mea Resdual Lfeme of ( + -ou-of- Sysems Dscree Seg Maryam Torab Sahboom Deparme of Sascs Scece ad

More information

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles Ope Joural of Dsree Mahemas 2017 7 200-217 hp://wwwsrporg/joural/ojdm ISSN Ole: 2161-7643 ISSN Pr: 2161-7635 Cylally Ierval Toal Colorgs of Cyles Mddle Graphs of Cyles Yogqag Zhao 1 Shju Su 2 1 Shool of

More information

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination Lecure 3 Topc : Drbuo, hypohe eg, ad ample ze deermao The Sude - drbuo Coder a repeaed drawg of ample of ze from a ormal drbuo of mea. For each ample, compue,,, ad aoher ac,, where: The ac he devao of

More information

Chapter 8. Simple Linear Regression

Chapter 8. Simple Linear Regression Chaper 8. Smple Lear Regresso Regresso aalyss: regresso aalyss s a sascal mehodology o esmae he relaoshp of a respose varable o a se of predcor varable. whe here s jus oe predcor varable, we wll use smple

More information

The Linear Regression Of Weighted Segments

The Linear Regression Of Weighted Segments The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed

More information

Solution set Stat 471/Spring 06. Homework 2

Solution set Stat 471/Spring 06. Homework 2 oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o

More information

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall 8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model

More information

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD Leas squares ad moo uo Vascocelos ECE Deparme UCSD Pla for oda oda we wll dscuss moo esmao hs s eresg wo was moo s ver useful as a cue for recogo segmeao compresso ec. s a grea eample of leas squares problem

More information

FORCED VIBRATION of MDOF SYSTEMS

FORCED VIBRATION of MDOF SYSTEMS FORCED VIBRAION of DOF SSES he respose of a N DOF sysem s govered by he marx equao of moo: ] u C] u K] u 1 h al codos u u0 ad u u 0. hs marx equao of moo represes a sysem of N smulaeous equaos u ad s me

More information

NOTE ON SIMPLE AND LOGARITHMIC RETURN

NOTE ON SIMPLE AND LOGARITHMIC RETURN Appled udes Agrbusess ad Commerce AAC Ceer-r ublshg House, Debrece DOI:.94/AAC/27/-2/6 CIENIFIC AE NOE ON IME AND OGAIHMIC EUN aa Mskolcz Uversy of Debrece, Isue of Accoug ad Face mskolczpaa@gmal.com Absrac:

More information

Fully Fuzzy Linear Systems Solving Using MOLP

Fully Fuzzy Linear Systems Solving Using MOLP World Appled Sceces Joural 12 (12): 2268-2273, 2011 ISSN 1818-4952 IDOSI Publcaos, 2011 Fully Fuzzy Lear Sysems Solvg Usg MOLP Tofgh Allahvraloo ad Nasser Mkaelvad Deparme of Mahemacs, Islamc Azad Uversy,

More information

Mixed Integral Equation of Contact Problem in Position and Time

Mixed Integral Equation of Contact Problem in Position and Time Ieraoal Joural of Basc & Appled Sceces IJBAS-IJENS Vol: No: 3 ed Iegral Equao of Coac Problem Poso ad me. A. Abdou S. J. oaquel Deparme of ahemacs Faculy of Educao Aleadra Uversy Egyp Deparme of ahemacs

More information

-distributed random variables consisting of n samples each. Determine the asymptotic confidence intervals for

-distributed random variables consisting of n samples each. Determine the asymptotic confidence intervals for Assgme Sepha Brumme Ocober 8h, 003 9 h semeser, 70544 PREFACE I 004, I ed o sped wo semesers o a sudy abroad as a posgraduae exchage sude a he Uversy of Techology Sydey, Ausrala. Each opporuy o ehace my

More information

Model for Optimal Management of the Spare Parts Stock at an Irregular Distribution of Spare Parts

