e-companion ONLY AVAILABLE IN ELECTRONIC FORM
|
|
- Lorin Goodman
- 6 years ago
- Views:
Transcription
1 OPERATIONS RESEARCH doi 0.87/opre ec e-copaio ONLY AVAILABLE IN ELECTRONIC FORM ifors 009 INFORMS Electroic Copaio Equilibriu Capacity Expasio Uder Stochastic Dead Growth by Alfredo Garcia ad Zhijiag She, Operatios Research, doi 0.87/opre
2 ec e-copaio to Author: Alfredo Garcia ad Zhijiag She Proofs of Stateets EC.. Proof of Theore. (cotiued) EC... Regio S I the paper, we have checked that there is o (strictly) profitable deviatio fro y whe κ > ρ(+g) θg i regio S. The proof follows by checkig this coditio i all reaiig regios. EC... Regio S If the iitial relative capacities (k, 0 k) 0 are i this regio, there will be o ivestet accordig to strategy y. If fir deviates by choosig a target of capacity k > δk, 0 the the derivative of fir s objective fuctio is: D(k ) = κ + βθ(), δk 0 ) + β( θ) (k, δk 0 ) (EC.) If (k 0, k 0 ) {k 0 k δ < k0 i (+g), i =, }, the assuptio δ < guaratees the relative capacities 3δ +g after depreciatio will be regio S. They are either (k, δk 0 ) if o growth o the dead side or, δk0 ) if dead grows. The forer capacity pair is i regio IV ad the latter oe is i regio I. Thus we have: (k, δk k ) 0 = δκ, δk 0 ) = R, δk 0 ) + δ κ Substitutig i EC. we obtai the followig expressio for the derivative: D(k ) = ( βδ)κ + βθ() R, δk 0 ) Sice R k < 0, R < 0 i regio I ad δk 0 k > k, k > δk 0 > k,so it is true that Thus, for all (k 0, k 0 ) {k 0 k δ D(k ) < ( βδ)κ + βθ() R ( k, k ) = 0 < k0 i (+g), i =, } fir ca ot be better off by ivestig. 3δ
3 e-copaio to Author: Alfredo Garcia ad Zhijiag She ec3 If (k, 0 k) 0 {k 0 ki 0 > (+g), i =, }, the relative capacities after depreciatio will reai i 3δ regio IV o atter whether dead growth or ot. We kow the short ter equilibriu reveue R is a costat i regio IV, so fir has o icetive to ivest i this case. Therefore, it is ot profitable for fir to ivest i regio S. EC..3. Regio S 3 If the iitial relative capacities (k, 0 k) 0 are i this regio, strategy y prescribes o ivestet for fir. We defie fir s ivestet target as ϕ(k) 0 which is solely decided by the relative capacity level of fir. If fir ivests to reach a relative capacity level k, we have: The derivative of the objective is: (k, δk k ) 0 = R (k, δk k ) 0 + δκ, δk 0 ) = R, δk 0 ) + δ κ D(k ) = ( βδ)κ + βθ() R, δk 0 ) + β( θ) R (k, δk 0 ) (EC.) We ow show that it is ever optial for fir to choose a capacity level i regio I uder our assuptio > ρ(+g). The derivative for this case is: κ θg D(k ) = ( βδ)κ + β βδk0 β ( θg) ( θg)k which is decreasig i k. Whe k reaches the right boudary of regio I, that is k = δk 0, D(k ) is iiized: ( θg) D(k ) > ( βδ)κ β + Sice δk 0 > k for ay iitial k 0 capacity i regio S 3, we have: ( θg) D(k ) > ( βδ)κ β + 3( θg) βδk 0 So it is ever optial for fir to choose a capacity level i regio I. 3( θg) βk > 0 Based o the relative capacities (k, δk) 0 ad, δk0 ), EC. ay have differet expressios for a give k 0.
4 ec4 e-copaio to Author: Alfredo Garcia ad Zhijiag She Case : If (k, δk 0 ) IV ad +g, δk0 +g ) I: D (k ) = ( βδ)κ + βθ[ (k + δk 0 )] Case : If (k, δk 0 ) II ad +g, δk0 +g ) I: D (k ) = ( βδ)κ + βθ[ (k + δk 0 )] + β( θ) ( k ) Notice that D (k ) > D (k ). That eas for the sae δk 0, if the locally optial poit appears i Regio IV (D (k ) = 0) the D (k ) ust be positive. It is optial for fir to ivest to ake the capacity pair (k, δk 0 ) eter regio IV. Case 3: If (k, δk) 0 II ad, δk0 ) II:, the derivative of the objective is: D 3 (k ) = ( βδ)κ + θ β β( + θ)k Case 4: Ad if (k, δk 0 ) IV ad +g, δk0 +g ) II: D 4 (k ) = θ β ( βδ)κ β( + θ)k Sice there will be o ivestet i regio S, fir s ivestet level is bouded by (+g) 3. For a fixed δk 0, if there is oly oe locally optial k (that is D(k ) = 0), this k ust be the optial target of capacity ivestet for fir ; If for exaple, D 3 (k ) > 0 ad D 4 (k ) < 0, the optial level of ivestet for fir will be lie k = ; If there are ore tha oe locally optial level 3 of k, we ust copare the value of objectives correspodig to each targets levels i order to idetify the real optial level of ivestet level of capacity for fir. EC..4. Regio S 4 If the iitial relative capacities (k, 0 k) 0 are i this regio, fir will ivest to ake its relative capacity i the ext period equal to ϕ(k). 0 We are goig to check if it is profitable for fir to deviate by akig a little ivestet i this regio. Fir will ivest oly if the relative capacities after ivestet, ϕ(k0 ) ) are i regio I, otherwise fir s oe period reveue is oly decided
5 e-copaio to Author: Alfredo Garcia ad Zhijiag She ec5 by fir s relative capacity. (accordig to the clai i the previous part, (k, ϕ(k 0 )) ca ot be i regio I). We have: The derivative of the objective is: (k, ϕ(k k )) 0 = δκ, ϕ(k0 ) ) = R, ϕ(k0 ) ) + δ κ D(k ) = ( βδ)κ + βθ() R, ϕ(k0 ) ) + β( θ) R (k, ϕ(k 0 )) (EC.3) Case : If the relative capacities (k, ϕ(k 0 )) IV ad, ϕ(k0 ) ) I: we kow that for fir Thus, D (ϕ(k 0 )) = ( βδ)κ + βθ[ (δk0 + ϕ(k 0 ))] = 0 ϕ(k 0 ) = () [ ( βδ)κ ] δk0 βθ ad if fir ivests to reach k > δk 0, the D(k ) = ( βδ)κ + βθ[ (k + ϕ(k 0 ))] = [ ( βδ)κ + βθ( 4k δk 0 )] < [ ( βδ)κ + βθ( 3k )] < 3k [ ( βδ)κ + βθ( )] = 0 Hece, it is ot profitable for fir to ivest i this case. Case : If (k, δk) 0 III ad, δk0 ) I: we kow fro the previous part that the ivest- et target for fir is: D (ϕ(k 0 )) = ( βδ)κ + βθ[ (ϕ(k0 ) + δk 0 )] + β( θ) ( ϕ(k0 )) = 0 Thus, ϕ(k 0 ) = ( + ( θ)(+g) θ ) ( [ ) β( + θ) ( βδ)κ]() δk 0 βθ
6 ec6 e-copaio to Author: Alfredo Garcia ad Zhijiag She ad k + ϕ(k 0 ) > δk 0 + ϕ(k 0 ) = [ β( + θ) ( βδ)κ]() [ + βθ > [ β( + θ) ( βδ)κ]() βθ > 3k ( θ)() ]ϕ(k 0 θ ) Hece, D(k ) = ( βδ)κ + βθ[ (k + ϕ(k 0 ))] < 3k [ ( βδ)κ + βθ( )] = 0 ad it is ot profitable for fir to ivest i this case. EC.. Proof of Theore, whe ρ < κ ρ(+g) θg Siilar with the proof i the previous part, based o the iitial relative capacity prior depreciatio, we partitio the etire relative capacity space R + ito 4 regios slightly differet with before as figure EC. idicates: k S 3 S ( + g) 3 S S 4 * k 3δ ( + g) 3δ k Figure EC. The partitio of relative capacity space whe ρ < κ ρ(+g) θg.
