1 A Primer on Linear Models. 2 Chapter 1 corrections. 3 Chapter 2 corrections. 4 Chapter 3 corrections. 1.1 Corrections 23 May 2015

Size: px
Start display at page:

Download "1 A Primer on Linear Models. 2 Chapter 1 corrections. 3 Chapter 2 corrections. 4 Chapter 3 corrections. 1.1 Corrections 23 May 2015"

Transcription

1 A Prmer on Lnear Models. Correctons 23 May Chapter correctons Fx page: 9 lne -7 denomnator ( ρ 2 ) s mssng, should read Cov(e t, e s ) = V ar(a t )/( ρ 2 ) Fx page: lne -5 sgn needs changng: cos(a b) = cos(a)cos(b) + sn(a)sn(b) 3 Chapter 2 correctons Fx page: 5 lne - mssng dot n subscrpt, end of Example 2.2 should read any value of c, where y. = y j /n. Fx page: 23 lne 7 matrx n dsplay s P X, not I P X ; should read (I P X )z = = Fx page: 27 lne 4 error e should be ê for resduals orthogonalty of the resduals ê to the columns of the desgn matrx X Fx page: 3 lne dmenson of matrx A should be p p Fx page: 34 lne 7 mssng sgn choosng u = x + se () leads to Ux = se () 4 Chapter 3 correctons Fx page: 44 lne 4 parameter vector b s mssng, should read N n n 2... n a n n 0 0 X T Xb = n 2 0 n n a n a µ α α 2... α a = Ny.. n y. n 2 y n a y a. = XT y

2 Fx page: 46 lne 2 3, 2 element of X T X matrx should be b T a Fx page: 46 lne - n should be b seven of the a b = 4 3 = 2 cells are observed. Fx page: 49 lne 5 frst vector n second dsplay should have frst component equal to 2, not, so the vector s ( 2,,,,,, 0, 0, 0, 0, 0, 0) 5 Chapter 4 correctons Fx page: 75 lne 2 a should be d where d T X = λ T. Fx page: 75 lne 3 lnear should be lnearly Fx page: 83 lne -, -7, and -0 mssng transposes and... = (y Xb) T V (y Xb) = (y Xˆb GLS ) T V (y Xˆb GLS ) ˆσ 2 GLS =... = (y Xˆb GLS ) T V (y Xˆb GLS )/(N r) Fx page: 84 lne 7 mssng subscrpt of σ 2 Then t can be shown that V ar(e ) = σa/( 2 ρ 2 ) and the covarance Fx page: 86 lne 6 mssng σ 2 σ 2 VX = (σ 2 I N + τ 2 N T N ) Fx page: 88 lne 3,4 subscrpt errors E(y. ) = β 0 + β + β 2 n x 2 j j = β 0 + β + β 2 x 2. + β 2 n (x 2 j x. ) 2 Fx page: 9 lne -5 Example 4.7 should be Example 4.8 Fx page: 92 lne -5 transposes n wrong place, should read... f R s square and nonsngular and RVR T = I, then... Fx page: 95 lne -7 square n wrong place n Exercse 4.28, should read j 2

3 V ar( ˆβ ) = σ 2 /( (x j x.. ) 2 ) j Fx page: 96 lne 7,8 mssng parentheses n second pece, should read V ar((µ + e) T P(µ + e)) = E (µ + e) T P(µ + e)(µ + e) T P(µ + e) (E (µ + e) T P(µ + e) ) 2 Fx page: 07 lne both fgures the horzontal axes (u) n both graphs should go to 30, not 3 6 Chapter 5 correctons Fx page: 5 lne 4 In ANOVA table for Example 5.3, the noncentralty parameter for group s mssng n n the sum and should read a n(α α)2 = Fx page: 7 lne 5 projecton matrx n denomnator of (5.) s ncorrect, should read r 2 = yt (P X P )y y T (I P )y. Fx page: 7 lne -8 rght hand sde of (5.2) should be squared... and the expresson s the squared sample correlaton between the response and ftted values R 2 = ( (ŷ y)(y y)) 2 (ŷ y) 2 (y y) 2 Fx page: 23 lne ndex subscrpt s upper case Let X N, N =, 2,... be a sequence of random... 7 Chapter 6 correctons Fx page: 25 lne -7 Corollary 5.2, not (nonexstent) Corollary 5.5 Fx page: 26 lne -2,-3 no need for gant braces; equaton (6.4) should read f(y b, σ 2 ) = (2π) N/2 (σ 2 ) N/2 exp{ 2σ 2 (bt X T Xb)} exp{w (b, σ 2 )T (y)+w 2 (b, sgma 2 )T 2 (y)}. 3