Model for Optimal Management of the Spare Parts Stock at an Irregular Distribution of Spare Parts Joural of Evromeal cece ad Egeerg A 7 (08) 8-45 do:0.765/6-598/08.06.00 D DAVID UBLIHING Model for Opmal Maageme of he pare ars ock a a Irregular Dsrbuo of pare ars veozar Madzhov Fores Research Isue,

More information

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse P a g e Vol Issue7Ver,oveber Global Joural of Scece Froer Research Asypoc Behavor of Soluos of olear Delay Dffereal Equaos Wh Ipulse Zhag xog GJSFR Classfcao - F FOR 3 Absrac Ths paper sudes he asypoc

More information

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits. ose ad Varably Homewor # (8), aswers Q: Power spera of some smple oses A Posso ose A Posso ose () s a sequee of dela-fuo pulses, eah ourrg depedely, a some rae r (More formally, s a sum of pulses of wdh

More information

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model . Projec Iroduco Fudameals of Speech Recogo Suggesed Projec The Hdde Markov Model For hs projec, s proposed ha you desg ad mpleme a hdde Markov model (HMM) ha opmally maches he behavor of a se of rag sequeces

More information

FALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below.

FALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below. Jorge A. Ramírez HOMEWORK NO. 6 - SOLUTION Problem 1.: Use he Sorage-Idcao Mehod o roue he Ipu hydrograph abulaed below. Tme (h) Ipu Hydrograph (m 3 /s) Tme (h) Ipu Hydrograph (m 3 /s) 0 0 90 450 6 50

More information

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions:

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions: Paramerc coug process models Cosder coug processes: N,,..., ha cou he occurreces of a eve of eres for dvduals Iesy processes: Lelhood λ ( ;,,..., N { } λ < Log-lelhood: l( log L( Score fucos: U ( l( log

More information

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS Vol.7 No.4 (200) p73-78 Joural of Maageme Scece & Sascal Decso IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS TIANXIANG YAO AND ZAIWU GONG College of Ecoomcs &

More information

Moments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables

Moments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables Joural of Mahemacs ad Sascs 6 (4): 442-448, 200 SSN 549-3644 200 Scece Publcaos Momes of Order Sascs from Nodecally Dsrbued Three Parameers Bea ype ad Erlag Trucaed Expoeal Varables A.A. Jamoom ad Z.A.

More information

AML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending

AML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending CUIC SLINE CURVES Cubc Sples Marx formulao Normalsed cubc sples Alerae ed codos arabolc bledg AML7 CAD LECTURE CUIC SLINE The ame sple comes from he physcal srume sple drafsme use o produce curves A geeral

More information

Review Exercises for Chapter 9

Review Exercises for Chapter 9 0_090R.qd //0 : PM Page 88 88 CHAPTER 9 Ifiie Series I Eercises ad, wrie a epressio for he h erm of he sequece..,., 5, 0,,,, 0,... 7,... I Eercises, mach he sequece wih is graph. [The graphs are labeled

More information

Interval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X

Interval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X ECON 37: Ecoomercs Hypohess Tesg Iervl Esmo Wh we hve doe so fr s o udersd how we c ob esmors of ecoomcs reloshp we wsh o sudy. The queso s how comforble re we wh our esmors? We frs exme how o produce

More information

Real-time Classification of Large Data Sets using Binary Knapsack

Real-time Classification of Large Data Sets using Binary Knapsack Real-me Classfcao of Large Daa Ses usg Bary Kapsack Reao Bru bru@ds.uroma. Uversy of Roma La Sapeza AIRO 004-35h ANNUAL CONFERENCE OF THE ITALIAN OPERATIONS RESEARCH Sepember 7-0, 004, Lecce, Ialy Oule

More information

( ) () we define the interaction representation by the unitary transformation () = ()

( ) () we define the interaction representation by the unitary transformation () = () Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger

More information

Continuous Indexed Variable Systems

Continuous Indexed Variable Systems Ieraoal Joural o Compuaoal cece ad Mahemacs. IN 0974-389 Volume 3, Number 4 (20), pp. 40-409 Ieraoal Research Publcao House hp://www.rphouse.com Couous Idexed Varable ysems. Pouhassa ad F. Mohammad ghjeh