7 e-copaio to Author: Alfredo Garcia ad Zhijiag She ec7 S = {k 0 ki 0, i =, } 3δ S = {k 0 ki 0 > (+g), i =, } 3δ S 3 = {k 0 k 0 < k}\(s 0 S ) S 4 = {k 0 k 0 > k}\(s 0 S ) We propose a ivestet strategy cobiatio ŷ whe ρ < ρ(+g), forally described as κ θg follows where ax{k δk ; 0} k i S ŷ (k) = ax{ϕ(k) δk ; 0} k i S 3 0 otherwise k = [β ( βδ)κ]() 3β( θg) ad ϕ(k) is a fuctio to be defied later. Because of syetry, it is eough to check the optiality of strategy ŷ whe fir follows strategy ŷ. We defie BR (k ) = k, BR (k ) = (+g) k whe k k ad defie BR (k ), BR (k ) accordigly whe k > k. EC... Regio S Suppose the iitial relative capacities are such that δk 0 i k ad fir s capacity at the begiig of the ext period after ivestet is k = δk 0 + y. Accordig to strategy ŷ, fir s decisio is to achieve a relative capacity level of k. So fir s objective fuctio is ax{r k (k, 0 k) 0 (k δk)κ 0 + β[θ()v, k ) + ( θ)v (k, k )]} The derivative D(k ) of the objective fuctio above with respect to k is: D(k ) = κ + βθ(), k ) + β( θ) (k, k ) (EC.4) If fir chooses a target capacity ivestet level k k, v (k, k ) = R (k, k ) (k δk )κ + C(k ) v, k ) = R, k ) (k δk )κ + C(k ) where C(k ) = β[θ()v ( k +g, k +g ) + ( θ)v (k, k )] is a costat oly related to the capacity level i equilibriu k. Thus, (k, k ) = R (k, k ) + δκ
8 ec8 e-copaio to Author: Alfredo Garcia ad Zhijiag She, k ) = R, k ) + δ κ Substitutig i EC.4 we obtai the followig expressio for the derivative: Note that D(k ) = ( βδ)κ + βθ() R, k ) + β( θ) R (k, k ) = ( βδ)κ + βθ[ ( k + k )] + β( θ)[ (k + k )] D(k ) = ( βδ)κ + βθ() R, k ) + β( θ) R (k, k ) k =k k = ( βδ)κ + βθ( 3k ) + β( θ)( 3k ) = β ( βδ)κ 3βk ( θg ) = 0 k [β ( βδ)κ]() = 3β( θg) k is i regio I oly if ρ < ρ(+g), where ρ = βδ. κ θg β The differece betwee D(k ) ad D(k ) is D(k ) D(k ) = βσ( θg )(k k ) We have that D(k ) > D(k ) = 0 whe k < k ad D(k ) < D(k ) = 0 for all k < k k δ. If the iitial relative capacities satisfy k 0 i S {k 0 i k < δk 0 k () ad k 0 k 0 }, ad fir chooses a target capacity ivestet level k, fir will ot ivest accordig to strategy ŷ. We have k =k v (k, δk 0 ) = R (k, δk 0 ) (k δk )κ + C(k ) v, δk 0 ) = R, δk 0 ) (k δk )κ + C(k ) Siilar to EC.4, the first order coditio of fir s objective is: D(k ) = κ + βθ()[ R, δk 0 ) + δκ ] + β( θ)[ R (k, δk 0 ) + δκ] = ( βδ)κ + βθ() R, δk 0 ) + β( θ) R (k, δk 0 )
9 e-copaio to Author: Alfredo Garcia ad Zhijiag She ec9 = ( βδ)κ + βθ[ ( k + δk0 )] + β( θ)[ (k + δk 0 )] = 0 We get the ivestet target for fir i this case k (k 0 ) = [β ( βδ)κ]() β( θg) δk0 = 3k δk0 If the iitial relative capacities satisfy k 0 i S {k 0 i k < δk 0 k () ad k 0 < k 0 }, ad fir chooses a target capacity ivestet level k slightly greater tha δk 0, we have v (k, k (k 0 )) = R (k, k (k 0 )) (k δk )κ + C(k ) v, k (k 0 ) ) = R, k (k 0 ) ) (k δk )κ + C(k ) The derivative of fir s objective is: D(k ) = κ + βθ()[ R, k (k 0 ) ) + δκ ] + β( θ)[ R (k, k (k 0 )) + δκ] Sice k (k 0 ) = 3k = ( βδ)κ + βθ[ σ( k + k (k 0 ) )] + β( θ)[ σ(k + k (k 0 ))] δk0, the derivative becoes D(k ) = ( βδ)κ + βθ[ σ( k + k (k 0 ) )] + β( θ)[ σ(k + k (k 0 ))] = β ( βδ)κ βσ( θg )(k + 3k δk0 ) < β ( βδ)κ βσ( θg )( 3δk0 + 3k ) < β ( βδ)κ 3βσk ( θg ) = 0 So it is ot profitable for fir to ivest i this case. The siilar aalysis ca be carried out for iitial relative capacities i regio k 0 i S {k 0 i k (+ g) t < δk 0 k () t+ ad k 0 k 0 } ad k 0 i S {k 0 i k () t < δk 0 k () t+ ad k 0 < k 0 } where t =,, 3,... Therefore, we coclude that for all the iitial relative capacities (k 0, k 0 ) i regio S, k (k 0 ) ad k (k 0 ) describes the optial ivestet target for each fir. If both k 0 ad k 0 are less tha k /δ,
10 ec0 e-copaio to Author: Alfredo Garcia ad Zhijiag She they both ivest to reach k at the begiig of ext period; if both k 0 ad k 0 are greater tha the correspodig level described by k (k) 0 ad k (k), 0 there will be o ivestet at all; for the other cases, oly the fir with less relative capacity will ivest to a level of capacity decided by the iitial capacity of the oppoet fir. EC... Regio S If (k, 0 k) 0 {k 0 ki 0 > (+g), i =, }, the relative capacities after depreciatio will reai at regio 3δ IV o atter whether dead growth or ot. We kow the short ter equilibriu reveue R is a costat i regio IV, so o fir has icetive to ivest for this case. EC..3. Regio S 3 Sice we already kow the optial ivestet strategy ŷ i regio S, we ca idetifiy the states trajectories startig fro ay iitial relative capacities i regio S. Therefore, the expected value v (k) i regio S is kow. We divide regio S 3 ito a series of horizotal stripes I t = {k 0 i k 0 i S 3 ad 3 ()t < δk 0 ( + 3 g)t+ } where t = 0,,,... If (k, 0 k) 0 I 0, we defie fir s ivestet target as ϕ(k) 0 which is solely decided by the iitial relative capacity level of fir. If fir ivests to reach a relative capacity level k, the derivative of the objective is: D(k ) = κ + βθ(), δk 0 ) + β( θ) v (k, δk 0 ) (EC.5) Sice there will be o ivestet i regio S, fir s ivestet level is bouded by (+g) 3. For a fixed δk 0, if there is oly oe locally optial k (that is D(k ) = 0), this k ust be the optial target of capacity ivestet for fir ; If for exaple, D(k ) > 0 whe k < 3σ ad D(k ) < 0 whe k >, the optial level of ivestet for fir will be lie k 3σ = ; If there are ore tha 3σ oe locally optial level of k, we ust copare the value of objectives correspodig to each targets levels i order to idetify the real optial level of ivestet level of capacity for fir. After we get fir s ivestet target as ϕ(k 0 ) i regio I 0, we are able to calculate the expected value fuctio of fir v (k) i this stripe. The we ca idetify fir s ivestet target whe
