Blanche Dubois As A Tragic Hero In A Streetcar Named Desire

The Queen of the Nile Aristotle conceptualized how â€Å"tragedy arouses the emotions by bringing a person who is somewhat better than average into a reversal of fortune for which he or she is responsible; then, through the downfall of the hero and the resolution of the conflicts resulting from the hero’s tragic flaw, the tragedy achieves a purging of the audience’s emotions† (Masterpieces of World Literature). Tragic plays have one or more tragic heroes within them; A Streetcar Named Desire is no exception. According to Dr. Hebert, a tragic hero must meet the following criteria: they â€Å"must be Noble, have a tragic flaw such as hubris, they go through a sequence of fall, suffering, learning, and punishment, and there must be an emotional†¦show more content†¦This part of the film demonstrates the fact that Blanche thinks that she is better than others to the point of being stuck up. Early in the film, Blanche and Stella return home at night and Stan ley is playing cards with some friends. When Blanche is introduced, she says, â€Å"how do you do? Please, don’t get up.† Stanley replies, â€Å"nobody is getting up.† Blanche expects everyone to stand on her behalf when she walks by as if she were of grandeur social status. Even Stella notices Blanche acting better than everyone when Blanche tried to get Stella to leave Stanley. Stella says, â€Å"don’t you think your superior attitude is a little out of place?† Blanche has this idea that since they grew up in Belle Reve, they should not live in a small apartment, they deserve better than â€Å"hanging back with the brutes† (Williams). The relationship shared between Blanche and Mitch is unlike the relationship she shares with any other men in the film. She truly exposes herself to Mitch and reveals her tragic flaw, desire, during their date on the deck. Blanche begins describing her husband, Allan. Blanche says, â€Å"I killed him,† and further explains that while dancing, she told Allan, â€Å"You are weak, I’ve lost respect for you, I despise you.† Allan â€Å"broke away from† Blanche and â€Å"stuck a revolver in his mouth and fired† (Williams). Blanche had feel in love at a young age and her reality was destroyed with this single pull of the trigger. She felt a need to fill the void and didShow MoreRelatedA Streetcar Named Desire : A Tragic Desire969 Words   |  4 PagesA tragic hero in literature is a type of character who has fallen from grace, where the downfall suggests feelings of misfortune and distress among the audience. The tragic flaw of the hero leads to their demise or downfall that in turn brings a tragic end. Aristotle defines a tragic hero as â€Å"a person who must evoke a sense of pity and fear in the audience. He is considered a man of misfortune that comes to him through error of judgment.† The characteristics of a tragic hero described by AristotleR ead MoreA Streetcar Named Desire By Tennessee Williams1054 Words   |  5 PagesJamie Razo Mr. Baker Period 7 22 September 2017 Tragic Downfalls In the play and book called â€Å"A Streetcar Named Desire†, there are numerous amounts of tragic events that not only affected the person in the event, but others around them as well. A tragedy, or tragic event, is known to bring chaos, destruction, distress, and even discomfort such as a natural disaster or a serious accident. A tragedy in a story can also highlight the downfall of the main character, or sometimes one of the more importantRead More Tragic Comedy of Tennessee Williams A Streetcar Named Desire1350 Words   |  6 PagesA Streetcar Named Desire as Tragic Comedy      Ã‚   Tennessee Williams’ A Streetcar Named Desire is considered by many critics to be a â€Å"flawed† masterpiece. This is because William’s work utilizes and wonderfully blends both tragic and comic elements that serve to shroud the true nature of the hero and heroine, thereby not allowing the reader to judge them on solid actuality. Hence, Williams has been compared to writers such as Shakespeare who, in literature, have created a sense of ambiguity andRead MoreTo What Extent Does Williams Present Desire as a Tragic Flaw in Scene Six of ‘a Streetcar Named Desire’1632 Words   |  7 PagesTo what extent does Williams present desire as a tragic flaw in scene six of ‘A Streetcar Named Desire’ In A Streetcar Named Desire Blanche’s flaws that lead to her downfall are abundant. If we are to view Blanche Dubois as a tragic heroine, then it is in scene six that her tragic flaws are especially evident, and in particular desire. They are so prevalent here as it is arguably the beginning of Blanche’s demise and as in Shakespearean tragedy; it is in the centre of the play that we seeRead MoreStreetcar To Desire Character Analysis848 Words   |  4 PagesIn the play The Streetcar to Desire there are many tragic moments they are events that causing great suffering, destruction, and distress, such as a serious accident, crime, or natural catastrophe. The book takes place in a New Orleans during the 1940’s. Blanche is wanting to visit her sister for a little while but she realizes that she lives in crappy old apartment. She decided to stay with them and throughout her stay there were many tragic events that happened. The main tragic events in the playRead MoreTragic Heroes Throughout A Streetcar Named Desire And The Great Gatsby1961 Words   |  8 PagesBlanche Dubois and Jay Gatsby are portrayed as tragic heroes throughout A Streetcar Named Desire and The Great Gatsby. In tragic novels and plays protagonists are often dealing with a conflict that they will ultimately lose in some way. The protagonist is often trying to right a wrong that leads to the world returning to the way it was before the conflict. Blanche wants to return to the old south when she was a young girl and Gatsby to when he first met Daisy during the war. A tragic hero would haveRead MoreCompare And Contrast A Streetcar Named Desire And Death Of A Salesman1209 Words   |  5 PagesWhen she arrives on her sister’s doorstep, the tragic hero of Williams’ play A Streetcar Named Desire, Blanche DuBois, is blatantly out of place. However, with no where else to go, the former aristocrat arrives at the home of Stella and her husband Stanley in downtown New Orleans. Once there, Blanche seeks refuge from reality through the acceptance of men. However, Stanley, sees through Blanche’s compulsive lies and investigates her suspicions past. After being confronted and sexually abused Blache’sRead MoreA Streetcar Named Desire-A Tragic Hero1422 Words   |  6 Pagesplots, cliches etc. Among those is the classic tragic hero, one who is destined to fail no matter what. In a Streetcar Named Desire, the tragic hero is Blanche Dubois, an aging Southern Belle living in a state of perpetual panic about her fading beauty. In this essay it will be discussed what makes Blanche a tragic hero and how she compares to a typical tragic hero. A typical tragic hero is first and foremost, born of noble stature. This gives the hero something to fall from, so they can fall fromRead MoreStreetcar Named Desire1179 Words   |  5 Pagesis to follow in scene 1 of A Streetcar Named Desire? ‘A Streetcar Named Desire’ can be seen as a modern domestic tragedy, with base elements of traditional tragedy. Williams is able to alert us, with subtle hints in the very first scene of the play that a tragedy is going to occur, by creating an atmosphere that is both oppressive and claustrophobic. The portrayal of characters also adds to the tension as we realise that the two main protagonists, Blanche Dubois and Stanley Kowalski, are polarRead MoreJoseph O Neil : The Greatest American Playwright1499 Words   |  6 Pagesnorms and taboos through novels such as â€Å"A Streetcar Named Desire†. (3) His most popular work is perhaps â€Å"A Streetcar Named Desire† because it dealt with sexuality and psychology that had never been spoken of before in American Culture. â€Å"A Streetcar Named Desire† came out in the year 1947 and completely surprised the audience that read it.The book included every act of defiance in media that can be possible.The play is about a women name Blanche Dubois who has just lost everything, her home, her

Biology Essential Biology Discussion - 1189 Words

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Stochastic Calculus Solution Manual Free Essays

string(48) " consisting of a short position in stock and 0\." Stochastic Calculus for Finance, Volume I and II by Yan Zeng Last updated: August 20, 2007 This is a solution manual for the two-volume textbook Stochastic calculus for ? nance, by Steven Shreve. If you have any comments or ? nd any typos/errors, please email me at yz44@cornell. edu. We will write a custom essay sample on Stochastic Calculus Solution Manual or any similar topic only for you Order Now The current version omits the following problems. Volume I: 1. 5, 3. 3, 3. 4, 5. 7; Volume II: 3. 9, 7. 1, 7. 2, 7. 5–7. 9, 10. 8, 10. 9, 10. 10. Acknowledgment I thank Hua Li (a graduate student at Brown University) for reading through this solution manual and communicating to me several mistakes/typos. 1. 1. Stochastic Calculus for Finance I: The Binomial Asset Pricing Model 1. The Binomial No-Arbitrage Pricing Model Proof. If we get the up sate, then X1 = X1 (H) = ? 0 uS0 + (1 + r)(X0 ? ?0 S0 ); if we get the down state, then X1 = X1 (T ) = ? 0 dS0 + (1 + r)(X0 ? ?0 S0 ). If X1 has a positive probability of being strictly positive, then we must either have X1 (H) 0 or X1 (T ) 0. (i) If X1 (H) 0, then ? 0 uS0 + (1 + r)(X0 ? ?0 S0 ) 0. Plug in X0 = 0, we get u? 0 (1 + r)? 0 . By condition d 1 + r u, we conclude ? 0 0. In this case, X1 (T ) = ? 0 dS0 + (1 + r)(X0 ? ?0 S0 ) = ? 0 S0 [d ? (1 + r)] 0. (ii) If X1 (T ) 0, then we can similarly deduce ? 0 0 and hence X1 (H) 0. So we cannot have X1 strictly positive with positive probability unless X1 is strictly negative with positive probability as well, regardless the choice of the number ? 0 . Remark: Here the condition X0 = 0 is not essential, as far as a property de? nition of arbitrage for arbitrary X0 can be given. Indeed, for the one-period binomial model, we can de? ne arbitrage as a trading strategy such that P (X1 ? X0 (1 + r)) = 1 and P (X1 X0 (1 + r)) 0. First, this is a generalization of the case X0 = 0; second, it is â€Å"proper† because it is comparing the result of an arbitrary investment involving money and stock markets with that of a safe investment involving only money market. This can also be seen by regarding X0 as borrowed from money market account. Then at time 1, we have to pay back X0 (1 + r) to the money market account. In summary, arbitrage is a trading strategy that beats â€Å"safe† investment. Accordingly, we revise the proof of Exercise 1. 1. as follows. If X1 has a positive probability of being strictly larger than X0 (1 + r), the either X1 (H) X0 (1 + r) or X1 (T ) X0 (1 + r). The ? rst case yields ? 0 S0 (u ? 1 ? r) 0, i. e. ?0 0. So X1 (T ) = (1 + r)X0 + ? 0 S0 (d ? 1 ? r) (1 + r)X0 . The second case can be similarly analyzed. Hence we cannot have X1 strictly greater than X0 (1 + r) with positive probability unless X1 is strictly smaller than X0 (1 + r) with positive probability as well. Finally, we comment that the above formulation of arbitrage is equivalent to the one in the textbook. For details, see Shreve [7], Exercise 5. . 1. 2. 1 5 Proof. X1 (u) = ? 0 ? 8 + ? 0 ? 3 ? 5 (4? 0 + 1. 20? 0 ) = 3? 0 + 1. 5? 0 , and X1 (d) = ? 0 ? 2 ? 4 (4? 0 + 1. 20? 0 ) = 4 ? 3? 0 ? 1. 5? 0 . That is, X1 (u) = ? X1 (d). So if there is a positive probability that X1 is positive, then there is a positive probability that X1 is negative. Remark: Note the above relation X1 (u) = ? X1 (d) is not a coincidence. In general, let V1 denote the ? ? payo? of the derivative security at time 1. Suppose X0 and ? 0 are chosen in such a way that V1 can be ? 