ps1_sol_10_11

ps1_sol_10_11 - MS&E 221 CA: Hongsong Yuan Problem 1 (a)...

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Problem Set 1 Solutions CA: Hongsong Yuan January 19, 2011 Problem 1 (a) E [ X n ] = 0 (b) Var[ X ] = Var[ E [ X | P ]] + E [Var[ X | P ]] Var[ X n ] = Var[ E [ X n | X n - 1 ]] + E [Var[ X n | X n - 1 ]]] = 0 + E [ βX 2 n - 1 ] = β E [ X 2 n - 1 ] = β (Var[ X n - 1 ] + ( E [ X n - 1 ]) 2 ) = β Var[ X n - 1 ] Var[ X 1 ] = βx 2 0 Var[ X n ] = β n x 2 0 Note: The sequence is not necessarily a Markov chain. The current mean and variance just depend on the previous step, but the distribution itself could depend on all previous steps. Problem 2 Let Ω be the probability space and A,B,C be 3 disjoint subsets that evenly divides Ω , that is, P ( A ) = P ( B ) = P ( C ) = 1 3 . Define X,Y,Z as follows: X = - 2 I ( A ) + I ( B ) + I ( C ) Y = 0 Z = - I ( A ) - I ( B ) + 2 I ( C ) where I ( · ) is the indicator function. Then, P ( X > Y ) = P ( B ) + P ( C ) = 2 3 P ( Y > Z ) = P ( A ) + P ( B ) = 2 3 P ( Z > X ) = P ( C ) + P ( A ) = 2 3 Problem 3 For each part, I’ll give both the reason for modeling the process as a Markov chain and the reason for not doing so. 1
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(a) Yes: in a simple but widely used model, one assumes that the increase of a stock price from today to tomorrow consists of a fixed increasing rate plus a fluctuated increasing rate, i.e. S t +1 - S t = S t ( μ t + Z t ) , where μ t are constants and Z t are independent (possibly identically distributed) through t . No: in many cases, historical data of the stock prices contributes largely in predicting the future price. For example, if the stock price has been falling over the past few weeks, then probably for tomorrow we cannot expect a high rate of return as usual. (b) Yes: when a new bidder arrives, the current bid is the highest among all previous bids. One
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This note was uploaded on 02/03/2011 for the course MS&E 221 taught by Professor Ramesh during the Winter '11 term at Stanford.

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ps1_sol_10_11 - MS&E 221 CA: Hongsong Yuan Problem 1 (a)...

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