11 e-copaio to Author: Alfredo Garcia ad Zhijiag She ec (k, 0 k) 0 I. Fir s optial ivestet target ϕ(k) 0 for those stripes with low idex ubers I 0, I,... ay ot be cotiuous alog k, 0 the reaso for that is because the short ter reveue is oly piecewise cotious i the capacity space. Let s exaie ϕ(k) 0 for I t with relatively large idex uber. If fir ivests to reach a relative capacity level k, we have: The derivative of the objective is: (k, δk k ) 0 = R (k, δk k ) 0 + δκ, δk 0 ) = R, δk 0 ) + δ κ D(k ) = ( βδ)κ + βθ() R, δk 0 ) + β( θ) R (k, δk 0 ) Case : If (k, δk) 0 II ad, δk0 ) II:, the derivative of the objective is: Case : If (k, δk 0 ) IV ad +g, δk0 +g ) II: D (k ) = θ β ( βδ)κ β( + θ)k D (k ) = θ β ( βδ)κ β( )k Followig the guidlies we just etioed above, if there is oly oe local axiu, this is the optial target ivestet level; if D (k ) > 0 whe k < 3 ad D (k ) > 0 whe k > 3, ϕ(k0 ) = (+g) ; if D 3 (k ) > 0 whe k < ad D 3 (k ) < 0 whe k >, 3 ϕ(k0 ) = ; ad if there are two 3 local axia levels, we eed to copare the expected value fuctio explicitly. EC..4. Regio S 4 If the iitial relative capacities (k, 0 k) 0 are i this regio, fir will ivest to ake its relative capacity i the ext period equal to ϕ(k). 0 We are goig to check if it is profitable for fir to deviate by akig a little ivestet i this regio. Fir will ivest oly if the relative capacities after depreciatio ad after dead growth ( δk0, ϕ(k0 ) ) are i regio I, otherwise fir s oe period
12 ec e-copaio to Author: Alfredo Garcia ad Zhijiag She reveue is oly decided by fir s relative capacity. Siilar with the aalysis for regio S 3, we divide regio S 4 ito a series of vertical stripes H t = {k 0 i k 0 i S 4 ad 3 (+g)t < δk 0 3 (+g)t+ } where t = 0,,,... For the first stripe H 0, if (k 0, k 0 ) H 0 ad fir chooses a target level of ivestet k 3δ we have: v (k, ϕ(k 0 )) = R (k, ϕ(k 0 )) + β[θ()v, ϕ(k0 ) ) + ( θ)v (k, ϕ(k 0 ))] Thus, (k, ϕ(k 0 k )) = β( θ) [ R (k, ϕ(k k )) 0 + βθ(), ϕ(k0 ) )] The derivative of the objective is: D(k ) = κ + β( θ) [β( θ) R (k, ϕ(k k )) 0 + βθ(), ϕ(k0 ) )] (EC.6) If the relative capacities +g, ϕ(k0 ) +g ) are above the coectio of k (k 0 ) ad k (k 0 ), fir will ot ivest accordig to the optial strategy ŷ i regio S. So we have The derivative i EC.6 becoes, ϕ(k0 ) ) < κ D(k ) < κ + β( θ) [β( θ) R (k, ϕ(k 0 )) + βθκ] (i) If the relative capacities +g, ϕ(k0 ) +g ) are uder the coectio of k (k 0 ) ad k (k 0 ), fir will ivest to k (k 0 ) or k accordig to the optial strategy ŷ i regio S. So we have The derivative i EC.6 becoes, ϕ(k0 ) ) = R, ϕ(k0 ) ) + δ κ D(k ) = ( βδ)κ + βθ() R, ϕ(k0 ) ) + β( θ) R (k, ϕ(k 0 )) (ii) Case : If the relative capacities (k, ϕ(k 0 )) IV ad D(k ) < κ +, ϕ(k0 ) βθκ = ( β) < 0 β( θ) ) I: the derivative i (i) is
13 e-copaio to Author: Alfredo Garcia ad Zhijiag She ec3 Sice it is true for fir that ( βδ)κ + βθ[ (δk0 + ϕ(k 0 ))] = 0 Thus, ϕ(k 0 ) = () [ ( βδ)κ ] δk0 βθ ad the derivative i (ii) becoes D(k ) = ( βδ)κ + βθ[ (k + ϕ(k 0 ))] < [ ( βδ)κ + βθ( 3k )] < 3k [ ( βδ)κ + βθ( )] = 0 Hece, it is ot profitable for fir to ivest i this case. Case : If (k, δk) 0 III ad, δk0 ) I: the derivative i (i) is Sice it is ture for fir that: D(k ) < κ + βθκ = ( β) < 0 β( θ) ( βδ)κ + βθ[ (ϕ(k0 ) + δk 0 )] + β( θ) ( ϕ(k0 )) = 0 Thus, ad ϕ(k 0 ) = ( + ( θ)(+g) θ ) ( [ ) β( + θ) ( βδ)κ]() δk 0 βθ k + ϕ(k 0 ) > δk 0 + ϕ(k 0 ) = [ β( + θ) ( βδ)κ]() [ + βθ > [ β( + θ) ( βδ)κ]() βθ > 3k ( θ)() ]ϕ(k 0 θ ) Hece, the derivative i (ii) is D(k ) = ( βδ)κ + βθ[ (k + ϕ(k 0 ))]
14 ec4 e-copaio to Author: Alfredo Garcia ad Zhijiag She < 3k [ ( βδ)κ + βθ( )] = 0 ad it is ot profitable for fir to ivest i this case. S. Case 3: If (k, δk) 0 I ad, δk0 ) I: the aalysis is the sae as what we did i regio The siilar arguet holds for (k 0, k 0 ) H t, t =,,... I Suary, for ay iitial relative capacities (k 0, k 0 ), there exists a MPE strategy ŷ whe ρ < κ ρ(+g) θg so that ay deviatio fro which is ot profitable. EC.3. Uiqueess of y Cosider the class of ivestet strategies, say ỹ(k), that prescribe ivestets ỹ i (k) = k i δk i wheever δk i k i ad k k. Suppose the iitial capacity stock k 0 is such that δki 0 k i. Fir i s objective fuctio is ax{r i (k 0 ) (k i δk 0 k i i )κ + β[θ()v i ( k i, kj ỹ) + ( θ)v i(k i, k j ỹ)]} The (ecessary) first order coditio for equilibriu for i {, } is: ( βδ)κ = βθ() R i k i ( k i, kj ) + β( θ) R i ( k i, k k j ) i Without loss of geerality, it is eough to cosider the coditio where k i k j. The argial profits are as follows: Regio I: { R i k i ( k i, k j ) = ( k i + k j ) R i k j ( k i, k j ) = ( k i + k j ) Regio II: { R i k i ( k i, k j ) = k i R i k j ( k i, k j ) = 0 Regio IV : { R i k i ( k i, k j ) = 0 R i k j ( k i, k j ) = 0