0 ? ?0 S0 ) + ? 0 S1 = V1 . Using the notation of the problem, suppose an agent begins ? replicated: (1 + r)(X with 0 wealth and at time zero buys ? 0 shares of stock and ? 0 options. He then puts his cash position ? 0 S0 ? ?0 X0 in a money market account. At time one, the value of the agent’s portfolio of stock, option and money market assets is ? X1 = ? 0 S1 + ? 0 V1 ? (1 + r)(? 0 S0 + ? 0 X0 ). Plug in the expression of V1 and sort out terms, we have ? X1 = S0 (? 0 + ? 0 ? 0 )( S1 ? (1 + r)). S0 ? Since d (1 + r) u, X1 (u) and X1 (d) have opposite signs. So if the price of the option at time zero is X0 , then there will no arbitrage. 1. 3. S0 1 Proof. V0 = 1+r 1+r? d S1 (H) + u? ? r S1 (T ) = 1+r 1+r? d u + u? 1? r d = S0 . This is not surprising, since u? d u? d u? d u? d this is exactly the cost of replicating S1 . Remark: This illustrates an important point. The â€Å"fair price† of a stock cannot be determined by the risk-neutral pricing, as seen below. Suppose S1 (H) and S1 (T ) are given, we could have two current prices, S0 and S0 . Correspondingly, we can get u, d and u , d . Because they are determined by S0 and S0 , respectively, it’s not surprising that risk-neutral pricing formula always holds, in both cases. That is, 1+r? d u? d S1 (H) S0 = + u? 1? r u? d S1 (T ) 1+r S0 = 1+r? d u ? d S1 (H) + u ? 1? r u ? d S1 (T ) 1+r . Essentially, this is because risk-neutral pricing relies on fair price=replication cost. Stock as a replicating component cannot determine its own â€Å"fair† price via the risk-neutral pricing formula. 1. 4. Proof. Xn+1 (T ) = = ? n dSn + (1 + r)(Xn ? ?n Sn ) ?n Sn (d ? 1 ? r) + (1 + r)Vn pVn+1 (H) + q Vn+1 (T ) ? ? Vn+1 (H) ? Vn+1 (T ) (d ? 1 ? r) + (1 + r) = u? d 1+r = p(Vn+1 (T ) ? Vn+1 (H)) + pVn+1 (H) + q Vn+1 (T ) ? ? ? = pVn+1 (T ) + q Vn+1 (T ) ? ? = Vn+1 (T ). 1. 6. 2 Proof. The ba nk’s trader should set up a replicating portfolio whose payo? s the opposite of the option’s payo?. More precisely, we solve the equation (1 + r)(X0 ? ?0 S0 ) + ? 0 S1 = ? (S1 ? K)+ . 1 Then X0 = ? 1. 20 and ? 0 = ? 2 . This means the trader should sell short 0. 5 share of stock, put the income 2 into a money market account, and then transfer 1. 20 into a separate money market account. At time one, the portfolio consisting of a short position in stock and 0. You read "Stochastic Calculus Solution Manual" in category "Essay examples" 8(1 + r) in money market account will cancel out with the option’s payo?. Therefore we end up with 1. 20(1 + r) in the separate money market account. Remark: This problem illustrates why we are interested in hedging a long position. In case the stock price goes down at time one, the option will expire without any payo?. The initial money 1. 20 we paid at time zero will be wasted. By hedging, we convert the option back into liquid assets (cash and stock) which guarantees a sure payo? at time one. Also, cf. page 7, paragraph 2. As to why we hedge a short position (as a writer), see Wilmott [8], page 11-13. 1. 7. Proof. The idea is the same as Problem 1. 6. The bank’s trader only needs to set up the reverse of the replicating trading strategy described in Example 1. 2. 4. More precisely, he should short sell 0. 1733 share of stock, invest the income 0. 933 into money market account, and transfer 1. 376 into a separate money market account. The portfolio consisting a short position in stock and 0. 6933-1. 376 in money market account will replicate the opposite of the option’s payo?. After they cancel out, we end up with 1. 376(1 + r)3 in the separate money market account. 1. 8. (i) 2 s s Proof. vn (s, y) = 5 (vn+1 (2s, y + 2s) + vn+1 ( 2 , y + 2 )). (ii) Proof. 1. 696. (iii) Proof. ?n (s, y) = vn+1 (us, y + us) ? vn+1 (ds, y + ds) . (u ? d)s 1. 9. (i) Proof. Similar to Theorem 1. 2. 2, but replace r, u and d everywhere with rn , un and dn . More precisely, set pn = 1+rn ? dn and qn = 1 ? pn . Then un ? dn Vn = pn Vn+1 (H) + qn Vn+1 (T ) . 1 + rn (ii) Proof. ?n = (iii) 3 Vn+1 (H)? Vn+1 (T ) Sn+1 (H)? Sn+1 (T ) = Vn+1 (H)? Vn+1 (T ) . (un ? dn )Sn 10 10 Proof. un = Sn+1 (H) = Sn +10 = 1+ Sn and dn = Sn+1 (T ) = Sn ? 10 = 1? Sn . So the risk-neutral probabilities Sn Sn Sn Sn at time n are pn = u1? dnn = 1 and qn = 1 . Risk-neutral pricing implies the price of this call at time zero is ? ? 2 2 n ? d 9. 375. 2. Probability Theory on Coin Toss Space 2. 1. (i) Proof. P (Ac ) + P (A) = (ii) Proof. By induction, it su? ces to work on the case N = 2. When A1 and A2 are disjoint, P (A1 ? A2 ) = A1 ? A2 P (? ) = A1 P (? ) + A2 P (? ) = P (A1 ) + P (A2 ). When A1 and A2 are arbitrary, using the result when they are disjoint, we have P (A1 ? A2 ) = P ((A1 ? A2 ) ? A2 ) = P (A1 ? A2 ) + P (A2 ) ? P (A1 ) + P (A2 ). 2. 2. (i) 1 3 1 Proof. P (S3 = 32) = p3 = 8 , P (S3 = 8) = 3p2 q = 3 , P (S3 = 2) = 3pq 2 = 8 , and P (S3 = 0. 5) = q 3 = 8 . 8 Ac P (? ) + A P (? ) = P (? ) = 1. (ii) Proof. E[S1 ] = 8P (S1 = 8) + 2P (S1 = 2) = 8p + 2q = 5, E[S2 ] = 16p2 + 4  · 2pq + 1  · q 2 = 6. 25, and 3 1 E[S3 ] = 32  · 1 + 8  · 8 + 2  · 3 + 0.  · 8 = 7. 8125. So the average rates of growth of the stock price under P 8 8 5 are, respectively: r0 = 4 ? 1 = 0. 25, r1 = 6. 25 ? 1 = 0. 25 and r2 = 7. 8125 ? 1 = 0. 25. 5 6. 25 (iii) 8 1 Proof. P (S3 = 32) = ( 2 )3 = 27 , P (S3 = 8) = 3  · ( 2 )2  · 1 = 4 , P (S3 = 2) = 2  · 1 = 2 , and P (S3 = 0. 5) = 27 . 3 3 3 9 9 9 Accordingly, E[S1 ] = 6, E[S2 ] = 9 and E[S3 ] = 13. 5. So the aver age rates of growth of the stock price 9 6 under P are, respectively: r0 = 4 ? 1 = 0. 5, r1 = 6 ? 1 = 0. 5, and r2 = 13. 5 ? 1 = 0. 5. 9 2. 3. Proof. Apply conditional Jensen’s inequality. 2. 4. (i) Proof. En [Mn+1 ] = Mn + En [Xn+1 ] = Mn + E[Xn+1 ] = Mn . (ii) 2 n+1 Proof. En [ SSn ] = En [e? Xn+1 e? +e ] = 2 ? Xn+1 ] e? +e E[e = 1. 2. 5. (i) 2 2 Proof. 2In = 2 j=0 Mj (Mj+1 ? Mj ) = 2 j=0 Mj Mj+1 ? j=1 Mj ? j=1 Mj = 2 j=0 Mj Mj+1 + n? 1 n? 1 n? 1 n? 1 2 2 2 2 2 2 2 2 Mn ? j=0 Mj+1 ? j=0 Mj = Mn ? j=0 (Mj+1 ? Mj ) = Mn ? j=0 Xj+1 = Mn ? n. n? 1 n? 1 n? 1 n? 1 n? 1 (ii) Proof. En [f (In+1 )] = En [f (In + Mn (Mn+1 ? Mn ))] = En [f (In + Mn Xn+1 )] = 1 [f (In + Mn ) + f (In ? Mn )] = 2 v v v g(In ), where g(x) = 1 [f (x + 2x + n) + f (x ? 2x + n)], since 2In + n = |Mn |. 2 2. 6. 4 Proof. En [In+1 ? In ] = En [? n (Mn+1 ? Mn )] = ? n En [Mn+1 ? Mn ] = 0. 2. 7. Proof. We denote by Xn the result of n-th coin toss, where Head is represented by X = 1 and Tail is 1 represented by X = ? 1. We also suppose P (X = 1) = P (X = ? 1) = 2 . De? ne S1 = X1 and Sn+1 = n Sn +bn (X1 ,  ·  ·  · , Xn )Xn+1 , where bn ( ·) is a bounded function on {? 1, 1} , to be determined later on. Clearly (Sn )n? 1 is an adapted stochastic process, and we can show it is a martingale. Indeed, En [Sn+1 ? Sn ] = bn (X1 ,  ·  ·  · , Xn )En [Xn+1 ] = 0. For any arbitrary function f , En [f (Sn+1 )] = 1 [f (Sn + bn (X1 ,  ·  ·  · , Xn )) + f (Sn ? n (X1 ,  ·  ·  · , Xn ))]. Then 2 intuitively, En [f (Sn+1 ] cannot be solely dependent upon Sn when bn ’s are properly chosen. Therefore in general, (Sn )n? 1 cannot be a Markov process. Remark: If Xn is regarded as the gain/loss of n-th bet in a gambling game, then Sn would be the wealth at time n. bn is therefore the wager for the (n+1)- th bet and is devised according to past gambling results. 2. 8. (i) Proof. Note Mn = En [MN ] and Mn = En [MN ]. (ii) Proof. In the proof of Theorem 1. 2. 2, we proved by induction that Xn = Vn where Xn is de? ned by (1. 2. 14) of Chapter 1. In other words, the sequence (Vn )0? n? N can be realized as the value process of a portfolio, Xn which consists of stock and money market accounts. Since ( (1+r)n )0? n? N is a martingale under P (Theorem Vn 2. 4. 5), ( (1+r)n )0? n? N is a martingale under P . (iii) Proof. (iv) Proof. Combine (ii) and (iii), then use (i). 2. 9. (i) (H) S1 (H) 1 = 2, d0 = S1S0 = 2 , S0 (T and d1 (T ) = S21 (TT)) = 1. S 1 1 0 ? d So p0 = 1+r? d0 0 = 2 , q0 = 2 , p1 (H) u0 5 q1 (T ) = 6 . Therefore P (HH) = p0 p1 (H) = 1 , 4 5 q0 q1 (T ) = 12 . Vn (1+r)n = En VN (1+r)N , so V0 , V1 1+r ,  ·Ã‚ ·Ã‚ ·, VN ? 1 , VN (1+r)N ? 1 (1+r)N is a martingale under P . Proof. u0 = u1 (H) = = S2 (HH) S1 (H) = 1. 5, d1 (H) = S2 (HT ) S1 (H) = 1, u1 (T ) = S2 (T H) S1 (T ) =4 1+r1 (H)? d1 (H) u1 (H)? d1 (H) 1 = 1 , q1 (H) = 2 , p1 (T ) = 2 1 4, 1+r1 (T )? d1 (T ) u1 (T )? d1 (T ) 1 12 1 = 6 , and P (HT ) = p0 q1 (H) = P (T H) = q0 p1 (T ) = and P (T T ) = The proofs of Theorem 2. 4. 4, Theorem 2. 4. 5 and Theorem 2. 4. 7 still work for the random interest rate model, with proper modi? cations (i. e. P would be constructed according to conditional probabilities P (? n+1 = H|? 1 ,  ·  ·  · , ? n ) := pn and P (? n+1 = T |? 1 ,  ·  ·  · , ? n ) := qn . Cf. notes on page 39. ). So the time-zero value of an option that pays o? V2 at time two is given by the risk-neutral pricing formula V0 = E (1+r0V2 1 ) . )(1+r (ii) Proof. V2 (HH) = 5, V2 (HT ) = 1, V2 (T H) = 1 and V2 (T T ) = 0. So V1 (H) = 2. 4, V1 (T ) = p1 (T )V2 (T H)+q1 (T )V2 (T T ) 1+r1 (T ) p1 (H)V2 (HH)+q1 (H)V2 (HT ) 1+r1 (H) = = 1 9, and V0 = p0 V1 (H)+q0 V1 (T ) 1+r0 ? 1. 5 (iii) Proof. ?0 = (iv) Proof. ?1 (H) = 2. 10. (i) Xn+1 Proof. En [ (1+r)n+1 ] = En [ ? n Yn+1 Sn + (1+r)n+1 (1+r)(Xn n Sn ) ] (1+r)n+1 Xn (1+r)n . V2 (HH)? V2 (HT ) S2 (HH)? S2 (HT ) V1 (H)? V1 (T ) S1 (H)? S1 (T ) = 1 2. 4? 9 8? 2 = 0. 4 ? 1 54 ? 0. 3815. = 5? 1 12? 8 = 1. = ?n Sn (1+r)n+1 En [Yn+1 ] + Xn Sn (1+r)n = ?n Sn (1+r)n+1 (up + dq) + Xn n Sn (1+r)n = ?n Sn +Xn n Sn (1+r)n = (ii) Proof. From (2. 8. 2), we have ? n uSn + (1 + r)(Xn ? ?n Sn ) = Xn+1 (H) ? n dSn + (1 + r)(Xn ? ?n Sn ) = Xn+1 (T ). So ? n = Xn+1 (H)? Xn+1 (T ) uSn ? dSn and Xn = En [ Xn+1 ]. To make the portfolio replicate the payo? at time N , we 1+r VN X must have XN = VN . So Xn = En [ (1+r)N ? n ] = En [ (1+r)N ? n ]. Since (Xn )0? n? N is the value process of the N unique replicating portfolio (uniqueness is guaranteed by the uniqueness of the solution to the above linear VN equations), the no-arbitrage price of VN at time n is Vn = Xn = En [ (1+r)N ? ]. (iii) Proof. En [ Sn+1 ] (1 + r)n+1 = = = 1 En [(1 ? An+1 )Yn+1 Sn ] (1 + r)n+1 Sn [p(1 ? An+1 (H))u + q(1 ? An+1 (T ))d] (1 + r)n+1 Sn [pu + qd] (1 + r)n+1 Sn . (1 + r)n Sn (1+r)n+1 (1? a)(pu+qd) Sn+1 If An+1 is a constant a, then En [ (1+r)n+1 ] = Sn (1+r)n (1? a)n . = Sn (1+r)n (1? a). Sn+1 So En [ (1+r)n+1 (1? a)n+1 ] = 2. 11. (i) Proof. FN + PN = SN ? K + (K ? SN )+ = (SN ? K)+ = CN . (ii) CN FN PN Proof. Cn = En [ (1+r)N ? n ] = En [ (1+r)N ? n ] + En [ (1+r)N ? n ] = Fn + Pn . (iii) FN Proof. F0 = E[ (1+r)N ] = 1 (1+r)N E[SN ? K] = S0 ? K (1+r)N . (iv) 6 Proof. At time zero, the trader has F0 = S0 in money market account and one share of stock. At time N , the trader has a wealth of (F0 ? S0 )(1 + r)N + SN = ? K + SN = FN . (v) Proof. By (ii), C0 = F0 + P0 . Since F0 = S0 ? (vi) SN ? K Proof. By (ii), Cn = Pn if and only if Fn = 0. Note Fn = En [ (1+r)N ?n ] = Sn ? So Fn is not necessarily zero and Cn = Pn is not necessarily true for n ? 1. (1+r)N S0 (1+r)N ? n (1+r)N S0 (1+r)N = 0, C0 = P0 . = Sn ? S0 (1 + r)n . 2. 12. Proof. First, the no-arbitrage price of the chooser option at time m must be max(C, P ), where C=E (SN ? K)+ (K ? SN )+ , and P = E . (1 + r)N ? m (1 + r)N ? That is, C is the no-arbitrage price of a call option at time m and P is the no-arbitrage price of a put option at time m. Both of them have maturity date N and strike price K. Suppose the market is liquid, then the chooser option is equivalent to receiving a payo? of max(C, P ) at time m. Therefore, its current no-arbitrage price should be E[ max(C,P ) ]. (1+r)m K K By the put-call parity, C = Sm ? (1+r)N ? m + P . So max(C, P ) = P + (Sm ? (1+r)N ? m )+ . Therefore, the time-zero price of a chooser option is E K (Sm ? (1+r)N ? m )+ P +E (1 + r)m (1 + r)m =E K (Sm ? (1+r)N ? m )+ (K ? SN )+ . +E (1 + r)N (1 + r)m The ? rst term stands for the time-zero price of a put, expiring at time N and having strike price K, and the K second term stands for the time-zero price of a call, expiring at time m and having strike price (1+r)N ? m . If we feel unconvinced by the above argument that the chooser option’s no-arbitrage price is E[ max(C,P ) ], (1+r)m due to the economical argument involved (like â€Å"the chooser option is equivalent to receiving a payo? of max(C, P ) at time m†), then we have the following mathematically rigorous argument. First, we can construct a portfolio ? 