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16 Pages

1996-final

Course: CS 590, Fall 2008
School: Idaho
Rating:

Word Count: 9731

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Coursehero >> Idaho >> Idaho >> CS 590

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1996-midterm.ps

Idaho - CS - 590

%!PS-Adobe-2.0 %Creator: dvipsk 5.58f Copyright 1986, 1994 Radical Eye Software %Title: midterm.dvi %Pages: 4 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentPaperSizes: Letter %EndComments %DVIPSCommandLine: dvips midterm -o midterm.ps %DVIPSP

1997-final.ps

Idaho - CS - 590

%!PS-Adobe-2.0 %Creator: dvips 5.58 Copyright 1986, 1994 Radical Eye Software %Title: exam2.dvi %CreationDate: Fri May 2 14:37:28 1997 %Pages: 4 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %EndComments %DVIPSCommandLine: dvips exam2 -o exam2.ps %DVI

1997-optional-midterm.ps

Idaho - CS - 590

%!PS-Adobe-2.0 %Creator: dvipsk 5.58f Copyright 1986, 1994 Radical Eye Software %Title: exam1a.dvi %Pages: 4 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentPaperSizes: Letter %EndComments %DVIPSCommandLine: dvips exam1a -o exam1a.ps %DVIPSPara

classes.pdf

Idaho - CS - 590

Some Interesting SetsHere are some sets of functions which we will discuss. Notice, my de tions are slightly di erent to those in the book, but you will nd similar or identical sets in the book.K = fe : ee g fe : e 2 We g EQ = fhx; yi : x = y g fe

dna-computing-1.ps

Idaho - CS - 590

%!PS-Adobe-2.0 %Creator: dvips 5.58 Copyright 1986, 1994 Radical Eye Software %Title: dna-slides.dvi %CreationDate: Mon May 5 17:18:57 1997 %Pages: 13 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %EndComments %DVIPSCommandLine: dvips dna-slides -o dn

hw0-ans.ps

Idaho - CS - 590

%!PS-Adobe-2.0 %Creator: dvips 5.58 Copyright 1986, 1994 Radical Eye Software %Title: hw0.dvi %CreationDate: Wed Jan 13 11:50:25 1999 %Pages: 5 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %EndComments %DVIPSCommandLine: dvips hw0 -o hw0.ps %DVIPSPar

hw0.pdf

Idaho - CS - 590

CS 590 Theory of Computation: Spring 1999Homework 0Due 25 January 1999 Lecture 6Problems from the text are denoted by C or BC, depending on whether they are from the Cutland text or from the Bovet and Crescenzi text, followed by the chapter and

hw0.ps

Idaho - CS - 590

%!PS-Adobe-2.0 %Creator: dvips 5.58 Copyright 1986, 1994 Radical Eye Software %Title: hw0.dvi %CreationDate: Wed Jan 13 11:51:00 1999 %Pages: 1 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %EndComments %DVIPSCommandLine: dvips hw0 -o hw0.ps %DVIPSPar

hw1.pdf

Idaho - CS - 590

CS 590 Theory of Computation: Spring 1997Homework 1Due 3 February 1997 Lecture 5Problems from the text are denoted by C or BC, depending on whether they are from the Cutland text or from the Bovet and Crescenzi text, followed by the chapter and

hw2.pdf

Idaho - CS - 590

CS 590 Theory of Computation: Spring 1999Homework 2Due 26 February 1999 Lecture 18Problems from the text are denoted by C or BC, depending on whether they are from the Cutland text or from the Bovet and Crescenzi text, followed by the chapter an

hw3.pdf

Idaho - CS - 590

CS 590 Theory of Computation: Spring 1999Homework 3Due 12 March 1999 Lecture 24Problems from the text are denoted by C or BC, depending on whether they are from the Cutland text or from the Bovet and Crescenzi text, followed by the chapter and p

hw4.pdf

Idaho - CS - 590

CS 590 Theory of Computation: Spring 1999Homework 4Due 16 April 1999 Lecture 36Problems from the text are denoted by C followed by the chapter and problem number, separated by a period. For example, C 2.3 3 is problem 3 for chapter 2, section 3 of

hw5a-ans.ps

Idaho - CS - 590

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hw5a.pdf

Idaho - CS - 590

CS 590 Theory of Computation: Spring 1999Homework 5Due 3 May 1999 Lecture 44Problems from the text are denoted by BC, for Bovet and Crescenzi, followed by the chapter and problem number, separated by a period. For example, BC 3.5 is problem 5 fo