15 e-copaio to Author: Alfredo Garcia ad Zhijiag She ec5 Note that best reply fuctios for fir i ad j are liear with differet slopes whe ( k i, k j ) ad ( k i +g, k j +g ) {I, IV }. So there ca oly exists a syetric equilibriu i these cases. If ( k i, k j ) II ad ( k i, k j ) I. The best reply of fir i is: k (i) j The best reply of fir j is: = βθ [ θ β( + θ) ( βδ)κ β( + θ) k i ] k (j) j The slope of fir i s best reply equals = ( ρκ θ ) k i ( θ ()( θ) + θ) = θ θ which is steeper tha fir j s best reply. We check for a itersectio of best reply aps whe ( k i, k j ) II ad ( k i +g, k j +g ) I. Whe k i = 3, k i locates at the right boudary of this regio, the correspodig k j fro fir i s best reply is k (i) j The correspodig k j fro fir j s best reply is Hece, the differece is: = ()ρκ ( + θ + 5θg) 6θ θ k (j) j = 6 ( + 3g) ()ρκ θ k (i) (j) j k j = ()ρκ ( θ + θg) 6θ θ Reeber that we require κ > ρ(+g) θg i Theore, we get κ < θg. Plug this relatio ito the ρ(+g) above expressio, this leads to k (i) (j) j k j > θg ( θ + θg) 6θ θ = ()( θ) 0 6θ Therefore, fir i s best reply is always above fir j s best reply, so these two best replies will ot itersect i this regio. If (( k i, k j ) II ad ( k i +g, k j +g ) II)or (( k i, k j ) IV ad ( k i +g, k j +g ) II), we have that the right side of first order coditio for equilibriu for fir j is always equal to 0. This eas fir j has o icetive to ivest to these regios.
16 ec6 e-copaio to Author: Alfredo Garcia ad Zhijiag She EC.4. Algebraic Derivatio i 5. H() + F (r θg) = θ()r ( k (), k EC.5. Etry Deterrece = θ[ k () ]k () + ( θ) ) + ( θ)r (k (), k ) ρκk () ( ) ρκk () + = θ[ + ( ρκ θ ) ρκ θ )]k () + ( θ) = θ ( ρκ ) ( ) k () + ( θ) + θ + ( ρκ ) ( ) θ = θ() + ( θ) + + ( ) + The paraeters i this uerical illustratio are κ =, β = 0.87, δ =, θ = 0.6, g = 0., F = ad let free. With this choice of paraeters, > 5, provided >.5. Assue icubet firs wat to deter the etry by aitaiig a level of capacity k > k (). Note that i order to deter etry, k does ot eed to be greater tha (+g). Figure EC. shows the state trajectory whe etry occurs. Etry is ot profitable if ad oly if: ax x { F κx + β[θ()v( x, ( ) () Etry occurs whe there is dead growth. Sice ( ) (+g) ( ) ) + ( θ)v(x, )]} 0 (EC.7) ( )k (), the firs will retur to syetric capacity levels equal to k () just oe period after the dead growth has bee ( ) k' * ( ) k ( ) ( ) K i * * ( k ( ), ( ) k ( )) x + K i Figure EC. The Capacity Trajectory Whe Etry Occurs.
17 e-copaio to Author: Alfredo Garcia ad Zhijiag She ec7 observed. Sice there are firs havig a capacity stock greater tha k (), the optial scale x for the etrat (i.e. the -th fir) ust be saller tha k (). We have v(x, ad ( ) ) = R(x, ( ) x ) + β[θ()v(, ( ) () ( ) ) + ( θ)v(x, )] (EC.8) x ( ) v(, () ) = R( x ( ), () ) (k () x )κ (EC.9) +β[θ()v( k (), ( )k () ) + ( θ)v(k (), ( )k ())] Fro EC.7-EC.9, we copute the derivative of the left side of EC.7: ( ) R(x, ) β( θ) x + βθ() R( x, ( ) ) +g (+g) ( β)κ 0 x (EC.0). Suppose (x, ( ) ) falls i regio II. I this case, the derivative becoes β( θ)( ( ) x+ x) + βθ[ ] ( β)κ +g = x This derivative equals to 0 whe x = So the optial level is x = i{ , 6 }. Suppose (x, ( ) ) falls i regio IV. I this case, the derivative becoes ( ) x+ βθ[ ] ( β)κ +g = x 0.3 This derivative is ootoe decreasig i x ad equals to 0 whe x = So the optial level of x is: x = ax{ , 6 } I suary, the optial level of x should be <.735 x =.735 < First, let s see if the etrat ca acquire positive profit whe x = The expected value of the etrat is: F κx + β[θ()v( x F κx + β( θ) β( θ), ( ) +g (+g) = R(x, ( ) ) + ( θ)v(x, ( ) )] ) + βθ(+g) β( θ) v( x +g, ( ) (+g) )
18 ec8 e-copaio to Author: Alfredo Garcia ad Zhijiag She Also, x ( ) v(, () ) = R( x ( ), () ) (k () x )κ + β β( + θg) [θ()r i(k ()) + ( θ)r i ( k () ( θ)g ) κk ()] =.0839( 0.935)( ) Therefore, the expected value of the etrat whe.4943 <.735 is: F κx + β( θ) β( θ) which is positive whe > R(x, ( ) ) + βθ(+g) (.0839( 0.935)( )) β( θ) =.0609(.5096)( +.35) I a aalogous way, we ca fid out the rage of which ight deter etry for the other two cases: whe x =,.the expected value of the etrat is:.0566(.5097)( +.64) which is 6 always positive sice.735 < whe x = , the expected value of the etrat is:.0596(.55)(+.37) which is positive sice > i this case. This result idicates that whe >.5096, it is always profitable for a ew fir to eter the arket. The icubets ca ever deter the occurrece of etry by aitaiig a higher level of capacity. Ackowledgets The authors gratefully ackowledge all the coets ad observatios received fro the referees which have iproved the paper sigificatly. Refereces See refereces list i the ai paper.
2.6 Rational Functions and Their Graphs
.6 Ratioal Fuctios ad Their Graphs Sectio.6 Notes Page Ratioal Fuctio: a fuctio with a variable i the deoiator. To fid the y-itercept for a ratioal fuctio, put i a zero for. To fid the -itercept for a
More informationAUTOMATIC GENERATION OF FUZZY PAYOFF MATRIX IN GAME THEORY
AUTOMATIC GENERATION OF FUZZY PAYOFF MATRIX IN GAME THEORY Dr. Farha I. D. Al Ai * ad Dr. Muhaed Alfarras ** * College of Egieerig ** College of Coputer Egieerig ad scieces Gulf Uiversity * Dr.farha@gulfuiversity.et;
More informationELEMENTARY PORTFOLIO MATHEMATICS
QRMC06 9/7/0 4:44 PM Page 03 CHAPTER SIX ELEMENTARY PORTFOLIO MATHEMATICS 6. AN INTRODUCTION TO PORTFOLIO ANALYSIS (Backgroud readig: sectios 5. ad 5.5) A ivestor s portfolio is the set of all her ivestets.