0 ,  ·  ·  · , ? m? 1 , whose payo? at time m is max(C, P ). Fix ? , if C(? ) P (? ), we can construct a portfolio ? m ,  ·  ·  · , ? N ? 1 whose payo? at time N is (SN ? K)+ ; if C(? ) P (? ), we can construct a portfolio ? m ,  ·  ·  · , ? N ? 1 whose payo? at time N is (K ? SN )+ . By de? ning (m ? k ? N ? 1) ? k (? ) = ? k (? ) ? k (? ) if C(? ) P (? ) if C(? ) P (? ), we get a portfolio (? n )0? n? N ? 1 whose payo? is the same as that of the chooser option. So the no-arbitrage price process of the chooser option must be equal to the value process of the replicating portfolio. In Xm particular, V0 = X0 = E[ (1+r)m ] = E[ max(C,P ) ]. (1+r)m 2. 13. (i) Proof. Note under both actual probability P and risk-neutral probability P , coin tosses ? n ’s are i. i. d.. So n+1 without loss of generality, we work on P . For any function g, En [g(Sn+1 , Yn+1 )] = En [g( SSn Sn , Yn + = pg(uSn , Yn + uSn ) + qg(dSn , Yn + dSn ), which is a function of (Sn , Yn ). So (Sn , Yn )0? n? N is Markov under P . (ii) 7 Sn+1 Sn Sn )] Proof. Set vN (s, y) = f ( Ny ). Then vN (SN , YN ) = f ( +1 Vn = where En [ Vn+1 ] 1+r = n+1 En [ vn+1 (S1+r ,Yn+1 ) ] N n=0 Sn N +1 ) = VN . Suppose vn+1 is given, then = 1 1+r [pvn+1 (uSn , Yn + uSn ) + qvn+1 (dSn , Yn + dSn )] = vn (Sn , Yn ), vn (s, y) = n+1 (us, y + us) + vn+1 (ds, y + ds) . 1+r 2. 14. (i) Proof. For n ? M , (Sn , Yn ) = (Sn , 0). Since coin tosses ? n ’s are i. i. d. under P , (Sn , Yn )0? n? M is Markov under P . More precisely, for any function h, En [h(Sn+1 )] = ph(uSn ) + h(dSn ), for n = 0, 1,  ·  ·  · , M ? 1. For any function g of two variables, we have EM [g(SM +1 , YM +1 )] = EM [g(SM +1 , SM +1 )] = pg(uSM , uSM )+ n+1 n+1 qg(dSM , dSM ). And for n ? M +1, En [g(Sn+1 , Yn+1 )] = En [g( SSn Sn , Yn + SSn Sn )] = pg(uSn , Yn +uSn )+ qg(dSn , Yn + dSn ), so (Sn , Yn )0? n? N is Markov under P . (ii) y Proof. Set vN (s, y) = f ( N ? M ). Then vN (SN , YN ) = f ( N K=M +1 Sk N ? M ) = VN . Suppose vn+1 is already given. a) If n M , then En [vn+1 (Sn+1 , Yn+1 )] = pvn+1 (uSn , Yn + uSn ) + qvn+1 (dSn , Yn + dSn ). So vn (s, y) = pvn+1 (us, y + us) + qvn+1 (ds, y + ds). b) If n = M , then EM [vM +1 (SM +1 , YM +1 )] = pvM +1 (uSM , uSM ) + vn+1 (dSM , dSM ). So vM (s) = pvM +1 (us, us) + qvM +1 (ds, ds). c) If n M , then En [vn+1 (Sn+1 )] = pvn+1 (uSn ) + qvn+1 (dSn ). So vn (s) = pvn+1 (us) + qvn+1 (ds). 3. State Prices 3. 1. Proof. Note Z(? ) := P (? ) P (? ) = 1 Z(? ) . Apply Theorem 3. 1. 1 with P , P , Z replaced by P , P , Z, we get the nalogous of properties (i)-(iii) of Theorem 3. 1. 1. 3. 2. (i) Proof. P (? ) = (ii) Proof. E[Y ] = (iii) ? Proof. P (A) = (iv) Proof. If P (A) = A Z(? )P (? ) = 0, by P (Z 0) = 1, we conclude P (? ) = 0 for any ? ? A. So P (A) = A P (? ) = 0. (v) Proof. P (A) = 1 P (Ac ) = 0 P (Ac ) = 0 P (A) = 1. (vi) A P (? ) = Z(? )P (? ) = E[Z] = 1. Y (? )P (? ) = Y (? )Z(? )P (? ) = E[Y Z]. Z(? )P (? ). Since P (A) = 0, P (? ) = 0 for any ? ? A. So P (A) = 0. 8 Proof. Pick ? 0 such that P (? 0 ) 0, de? ne Z(? ) = 1 P (? 0 ) 0, 1 P (? 0 ) , if ? = ? 0 Then P (Z ? 0) = 1 and E[Z] = if ? = ? 0 .  · P (? 0 ) = 1. =? 0 Clearly P (? {? 0 }) = E[Z1? {? 0 } ] = Z(? )P (? ) = 0. But P (? {? 0 }) = 1 ? P (? 0 ) 0 if P (? 0 ) 1. Hence in the case 0 P (? 0 ) 1, P and P are not equivalent. If P (? 0 ) = 1, then E[Z] = 1 if and only if Z(? 0 ) = 1. In this case P (? 0 ) = Z(? 0 )P (? 0 ) = 1. And P and P have to be equivalent. In summary, if we can ? nd ? 0 such that 0 P (? 0 ) 1, then Z as constructed above would induce a probability P that is not equivalent to P . 3. 5. (i) Proof. Z(HH) = (ii) Proof. Z1 (H) = E1 [Z2 ](H) = Z2 (HH)P (? 2 = H|? 1 = H) + Z2 (HT )P (? 2 = T |? 1 = H) = 3 E1 [Z2 ](T ) = Z2 (T H)P (? 2 = H|? = T ) + Z2 (T T )P (? 2 = T |? 1 = T ) = 2 . (iii) Proof. V1 (H) = [Z2 (HH)V2 (HH)P (? 2 = H|? 1 = H) + Z2 (HT )V2 (HT )P (? 2 = T |? 1 = T )] = 2. 4, Z1 (H)(1 + r1 (H)) [Z2 (T H)V2 (T H)P (? 2 = H|? 1 = T ) + Z2 (T T )V2 (T T )P (? 2 = T |? 1 = T )] 1 = , Z1 (T )(1 + r1 (T )) 9 3 4. 9 16 , Z(HT ) = 9 , Z(T H) = 8 3 8 and Z(T T ) = 15 4 . Z1 (T ) = V1 (T ) = and V0 = Z2 (HH)V2 (HH) Z2 (HT )V2 (HT ) Z2 (T H)V2 (T H) P (HH) + P (T H) + 0 ? 1. 1 1 1 1 P (HT ) + 1 (1 + 4 )(1 + 4 ) (1 + 4 )(1 + 4 ) (1 + 4 )(1 + 1 ) 2 3. 6. Proof. U (x) = have XN = 1 x, (1+r)N ? Z so I(x) = = 1 Z] 1 x. Z (3. 3. 26) gives E[ (1+r)N 1 X0 (1 + r)n Zn En [Z  · X0 N Z (1 + r) . 0 = Xn , where ? Hence Xn = (1+r)N ? Z X En [ (1+r)N ? n ] N ] = X0 . So ? = = En [ X0 (1+r) Z n 1 X0 . By (3. 3. 25), we 1 ] = X0 (1 + r)n En [ Z ] = the second to last â€Å"=† comes from Lemma 3. 2. 6. 3. 7. Z ? Z Proof. U (x) = xp? 1 and so I(x) = x p? 1 . By (3. 3. 26), we have E[ (1+r)N ( (1+r)N ) p? 1 ] = X0 . Solve it for ? , we get ? ?p? 1 1 1 ? ? =? ? X0 p E 1 Z p? 1 Np ? ? ? = p? 1 X0 (1 + r)N p (E[Z p? 1 ])p? 1 1 p . (1+r) p? 1 ? Z So by (3. 3. 25), XN = ( (1+r)N ) p? 1 = 1 1 Np ? p? 1 Z p? 1 N (1+r) p? 1 = X0 (1+r) p? 1 E[Z p p? 1 Z p? 1 N (1+r) p? 1 = (1+r)N X0 Z p? 1 E[Z p p? 1 1 . ] ] 3. 8. (i) 9 d d Proof. x (U (x) ? yx) = U (x) ? y. So x = I(y) is an extreme point of U (x) ? yx. Because dx2 (U (x) ? yx) = U (x) ? 0 (U is concave), x = I(y) is a maximum point. Therefore U (x) ? y(x) ? U (I(y)) ? yI(y) for every x. 2 (ii) Proof. Following the hint of the problem, we have E[U (XN )] ? E[XN ? Z ? Z ? Z ? Z ] ? E[U (I( ))] ? E[ I( )], N N N (1 + r ) (1 + r) (1 + r) (1 + r)N ? ? ? ? ? i. e. E[U (XN )] ? ?X0 ? E[U (XN )] ? E[ (1+r)N XN ] = E[U (XN )] ? ?X0 . So E[U (XN )] ? E[U (XN )]. 3. 9. (i) X Proof. Xn = En [ (1+r)N ? n ]. So if XN ? 0, then Xn ? 0 for all n. N (ii) 1 Proof. a) If 0 ? x ? and 0 y ? ? , then U (x) ? yx = ? yx ? and U (I(y)) ? yI(y) = U (? ) ? y? = 1 ? y? ? 0. So U (x) ? yx ? U (I(y)) ? yI(y). 1 b) If 0 ? x ? and y ? , then U (x) ? yx = ? yx ? 0 and U (I(y)) ? yI(y) = U (0) ? y  · 0 = 0. So U (x) ? yx ? U (I(y)) ? yI(y). 1 c) If x ? ? and 0 y ? ? , then U (x) ? yx = 1 ? yx and U (I(y)) ? yI(y) = U (? ) ? y? = 1 ? y? ? 1 ? yx. So U (x) ? yx ? U (I(y)) ? yI(y). 1 d) If x ? ? and y ? , then U (x) ? yx = 1 ? yx 0 and U (I(y)) ? yI(y) = U (0) ? y  · 0 = 0. So U (x) ? yx ? U (I(y)) ? yI(y). (iii) XN ? Z Proof. Using (ii) and set x = XN , y = (1+r)N , where XN is a random variable satisfying E[ (1+r)N ] = X0 , we have ? Z ? Z ? E[U (XN )] ? E[ XN ] ? E[U (XN )] ? E[ X ? ]. (1 + r)N (1 + r)N N ? ? That is, E[U (XN )] ? ?X0 ? E[U (XN )] ? ?X0 . So E[U (XN )] ? E[U (XN )]. (iv) Proof. Plug pm and ? m into (3. 6. 4), we have 2N 2N X0 = m=1 pm ? m I( m ) = m=1 1 pm ? m ? 1{ m ? ? } . So X0 ? X0 ? {m : = we are looking for positive solution ? 0). Conversely, suppose there exists some K so that ? K ? K+1 and K X0 1 m=1 ? m pm = ? . Then we can ? nd ? 0, such that ? K ? K+1 . For such ? , we have Z ? Z 1 E[ I( )] = pm ? m 1{ m ? ? } ? = pm ? m ? = X0 . N (1 + r) (1 + r)N m=1 m=1 Hence (3. 6. 4) has a solution. 0 2N K 2N X0 1 m=1 pm ? m 1{ m ? ? } . Suppose there is a solution ? to (3. 6. 4), note ? 0, we then can conclude 1 1 1 m ? ? } = ?. Let K = max{m : m ? ? }, then K ? ? K+1 . So ? K ? K+1 and K N m=1 pm ? m (Note, however, that K could be 2 . In this case, ? K+1 is interpreted as ?. Also, note = (v) ? 1 Proof. XN (? m ) = I( m ) = ? 1{ m ? ? } = ?, if m ? K . 0, if m ? K + 1 4. American D erivative Securities Before proceeding to the exercise problems, we ? rst give a brief summary of pricing American derivative securities as presented in the textbook. We shall use the notation of the book. From the buyer’s perspective: At time n, if the derivative security has not been exercised, then the buyer can choose a policy ? with ? ? Sn . The valuation formula for cash ? ow (Theorem 2. 4. 8) gives a fair price for the derivative security exercised according to ? : N Vn (? ) = k=n En 1{? =k} 1 1 Gk = En 1{? ?N } G? . (1 + r)k? n (1 + r)? ?n The buyer wants to consider all the possible ? ’s, so that he can ? nd the least upper bound of security value, which will be the maximum price of the derivative security acceptable to him. This is the price given by 1 De? nition 4. 4. 1: Vn = max? ?Sn En [1{? ?N } (1+r)? n G? ]. From the seller’s perspective: A price process (Vn )0? n? N is acceptable to him if and only if at time n, he can construct a portfolio at cost Vn so that (i) Vn ? Gn and (ii) he needs no further investing into the portfolio as time goes by. Formally, the seller can ? nd (? n )0? n? N and (Cn )0? n? N so that Cn ? 0 and Sn Vn+1 = ? n Sn+1 + (1 + r)(Vn ? Cn ? ?n Sn ). Since ( (1+r)n )0? n? N is a martingale under the risk-neutral measure P , we conclude En Cn Vn+1 Vn =? ? 0, ? n+1 n (1 + r) (1 + r) (1 + r)n Vn i. e. ( (1+r)n )0? n? N is a supermartingale. This inspired us to check if the converse is also true. This is exactly the content of Theorem 4. 4. 4. So (Vn )0? n? N is the value process of a portfolio that needs no further investing if and only if Vn (1+r)n Vn (1+r)n is a supermartingale under P (note this is independent of the requirement 0? n? N Vn ? Gn ). In summary, a price process (Vn )0? n? N is acceptable to the seller if and only if (i) Vn ? Gn ; (ii) is a supermartingale under P . 0? n? N Theorem 4. 4. 2 shows the buyer’s upper bound is the seller’s lower bound. So it gives the price acceptable to both. Theorem 4. 4. 3 gives a speci? c algorithm for calculating the price, Theorem 4. 4. establishes the one-to-one correspondence between super-replication and supermartingale property, and ? nally, Theorem 4. 4. 5 shows how to decide on the optimal exercise policy. 4. 1. (i) Proof. V2P (HH) = 0, V2P (HT ) = V2P (T H) = 0. 8, V2P (T T ) = 3, V1P (H) = 0. 32, V1P (T ) = 2, V0P = 9. 28. (ii) Proof. V0C = 5. (iii) Proof. gS (s) = |4 ? s|. We apply Theorem 4. 4. 3 and have V2S (HH) = 12. 8, V2S (HT ) = V2S (T H) = 2. 4, V2S (T T ) = 3, V1S (H) = 6. 08, V1S (T ) = 2. 16 and V0S = 3. 296. (iv) 11 Proof. First, we note the simple inequality max(a1 , b1 ) + max(a2 , b2 ) ? max(a1 + a2 , b1 + b2 ). † holds if and only if b1 a1 , b2 a2 or b1 a1 , b2 a2 . By induction, we can show S Vn = max gS (Sn ), S S pVn+1 + Vn+1 1+r C P P pV C + Vn+1 pVn+1 + Vn+1 + n+1 1+r 1+r C C pVn+1 + Vn+1 1+r ? max gP (Sn ) + gC (Sn ), ? max gP (Sn ), P C = Vn + Vn . P P pVn+1 + Vn+1 1+r + max gC (Sn ), S P C As to when â€Å" C C pVn+1 +qVn+1 1+r or gP (Sn ) P P pVn+1 +qVn+1 1+r and gC (Sn ) C C pVn+1 +qVn+1 }. 1+r 4. 2. Proof. For this problem, we need Figure 4. 2. 1, Figure 4. 4. 1 and Figure 4. 4. 2. Then ? 1 (H) = and ? 0 = V2 (HH) ? V2 (HT ) 1 V2 (T H) ? V2 (T T ) = ? , ? 1 (T ) = = ? 1, S2 (HH) ? S2 (HT ) 12 S2 (T H) ? S2 (T T ) V1 (H) ? V1 (T ) ? ?0. 433. S1 (H) ? S1 (T ) The optimal exercise time is ? = inf{n : Vn = Gn }. So ? (HH) = ? , ? (HT ) = 2, ? (T H) = ? (T T ) = 1. Therefore, the agent borrows 1. 36 at time zero and buys the put. At the same time, to hedge the long position, he needs to borrow again and buy 0. 433 shares of stock at time zero. At time one, if the result of coin toss is tail and the stock price goes down to 2, the value of the portfolio 1 is X1 (T ) = (1 + r)(? 1. 36 ? 0. 433S0 ) + 0. 433S1 (T ) = (1 + 4 )(? 1. 36 ? 0. 433 ? 4) + 0. 433 ? 2 = ? 3. The agent should exercise the put at time one and get 3 to pay o? is debt. At time one, if the result of coin toss is head and the stock price goes up to 8, the value of the portfolio 1 is X1 (H) = (1 + r)(? 1. 36 ? 0. 433S0 ) + 0. 433S1 (H) = ? 0. 