More information

Supplement Material for Inverse Probability Weighted Estimation of Local Average Treatment Effects: A Higher Order MSE Expansion

Supplement Material for Inverse Probability Weighted Estimation of Local Average Treatment Effects: A Higher Order MSE Expansion Suppleme Maeral for Iverse Probably Weged Esmao of Local Average Treame Effecs: A Hger Order MSE Expaso Sepe G. Doald Deparme of Ecoomcs Uversy of Texas a Aus Yu-C Hsu Isue of Ecoomcs Academa Sca Rober

More information

Research on portfolio model based on information entropy theory

Research on portfolio model based on information entropy theory Avalable ole www.jocpr.com Joural of Chemcal ad Pharmaceucal esearch, 204, 6(6):286-290 esearch Arcle ISSN : 0975-7384 CODEN(USA) : JCPC5 esearch o porfolo model based o formao eropy heory Zhag Jusha,

More information

Exam Supply Chain Management January 17, 2008

Exam Supply Chain Management January 17, 2008 Exam Supply Cha Maageme Jauary 7, 008 IMPORTANT GUIELINES: The exam s closed book. Sudes may use a calculaor. The formularum s aached a he back of he assgme budle. Please wre your aswers o he blak pages

More information

Probability Bracket Notation and Probability Modeling. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA 94087, USA. Abstract

Probability Bracket Notation and Probability Modeling. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA 94087, USA. Abstract Probably Bracke Noao ad Probably Modelg Xg M. Wag Sherma Vsual Lab, Suyvale, CA 94087, USA Absrac Ispred by he Drac oao, a ew se of symbols, he Probably Bracke Noao (PBN) s proposed for probably modelg.

More information

Solution in semi infinite diffusion couples (error function analysis)

Solution in semi infinite diffusion couples (error function analysis) Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of

More information

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1) Aoucemes Reags o E-reserves Proec roosal ue oay Parameer Esmao Bomercs CSE 9-a Lecure 6 CSE9a Fall 6 CSE9a Fall 6 Paer Classfcao Chaer 3: Mamum-Lelhoo & Bayesa Parameer Esmao ar All maerals hese sles were

More information

The algebraic immunity of a class of correlation immune H Boolean functions

The algebraic immunity of a class of correlation immune H Boolean functions Ieraoal Coferece o Advaced Elecroc Scece ad Techology (AEST 06) The algebrac mmuy of a class of correlao mmue H Boolea fucos a Jgla Huag ad Zhuo Wag School of Elecrcal Egeerg Norhwes Uversy for Naoales

More information

Mathematical Formulation

Mathematical Formulation Mahemacal Formulao The purpose of a fe fferece equao s o appromae he paral ffereal equao (PE) whle maag he physcal meag. Eample PE: p c k FEs are usually formulae by Taylor Seres Epaso abou a po a eglecg

More information

Optimal Eye Movement Strategies in Visual Search (Supplement)

Optimal Eye Movement Strategies in Visual Search (Supplement) Opmal Eye Moveme Sraeges Vsual Search (Suppleme) Jr Naemk ad Wlso S. Gesler Ceer for Percepual Sysems ad Deparme of Psychology, Uversy of exas a Aus, Aus X 787 Here we derve he deal searcher for he case

More information

Fresnel Dragging Explained

Fresnel Dragging Explained Fresel Draggig Explaied 07/05/008 Decla Traill Decla@espace.e.au The Fresel Draggig Coefficie required o explai he resul of he Fizeau experime ca be easily explaied by usig he priciples of Eergy Field

More information

Application of the stochastic self-training procedure for the modelling of extreme floods

Application of the stochastic self-training procedure for the modelling of extreme floods The Exremes of he Exremes: Exraordary Floods (Proceedgs of a symposum held a Reyjav, Icelad, July 000). IAHS Publ. o. 7, 00. 37 Applcao of he sochasc self-rag procedure for he modellg of exreme floods