More information43. A 000 par value 5-year bod with 8.0% semiaual coupos was bought to yield 7.5% covertible semiaually. Determie the amout of premium amortized i the 6 th coupo paymet. (A).00 (B).08 (C).5 (D).5 (E).34
More informationMonopoly vs. Competition in Light of Extraction Norms. Abstract
Moopoly vs. Competitio i Light of Extractio Norms By Arkadi Koziashvili, Shmuel Nitza ad Yossef Tobol Abstract This ote demostrates that whether the market is competitive or moopolistic eed ot be the result
More information1 Random Variables and Key Statistics
Review of Statistics 1 Radom Variables ad Key Statistics Radom Variable: A radom variable is a variable that takes o differet umerical values from a sample space determied by chace (probability distributio,
More informationCHAPTER 8 Estimating with Confidence
CHAPTER 8 Estimatig with Cofidece 8.2 Estimatig a Populatio Proportio The Practice of Statistics, 5th Editio Stares, Tabor, Yates, Moore Bedford Freema Worth Publishers Estimatig a Populatio Proportio
More informationMath 312, Intro. to Real Analysis: Homework #4 Solutions
Math 3, Itro. to Real Aalysis: Homework #4 Solutios Stephe G. Simpso Moday, March, 009 The assigmet cosists of Exercises 0.6, 0.8, 0.0,.,.3,.6,.0,.,. i the Ross textbook. Each problem couts 0 poits. 0.6.
More informationWe learned: $100 cash today is preferred over $100 a year from now
Recap from Last Week Time Value of Moey We leared: $ cash today is preferred over $ a year from ow there is time value of moey i the form of willigess of baks, busiesses, ad people to pay iterest for its
More informationA random variable is a variable whose value is a numerical outcome of a random phenomenon.
The Practice of Statistics, d ed ates, Moore, ad Stares Itroductio We are ofte more iterested i the umber of times a give outcome ca occur tha i the possible outcomes themselves For example, if we toss
More information5. Best Unbiased Estimators
Best Ubiased Estimators http://www.math.uah.edu/stat/poit/ubiased.xhtml 1 of 7 7/16/2009 6:13 AM Virtual Laboratories > 7. Poit Estimatio > 1 2 3 4 5 6 5. Best Ubiased Estimators Basic Theory Cosider agai
More informationLecture 9: The law of large numbers and central limit theorem
Lecture 9: The law of large umbers ad cetral limit theorem Theorem.4 Let X,X 2,... be idepedet radom variables with fiite expectatios. (i) (The SLLN). If there is a costat p [,2] such that E X i p i i=
More informationEVEN NUMBERED EXERCISES IN CHAPTER 4
Joh Riley 7 July EVEN NUMBERED EXERCISES IN CHAPTER 4 SECTION 4 Exercise 4-: Cost Fuctio of a Cobb-Douglas firm What is the cost fuctio of a firm with a Cobb-Douglas productio fuctio? Rather tha miimie
More informationMATH : EXAM 2 REVIEW. A = P 1 + AP R ) ny
MATH 1030-008: EXAM 2 REVIEW Origially, I was havig you all memorize the basic compoud iterest formula. I ow wat you to memorize the geeral compoud iterest formula. This formula, whe = 1, is the same as
More informationForecasting bad debt losses using clustering algorithms and Markov chains
Forecastig bad debt losses usig clusterig algorithms ad Markov chais Robert J. Till Experia Ltd Lambert House Talbot Street Nottigham NG1 5HF {Robert.Till@uk.experia.com} Abstract Beig able to make accurate
More informationCAPITAL ASSET PRICING MODEL
CAPITAL ASSET PRICING MODEL RETURN. Retur i respect of a observatio is give by the followig formula R = (P P 0 ) + D P 0 Where R = Retur from the ivestmet durig this period P 0 = Curret market price P
More informationFINM6900 Finance Theory How Is Asymmetric Information Reflected in Asset Prices?
FINM6900 Fiace Theory How Is Asymmetric Iformatio Reflected i Asset Prices? February 3, 2012 Referece S. Grossma, O the Efficiecy of Competitive Stock Markets where Traders Have Diverse iformatio, Joural
More informationSELECTING THE NUMBER OF CHANGE-POINTS IN SEGMENTED LINE REGRESSION
1 SELECTING THE NUMBER OF CHANGE-POINTS IN SEGMENTED LINE REGRESSION Hyue-Ju Kim 1,, Bibig Yu 2, ad Eric J. Feuer 3 1 Syracuse Uiversity, 2 Natioal Istitute of Agig, ad 3 Natioal Cacer Istitute Supplemetary
More informationDr. Maddah ENMG 624 Financial Eng g I 03/22/06. Chapter 6 Mean-Variance Portfolio Theory
Dr Maddah ENMG 64 Fiacial Eg g I 03//06 Chapter 6 Mea-Variace Portfolio Theory Sigle Period Ivestmets Typically, i a ivestmet the iitial outlay of capital is kow but the retur is ucertai A sigle-period
More informationCHAPTER 2 PRICING OF BONDS
CHAPTER 2 PRICING OF BONDS CHAPTER SUARY This chapter will focus o the time value of moey ad how to calculate the price of a bod. Whe pricig a bod it is ecessary to estimate the expected cash flows ad
More informationRafa l Kulik and Marc Raimondo. University of Ottawa and University of Sydney. Supplementary material
Statistica Siica 009: Supplemet 1 L p -WAVELET REGRESSION WITH CORRELATED ERRORS AND INVERSE PROBLEMS Rafa l Kulik ad Marc Raimodo Uiversity of Ottawa ad Uiversity of Sydey Supplemetary material This ote
More information14.30 Introduction to Statistical Methods in Economics Spring 2009
MIT OpeCourseWare http://ocwmitedu 430 Itroductio to Statistical Methods i Ecoomics Sprig 009 For iformatio about citig these materials or our Terms of Use, visit: http://ocwmitedu/terms 430 Itroductio
More informationSupersedes: 1.3 This procedure assumes that the minimal conditions for applying ISO 3301:1975 have been met, but additional criteria can be used.
Procedures Category: STATISTICAL METHODS Procedure: P-S-01 Page: 1 of 9 Paired Differece Experiet Procedure 1.0 Purpose 1.1 The purpose of this procedure is to provide istructios that ay be used for perforig
More informationMaximum Empirical Likelihood Estimation (MELE)
Maximum Empirical Likelihood Estimatio (MELE Natha Smooha Abstract Estimatio of Stadard Liear Model - Maximum Empirical Likelihood Estimator: Combiatio of the idea of imum likelihood method of momets,
More informationModels of Asset Pricing
4 Appedix 1 to Chapter Models of Asset Pricig I this appedix, we first examie why diversificatio, the holdig of may risky assets i a portfolio, reduces the overall risk a ivestor faces. The we will see
More informationCombining imperfect data, and an introduction to data assimilation Ross Bannister, NCEO, September 2010
Combiig imperfect data, ad a itroductio to data assimilatio Ross Baister, NCEO, September 00 rbaister@readigacuk The probability desity fuctio (PDF prob that x lies betwee x ad x + dx p (x restrictio o
More informationStatistics for Economics & Business
Statistics for Ecoomics & Busiess Cofidece Iterval Estimatio Learig Objectives I this chapter, you lear: To costruct ad iterpret cofidece iterval estimates for the mea ad the proportio How to determie
More informationNotes on Expected Revenue from Auctions
Notes o Epected Reveue from Auctios Professor Bergstrom These otes spell out some of the mathematical details about first ad secod price sealed bid auctios that were discussed i Thursday s lecture You
More informationEstimating Volatilities and Correlations. Following Options, Futures, and Other Derivatives, 5th edition by John C. Hull. Chapter 17. m 2 2.