4. The agent should borrow to buy 12 shares of stock. At time two, if the result of coin toss is head and the stock price goes up to 16, the value of the 1 1 portfolio is X2 (HH) = (1 + r)(X1 (H) ? 12 S1 (H)) + 12 S2 (HH) = 0, and the agent should let the put expire. If at time two, the result of coin toss is tail and the stock price goes down to 4, the value of the portfolio is 1 1 X2 (HT ) = (1 + r)(X1 (H) ? 12 S1 (H)) + 12 S2 (HT ) = ? 1. The agent should exercise the put to get 1. This will pay o? his debt. 4. 3. Proof. We need Figure 1. 2. 2 for this problem, and calculate the intrinsic value process and price process of the put as follows. 2 For the intrinsic value process, G0 = 0, G1 (T ) = 1, G2 (T H) = 3 , G2 (T T ) = 5 , G3 (T HT ) = 1, 3 G3 (T T H) = 1. 75, G3 (T T T ) = 2. 125. All the other outcomes of G is negative. 12 2 5 For the price process, V0 = 0. 4, V1 (T ) = 1, V1 (T H) = 3 , V1 (T T ) = 3 , V3 (T HT ) = 1, V3 (T T H) = 1. 75, V3 (T T T ) = 2. 125. All the other outcomes of V is zero. Therefore the time-zero price of the derivative security is 0. and the optimal exercise time satis? es ? (? ) = ? if ? 1 = H, 1 if ? 1 = T . 4. 4. Proof. 1. 36 is the cost of super-replicating the American derivative security. It enables us to construct a portfolio su? cient to pay o? the derivative security, no matter when the derivative security is exercised. So to hedge our short position after selling the put, ther e is no need to charge the insider more than 1. 36. 4. 5. Proof. The stopping times in S0 are (1) ? ? 0; (2) ? ? 1; (3) ? (HT ) = ? (HH) = 1, ? (T H), ? (T T ) ? {2, ? } (4 di? erent ones); (4) ? (HT ), ? (HH) ? {2, ? }, ? (T H) = ? (T T ) = 1 (4 di? rent ones); (5) ? (HT ), ? (HH), ? (T H), ? (T T ) ? {2, ? } (16 di? erent ones). When the option is out of money, the following stopping times do not exercise (i) ? ? 0; (ii) ? (HT ) ? {2, ? }, ? (HH) = ? , ? (T H), ? (T T ) ? {2, ? } (8 di? erent ones); (iii) ? (HT ) ? {2, ? }, ? (HH) = ? , ? (T H) = ? (T T ) = 1 (2 di? erent ones). ? 4 For (i), E[1{? ?2} ( 4 )? G? ] = G0 = 1. For (ii), E[1{? ?2} ( 5 )? G? ] ? E[1{? ? ? 2} ( 4 )? G? ? ], where ? ? (HT ) = 5 5 1 4 4 ? 2, ? ? (HH) = ? , ? ? (T H) = ? ? (T T ) = 2. So E[1{? ? ? 2} ( 5 )? G? ? ] = 4 [( 4 )2  · 1 + ( 5 )2 (1 + 4)] = 0. 96. For 5 (iii), E[1{? ?2} ( 4 )? G? has the biggest value when ? satis? es ? (HT ) = 2, ? (HH) = ? , ? (T H) = ? (T T ) = 1. 5 This value is 1. 36. 4. 6. (i) Proof. The value of the put at time N , if it is not exercised at previous times, is K ? SN . Hence VN ? 1 = VN K max{K ? SN ? 1 , EN ? 1 [ 1+r ]} = max{K ? SN ? 1 , 1+r ? SN ? 1 } = K ? SN ? 1 . The second equality comes from the fact that discounted stock price process is a martingale under risk-neutral probability. By induction, we can show Vn = K ? Sn (0 ? n ? N ). So by Theorem 4. 4. 5, the optimal exercise policy is to sell the stock at time zero and the value of this derivative security is K ? S0 . Remark: We cheated a little bit by using American algorithm and Theorem 4. 4. 5, since they are developed for the case where ? is allowed to be ?. But intuitively, results in this chapter should still hold for the case ? ? N , provided we replace â€Å"max{Gn , 0}† with â€Å"Gn †. (ii) Proof. This is because at time N , if we have to exercise the put and K ? SN 0, we can exercise the European call to set o? the negative payo?. In e? ect, throughout the portfolio’s lifetime, the portfolio has intrinsic values greater than that of an American put stuck at K with expiration time N . So, we must have V0AP ? V0 + V0EC ? K ? S0 + V0EC . (iii) 13 Proof. Let V0EP denote the time-zero value of a European put with strike K and expiration time N . Then V0AP ? V0EP = V0EC ? E[ K SN ? K ] = V0EC ? S0 + . (1 + r)N (1 + r)N 4. 7. VN K K Proof. VN = SN ? K, VN ? 1 = max{SN ? 1 ? K, EN ? 1 [ 1+r ]} = max{SN ? 1 ? K, SN ? 1 ? 1+r } = SN ? 1 ? 1+r . K By induction, we can prove Vn = Sn ? (1+r)N ? n (0 ? n ? N ) and Vn Gn for 0 ? n ? N ? 1. So the K time-zero value is S0 ? (1+r)N and the optimal exercise time is N . 5. Random Walk 5. 1. (i) Proof. E[ 2 ] = E[? (? 2 1 )+? 1 ] = E[? (? 2 1 ) ]E[ 1 ] = E[ 1 ]2 . (ii) Proof. If we de? ne Mn = Mn+? ? M? m (m = 1, 2,  ·  ·  · ), then (M · )m as random functions are i. i. d. with (m) distributions the same as that of M . So ? m+1 ? ?m = inf{n : Mn = 1} are i. i. d. with distributions the same as that of ? 1 . Therefore E[ m ] = E[? (? m m? 1 )+(? m? 1 m? 2 )+ ·Ã‚ ·Ã‚ ·+? 1 ] = E[ 1 ]m . (m) (m) (iii) Proof. Yes, since the argument of (ii) still works for asymm etric random walk. 5. 2. (i) Proof. f (? ) = pe? ? qe , so f (? ) 0 if and only if ? f (? ) f (0) = 1 for all ? 0. (ii) 1 1 1 n+1 Proof. En [ SSn ] = En [e? Xn+1 f (? ) ] = pe? f (? ) + qe f (? ) = 1. 1 2 (ln q ? ln p). Since 1 2 (ln q ln p) 0, (iii) 1 Proof. By optional stopping theorem, E[Sn 1 ] = E[S0 ] = 1. Note Sn 1 = e? Mn 1 ( f (? ) )n 1 ? e?  ·1 , by bounded convergence theorem, E[1{? 1 1 for all ? ? 0 . v (ii) 1 1 Proof. As in Exercise 5. 2, Sn = e? Mn ( f (? ) )n is a martingale, and 1 = E[S0 ] = E[Sn 1 ] = E[e? Mn 1 ( f (? ) )? 1 ? n ]. Suppose ? ? 0 , then by bounded convergence theorem, 1 = E[ lim e? Mn 1 ( n? 1 n 1 1 ? 1 ) ] = E[1{? 1 K} ] = P (ST K). Moreover, by Girsanov’s Theorem, Wt = Wt + in Theorem 5. 4. 1. ) (iii) Proof. ST = xe? WT +(r? 2 ? 1 2 1 2 t ( )du 0 = Wt ? ?t is a P -Brownian motion (set ? )T = xe? WT +(r+ 2 ? 1 2 1 2 )T . So WT v ? d+ (T, x) T = N (d+ (T, x)). P (ST K) = P (xe? WT +(r+ 2 ? )T K) = P 46 5. 4. First, a few typos. In the SDE for S, â€Å"? (t)dW (t)† â€Å"? (t)S(t)dW (t)†. In the ? rst equation for c(0, S(0)), E E. In the second equation for c(0, S(0)), the variable for BSM should be ? ? 1 T 2 1 T r(t)dt, ? (t)dt? . BSM ? T, S(0); K, T 0 T 0 (i) Proof. d ln St = X = ? is a Gaussian with X ? N ( (ii) Proof. For the standard BSM model with constant volatility ? and interest rate R, under the risk-neutral measure, we have ST = S0 eY , where Y = (R? 1 ? 2 )T +? WT ? N ((R? 1 ? )T, ? 2 T ), and E[(S0 eY ? K)+ ] = 2 2 eRT BSM (T, S0 ; K, R, ? ). Note R = 1 T (rt 0 T T dSt 1 2 1 1 2 2 St ? 2St d S t = rt dt + ? t dWt ? 2 ? t dt. So ST = S0 exp{ 0 (rt ? 2 ? t )dt + 0 T 1 2 2 ? t )dt + 0 ? t dWt . The ? rst term in the expression of X is a number and the T 2 random variable N (0, 0 ? t dt), since both r and ? ar deterministic. Therefore, T T 2 2 (rt ? 1 ? t )dt, 0 ? t dt),. 2 0 ?t dWt }. Let second term ST = S0 eX , 1 T (E[Y ] + 1 V ar(Y )) and ? = 2 T, S0 ; K, 1 T 1 T V ar(Y ), we can get 1 V ar(Y ) . T E[(S0 eY ? K)+ ] = eE[Y ]+ 2 V ar(Y ) BSM So for the model in this problem, c(0, S0 ) = = e? ? T 0 1 E[Y ] + V ar(Y ) , 2 rt dt E[(S0 eX ? K)+ ] e BSM T, S0 ; K, 1 T T 0 T 0 1 rt dt E[X]+ 2 V ar(X) 1 T ? 1 E[X] + V ar(X) , 2 1 V ar(X) T ? = 1 BSM ? T, S0 ; K, T 0 T rt dt, 2 ? t dt? . 5. 5. (i) 1 1 Proof. Let f (x) = x , then f (x) = ? x2 and f (x) = 2 x3 . Note dZt = ? Zt ? t dWt , so d 1 Zt 1 1 1 2 2 2 ? t ? 2 t = f (Zt )dZt + f (Zt )dZt dZt = ? 2 (? Zt )? t dWt + 3 Zt ? t dt = Z dWt + Z dt. 2 Zt 2 Zt t t (ii) Proof. By Lemma 5. 2. 2. , for s, t ? 0 with s t, Ms = E[Mt |Fs ] = E Zs Ms . So M = Z M is a P -martingale. (iii) Zt Mt Zs |Fs . That is, E[Zt Mt |Fs ] = 47 Proof. dMt = d Mt  · 1 Zt = 1 1 1 ? M t ? t M t ? 2 ? t ? t t dMt + Mt d + dMt d = dWt + dWt + dt + dt. Zt Zt Zt Zt Zt Zt Zt (iv) Proof. In part (iii), we have dMt = Let ? t = 5. 6. Proof. By Theorem 4. 6. 5, it su? ces to show Wi (t) is an Ft -martingale under P and [Wi , Wj ](t) = t? ij (i, j = 1, 2). Indeed, for i = 1, 2, Wi (t) is an Ft -martingale under P if and only if Wi (t)Zt is an Ft -martingale under P , since Wi (t)Zt E[Wi (t)|Fs ] = E |Fs . Zs By It? ’s product formula, we have o d(Wi (t)Zt ) = Wi (t)dZt + Zt dWi (t) + dZt dWi (t) = Wi (t)(? Zt )? (t)  · dWt + Zt (dWi (t) + ? i (t)dt) + (? Zt ? t  · dWt )(dWi (t) + ? i (t)dt) d t M t ? t M t ? 2 ? t ? t ? t M t ? t t dWt + dWt + dt + dt = (dWt + ? t dt) + (dWt + ? t dt). Zt Zt Zt Zt Zt Zt then dMt = ? t dWt . This proves Corollary 5. 3. 2. ?t +Mt ? t , Zt = Wi (t)(? Zt ) j=1 d ?j (t)dWj (t) + Zt (dWi (t) + ? i (t)dt) ? Zt ? i (t)dt = Wi (t)(? Zt ) j=1 ?j (t)dWj (t) + Zt dWi (t) This shows Wi (t)Zt is an Ft -martingale under P . So Wi (t) is an Ft -martingale under P . Moreover,  ·  · [Wi , Wj ](t) = Wi + 0 ?i (s)ds, Wj + 0 ?j (s)ds (t) = [Wi , Wj ](t) = t? ij . Combined, this proves the two-dimensional Girsanov’s Theorem. 5. 7. (i) Proof. Let a be any strictly positive number. We de? e X2 (t) = (a + X1 (t))D(t)? 1 . Then P X2 (T ) ? X2 (0) D(T ) = P (a + X1 (T ) ? a) = P (X1 (T ) ? 0) = 1, and P X2 (T ) X2 (0) = P (X1 (T ) 0) 0, since a is arbitrary, we have proved the claim of this problem. D(T ) Remark: The intuition is that we invest the positive starting fund a into the money market account, and construct portfolio X1 from zero cost. Their sum should be able to beat the return of money market account. (ii) 48 Proof. We de? ne X1 (t) = X2 (t)D(t) ? X2 (0). Then X1 (0) = 0, P (X1 (T ) ? 0) = P X2 (T ) ? X2 (0) D(T ) = 1, P (X1 (T ) 0) = P X2 (T ) X2 (0) D(T ) 0. 5. 8. The basic idea is that for any positive P -martingale M , dMt = Mt  · sentation Theorem, dMt = ? t dWt for some adapted process ? t . So martingale must be the exponential of an integral w. r. t. Brownian motion. Taking into account discounting factor and apply It? ’s product rule, we can show every strictly positive asset is a generalized geometric o Brownian motion. (i) Proof. Vt Dt = E[e? 0 Ru du VT |Ft ] = E[DT VT |Ft ]. So (Dt Vt )t? 0 is a P -martingale. By Martingale Represent tation Theorem, there exists an adapted process ? t , 0 ? t ? T , such that Dt Vt = 0 ? s dWs , or equivalently, ? 1 t ? 1 t ? 1 Vt = Dt 0 ? dWs . Di? erentiate both sides of the equation, we get dVt = Rt Dt 0 ? s dWs dt + Dt ? t dWt , i. e. dVt = Rt Vt dt + (ii) Proof. We prove the following more general lemma. Lemma 1. Let X be an almost surely positive random variable (i. e. X 0 a. s. ) de? ned on the probability space (? , G, P ). Let F be a sub ? -algebra of G, then Y = E[X|F] 0 a. s. Pro of. By the property of conditional expectation Yt ? 0 a. s. Let A = {Y = 0}, we shall show P (A) = 0. In? 1 1 deed, note A ? F, 0 = E[Y IA ] = E[E[X|F]IA ] = E[XIA ] = E[X1A? {X? 1} ] + n=1 E[X1A? { n X? n+1 } ] ? 1 1 1 1 1 P (A? {X ? 1})+ n=1 n+1 P (A? n X ? n+1 }). So P (A? {X ? 1}) = 0 and P (A? { n X ? n+1 }) = 0, ? 1 1 ? n ? 1. This in turn implies P (A) = P (A ? {X 0}) = P (A ? {X ? 1}) + n=1 P (A ? { n X ? n+1 }) = 0. ? ? t Dt dWt . T 1 Mt dMt . By Martingale Repre? dMt = Mt ( Mtt )dWt , i. e. any positive By the above lemma, it is clear that for each t ? [0, T ], Vt = E[e? t Ru du VT |Ft ] 0 a. s.. Moreover, by a classical result of martingale theory (Revuz and Yor [4], Chapter II, Proposition (3. 4)), we have the following stronger result: for a. s. ?, Vt (? ) 0 for any t ? [0, T ]. (iii) 1 1 Proof. By (ii), V 0 a. s. so dVt = Vt Vt dVt = Vt Vt Rt Vt dt + ? t Dt dWt ? t = Vt Rt dt + Vt Vt Dt dWt = Rt Vt dt + T ?t Vt dWt , where ? t = 5. 9. ?t Vt Dt . This shows V fol lows a generalized geometric Brownian motion. Proof. c(0, T, x, K) = xN (d+ ) ? Ke? rT N (d? ) with d ± = then f (y) = ? yf (y), cK (0, T, x, K) = xf (d+ ) 1 v ? T x (ln K + (r  ± 1 ? 2 )T ). Let f (y) = 2 y v1 e? 2 2? 2 , ?d+ ? d? ? e? rT N (d? ) ? Ke? rT f (d? ) ? y ? y ? 1 1 = xf (d+ ) v ? e? rT N (d? ) + e? rT f (d? ) v , ? TK ? T 49 and cKK (0, T, x, K) x ? d? e? rT 1 ? d+ d? ? v ? e? rT f (d? ) + v (? d? )f (d? ) xf (d+ ) v f (d+ )(? d+ ) 2 ? y ? y ? y ? TK ? TK ? T x xd+ ? 1 ? 1 e? rT d? ?1 v v ? e? rT f (d? ) v ? v f (d? ) v f (d+ ) + v f (d+ ) ? T K2 ? TK K? T K? T ? T K? T x d+ e? rT f (d? ) d? v [1 ? v ] + v f (d+ ) [1 + v ] 2? T K ? T K? T ? T e? rT x f (d? )d+ ? 2 2 f (d+ )d? . K? 2 T K ? T = = = = 5. 