More information

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations Joural of aheacs ad copuer Scece (4 39-38 Soluo of Ipulsve Dffereal Equaos wh Boudary Codos Ters of Iegral Equaos Arcle hsory: Receved Ocober 3 Acceped February 4 Avalable ole July 4 ohse Rabba Depare

More information

Complementary Tree Paired Domination in Graphs

Complementary Tree Paired Domination in Graphs IOSR Joural of Mahemacs (IOSR-JM) e-issn: 2278-5728, p-issn: 239-765X Volume 2, Issue 6 Ver II (Nov - Dec206), PP 26-3 wwwosrjouralsorg Complemeary Tree Pared Domao Graphs A Meeaksh, J Baskar Babujee 2

More information

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS Opimal ear Forecasig Alhough we have o meioed hem explicily so far i he course, here are geeral saisical priciples for derivig he bes liear forecas, ad

More information

General Complex Fuzzy Transformation Semigroups in Automata

General Complex Fuzzy Transformation Semigroups in Automata Joural of Advaces Compuer Research Quarerly pissn: 345-606x eissn: 345-6078 Sar Brach Islamc Azad Uversy Sar IRIra Vol 7 No May 06 Pages: 7-37 wwwacrausaracr Geeral Complex uzzy Trasformao Semgroups Auomaa

More information

Brownian Motion and Stochastic Calculus. Brownian Motion and Stochastic Calculus

Brownian Motion and Stochastic Calculus. Brownian Motion and Stochastic Calculus Browa Moo Sochasc Calculus Xogzh Che Uversy of Hawa a Maoa earme of Mahemacs Seember, 8 Absrac Ths oe s abou oob decomoso he bascs of Suare egrable margales Coes oob-meyer ecomoso Suare Iegrable Margales

More information

International Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No.

International Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No. www.jecs. Ieraoal Joural Of Egeerg Ad Compuer Scece ISSN: 19-74 Volume 5 Issue 1 Dec. 16, Page No. 196-1974 Sofware Relably Model whe mulple errors occur a a me cludg a faul correco process K. Harshchadra

More information

Economics 8723 Macroeconomic Theory Problem Set 2 Professor Sanjay Chugh Spring 2017

Economics 8723 Macroeconomic Theory Problem Set 2 Professor Sanjay Chugh Spring 2017 Deparme of Ecoomics The Ohio Sae Uiversiy Ecoomics 8723 Macroecoomic Theory Problem Se 2 Professor Sajay Chugh Sprig 207 Labor Icome Taxes, Nash-Bargaied Wages, ad Proporioally-Bargaied Wages. I a ecoomy

More information

Extremal graph theory II: K t and K t,t

Extremal graph theory II: K t and K t,t Exremal graph heory II: K ad K, Lecure Graph Theory 06 EPFL Frak de Zeeuw I his lecure, we geeralize he wo mai heorems from he las lecure, from riagles K 3 o complee graphs K, ad from squares K, o complee

More information

Solving fuzzy linear programming problems with piecewise linear membership functions by the determination of a crisp maximizing decision

Solving fuzzy linear programming problems with piecewise linear membership functions by the determination of a crisp maximizing decision Frs Jo Cogress o Fuzzy ad Iellge Sysems Ferdows Uversy of Mashhad Ira 9-3 Aug 7 Iellge Sysems Scefc Socey of Ira Solvg fuzzy lear programmg problems wh pecewse lear membershp fucos by he deermao of a crsp

More information

Common MidPoint (CMP) Records and Stacking

Common MidPoint (CMP) Records and Stacking Evromeal ad Explorao Geophyscs II Commo MdPo (CMP) Records ad Sackg om.h.wlso om.wlso@mal.wvu.edu Deparme of Geology ad Geography Wes rga Uversy Morgaow, W Commo Mdpo (CMP) gaher, also ofe referred o as