hw5a.ps

Idaho - CS - 590

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syllabus.ps

Idaho - CS - 590

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lecture-notes.ps

Idaho - CS - 596

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syllabus.ps

Idaho - CS - 596

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hw1-ans.ps

Idaho - CS - 495

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hw2-ans.ps

Idaho - CS - 495

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hw3-ans.ps

Idaho - CS - 495

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hw4-ans.ps

Idaho - CS - 495

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hw5-ans.ps

Idaho - CS - 495

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1994-midterm.ps

Idaho - CS - 495

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1996-midterm.ps

Idaho - CS - 495

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1994-final.ps

Idaho - CS - 495

1996-final.ps

Idaho - CS - 495

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Loomba.doc

SUNY Buffalo - SHAKE - 587

The `infinite variety' of patriarchal discourseAnia Loomba Fragmentation, mobility and women The theme of racial difference in Othello allows us to locate the heterogeneity of patriarchal power and the oppositions to it. But what of other texts of t

sga-worksheet.ps

Idaho - CS - 572

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Greenblatt.doc

SUNY Buffalo - SHAKE - 587

Shakespeare BewitchedSTEPHEN GREENBLATT For the great witchmongers of the late middle ages and early Renaissance, those who wrote that there should be more fear, more denunciations of women, more confessions extorted under torture, and abave all mor

barnsley.txt

UNC Asheville - CS - 273

4 .01 .85 .07 .074 3 .00 .00 .500 .85 .04 .075 .20 -.26 .400-.15 .28 .5754 3 .00 .16 .000-.04 .85 .180.23 .22 .045.26 .24 -.086

major.txt

UNC Asheville - CS - 273

0 0.502 0.504 0.505 0.507 0.509 0.5011 0.5012 0.50

4body.txt

UNC Asheville - CS - 273

4 5.0e10-3.5e10 0.0e00 0.0e00 1.4e03 3.0e28-1.0e10 0.0e00 0.0e00 1.4e04 3.0e28 1.0e10 0.0e00 0.0e00 -1.4e04 3.0e28 3.5e10 0.0e00 0.0e00 -1.4e03 3.0e28

coral.txt

UNC Asheville - CS - 273

3 .40 .15 .453 3 .3077 -.5315 .8863 .3077 -.0769 .2166 .0000 .5455 .01063 3-.4615 -.2937 1.0962 .1538 -.4476 .3384 .6923 -.1958 .3808

minor.txt

UNC Asheville - CS - 273

0 0.502 0.503 0.505 0.507 0.508 0.5010 0.5012 0.50

3body.txt

UNC Asheville - CS - 273

3 1.25e11 0.0e00 0.0e00 0.05e04 0.0e00 5.97e24 0.0e00 4.5e10 3.0e04 0.0e00 1.989e30 0.0e00 -4.5e10 -3.0e04 0.0e00 1.989e30

2body.txt

UNC Asheville - CS - 273

2 5.0e10 0.0e00 4.5e10 1.0e04 0.0e00 1.5e30 0.0e00 -4.5e10 -1.0e04 0.0e00 1.5e30

chromatic.txt

UNC Asheville - CS - 273

0 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59 0.510 0.511 0.512 0.5

hw1-worksheet.ps

Idaho - CS - 572

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review2.ps

Idaho - CS - 572

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lecture2.pdf

Brookdale - ECON - 3338

Historical Origin Francis Galton (1886) Although there was a tendency for tall parents to have tall children and short parents to have short children, the average height of children born of parents of a given height tended to move or "regress" towar

Summary of Bond Features.ppt

Kennesaw - FIN - 4320

Summary of Bond Features The most important features of a bond are: nominal, principal or face amountthe amount over which the issuer pays interest, and which has to be repaid at the end. issue pricethe price at which investors buy the bonds whe

Chapter3.ppt

Kennesaw - FIN - 4320

FIXED-INCOME SECURITIESChapter 3Term Structure of Interest Rates: Empirical Properties and Classical TheoriesOutline Types of TS Shapes of the TS Dynamics of the TS Stylized Facts Theories of the TSTypes of Term Structures The term st

Lab2.xls

Kennesaw - FIN - 4320

Series Description yield on U.S. Treasury yield on U.S. 1-month at 3-monthmaturity, quoted Marketat 2-yearquoted on investmen Market Market yield on U.S. Treasury yield on U.S. Treasury yield on U.S. 1-yearquoted Marketat 3-yearmaturity, Market secur

Lab3_solutions.xls

Kennesaw - FIN - 4320

Plot the price of 10 year bond, nominal value 1000, coupon 6% with annual payment, as a function of the yield to m Use a range of yields from 0 to 20% with increments of 2% What do you see. Estimate the slope of the price function around 6% by comput