10. (i) Proof. At time t0 , the value of the chooser option is V (t0 ) = max{C(t0 ), P (t0 )} = max{C(t0 ), C(t0 ) ? F (t0 )} = C(t0 ) + max{0, ? F (t0 )} = C(t0 ) + (e? r(T ? t0 ) K ? S(t0 ))+ . (ii) Proof. By the risk-neutral pricing formula, V (0) = E[e? rt0 V (t0 )] = E[e? rt0 C(t0 )+(e? rT K ? e? rt0 S(t0 )+ ] = C(0) + E[e? rt0 (e? r(T ? t0 ) K ? S(t0 ))+ ]. The ? st term is the value of a call expiring at time T with strike price K and the second term is the value of a put expiring at time t0 with strike price e? r(T ? t0 ) K. 5. 11. Proof. We ? rst make an analysis which leads to the hint, then we give a formal proof. (Analysis) If we want to construct a portfolio X that exactly replicates the cash ? ow, we must ? nd a solution to t he backward SDE dXt = ? t dSt + Rt (Xt ? ?t St )dt ? Ct dt XT = 0. Multiply Dt on both sides of the ? rst equation and apply It? ’s product rule, we get d(Dt Xt ) = ? t d(Dt St ) ? o T T Ct Dt dt. Integrate from 0 to T , we have DT XT ? D0 X0 = 0 ? d(Dt St ) ? 0 Ct Dt dt. By the terminal T T ? 1 condition, we get X0 = D0 ( 0 Ct Dt dt ? 0 ? t d(Dt St )). X0 is the theoretical, no-arbitrage price of the cash ? ow, provided we can ? nd a trading strategy ? that solves the BSDE. Note the SDE for S ? R gives d(Dt St ) = (Dt St )? t (? t dt + dWt ), where ? t = ? t? t t . Take the proper change of measure so that Wt = t ? ds 0 s + Wt is a Brownian motion under the new measure P , we get T T T Ct Dt dt = D0 X0 + 0 T 0 ?t d(Dt St ) = D0 X0 + 0 ?t (Dt St )? t dWt . T This says the random variable 0 Ct Dt dt has a stochastic integral representation D0 X0 + 0 ? t Dt St ? dWt . T This inspires us to consider the martingale generated by 0 Ct Dt dt, so that we can apply Martingale Represen tation Theorem and get a formula for ? by comparison of the integrands. 50 (Formal proof) Let MT = Xt = ?1 Dt (D0 X0 T 0 Ct Dt dt, and Mt = E[MT |Ft ]. Then by Martingale Representation Theot 0 rem, we can ? nd an adapted process ? t , so that Mt = M0 + + t 0 ?t dWt . If we set ? t = T 0 ?u d(Du Su ) ? t 0 ?t Dt St ? t , we can check Cu Du du), with X0 = M0 = E[ Ct Dt dt] solves the SDE dXt = ? t dSt + Rt (Xt ? ?t St )dt ? Ct dt XT = 0. Indeed, it is easy to see that X satis? es the ? rst equation. To check the terminal condition, we note T T T XT DT = D0 X0 + 0 ? t Dt St ? t dWt ? 0 Ct Dt dt = M0 + 0 ? t dWt ? MT = 0. So XT = 0. Thus, we have found a trading strategy ? , so that the corresponding portfolio X replicates the cash ? ow and has zero T terminal value. So X0 = E[ 0 Ct Dt dt] is the no-arbitrage price of the cash ? ow at time zero. Remark: As shown in the analysis, d(Dt Xt ) = ? t d(Dt St ) ? Ct Dt dt. Integrate from t to T , we get T T 0 ? Dt Xt = t ? u d(Du Su ) ? t Cu Du du. Take conditional expectation w. r. t. Ft on both sides, we get T T ? 1 ? Dt Xt = ? E[ t Cu Du du|Ft ]. So Xt = Dt E[ t Cu Du du|Ft ]. This is the no-arbitrage price of the cash ? ow at time t, and we have justi? ed formula (5. 6. 10) in the textbook. 5. 12. (i) Proof. dBi (t) = dBi (t) + ? i (t)dt = martingale. Since dBi (t)dBi (t) = P. (ii) Proof. dSi (t) = = = R(t)Si (t)dt + ? i (t)Si (t)dBi (t) + (? i (t) ? R(t))Si (t)dt ? ?i (t)Si (t)? i (t)dt d d ? ij (t) ? ij (t) d d j=1 ? i (t) ? j (t)dt = j=1 ? i (t) dWj (t) + ? ij (t)2 d e j=1 ? i (t)2 dt = dt, by L? vy’s Theorem, Bi ? ij (t) d j=1 ? i (t) dWj (t). So Bi is a is a Brownian motion under R(t)Si (t)dt + ? i (t)Si (t)dBi (t) + j=1 ?ij (t)? j (t)Si (t)dt ? Si (t) j=1 ?ij (t)? j (t)dt R(t)Si (t)dt + ? (t)Si (t)dBi (t). (iii) Proof. dBi (t)dBk (t) = (dBi (t) + ? i (t)dt)(dBj (t) + ? j (t)dt) = dBi (t)dBj (t) = ? ik (t)dt. (iv) Proof. By It? ’s product rule and martingale property, o t t t E[Bi (t)Bk (t)] = E[ 0 t Bi (s)dBk (s)] + E[ 0 t Bk (s)dBi (s)] + E[ 0 dBi (s)dBk (s)] = E[ 0 ?ik (s)ds] = 0 ?ik (s)ds. t 0 Similarly, by part (iii), we can show E [Bi (t)Bk (t)] = (v) ?ik (s)ds. 51 Proof. By It? ’s product formula, o t t E[B1 (t)B2 (t)] = E[ 0 sign(W1 (u))du] = 0 [P (W1 (u) ? 0) ? P (W1 (u) 0)]du = 0. Meanwhile, t E[B1 (t)B2 (t)] = E[ 0 t sign(W1 (u))du [P (W1 (u) ? 0) ? P (W1 (u) 0)]du = 0 t = 0 t [P (W1 (u) ? ) ? P (W1 (u) u)]du 2 0 = 0, 1 ? P (W1 (u) u) du 2 for any t 0. So E[B1 (t)B2 (t)] = E[B1 (t)B2 (t)] for all t 0. 5. 13. (i) Proof. E[W1 (t)] = E[W1 (t)] = 0 and E[W2 (t)] = E[W2 (t) ? (ii) Proof. Cov[W1 (T ), W2 (T )] = E[W1 (T )W2 (T )] T T t 0 W1 (u)du] = 0, for all t ? [0, T ]. = E 0 T W1 (t)dW2 (t) + 0 W2 (t)dW1 (t) T = E 0 W1 (t)(dW2 (t) ? W1 (t)dt) + E 0 T W2 (t)dW1 (t) = ? E 0 T W1 (t)2 dt tdt = ? 0 1 = ? T 2. 2 5. 14. Equation (5. 9. 6) can be transformed into d(e? rt Xt ) = ? t [d(e? rt St ) ? ae? rt dt] = ? t e? rt [dSt ? rSt dt ? adt]. So, to make the discounted portfolio value e? t Xt a martingale, we are motivated to change the measure t in such a way that St ? r 0 Su du? at is a martingale under the new measure. To do this, we note the SDE for S is dSt = ? t St dt+? St dWt . Hence dSt ? rSt dt? adt = [(? t ? r)St ? a]dt+? St dWt = ? St Set ? t = (? t ? r)St ? a ? St (? t ? r)St ? a dt ? St + dWt . and Wt = t ? ds 0 s + Wt , we can ? nd an equivalent probability measure P , under which S satis? es the SDE dSt = rSt dt + ? St dWt + adt and Wt is a BM. This is the rational for formula (5. 9. 7). This is a good place to pause and think about the meaning of â€Å"martingale measure. † What is to be a martingale? The new measure P should be such that the discounted value process of the replicating 52 portfolio is a martingale, not the discounted price process of the underlying. First, we want Dt Xt to be a martingale under P because we suppose that X is able to replicate the derivative payo? at terminal time, XT = VT . In order to avoid arbitrage, we must have Xt = Vt for any t ? [0, T ]. The di? culty is how to calculate Xt and the magic is brought by the martingale measure in the following line of reasoning: ? 1 ? 1 Vt = Xt = Dt E[DT XT |Ft ] = Dt E[DT VT |Ft ]. You can think of martingale measure as a calculational convenience. That is all about martingale measure! Risk neutral is a just perception, referring to the actual e? ect of constructing a hedging portfolio! Second, we note when the portfolio is self-? nancing, the discounted price process of the underlying is a martingale under P , as in the classical Black-Scholes-Merton model without dividends or cost of carry. This is not a coincidence. Indeed, we have in this case the relation d(Dt Xt ) = ? t d(Dt St ). So Dt Xt being a martingale under P is more or less equivalent to Dt St being a martingale under P . However, when the underlying pays dividends, or there is cost of carry, d(Dt Xt ) = ? d(Dt St ) no longer holds, as shown in formula (5. 9. 6). The portfolio is no longer self-? nancing, but self-? nancing with consumption. What we still want to retain is the martingale property of Dt Xt , not that of Dt St . This is how we choose martingale measure in the above paragraph. Let VT be a payo? at time T , then for the martingale Mt = E[e? rT VT |Ft ], by Martingale Representation rt t Theorem, we can ? nd an adapted process ? t , so that Mt = M0 + 0 ? s dWs . If we let ? t = ? t e t , then the ? S value of the corresponding portfolio X satis? es d(e? rt Xt ) = ? t dWt . So by setting X0 = M0 = E[e? T VT ], we must have e? rt Xt = Mt , for all t ? [0, T ]. In particular, XT = VT . Thus the portfolio perfectly hedges VT . This justi? es the risk-neutral pricing of European-type contingent claims in the model where cost of carry exists. Also note the risk-neutral measure is di? erent from the one in case of no cost of carry. Another perspective for perfect replication is the following. We need to solve the backward SDE dXt = ? t dSt ? a? t dt + r(Xt ? ?t St )dt XT = VT for two unknowns, X and ?. To do so, we ? nd a probability measure P , under which e? rt Xt is a martingale, t then e? rt Xt = E[e? T VT |Ft ] := Mt . Martingale Representation Theorem gives Mt = M0 + 0 ? u dWu for some adapted process ?. This would give us a theor etical representation of ? by comparison of integrands, hence a perfect replication of VT . (i) Proof. As indicated in the above analysis, if we have (5. 9. 7) under P , then d(e? rt Xt ) = ? t [d(e? rt St ) ? ae? rt dt] = ? t e? rt ? St dWt . So (e? rt Xt )t? 0 , where X is given by (5. 9. 6), is a P -martingale. (ii) 1 1 Proof. By It? ’s formula, dYt = Yt [? dWt + (r ? 2 ? 2 )dt] + 2 Yt ? 2 dt = Yt (? dWt + rdt). So d(e? rt Yt ) = o t a ? e? rt Yt dWt and e? rt Yt is a P -martingale. Moreover, if St = S0 Yt + Yt 0 Ys ds, then t dSt = S0 dYt + 0 a dsdYt + adt = Ys t S0 + 0 a ds Yt (? dWt + rdt) + adt = St (? dWt + rdt) + adt. Ys This shows S satis? es (5. 9. 7). Remark: To obtain this formula for S, we ? rst set Ut = e? rt St to remove the rSt dt term. The SDE for U is dUt = ? Ut dWt + ae? rt dt. Just like solving linear ODE, to remove U in the dWt term, we consider Vt = Ut e Wt . It? ’s product formula yields o dVt = = e Wt dUt + Ut e Wt 1 ( )dWt + ? 2 dt + dUt  · e Wt 2 1 ( )dWt + ? 2 dt 2 1 e Wt ae? rt dt ? ? 2 Vt dt. 2 53 Note V appears only in the dt term, so multiply the integration factor e 2 ? e get 1 2 1 2 d(e 2 ? t Vt ) = ae? rt Wt + 2 ? t dt. Set Yt = e? Wt +(r? 2 ? (iii) Proof. t 1 2 1 2 t on both sides of the equation, )t , we have d(St /Yt ) = adt/Yt . So St = Yt (S0 + t ads ). 0 Ys E[ST |Ft ] = S0 E[YT |Ft ] + E YT 0 t a ds + YT Ys T t T a ds|Ft Ys E YT |Ft ds Ys E[YT ? s ]ds t = S0 E[YT |Ft ] + 0 a dsE[YT |Ft ] + a Ys t t T = S0 Yt E[YT ? t ] + 0 t a dsYt E[YT ? t ] + a Ys T t = = S0 + 0 t a ds Yt er(T ? t) + a Ys ads Ys er(T ? s) ds S0 + 0 a Yt er(T ? t) ? (1 ? er(T ? t) ). r In particular, E[ST ] = S0 erT ? a (1 ? erT ). r (iv) Proof. t dE[ST |Ft ] = aer(T ? t) dt + S0 + 0 t ads Ys a (er(T ? ) dYt ? rYt er(T ? t) dt) + er(T ? t) (? r)dt r = S0 + 0 ads Ys er(T ? t) ? Yt dWt . So E[ST |Ft ] is a P -martingale. As we have argued at the beginning of the solution, risk-neutral pricing is valid even in the presence of cost of carry. So by an argument similar to that of  §5. 6. 2, the process E[ST |Ft ] is the futures price process for the commodity. (v) Proof. We solve the equation E[e? r(T ? t) (ST ? K)|Ft ] = 0 for K, and get K = E[ST |Ft ]. So F orS (t, T ) = F utS (t, T ). (vi) Proof. We follow the hint. First, we solve the SDE dXt = dSt ? adt + r(Xt ? St )dt X0 = 0. By our analysis in part (i), d(e? t Xt ) = d(e? rt St ) ? ae? rt dt. Integrate from 0 to t on both sides, we get Xt = St ? S0 ert + a (1 ? ert ) = St ? S0 ert ? a (ert ? 1). In particular, XT = ST ? S0 erT ? a (erT ? 1). r r r Meanwhile, F orS (t, T ) = F uts (t, T ) = E[ST |Ft ] = S0 + t ads 0 Ys Yt er(T ? t) ? a (1? er(T ? t) ). So F orS (0, T ) = r S0 erT ? a (1 ? erT ) and hence XT = ST ? F orS (0, T ). After the agent delivers the commodity, whose value r is ST , and receives the forward price F orS (0, T ), the portfolio has exactly zero value. 54 6. Connections with Partial Di? erential Equations 6. 1. (i) Proof. Zt = 1 is obvious. Note the form of Z is similar to that of a geometric Brownian motion. So by It? ’s o formula, it is easy to obtain dZu = bu Zu du + ? u Zu dWu , u ? t. (ii) Proof. If Xu = Yu Zu (u ? t), then Xt = Yt Zt = x  · 1 = x and dXu = = = = Yu dZu + Zu dYu + dYu Zu au ? ?u ? u ? u du + dWu Zu Zu [Yu bu Zu + (au ? ?u ? u ) + ? u ? u ]du + (? u Zu Yu + ? u )dWu Yu (bu Zu du + ? u Zu dWu ) + Zu (bu Xu + au )du + (? u Xu + ? u )dWu . + ? u Z u ? u du Zu Remark: To see how to ? nd the above solution, we manipulate the equation (6. 2. 4) as follows. First, to u remove the term bu Xu du, we multiply on both sides of (6. 2. 4) the integrating factor e? bv dv . Then d(Xu e? ? Let Xu = e? u t u t bv dv ) = e? u t bv dv (au du + (? u + ? u Xu )dWu ). u t bv dv Xu , au = e? ? u t bv dv au and ? u = e? ? bv dv ? ? u , then X satis? es the SDE ? ? ? dXu = au du + (? u + ? u Xu )dWu = (? u du + ? u dWu ) + ? u Xu dWu . ? ? a ? ? ? ? To deal with the term ? u Xu dWu , we consider Xu = Xu e? ? dXu = e? u t u t ?v dWv . Then ?v dWv ?v dWv ? ? [(? u du + ? u dWu ) + ? u Xu dWu ] + Xu e? a ? u t u t 1 ( u )dWu + e? 2 u t ?v dWv 2 ? u du ? +(? u + ? u Xu )( u )e? ? ?v dWv du 1 ? 