More information

Conditional Probability and Conditional Expectation

Conditional Probability and Conditional Expectation Hadou #8 for B902308 prig 2002 lecure dae: 3/06/2002 Codiioal Probabiliy ad Codiioal Epecaio uppose X ad Y are wo radom variables The codiioal probabiliy of Y y give X is } { }, { } { X P X y Y P X y Y

More information

Pricing Asian Options with Fourier Convolution

Pricing Asian Options with Fourier Convolution Prcg Asa Opos wh Fourer Covoluo Cheg-Hsug Shu Deparme of Compuer Scece ad Iformao Egeerg Naoal Tawa Uversy Coes. Iroduco. Backgroud 3. The Fourer Covoluo Mehod 3. Seward ad Hodges facorzao 3. Re-ceerg

More information

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory Liear Time-Ivaria Sysems (LTI Sysems) Oulie Basic Sysem Properies Memoryless ad sysems wih memory (saic or dyamic) Causal ad o-causal sysems (Causaliy) Liear ad o-liear sysems (Lieariy) Sable ad o-sable

More information

HYDROSTATIC HEAD CORRECTION

HYDROSTATIC HEAD CORRECTION XVI IMKO World oress Measureme - Suppors Scece - Improves Tecoloy - Proecs vrome... ad Provdes mployme - Now ad e Fuure Vea, AUSTIA,, Sepember 5-8 YDOSTATI AD OTION W. Kolacza Seco for e, Area, Ale, Poomery,

More information

Reliability Analysis of Sparsely Connected Consecutive-k Systems: GERT Approach

Reliability Analysis of Sparsely Connected Consecutive-k Systems: GERT Approach Relably Aalyss of Sparsely Coece Cosecuve- Sysems: GERT Approach Pooa Moha RMSI Pv. L Noa-2131 poalovely@yahoo.com Mau Agarwal Deparme of Operaoal Research Uversy of Delh Delh-117, Ia Agarwal_maulaa@yahoo.com

More information

Big O Notation for Time Complexity of Algorithms

Big O Notation for Time Complexity of Algorithms BRONX COMMUNITY COLLEGE of he Ciy Uiversiy of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE CSI 33 Secio E01 Hadou 1 Fall 2014 Sepember 3, 2014 Big O Noaio for Time Complexiy of Algorihms Time

More information

. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue.

. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue. Lnear Algebra Lecure # Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons

More information

Competitive Facility Location Problem with Demands Depending on the Facilities

Competitive Facility Location Problem with Demands Depending on the Facilities Aa Pacc Maageme Revew 4) 009) 5-5 Compeve Facl Locao Problem wh Demad Depedg o he Facle Shogo Shode a* Kuag-Yh Yeh b Hao-Chg Ha c a Facul of Bue Admrao Kobe Gau Uver Japa bc Urba Plag Deparme Naoal Cheg

More information

14.02 Principles of Macroeconomics Fall 2005

14.02 Principles of Macroeconomics Fall 2005 14.02 Priciples of Macroecoomics Fall 2005 Quiz 2 Tuesday, November 8, 2005 7:30 PM 9 PM Please, aswer he followig quesios. Wrie your aswers direcly o he quiz. You ca achieve a oal of 100 pois. There are

More information

SUMMATION OF INFINITE SERIES REVISITED

SUMMATION OF INFINITE SERIES REVISITED SUMMATION OF INFINITE SERIES REVISITED I several aricles over he las decade o his web page we have show how o sum cerai iiie series icludig he geomeric series. We wa here o eed his discussio o he geeral

More information

SYRIAN SEISMIC CODE :

SYRIAN SEISMIC CODE : SYRIAN SEISMIC CODE 2004 : Two sac mehods have bee ssued Syra buldg code 2004 o calculae he laeral sesmc forces he buldg. The Frs Sac Mehod: I s he same mehod he prevous code (995) wh few modfcaos. I s

More information

Actuarial Society of India

Actuarial Society of India Acuarial Sociey of Idia EXAMINAIONS Jue 5 C4 (3) Models oal Marks - 5 Idicaive Soluio Q. (i) a) Le U deoe he process described by 3 ad V deoe he process described by 4. he 5 e 5 PU [ ] PV [ ] ( e ).538!