Lab2.xls

Kennesaw - FIN - 8340

Lab2_solutions.xls

Kennesaw - FIN - 8340

Matrix Computation in Excel.pdf

Kennesaw - FIN - 8340

1Fixed Income SecuritiesThe purpose of this exercise is to refresh (or to learn) some element of matrix algebra which will enable you to solve linear systems in excel.1.1LetNotation.a1;1 a2;1 a1;2 a2;2A=be a matrix of coe cients x1 x2 y

Matrix Computation Exercise.xls

Kennesaw - FIN - 8340

Exercise 1) Compute the Matrix Inverse of T 2) Verify that Inverse(T) . T = Indentity. Recall that an identity matrix has ones on the main diagonal and zeros elsewhere 3) Use inverse(T) ito solve the linear system, i.e. find the interpolating functio

Lab3.pdf

Kennesaw - FIN - 8340

1Fixed Income. Business Application. Duration Hedging.(c) Stefano Mazzotta 2008 1. Plot the price of 10 year bond, nominal value 1000, coupon 6% with annual payment, as a function of the yield to maturity. Use a range of yields from 0 to 20% with

Lab3.xls

Kennesaw - FIN - 8340

Lab3_solutions.xls

Kennesaw - FIN - 8340

Duration exercise.pdf

Kennesaw - FIN - 8340

1Fixed Income. In class exerciseThe purpose of this exercise is to understand, compute, and apply duration to hedge a one bond portfolio. Assume that you own one million certi.cates of a 3 year bond, coupon 6%, annually paid, price equal to 98, p

Duration exercise_solution.pdf

Kennesaw - FIN - 8340

1Fixed Income. In class exercise.(c) Stefano Mazzotta 2006 The purpose of this exercise is to understand, compute, and apply duration to hedge a one bond portfolio. Assume that you own one million certi.cates of a 3 year bond, coupon 6%, annually

Lab4_numericalConvexity.pdf

Kennesaw - FIN - 8340

1Fixed Income. Numerical Convexity.The purpose of this exercise is to understand, and compute convexity.(c) Stefano Mazzotta 2006Assume that you own a 10 year bond, coupon 6%, annually paid, yield 5:5%, par value equal to 100.1. Plot the val

Lab4_numericalConvexity_solutions.pdf

Kennesaw - FIN - 8340

1Fixed Income. Numerical convexity. Solution.The purpose of this exercise is to understand, and compute convexity.(c) Stefano Mazzotta 2006Assume that you own a 10 year bond, coupon 6%, annually paid, yield 5:5%, par value equal to 100.1. Pl

Lab4.xls

Kennesaw - FIN - 8340

y= t 1 2 3 4 5 6 7 8 9 10 CF DF5.500000% DCFdy = t0.001% y+dy = CF 1 2 3 4 5 6 7 8 9 105.501000% DFPrice $DURChange in $duration i.e. Convexityrate nper pmt fv Price1%2%3%4%5%6%1st order approximation P(y)= 103.76881 +(-77

Lab4_solutions.xls

Kennesaw - FIN - 8340

y= CF 1 2 3 4 5 6 7 8 9 10 6 6 6 6 6 6 6 6 6 106 DF5.500000% DCF 0.95 0.9 0.85 0.81 0.77 0.73 0.69 0.65 0.62 0.59 Price $DUR 5.69 5.39 5.11 4.84 4.59 4.35 4.12 3.91 3.71 62.06 103.76881 -771.8dy =0.001% y+dy = CF 1 2 3 4 5 6 7 8 9 10 6 6 6 6 6

Lab7_PassivePortfolioReplication.pdf

Kennesaw - FIN - 8340

1Fixed Income. Business Application. Passive Portfolio replication.(c) Stefano Mazzotta 2007 The purpose of this exercise is to understand and apply the tracking error minimization approach to a passive bond portfolio replication. We will use bon

Lab7_PassivePortfolioReplication Solutions.pdf

Kennesaw - FIN - 8340

1Fixed Income. Business Application. Passive Portfolio replication. Solutions.(c) Stefano Mazzotta 2007 The purpose of this exercise is to understand and apply the tracking error minimization approach to a passive bond portfolio replication. We w

Lab7_passive_bond_portfolio_problem.xls

Kennesaw - FIN - 8340

Date6.25 31-Jan-20024.75 15-Feb-2004 Dirty Mid Price 5.88 15-Nov-2005 Dirty Mid Price 6.13 15-Aug-2007 Dirty Mid2-Aug-01 3-Aug-01 6-Aug-01 7-Aug-01 8-Aug-01 9-Aug-01 10-Aug-01 13-Aug-01 14-Aug-01 15-Aug-01 16-Aug-01 17-Aug-01 20-Aug-01 21-Aug-0