2 ? ? ? = au du + ? u dWu + ? u Xu dWu ? ?u Xu dWu + Xu ? u du ? ?u (? u + ? u Xu )du ? ? ? 1 ? 2 = (? u ? ?u ? u ? Xu ? u )du + ? u dWu , a ? ? 2 where au = au e? ? ? ? 1 d Xu e 2 u t ?v dWv 2 ? v dv and ? u = ? u e? ? ? = e2 1 u t 2 ? v dv u t ?v dWv . Finally, use the integrating factor e u t 2 ? v dv u 1 2 ? dv t 2 v , we have u t 1 ? ? 1 2 (dXu + Xu  · ? u du) = e 2 2 [(? u ? ?u ? u )du + ? u dWu ]. a ? ? Write everything back into the original X, a and ? , we get d Xu e? i. e. d u t bv dv? u t 1 ? v dWv + 2 u t 2 ? v dv = e2 1 u t 2 ? v dv? u t ?v dWv ? u t bv dv [(au ? ?u ? u )du + ? u dWu ], Xu Zu = 1 [(au ? ?u ? u )du + ? u dWu ] = dYu . Zu This inspired us to try Xu = Yu Zu . 6. 2. (i) 55 Proof. The portfolio is self-? nancing, so for any t ? T1 , we have dXt = ? 1 (t)df (t, Rt , T1 ) + ? 2 (t)df (t, Rt , T2 ) + Rt (Xt ? ?1 (t)f (t, Rt , T1 ) ? ?2 (t)f (t, Rt , T2 ))dt, and d(Dt Xt ) = ? Rt Dt Xt dt + Dt dXt = Dt [? 1 (t)df (t, Rt , T1 ) + ? 2 (t)df (t, Rt , T2 ) ? Rt (? 1 (t)f (t, Rt , T1 ) + ? 2 (t)f (t, Rt , T2 ))dt] 1 = Dt [? 1 (t) ft (t, Rt , T1 )dt + fr (t, Rt , T1 )dRt + frr (t, Rt , T1 )? 2 (t, Rt )dt 2 1 +? 2 (t) ft (t, Rt , T2 )dt + fr (t, Rt , T2 )dRt + frr (t, Rt , T2 )? 2 (t, Rt )dt 2 ? Rt (? 1 (t)f (t, Rt , T1 ) + ? 2 (t)f (t, Rt , T2 ))dt] 1 = ? 1 (t)Dt [? Rt f (t, Rt , T1 ) + ft (t, Rt , T1 ) + ? t, Rt )fr (t, Rt , T1 ) + ? 2 (t, Rt )frr (t, Rt , T1 )]dt 2 1 +? 2 (t)Dt [? Rt f (t, Rt , T2 ) + ft (t, Rt , T2 ) + ? (t, Rt )fr (t, Rt , T2 ) + ? 2 (t, Rt )frr (t, Rt , T2 )]dt 2 +Dt ? (t, Rt )[Dt ? (t, Rt )[? 1 (t)fr (t, Rt , T1 ) + ? 2 (t)fr (t, Rt , T2 )]]dWt = ? 1 (t)Dt [? (t, Rt ) ? ?(t, Rt , T1 )]fr (t, Rt , T1 )dt + ? 2 (t)Dt [? (t, Rt ) ? ?(t, Rt , T2 )]fr (t, Rt , T2 )dt +Dt ? (t, Rt )[? 1 (t)fr (t, Rt , T1 ) + ? 2 (t)fr (t, Rt , T2 )]dWt . (ii) Proof. Let ? 1 (t) = St fr (t, Rt , T2 ) and ? 2 (t) = ? St fr (t, Rt , T1 ), then d(Dt Xt ) = Dt St [? (t, Rt , T2 ) ? ?(t, Rt , T1 )]fr (t, Rt , T1 )fr (t, Rt , T2 )dt = Dt |[? t, Rt , T1 ) ? ?(t, Rt , T2 )]fr (t, Rt , T1 )fr (t, Rt , T2 )|dt. Integrate from 0 to T on both sides of the above equation, we get T DT XT ? D0 X0 = 0 Dt |[? (t, Rt , T1 ) ? ?(t, Rt , T2 )]fr (t, Rt , T1 )fr (t, Rt , T2 )|dt. If ? (t, Rt , T1 ) = ? (t, Rt , T2 ) for some t ? [0, T ], under the assumption that fr (t, r, T ) = 0 for all values of r and 0 ? t ? T , DT XT ? D0 X0 0. To avoid arbitrage (see, for example, Exercise 5. 7), we must have for a. s. ?, ? (t, Rt , T1 ) = ? (t, Rt , T2 ), ? t ? [0, T ]. This implies ? (t, r, T ) does not depend on T . (iii) Proof. In (6. 9. 4), let ? 1 (t) = ? (t), T1 = T and ? (t) = 0, we get d(Dt Xt ) = 1 ? (t)Dt ? Rt f (t, Rt , T ) + ft (t, Rt , T ) + ? (t, Rt )fr (t, Rt , T ) + ? 2 (t, Rt )frr (t, Rt , T ) dt 2 +Dt ? (t, Rt )? (t)fr (t, Rt , T )dWt . This is formula (6. 9. 5). 1 If fr (t, r, T ) = 0, then d(Dt Xt ) = ? (t)Dt ? Rt f (t, Rt , T ) + ft (t, Rt , T ) + 2 ? 2 (t, Rt )frr (t, Rt , T ) dt. We 1 2 choose ? (t) = sign ? Rt f (t, Rt , T ) + ft (t, Rt , T ) + 2 ? (t, Rt )frr (t, Rt , T ) . To avoid arbitrage in this case, we must have ft (t, Rt , T ) + 1 ? 2 (t, Rt )frr (t, Rt , T ) = Rt f (t, Rt , T ), or equivalently, for any r in the 2 range of Rt , ft (t, r, T ) + 1 ? (t, r)frr (t, r, T ) = rf (t, r, T ). 2 56 6. 3. Proof. We note d ? e ds s 0 bv dv C(s, T ) = e? s 0 bv dv [C(s, T )(? bs ) + bs C(s, T ) ? 1] = ? e? s 0 bv dv . So integrate on both sides of the equation from t to T, we obtain e? T 0 bv dv C(T, T ) ? e? t 0 t 0 T bv dv C(t, T ) = ? t s 0 e? T t s 0 bv dv ds. Since C(T, T ) = 0, we have C(t, T ) = e 1 ? a(s)C(s, T ) + 2 ? 2 (s)C 2 (s, T ), we get A(T, T ) ? A(t, T ) = ? bv dv T t e? bv dv ds = T e t s bv dv ds. Finally, by A (s, T ) = T a(s)C(s, T )ds + t 1 2 ? 2 (s)C 2 (s, T )ds. t How to cite Stochastic Calculus Solution Manual, Essay examples

THE FLIELD AT LEUCTRA Essay Example For Students

THE FLIELD AT LEUCTRA Essay Imagine if you will, It is a bright sun soaked day in Thebes. You are a soldier in theTheban Army under the command of General Epaminonads. To your front are the lowgreen plains of Boeotia. Plains that would latter become known as the blood alley(Hanson, 55) of Greece. In the distance you can see the sea of Spartan phalanxes, thereshields gleaming in the sun. To your left the sound of Cavalry as they rush out to meet theon coming Spartan horsemen. Another uneventful day in the live of a soldier? Most allhistorians of the day and of times past will tell you differently. The battle between the Spartans and the Thebans at the battle of Leuctra has beenstudied for a great many years. There are a great many arguments both for and against thepivotal engagement. Was it superior tactics or luck that defeated the Spartans. Thedifficulties lay in the fact that there is only one person present during the engagement thatrecorded the action. The person was a Athenian by the name of Xenophon. His the onlycontemporary description of Leuctra, and thus must over shadow all subsequentaccounts. (Hanson, 55) These accounts can be found in Xenophon`s papers calledHellenica. The first thing that needed is a description of the engagement itself. Thedescription of the engagement can be found everywhere from college history books toin-depth military history novels. The engagement began with standard linear tactics. The Spartans and their allies tookup positions head of the Theban column. At the time it was standard procedure to havethe commander and his elite troops placed to the right of his column, but Epaminondas diddifferently that day. He placed his elite troops and himself to the left. Now bothcommanders, Epaminondas and Cleombrotus face each other with their fines troops. Before the Thebans began movement against the Spartans the army resized its elitehoplites into forty to fifty shields deep instead of the standard twelve to fifteen man deepphalanx. The Spartan sent in their outnumbered cavalry in front of them at the beginningof the assault. The Thebans sent in their cavalry to meet them. The brief skirmish to followwas to although the Theban generals time to drive some of his infantry into the fray. Theresult was to force the enemies horsemen fleeing into the Spartan infantrys advance,breaking them up. At this point the Thebans began a left echelon march toward theSpartan right. Exploiting the gaps caused by the cavalry flight the Thebans preceded tosmash the Spartans. During the fighting that ensued the Cleombrotus was slain. After thefall of the king, the Spartans began to give ground. The event of their leader being killedthe Spartan allies fled the field. The Spartan right, although now alone, leaderless, andpressed by the Theban mass, withdrew undaunted-and in formation. (Hanson, 56) Thebattle for Lecutra was over the Spartans and their allies were defeated, Thebes and wonthe day, no longer would Sparta be an invincible nation. Now we must look at the controversies surrounding the battle at Lecutra. Was militarygenius or luck that won the day? First let use look at the commander of the Thebantroops. Many historians wish to place much of the victory itself on the genius ofEpaminonda. Evidence can be found in many of the historical novels. Phalanx warfarewas revolutionized at the battle of Leuctra in 371 by Epaminonda the Theban general. (Montgomery, 70) The simple fact is, Epaminonda was not alone in his command of theTheban forces at Leuctra. Epaminonda was joined by another general by the name ofPelopidas along with other Boeotian commanders. These tactics, battles and decision werejoint endeavors. .uf5298918f660a08d6ed20a06593bc7c9 , .uf5298918f660a08d6ed20a06593bc7c9 .postImageUrl , .uf5298918f660a08d6ed20a06593bc7c9 .centered-text-area { min-height: 80px; position: relative; } .uf5298918f660a08d6ed20a06593bc7c9 , .uf5298918f660a08d6ed20a06593bc7c9:hover , .uf5298918f660a08d6ed20a06593bc7c9:visited , .uf5298918f660a08d6ed20a06593bc7c9:active { border:0!important; } .uf5298918f660a08d6ed20a06593bc7c9 .clearfix:after { content: ""; display: table; clear: both; } .uf5298918f660a08d6ed20a06593bc7c9 { display: block; transition: background-color 250ms; webkit-transition: background-color 250ms; width: 100%; opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #95A5A6; } .uf5298918f660a08d6ed20a06593bc7c9:active , .uf5298918f660a08d6ed20a06593bc7c9:hover { opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #2C3E50; } .uf5298918f660a08d6ed20a06593bc7c9 .centered-text-area { width: 100%; position: relative ; } .uf5298918f660a08d6ed20a06593bc7c9 .ctaText { border-bottom: 0 solid #fff; color: #2980B9; font-size: 16px; font-weight: bold; margin: 0; padding: 0; text-decoration: underline; } .uf5298918f660a08d6ed20a06593bc7c9 .postTitle { color: #FFFFFF; font-size: 16px; font-weight: 600; margin: 0; padding: 0; width: 100%; } .uf5298918f660a08d6ed20a06593bc7c9 .ctaButton { background-color: #7F8C8D!important; color: #2980B9; border: none; border-radius: 3px; box-shadow: none; font-size: 14px; font-weight: bold; line-height: 26px; moz-border-radius: 3px; text-align: center; text-decoration: none; text-shadow: none; width: 80px; min-height: 80px; background: url(https://artscolumbia.org/wp-content/plugins/intelly-related-posts/assets/images/simple-arrow.png)no-repeat; position: absolute; right: 0; top: 0; } .uf5298918f660a08d6ed20a06593bc7c9:hover .ctaButton { background-color: #34495E!important; } .uf5298918f660a08d6ed20a06593bc7c9 .centered-text { display: table; height: 80px; padding-left : 18px; top: 0; } .uf5298918f660a08d6ed20a06593bc7c9 .uf5298918f660a08d6ed20a06593bc7c9-content { display: table-cell; margin: 0; padding: 0; padding-right: 108px; position: relative; vertical-align: middle; width: 100%; } .uf5298918f660a08d6ed20a06593bc7c9:after { content: ""; display: block; clear: both; } READ: Microsoft Antitrusst Case EssaySecond, a review of the revolutionary tactics applied at Leuctra. First the innovation ofadjusting in depth of the Phalanxes. At face value the increase in the number of depthseems to be a stroke of genius, where the need for a hard hitting strike is needed. Thereare only two problems with this thinking. One this tactic was used before in otherengagements of the time. The fifty-shield mass at Leuctra was not unheard of. As mostGreek commanders knew, such an attack in column ordinarily had few advantages. (Hanson, 56) The placing of the command and elite troops on the left to meet withSpartans best and their command was also not a new tactic at the battle of Lecutra. Pelopidas, for example, who was in the field with the Theban army at Lecutra, had puthis best troops on the left wing four years earlier at Tegyra. (Hanson, 56) The use of aleft oblique appears to be the first account of such a tactic, but there is to still argument ofthe reasoning for this. Was it well planned manner of attack to keep the long Spartan linebusy while the Thebans smashed into the Spartan right? Or was it just a means forEpaminonda`s army who was outnumbered to keep from being enveloped. The infantryof the refused center and left advanced slowly, occupying the attention of the Spartans totheir front, but without engaging them. (Dupuy, 43) This to is in doubt as of the onlydirect writings on the subject of the maneuver are left out by Xenophon. The use of thecavalry with the infantry was also nothing new in combat of the time. The army ofDionysius I of Syracuse consisted of integrated bodies of hoplites, light infantry andcavalry. (Montgomery, 70) Epaminonda did make us e of this confusion caused by thefleeing Spartan cavalry back into their own lines. He recognized the confusion betweenhorse and foot among the enemy ranks as a gift. This proves him an able hoplitecommander, but hardly a military genius. (Hanson, 58) In the end it was probably thedeath of the Spartan commander Cleombrotus, that defeated the