Estiatig Volatilities ad Correlatios Followig Optios, Futures, ad Other Derivatives, 5th editio by Joh C. Hull Chapter 17 Stadard Approach to Estiatig Volatility Defie as the volatility per day betwee
More informationINTERVAL GAMES. and player 2 selects 1, then player 2 would give player 1 a payoff of, 1) = 0.
INTERVAL GAMES ANTHONY MENDES Let I ad I 2 be itervals of real umbers. A iterval game is played i this way: player secretly selects x I ad player 2 secretly ad idepedetly selects y I 2. After x ad y are
More informationModels of Asset Pricing
APPENDIX 1 TO CHAPTER 4 Models of Asset Pricig I this appedix, we first examie why diversificatio, the holdig of may risky assets i a portfolio, reduces the overall risk a ivestor faces. The we will see
More informationModels of Asset Pricing
APPENDIX 1 TO CHAPTER4 Models of Asset Pricig I this appedix, we first examie why diversificatio, the holdig of may risky assets i a portfolio, reduces the overall risk a ivestor faces. The we will see
More information1 Estimating sensitivities
Copyright c 27 by Karl Sigma 1 Estimatig sesitivities Whe estimatig the Greeks, such as the, the geeral problem ivolves a radom variable Y = Y (α) (such as a discouted payoff) that depeds o a parameter
More informationAn Introduction to Certificates of Deposit, Bonds, Yield to Maturity, Accrued Interest, and Duration
1 A Itroductio to Certificates of Deposit, Bods, Yield to Maturity, Accrued Iterest, ad Duratio Joh A. Guber Departet of Electrical ad Coputer Egieerig Uiversity of Wiscosi Madiso Abstract A brief itroductio
More informationCombinatorial Proofs of Fibonomial Identities
Clareot Colleges Scholarship @ Clareot All HMC aculty Publicatios ad Research HMC aculty Scholarship 12-1-2014 Cobiatorial Proofs of ibooial Idetities Arthur Bejai Harvey Mudd College Elizabeth Reilad
More informationSequences and Series
Sequeces ad Series Matt Rosezweig Cotets Sequeces ad Series. Sequeces.................................................. Series....................................................3 Rudi Chapter 3 Exercises........................................
More information11.7 (TAYLOR SERIES) NAME: SOLUTIONS 31 July 2018
.7 (TAYLOR SERIES NAME: SOLUTIONS 3 July 08 TAYLOR SERIES ( The power series T(x f ( (c (x c is called the Taylor Series for f(x cetered at x c. If c 0, this is called a Maclauri series. ( The N-th partial
More informationInferential Statistics and Probability a Holistic Approach. Inference Process. Inference Process. Chapter 8 Slides. Maurice Geraghty,
Iferetial Statistics ad Probability a Holistic Approach Chapter 8 Poit Estimatio ad Cofidece Itervals This Course Material by Maurice Geraghty is licesed uder a Creative Commos Attributio-ShareAlike 4.0
More informationOptimizing of the Investment Structure of the Telecommunication Sector Company
Iteratioal Joural of Ecoomics ad Busiess Admiistratio Vol. 1, No. 2, 2015, pp. 59-70 http://www.aisciece.org/joural/ijeba Optimizig of the Ivestmet Structure of the Telecommuicatio Sector Compay P. N.
More informationSUPPLEMENTAL MATERIAL
A SULEMENTAL MATERIAL Theorem (Expert pseudo-regret upper boud. Let us cosider a istace of the I-SG problem ad apply the FL algorithm, where each possible profile A is a expert ad receives, at roud, a
More informationA New Constructive Proof of Graham's Theorem and More New Classes of Functionally Complete Functions
A New Costructive Proof of Graham's Theorem ad More New Classes of Fuctioally Complete Fuctios Azhou Yag, Ph.D. Zhu-qi Lu, Ph.D. Abstract A -valued two-variable truth fuctio is called fuctioally complete,
More informationSampling Distributions and Estimation
Cotets 40 Samplig Distributios ad Estimatio 40.1 Samplig Distributios 40. Iterval Estimatio for the Variace 13 Learig outcomes You will lear about the distributios which are created whe a populatio is
More informationOnline appendices from Counterparty Risk and Credit Value Adjustment a continuing challenge for global financial markets by Jon Gregory
Olie appedices from Couterparty Risk ad Credit Value Adjustmet a APPENDIX 8A: Formulas for EE, PFE ad EPE for a ormal distributio Cosider a ormal distributio with mea (expected future value) ad stadard
More informationIntroduction to Statistical Inference
Itroductio to Statistical Iferece Fial Review CH1: Picturig Distributios With Graphs 1. Types of Variable -Categorical -Quatitative 2. Represetatios of Distributios (a) Categorical -Pie Chart -Bar Graph
More informationEXERCISE - BINOMIAL THEOREM
BINOMIAL THOEREM / EXERCISE - BINOMIAL THEOREM LEVEL I SUBJECTIVE QUESTIONS. Expad the followig expressios ad fid the umber of term i the expasio of the expressios. (a) (x + y) 99 (b) ( + a) 9 + ( a) 9
More informationpoint estimator a random variable (like P or X) whose values are used to estimate a population parameter
Estimatio We have oted that the pollig problem which attempts to estimate the proportio p of Successes i some populatio ad the measuremet problem which attempts to estimate the mea value µ of some quatity
More informationChapter 8. Confidence Interval Estimation. Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 8, Slide 1
Chapter 8 Cofidece Iterval Estimatio Copyright 2015, 2012, 2009 Pearso Educatio, Ic. Chapter 8, Slide 1 Learig Objectives I this chapter, you lear: To costruct ad iterpret cofidece iterval estimates for
More informationT4032-BC, Payroll Deductions Tables CPP, EI, and income tax deductions British Columbia Effective January 1, 2016
T4032-BC, Payroll Deductios Tables CPP, EI, ad icome tax deductios British Columbia Effective Jauary 1, 2016 T4032-BC What s ew as of Jauary 1, 2016 The major chages made to this guide, sice the last editio,
More information. The firm makes different types of furniture. Let x ( x1,..., x n. If the firm produces nothing it rents out the entire space and so has a profit of
Joh Riley F Maimizatio with a sigle costrait F3 The Ecoomic approach - - shadow prices Suppose that a firm has a log term retal of uits of factory space The firm ca ret additioal space at a retal rate
More informationAppendix 1 to Chapter 5
Appedix 1 to Chapter 5 Models of Asset Pricig I Chapter 4, we saw that the retur o a asset (such as a bod) measures how much we gai from holdig that asset. Whe we make a decisio to buy a asset, we are
More informationExam 2. Instructor: Cynthia Rudin TA: Dimitrios Bisias. October 25, 2011
15.075 Exam 2 Istructor: Cythia Rudi TA: Dimitrios Bisias October 25, 2011 Gradig is based o demostratio of coceptual uderstadig, so you eed to show all of your work. Problem 1 You are i charge of a study
More informationBASIC STATISTICS ECOE 1323
BASIC STATISTICS ECOE 33 SPRING 007 FINAL EXAM NAME: ID NUMBER: INSTRUCTIONS:. Write your ame ad studet ID.. You have hours 3. This eam must be your ow work etirely. You caot talk to or share iformatio
More informationProblem Set 1a - Oligopoly
Advaced Idustrial Ecoomics Sprig 2014 Joha Steek 6 may 2014 Problem Set 1a - Oligopoly 1 Table of Cotets 2 Price Competitio... 3 2.1 Courot Oligopoly with Homogeous Goods ad Differet Costs... 3 2.2 Bertrad
More information0.1 Valuation Formula:
0. Valuatio Formula: 0.. Case of Geeral Trees: q = er S S S 3 S q = er S S 4 S 5 S 4 q 3 = er S 3 S 6 S 7 S 6 Therefore, f (3) = e r [q 3 f (7) + ( q 3 ) f (6)] f () = e r [q f (5) + ( q ) f (4)] = f ()
More informationof Asset Pricing R e = expected return
Appedix 1 to Chapter 5 Models of Asset Pricig EXPECTED RETURN I Chapter 4, we saw that the retur o a asset (such as a bod) measures how much we gai from holdig that asset. Whe we make a decisio to buy
More informationOverlapping Generations
Eco. 53a all 996 C. Sims. troductio Overlappig Geeratios We wat to study how asset markets allow idividuals, motivated by the eed to provide icome for their retiremet years, to fiace capital accumulatio
More informationFirst determine the payments under the payment system
Corporate Fiace February 5, 2008 Problem Set # -- ANSWERS Klick. You wi a judgmet agaist a defedat worth $20,000,000. Uder state law, the defedat has the right to pay such a judgmet out over a 20 year
More information1. Suppose X is a variable that follows the normal distribution with known standard deviation σ = 0.3 but unknown mean µ.