More information

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables Joural of Sceces Islamc epublc of Ira 6(: 63-67 (005 Uvers of ehra ISSN 06-04 hp://scecesuacr Some Probabl Iequales for Quadrac Forms of Negavel Depede Subgaussa adom Varables M Am A ozorga ad H Zare 3

More information

VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS. Hunan , China,

VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS. Hunan , China, Mahemacal ad Compuaoal Applcaos Vol. 5 No. 5 pp. 834-839. Assocao for Scefc Research VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS Hoglag Lu Aguo Xao Yogxag Zhao School of Mahemacs

More information

Density estimation III.

Density estimation III. Lecure 4 esy esmao III. Mlos Hauskrec mlos@cs..edu 539 Seo Square Oule Oule: esy esmao: Mamum lkelood ML Bayesa arameer esmaes MP Beroull dsrbuo. Bomal dsrbuo Mulomal dsrbuo Normal dsrbuo Eoeal famly Eoeal

More information

SPECIAL CONSIDERATIONS FOR VOLUMETRIC Z-TEST FOR PROPORTIONS

SPECIAL CONSIDERATIONS FOR VOLUMETRIC Z-TEST FOR PROPORTIONS SPECIAL CONSIDERAIONS FOR VOLUMERIC Z-ES FOR PROPORIONS Oe s stctve reacto to the questo of whether two percetages are sgfcatly dfferet from each other s to treat them as f they were proportos whch the

More information

As evident from the full-sample-model, we continue to assume that individual errors are identically and

As evident from the full-sample-model, we continue to assume that individual errors are identically and Maxmum Lkelhood smao Greee Ch.4; App. R scrp modsa, modsb If we feel safe makg assumpos o he sascal dsrbuo of he error erm, Maxmum Lkelhood smao (ML) s a aracve alerave o Leas Squares for lear regresso

More information

The Signal, Variable System, and Transformation: A Personal Perspective

The Signal, Variable System, and Transformation: A Personal Perspective The Sgal Varable Syem ad Traformao: A Peroal Perpecve Sherv Erfa 35 Eex Hall Faculy of Egeerg Oule Of he Talk Iroduco Mahemacal Repreeao of yem Operaor Calculu Traformao Obervao O Laplace Traform SSB A

More information

4. Runge-Kutta Formula For Differential Equations

4. Runge-Kutta Formula For Differential Equations NCTU Deprme o Elecrcl d Compuer Egeerg 5 Sprg Course by Pro. Yo-Pg Ce. Ruge-Ku Formul For Derel Equos To solve e derel equos umerclly e mos useul ormul s clled Ruge-Ku ormul

More information

Final Exam Applied Econometrics

Final Exam Applied Econometrics Fal Eam Appled Ecoomercs. 0 Sppose we have he followg regresso resl: Depede Varable: SAT Sample: 437 Iclded observaos: 437 Whe heeroskedasc-cosse sadard errors & covarace Varable Coeffce Sd. Error -Sasc

More information

Stat 6863-Handout 5 Fundamentals of Interest July 2010, Maurice A. Geraghty

Stat 6863-Handout 5 Fundamentals of Interest July 2010, Maurice A. Geraghty S 6863-Hou 5 Fuels of Ieres July 00, Murce A. Gerghy The pror hous resse beef cl occurreces, ous, ol cls e-ulero s ro rbles. The fl copoe of he curl oel oles he ecooc ssupos such s re of reur o sses flo.