Army. Losses ofcommanders throughout history have resulted in like defeats of the time. Withoutleadership the troops new not what to do. Lastly I will touch on some the controversy of the battle. In many cases the victory atLecutra is portrayed as a stroke of genius. As mentioned earlier, almost every so calledrevolutionary tactic was used at some point and time before Lecutra. Historians speak ofthe grand defeat of the Spartans and their total lack of cohesion in the face of these newtactics of warfare. The Spartans were hopelessly confused by these novel tactics. (Dupuy, 43) If this was so why when Cleombrotus was killed did the Spartan exit the fieldin formation. Xenophon correctly points out that they were holding their own until theirking fell. And the fact after the Theban onslaught, they were able to maintain enoughcohesion to withdraw in formation and carry his body out of the melee. (Hanson, 58)This citations not only confirms the effect of killing the leader but the fact of howorganized the Spartans during the battle really were. It proves that although the Spartanswere broken up by the friendly cavalry they were by no means rendered combatineffective. In conclusion, their will always seem to be contrary on the action at Lecutra. Fromwhat were the real reasons the once invincible Spartans were defeated. As said earlier itseems to be a combination of quite a few things. From friendly cavalry retreating into theirown lines to the tactic of Epaminonda putting his best against the Spartans best in anattempt to overwhem the Spartan king to the fact that in the battle king Cleombrotus waskilled. These truths are just that but in no way new or innovative at the time of theegagement. Time and time again historians will argue on the subject. Few take the time todig through the ancient texts and discover that most of the tactics used by Epaminondawere used before him, in either minor battles or without as great as success that wouldforever leave the innovator over shadowed by someone luckier with the same tactics. Work CitiedDupuy R. Ernest and Dupuy Trevor N., The Encyclopedia of Militarty History, NewYork and Evanston, Harper Row, 1970Hanson Victor Davis, The Leuctra Mirage, MHQ: The Quarterly Journal of MilitaryHistory, Volume 2, Number 2, Winter 1990, 54-59Montgomery Viscount, Field-Marshal , A History of Warfare, Ceveland and NewYork, The World Publishing Company, 1968

Ways through which space is defined by cultural ornamentation

Introduction In architecture and decorative art, ornamentation is a decoration used to embellish parts of a building or object. Monumental sculpture and their equivalent in decorative art are excluded from the term; most ornaments do not include human figures, and if present, they are small compared to the overall scale. The most common types of architectural ornaments even with the advancement of technology since civilization remain the imitative ornament, applied ornament, and the organic ornament.Advertising We will write a custom essay sample on Ways through which space is defined by cultural ornamentation specifically for you for only $16.05 $11/page Learn More The imitative ornament as the name suggest, is a decoration embedded on the structure imitating a form of definite meaning and with a symbolic significant. The applied ornament generally adds decorative beauty in the structure and forms bearing with them. The organic ornaments on the other ha nd are the inherent decorations of the art representing the piece of art in its organic form. Of these three major categories of architectural ornaments, the applied architectural ornament remains the most common and widely used form of architectural ornamentation. Different cultural societies have continued to use the applied ornament symbolically to express their cultures and poster their communities globally through their fine arts and decorations. Architectural ornament can be carved from stone, wood or precious metals, formed with plaster or clay, or painted or impressed onto a surface as applied ornament creating the impression of beauty as aforementioned. Wide varieties of decorative styles and motifs have developed for architecture and the applied arts including pottery, furniture, metal works. In textiles, wallpaper and other objects where the decoration maybe the main justification for its existence, the term pattern or design are more likely to be used. Textile, especiall y decoration and design, play an important role because different cultures and communities prefer specific fabric decoration and designs with specific colors and patterns. These specifications for communities over time have led to easy identification of these communities from the mode of their dressing particularly the traditional attire. For wallpapers, solemnly made for decoration, their designing determines their attractiveness to the target group and therefore expanded demand in the market. Textile and wallpaper decoration designs and patterns have changed with time since civilization as it has been with the architectural decorations due to the changes in technology. In a 1941 essay, the architectural historian, Sir John Summerson, called it â€Å"surface modulation†. This particularly meant that, the application of the common form of architectural ornamentation on the surfaces of structures led to the modulation or modification of the same surfaces creating attractivenes s.Advertising Looking for essay on architecture? Let's see if we can help you! Get your first paper with 15% OFF Learn More Decoration and ornament has been evident in civilization since the beginning of recorded history, ranging from Ancient Egyptian architecture to the apparent lack of ornament of the 20th century modernist architecture. Style of ornamentation clearly comes out in studying the cultures of different communities that developed the decorations and ornaments from their preceding cultures or modified unique decoration forms from other cultures. Architectural decoration started in ancient Egypt, where civilization started. The first decorations on the walls of buildings with pure natural theme dominated with figures of animals and plants. Not all welcomed this advancement of decoration and ornamentation. Some critics of the then architectural technological advancement did not imagine that decoration was necessary. Adolf Loos wrote his famous essay, â€Å" ornament and crime† in 1908, dismissing embellished ornaments as merely unnecessary decoration. According to Adolf, there was nothing important in decorating buildings and to him; anyone doing decoration was a criminal and a degenerate in the society. Furthermore, Adolf compared decorating a building to a person doing tattoo in their faces, which was crime; at least to him. Decorating objects created by people were like tossing them from sides until they ruined and wracked. The ban proclaimed against this extremely harmonious formal language this intersection between high art and folklore, prevailed for almost a century. Only since the return of the millennium, ornament has reestablished itself as decorative and yet subversive and allusive elements, abstract, and floral patterns adorn and dominate works in the contemporary visual arts. Bespeak beauty and seduction and they also always refer to society and gender – the way reality is constructed. Culture is looked upon a s living ways of various groups of individuals, which may consist of aspects like interaction, social activity, spirituality, thought, Sciences, and arts (Smyth, 2001, p.56). These may be explained as follows: Interaction refers to human contact and social aspects, which include give-and-take, regarding conversations, protocol, negotiations, and socialization.Advertising We will write a custom essay sample on Ways through which space is defined by cultural ornamentation specifically for you for only $16.05 $11/page Learn More These are useful aspects regarding living ways because individuals are usually dynamic and social in nature; therefore, they have to involve themselves in various interaction types with each other within their environments (Low, 2005, p.15). During these interactions of cultures, people copied decorative and ornamental forms which they modified coming up with better-decorated ornaments. Social activity is shared pursuits and experi ences in cultural communities, which are usually demonstrated, by various life-celebrating and festivity events. These social activities including the celebrations and festivities provide an opportunity for different cultures to interact, exchange ideas, and learn from each other in terms of ornamentation and decoration among other things. Spirituality refers to belief systems, which help to build moral codes that are usually passed on through generations, which promotes human beings’ well-being. In addition, spirituality is usually highlighted through actions and languages. Thoughts are expressed ways through which people understand, interpret, and perceive the world around. Sciences and arts are looked upon as the most refined and advanced human expression forms (Smyth, 2001, p.48). Science and art promoted the expression of the skills that different cultures had in their possession and those learned from others during their regular interactions and festivities. Language re fers to the earliest human institution or expression medium which is usually sophisticated. These aspects indicate that culture is usually very important within society because it makes it possible for people to understand the various living ways, which exist among individuals. Cultural studies may be perceived as an area of great importance because of its ability to offer appropriate principles for understanding and explaining human behavior. It is usually among the unique elements regarding contemporary social thought and it is very essential in contemporary social science research and specifically for the study of anthropology in particular. Ornaments can be conceived in many ways; they appear in different places, colors, scales, and patterns depending on the culture from where the ornaments originate for cultures have distinct and unique colors and patterns symbolizing different themes. In most cases, these ornaments are worn on specific times for a meaning and by a particular c lass of people. Various are also the reasons to use ornaments, sometimes they are planned, sometimes they occur unintentionally – certainly, however, they are part of the local culture. Ornamentation on the other hand is usually looked upon as the process or act of embellishing, adorning, or decorating (Low, 2005). Especially where a combination of both color and pattern decoration are applied, the patterns on the form or structure or figure adds interest in form of beauty more so where the image intended is solely imagination.Advertising Looking for essay on architecture? Let's see if we can help you! Get your first paper with 15% OFF Learn More These decorations and ornaments differ from culture to another and from one community to the other. This brings to our attention that cultural ornamentation is the aspect, which makes it possible for various cultural aspects to be embellished, adorned, or decorated. This implies that cultural ornamentation involves processes, which make it possible for cultural aspects to be attractive or appear as midpoints of interest (Winch, 1997). It therefore becomes apparent that, different cultures bear different decorative forms, which consequently express different cultural aspects. From the aspects brought to light above, it is apparent that space according to cultural ornamentation is usually perceived as the various cultural differences, which are experienced amid various cultures (Low, 2005). It is apparent that culture or living ways vary from place to place and these variations are