Chapter 9 Exercises Suppose X is a variable that follows the ormal distributio with kow stadard deviatio σ = 03 but ukow mea µ (a) Costruct a 95% cofidece iterval for µ if a radom sample of = 6 observatios
More informationParametric Density Estimation: Maximum Likelihood Estimation
Parametric Desity stimatio: Maimum Likelihood stimatio C6 Today Itroductio to desity estimatio Maimum Likelihood stimatio Itroducto Bayesia Decisio Theory i previous lectures tells us how to desig a optimal
More informationT4032-MB, Payroll Deductions Tables CPP, EI, and income tax deductions Manitoba Effective January 1, 2016
T4032-MB, Payroll Deductios Tables CPP, EI, ad icome tax deductios Maitoba Effective Jauary 1, 2016 T4032-MB What s ew as of Jauary 1, 2016 The major chages made to this guide sice the last editio are
More informationT4032-ON, Payroll Deductions Tables CPP, EI, and income tax deductions Ontario Effective January 1, 2016
T4032-ON, Payroll Deductios Tables CPP, EI, ad icome tax deductios Otario Effective Jauary 1, 2016 T4032-ON What s ew as of Jauary 1, 2016 The major chages made to this guide sice the last editio are outlied.
More information5 Statistical Inference
5 Statistical Iferece 5.1 Trasitio from Probability Theory to Statistical Iferece 1. We have ow more or less fiished the probability sectio of the course - we ow tur attetio to statistical iferece. I statistical
More informationInstitute of Actuaries of India Subject CT5 General Insurance, Life and Health Contingencies
Istitute of Actuaries of Idia Subject CT5 Geeral Isurace, Life ad Health Cotigecies For 2017 Examiatios Aim The aim of the Cotigecies subject is to provide a groudig i the mathematical techiques which
More informationEstimating Proportions with Confidence
Aoucemets: Discussio today is review for midterm, o credit. You may atted more tha oe discussio sectio. Brig sheets of otes ad calculator to midterm. We will provide Scatro form. Homework: (Due Wed Chapter
More informationAPPLICATION OF GEOMETRIC SEQUENCES AND SERIES: COMPOUND INTEREST AND ANNUITIES
APPLICATION OF GEOMETRIC SEQUENCES AND SERIES: COMPOUND INTEREST AND ANNUITIES Example: Brado s Problem Brado, who is ow sixtee, would like to be a poker champio some day. At the age of twety-oe, he would
More information1 The Power of Compounding
1 The Power of Compoudig 1.1 Simple vs Compoud Iterest You deposit $1,000 i a bak that pays 5% iterest each year. At the ed of the year you will have eared $50. The bak seds you a check for $50 dollars.
More informationIntra-Industry Trade in Intermediate Products, Pollution and Internationally Increasing Returns *
Itra-Idustry rade i Iterediate Products, Pollutio ad Iteratioally Icreasig Returs * By Michael Bearroch ad Rolf Weder** his Versio: Noveber 2003 [he paper is curretly (March 2004 i revisio. If you quote
More informationStandard Deviations for Normal Sampling Distributions are: For proportions For means _
Sectio 9.2 Cofidece Itervals for Proportios We will lear to use a sample to say somethig about the world at large. This process (statistical iferece) is based o our uderstadig of samplig models, ad will
More informationB = A x z
114 Block 3 Erdeky == Begi 6.3 ============================================================== 1 / 8 / 2008 1 Correspodig Areas uder a ormal curve ad the stadard ormal curve are equal. Below: Area B = Area
More informationCreditRisk + Download document from CSFB web site:
CreditRis + Dowload documet from CSFB web site: http://www.csfb.com/creditris/ Features of CreditRis+ pplies a actuarial sciece framewor to the derivatio of the loss distributio of a bod/loa portfolio.
More informationSolutions to Problem Sheet 1
Solutios to Problem Sheet ) Use Theorem.4 to prove that p log for all real x 3. This is a versio of Theorem.4 with the iteger N replaced by the real x. Hit Give x 3 let N = [x], the largest iteger x. The,
More informationThe material in this chapter is motivated by Experiment 9.
Chapter 5 Optimal Auctios The material i this chapter is motivated by Experimet 9. We wish to aalyze the decisio of a seller who sets a reserve price whe auctioig off a item to a group of bidders. We begi
More informationSubject CT5 Contingencies Core Technical. Syllabus. for the 2011 Examinations. The Faculty of Actuaries and Institute of Actuaries.
Subject CT5 Cotigecies Core Techical Syllabus for the 2011 Examiatios 1 Jue 2010 The Faculty of Actuaries ad Istitute of Actuaries Aim The aim of the Cotigecies subject is to provide a groudig i the mathematical
More informationIntroduction to Probability and Statistics Chapter 7
Itroductio to Probability ad Statistics Chapter 7 Ammar M. Sarha, asarha@mathstat.dal.ca Departmet of Mathematics ad Statistics, Dalhousie Uiversity Fall Semester 008 Chapter 7 Statistical Itervals Based
More informationof Asset Pricing APPENDIX 1 TO CHAPTER EXPECTED RETURN APPLICATION Expected Return
APPENDIX 1 TO CHAPTER 5 Models of Asset Pricig I Chapter 4, we saw that the retur o a asset (such as a bod) measures how much we gai from holdig that asset. Whe we make a decisio to buy a asset, we are
More informationThe Time Value of Money in Financial Management
The Time Value of Moey i Fiacial Maagemet Muteau Irea Ovidius Uiversity of Costata irea.muteau@yahoo.com Bacula Mariaa Traia Theoretical High School, Costata baculamariaa@yahoo.com Abstract The Time Value
More informationSubject CT1 Financial Mathematics Core Technical Syllabus
Subject CT1 Fiacial Mathematics Core Techical Syllabus for the 2018 exams 1 Jue 2017 Subject CT1 Fiacial Mathematics Core Techical Aim The aim of the Fiacial Mathematics subject is to provide a groudig
More informationNon-Inferiority Logrank Tests
Chapter 706 No-Iferiority Lograk Tests Itroductio This module computes the sample size ad power for o-iferiority tests uder the assumptio of proportioal hazards. Accrual time ad follow-up time are icluded
More informationx satisfying all regularity conditions. Then
AMS570.01 Practice Midterm Exam Sprig, 018 Name: ID: Sigature: Istructio: This is a close book exam. You are allowed oe-page 8x11 formula sheet (-sided). No cellphoe or calculator or computer is allowed.