More information

The MacWilliams Identity of the Linear Codes over the Ring F p +uf p +vf p +uvf p

The MacWilliams Identity of the Linear Codes over the Ring F p +uf p +vf p +uvf p Reearch Joural of Aled Scece Eeer ad Techoloy (6): 28-282 22 ISSN: 2-6 Maxwell Scefc Orazao 22 Submed: March 26 22 Acceed: Arl 22 Publhed: Auu 5 22 The MacWllam Idey of he Lear ode over he R F +uf +vf

More information

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several

More information

A Second Kind Chebyshev Polynomial Approach for the Wave Equation Subject to an Integral Conservation Condition

A Second Kind Chebyshev Polynomial Approach for the Wave Equation Subject to an Integral Conservation Condition SSN 76-7659 Eglad K Joural of forao ad Copug Scece Vol 7 No 3 pp 63-7 A Secod Kd Chebyshev olyoal Approach for he Wave Equao Subec o a egral Coservao Codo Soayeh Nea ad Yadollah rdokha Depare of aheacs

More information

Stabilization of LTI Switched Systems with Input Time Delay. Engineering Letters, 14:2, EL_14_2_14 (Advance online publication: 16 May 2007) Lin Lin

Stabilization of LTI Switched Systems with Input Time Delay. Engineering Letters, 14:2, EL_14_2_14 (Advance online publication: 16 May 2007) Lin Lin Egeerg Leers, 4:2, EL_4_2_4 (Advace ole publcao: 6 May 27) Sablzao of LTI Swched Sysems wh Ipu Tme Delay L L Absrac Ths paper deals wh sablzao of LTI swched sysems wh pu me delay. A descrpo of sysems sablzao

More information

The Properties of Probability of Normal Chain

The Properties of Probability of Normal Chain I. J. Coep. Mah. Sceces Vol. 8 23 o. 9 433-439 HIKARI Ld www.-hkar.co The Properes of Proaly of Noral Cha L Che School of Maheacs ad Sascs Zheghou Noral Uversy Zheghou Cy Hea Provce 4544 Cha cluu6697@sa.co

More information

OLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17

OLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17 OLS bias for ecoomeric models wih errors-i-variables. The Lucas-criique Supplemeary oe o Lecure 7 RNy May 6, 03 Properies of OLS i RE models I Lecure 7 we discussed he followig example of a raioal expecaios

More information

4. Runge-Kutta Formula For Differential Equations. A. Euler Formula B. Runge-Kutta Formula C. An Example for Fourth-Order Runge-Kutta Formula

4. Runge-Kutta Formula For Differential Equations. A. Euler Formula B. Runge-Kutta Formula C. An Example for Fourth-Order Runge-Kutta Formula NCTU Deprme o Elecrcl d Compuer Egeerg Seor Course By Pro. Yo-Pg Ce. Ruge-Ku Formul For Derel Equos A. Euler Formul B. Ruge-Ku Formul C. A Emple or Four-Order Ruge-Ku Formul

More information

On Metric Dimension of Two Constructed Families from Antiprism Graph

On Metric Dimension of Two Constructed Families from Antiprism Graph Mah S Le 2, No, -7 203) Mahemaal Sees Leers A Ieraoal Joural @ 203 NSP Naural Sees Publhg Cor O Mer Dmeso of Two Cosrued Famles from Aprm Graph M Al,2, G Al,2 ad M T Rahm 2 Cere for Mahemaal Imagg Tehques

More information

Bianchi Type II Stiff Fluid Tilted Cosmological Model in General Relativity

Bianchi Type II Stiff Fluid Tilted Cosmological Model in General Relativity Ieraoal Joural of Mahemacs esearch. IN 0976-50 Volume 6, Number (0), pp. 6-7 Ieraoal esearch Publcao House hp://www.rphouse.com Bach ype II ff Flud led Cosmologcal Model Geeral elay B. L. Meea Deparme

More information

Lecture 3. Diffusion. Reading: Chapter 3

Lecture 3. Diffusion. Reading: Chapter 3 Lecure 3 ffuso Readg: haper 3 EE 6450 - r. Ala oolle Impury ffuso: Pfa paeed he dea of usg dffusos S ad Ge 195. ffusos are mos commoly used for: Whe o use ad whe o o use : ffusos sources clude: 1.) Bases,

More information