the ones, which are typically perceived as cultural spaces. The variations in this case are experienced in interaction, social activity, spirituality, thought, Sciences and arts as well as language. These may be put to light as follows. For instance, interaction modes have been perceived as main cultural aspects because individuals have been found to be dynamic and social in nature and they end up involving themselves in various interaction types. These interactions among different people of different cultures involve also interaction and copying of cultures themselves between the people interacting. These interactions have been found to vary from area to another and therefore cultural spaces exist between various areas. Secondly, social activities also vary from place to another and therefore justify cultural space existence amid communities (Smyth, 2001). These social activities give a platform or a better forum for the different cultural societies gathering to express fully their arts. It is apparent that various societies have varying social activities and the felt differences in this case are cultural spaces. Living ways of various communities are highly influenced by factors like surrounding environment and interaction with other communities. Research has highlighted that, communities that highly interact with other communities end up incorporating their living ways and therefore the cultural spaces between them may end up being trimmed down. However, minimum interaction among cultures brings about limited learning regarding other individuals’ cultures and therefore they end up bringing about increased cultural spaces among them (Low, 2005). Increased cultural space makes it difficult for individuals to understand cultures exhibited by other individuals due to the limited interactions between them whereas reduced cultural space brings about situations whereby easier understanding regarding various cultures is experienced across cultures. This insight becomes clear in that the more the societies and cultures interact, the less the space between them reduce. The differences in sophistication of the arts from the two or more interacting societies show the space between the societies. The regularly the cultures interact; the great the understanding between the two cultures exist. This is usually very essential due to its ability to sustain successfully the respect from other cultures because of the understanding, which is experienced among various cultures or individuals with varying living ways. Spirituality varies from one culture to another; therefore, it may be used to express cultural space meaning. In spirituality, different people from different cultural backgrounds have different ways of carrying out their spiritual activities and the different forms of arts in their places of worship. Spirituality in this case is value systems, which are usually passed on through generations (Winch, 1997). These value systems highly determine aspects, which are considered bad or good. Individuals from varying cultures may end up perceivin g cultural spaces among them because they hold varying value systems. African art, for example constitutes one of the most diverse legacies on earth. Though many casual observers tend to generalize â€Å"traditional† African art, the continent is full of people, societies, and civilizations, each with a unique visual special culture. The definition also includes the art of the African, such as the art of  African Americans. Despite this diversity, there are some unifying artistic themes when considering the totality of the  visual culture  from the continent of Africa. The human figure has always been the primary subject matter for most African art, and this emphasis even influenced certain European traditions. Most Europeans admired the cultures portrayed by the beautiful pieces of arts of African human figures and opted to adopt them, because the theme portrayed by these figures shows a rich African culture. In most cases, these figures in their making signify a parti cular important cultural aspect for the community from which the piece of art comes from. The human figure may symbolize the living or the dead, may reference chiefs, dancers, or various trades such as drummers or hunters, or even may be an anthropomorphic representation of a god or have other votive function. Another common theme is the inter-morphosis of human and animal. African artworks tend to favor visual abstraction over naturalistic representation. This is because many African artworks generalize stylistic norms. Ancient Egyptian art, also usually thought of as naturalistically depictive, makes use of highly abstracted and regimented visual canons, especially in painting, as well as the use of different colors to represent the qualities and characteristics of an individual being depicted. African artists tend to favor three-dimensional artworks over two-dimensional works. Even many African paintings or cloth works were meant to be experienced three-dimensionally. House paint ings are often seen as a continuous design wrapped around a house, forcing the viewer to walk around the work to experience it fully; while decorated cloths are worn as decorative or ceremonial garments, transforming the wearer into a living sculpture. Distinct from the static form of traditional Western sculpture African art displays animation, a readiness to move. An extension of the utilitarianism and three-dimensionality of traditional African art is the fact that much of it is crafted for use in performance contexts, rather than in static one. For example, masks and costumes very often are used in communal, ceremonial contexts, where they are â€Å"danced.† Most societies in Africa have names for their masks, but this single name incorporates not only the sculpture, but also the meanings of the mask, the dance associated with it, and the spirits that reside within. In African thought, the three cannot be differentiated. Often a small part of an African design will look s imilar to a larger part, such as the diamonds at different scales in the Kasai pattern at right. Louis Senghor, Senegal’s first president, referred to this as â€Å"dynamic symmetry.† William Fagg, the British art historian, compared it to the logarithmic mapping of natural growth by biologist D’Arcy Thompson. More recently, it has been described in terms of  fractal  geometry. The origins of African art lie long before recorded history. African rock art in the  Sahara  in  Niger  preserves 6000-year-old carvings. The earliest known sculptures are from the  Nok culture  of  Nigeria, made around 500 BC. Along with sub-Saharan Africa, the cultural arts of the western tribes,  ancient Egyptian  paintings and artifacts, and indigenous southern crafts also contributed greatly to African art. Often depicting the abundance of surrounding nature, the art was often abstract interpretations of animals, plant life, or natural designs and shapes. In Is lamic art, unless space is infinite, whatever goes on in that space will have to end at some point. Many have commented on what is taken to be a horror vacuity in Islamic ornamentation. A dislike of the empty, and this accounts for the ways in which space is filled up so comprehensively in Islamic art. Yet space cannot be filled up entirely, for if it were, there would be no ornamentation. Geometric patterns are often said to be empty of content, and so to stimulate the mind to think of a deity existing without companions. However, it could also get the mind to think all sorts of thing. How geometric shapes are infinite? There is nothing infinite about a square or a triangle; on the contrary, such a specific shape is precisely finite, with recognizable and visible limits that define it. That is not to suggest that in Islamic art these forms of ornamentation are not used effectively to produce beautiful designs and consequently objects, but whether they are really supposed to produce particular ideas in us, their viewer, is questionable. There is no reason to think that we have to see geometrical design as having any religious meaning whatsoever. There is a saying in Arabic, ‘al-fann ihsas’ (‘art is feeling’). In addition, thought expresses cultural space because individuals from varying communities express varying views regarding various aspects (Winch, 1997). Thought refers to expressed ways through which people understand, interpret and perceive would which surrounds them. It is apparent that individuals within varying localities and cultures have varying understanding, interpretation, and perception regarding various aspects. Sciences and arts are looked upon as the most refined and advanced human expression forms. Human expressions differ from community to community, location to location, and among individual groups (Smyth, 2001). These variations are called cultural spaces among the various communities or groups. Human expression forms are usually influenced by various factors including environment and interactions with other individuals holding varying cultures. It is apparent that substantial interactions among individuals brings about trimmed down cultural spaces whereas minimum interactions yield increased cultural spaces. Language refers to the earliest human institution or expression medium which is usually sophisticated (Low, 2005). This expresses cultural space because various communities have various opinions regarding varying cultural aspects and they hold varying importance to them. The experienced variations are therefore expressed as cultural spaces among the various communities. Conclusion From the various aspects exposed above, it is apparent that space according to cultural ornamentation is the variations experienced regarding cultural aspects from one culture to another. The cultural ornamentation aspects, which determine cultural variations, include interaction, social activity, spirituali ty, thought, Sciences and arts as well as language. Various individual groups experience varying cultural aspects and therefore the variations demonstrate cultural variations. Cultural space may be either narrow or wide depending on experienced interactions among individuals. Extended interactions regarding individuals from various cultures have the capacity to trim down cultural space whereas minimum interactions yield wider economic spaces. This phenomenon happens for the obvious reasons that the more people stay together, the more they tend to understand each other; therefore, in the wake of different cultures associating, the cultural space melts down and vanishes without anyone noticing. Culture can define art by determining the type of art produced, by genre or the medium. In the light of this acknowledgement, it suffices to concur that different cultures will have different types of art, genre and medium and because culture defines all these elements, then it (culture) become s a determinant and a defining element of art. Art and culture are intertwined. References Blackmun, M. (2001) A history of Art in Africa, visiona et al. Prentice Hall, New York Low, S. (2005). Rethinking Urban Parks: Public Space and Cultural Diversity. Austin, TX: University of Texas Press. Piotrovsky, M. J. Vrieze (1999) Art of Islam: Heavenly Art, Earthly Art, ed. London, Lund Humpries Smyth, G. (2001). Space and the Irish Cultural Imagination. New York: Palgrave. Winch, S. (1997). Mapping the Cultural Space of Journalism: How Journalists Distinguish News From Entertainment. Westport, CT: Praeger. This essay on Ways through which space is defined by cultural ornamentation was written and submitted by user Rihanna Nelson to help you with your own studies. You are free to use it for research and reference purposes in order to write your own paper; however, you must cite it accordingly. You can donate your paper here.