More informationDESCRIPTION OF MATHEMATICAL MODELS USED IN RATING ACTIVITIES
July 2014, Frakfurt am Mai. DESCRIPTION OF MATHEMATICAL MODELS USED IN RATING ACTIVITIES This documet outlies priciples ad key assumptios uderlyig the ratig models ad methodologies of Ratig-Agetur Expert
More informationConfidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.
Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).
More information0.07. i PV Qa Q Q i n. Chapter 3, Section 2
Chapter 3, Sectio 2 1. (S13HW) Calculate the preset value for a auity that pays 500 at the ed of each year for 20 years. You are give that the aual iterest rate is 7%. 20 1 v 1 1.07 PV Qa Q 500 5297.01
More informationCorrelation possibly the most important and least understood topic in finance
Correlatio...... possibly the most importat ad least uderstood topic i fiace 2014 Gary R. Evas. May be used oly for o-profit educatioal purposes oly without permissio of the author. The first exam... Eco
More informationUnbiased estimators Estimators
19 Ubiased estimators I Chapter 17 we saw that a dataset ca be modeled as a realizatio of a radom sample from a probability distributio ad that quatities of iterest correspod to features of the model distributio.
More informationAn Empirical Study of the Behaviour of the Sample Kurtosis in Samples from Symmetric Stable Distributions
A Empirical Study of the Behaviour of the Sample Kurtosis i Samples from Symmetric Stable Distributios J. Marti va Zyl Departmet of Actuarial Sciece ad Mathematical Statistics, Uiversity of the Free State,
More information2. The Time Value of Money
2. The Time Value of Moey Problem 4 Suppose you deposit $100 i the bak today ad it ears iterest at a rate of 10% compouded aually. How much will be i the accout 50 years from today? I this case, $100 ivested
More informationAnalysis III (BAUG) Assignment 1 Prof. Dr. Alessandro Sisto Due 29 of September 2017
Aalysis III (BAUG) Assigmet 1 Prof. Dr. Alessadro Sisto Due 29 of September 217 1. Verify that the each of the followig fuctios satisfy the PDE u rr + u tt =. u(r, t) = e 2r cos 2(t) u rr + u tt = 2 (e
More informationSection 3.3 Exercises Part A Simplify the following. 1. (3m 2 ) 5 2. x 7 x 11
123 Sectio 3.3 Exercises Part A Simplify the followig. 1. (3m 2 ) 5 2. x 7 x 11 3. f 12 4. t 8 t 5 f 5 5. 3-4 6. 3x 7 4x 7. 3z 5 12z 3 8. 17 0 9. (g 8 ) -2 10. 14d 3 21d 7 11. (2m 2 5 g 8 ) 7 12. 5x 2
More informationSystems Analysis Laboratory Research Reports E16, June 2005 PROJECT VALUATION IN MIXED ASSET PORTFOLIO SELECTION
Helsii Uiversity of Techology Systes Aalysis Laboratory Research Reports E6, Jue 25 PROJECT VALUATION IN MIXED ASSET PORTFOLIO SELECTION Jae Gustafsso Bert De Reyc Zeger Degraeve Ahti Salo ABTEKNILLINEN
More informationIntroduction to Financial Derivatives
550.444 Itroductio to Fiacial Derivatives Determiig Prices for Forwards ad Futures Week of October 1, 01 Where we are Last week: Itroductio to Iterest Rates, Future Value, Preset Value ad FRAs (Chapter
More informationCHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Means and Proportions
CHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Meas ad Proportios Itroductio: I this chapter we wat to fid out the value of a parameter for a populatio. We do t kow the value of this parameter for the etire
More informationad covexity Defie Macaulay duratio D Mod = r 1 = ( CF i i k (1 + r k) i ) (1.) (1 + r k) C = ( r ) = 1 ( CF i i(i + 1) (1 + r k) i+ k ) ( ( i k ) CF i
Fixed Icome Basics Cotets Duratio ad Covexity Bod Duratios ar Rate, Spot Rate, ad Forward Rate Flat Forward Iterpolatio Forward rice/yield, Carry, Roll-Dow Example Duratio ad Covexity For a series of cash
More informationTwitter: @Owe134866 www.mathsfreeresourcelibrary.com Prior Kowledge Check 1) State whether each variable is qualitative or quatitative: a) Car colour Qualitative b) Miles travelled by a cyclist c) Favourite
More informationKEY INFORMATION DOCUMENT CFD s Generic
KEY INFORMATION DOCUMENT CFD s Geeric KEY INFORMATION DOCUMENT - CFDs Geeric Purpose This documet provides you with key iformatio about this ivestmet product. It is ot marketig material ad it does ot costitute
More informationCAUCHY'S FORMULA AND EIGENVAULES (PRINCIPAL STRESSES) IN 3-D
GG303 Lecture 19 11/5/0 1 CAUCHY'S FRMULA AN EIGENVAULES (PRINCIPAL STRESSES) IN 3- I II Mai Topics A Cauchy s formula Pricipal stresses (eigevectors ad eigevalues) Cauchy's formula A Relates tractio vector
More information1 + r. k=1. (1 + r) k = A r 1
Perpetual auity pays a fixed sum periodically forever. Suppose a amout A is paid at the ed of each period, ad suppose the per-period iterest rate is r. The the preset value of the perpetual auity is A
More informationCAPITAL PROJECT SCREENING AND SELECTION
CAPITAL PROJECT SCREEIG AD SELECTIO Before studyig the three measures of ivestmet attractiveess, we will review a simple method that is commoly used to scree capital ivestmets. Oe of the primary cocers
More informationMethodology on setting the booking prices Project Development and expansion of Bulgartransgaz EAD gas transmission system
Methodology o settig the bookig prices Project Developmet ad expasio of Bulgartrasgaz EAD gas trasmissio system Art.1. The preset Methodology determies the coditios, order, major requiremets ad model of
More informationHopscotch and Explicit difference method for solving Black-Scholes PDE
Mälardale iversity Fiacial Egieerig Program Aalytical Fiace Semiar Report Hopscotch ad Explicit differece method for solvig Blac-Scholes PDE Istructor: Ja Röma Team members: A Gog HaiLog Zhao Hog Cui 0
More informationSTAT 135 Solutions to Homework 3: 30 points
STAT 35 Solutios to Homework 3: 30 poits Sprig 205 The objective of this Problem Set is to study the Stei Pheomeo 955. Suppose that θ θ, θ 2,..., θ cosists of ukow parameters, with 3. We wish to estimate
More information