4 Fx page: 27 lne - change sgn of second term (N/2) σ 2 + 2(σ 2 ) 2 Q(ˆb) Fx page: 33 lne 7 mssng n n frst sum a a = n (y. y. ) 2 n (y N. y. ) =2 Fx page: 42 lne -9 and -9 τ should be m n the probablty statements or =2 P r( t j < t N r,α/2 for all j) = P r(m B) < α P r( t j t N r,α/2 for at least one j) = P r(m / B) > α Fx page: 46 lne -6 constant and degrees of freedom should both be a(n )... and ndependently a(n )ˆσ 2 /σ 2 χ 2 a(n ) Fx page: 46 lne -3 degrees of freedom n Equaton (6.29) should be a(n ) to read 2 (y. y j. ) ˆσ n q a,a(n ) α α j (y. y j. ) + ˆσ n q a,a(n ) Fx page: 47 lne 6,7 degrees of freedom n Equaton (6.30) should be a(n ) to read u y. ˆσ q n a,a(n ) u u τ u y 2. + ˆσ q n a,a(n ) 2 u Fx page: 5 lne -4 Prove Corollary 6., not 6.2. Fx page: 5 lne -2 add where to evaluate dervatve normal Gauss-Markov model wth respect to σ 2 evaluated at σ 2 = ˆσ MLE 2 s negatve: Fx page: 52 lne -8,-7 reword second sentence of Exercse 6.9 Construct the remanng a + n lnearly ndependent functons of γ j s and show how they are confounded wth the man effects α s and β j s. Fx page: 53 lne 3 Add nstructon to Exercse 6. Use σ 2 = 0.,, 0. Fx page: 53 lne 9 Correct the Cauchy-Schwarz nequalty (u T w) 2 u 2 w 2 4

5 8 Chapter 7 correctons Fx page: 73 lne -4 mssng parentheses, wrong projecton matrx n denomnator F = yt (P Z P X )y/(r(z) r(x)) y T. (I P Z )y/(n r(z)) 9 Chapter 8 correctons Fx page: 8 lne - drop bar to fx expresson for SSE SSE = y T (I P X )y = Fx page: 83 lne 3,4 dvde by upper case N, not lower case n 2φ a σ 2 /N = N a n (y j y. ) 2 = j= n α 2 α 2 = α T N Dα N 2 αt D T Dα = α T Fx page: 83 lne 9 long expresson n exponent n equaton (8.4) E(E(e Us α s)) = ( 2sσ 2 ) (a )/2 E(e sσ2 /( 2sσ 2 ) 2λ a ) Fx page: 88 lne -8 γ not tau by wrtng γ j = v j v. Fx page: 88 lne -6 dsplay not broken up N D N 2 DT D α γ. = v.. γ.j γ.. = v.j v.. γ j γ. γ.j + γ.. = v j v. v.j + v.. Fx page: 89 lne 20 subscrpt upper case N e N N (0, R) Fx page: 92 lne -7 Result A.8(e), not A.7(e) Fx page: 93 lne 4 determnant as wrtten s not correct, should read X T V X = a = n /(σ 2 + n σ 2 a). 5

6 Fx page: 200 lne 5 correct mean y E y X = Fx page: 200 lne 23 subscrpt, not superscrpt * whose varance s σ 2 (a T a + ) = σ 2 (x T (X T X) g x + ). Fx page: 200 lne 26 subscrpt, not superscrpt * Fx page: 200 lne 27 subscrpt, not superscrpt * P r( t α/2 < Fx page: 200 lne 28,29 subscrpt, not superscrpt * x T b x T ˆb y N(0, σ 2 (x T (X T X) g x + )) x T ˆb y ˆσ(x T (X T X) g x + ) /2 < t α/2) = α = P r(x T ˆb t α/2ˆσ(x T (X T X) g x +) /2 < y < x T ˆb+t α/2ˆσ(x T (X T X) g x +) /2 ) Fx page: 200 lne 3 or -3 subscrpt, not superscrpt * Fx page: 20 lne sgn of second term x T ˆb ± t α/2ˆσ(x T (X T X) g x + ) /2. V ar(a T y y ) = a T Ωa 2a T ω + ω. Fx page: 20 lne 3 mssng zero RHS Unbasedness agan means that a T Xb x T b = 0 for all b, or X T a x = 0. Fx page: 20 lne 5 change both sgns Fx page: 204 lne -0 Exercse 6.7 should be Exercse 6.25 Ωa ω Xλ = 0, 0 Chapter 9 correctons Fx page: 20 lne -0 second (I N P X ) s correct, but superfluous and msleadng... = tr (I N P X )(XB.k B Ṭ jx T + Σ jk I N ) = (N rank(x))σ jk. 6

7 Appendx A correctons Fx page: 250 lne 5 subscrpt of dentty matrx should be n r nstead of m r, should read x x 2 = B b B B 2x 2 x 2 = B b 0 + Fx page: 263 lne -6 subscrpt mssng on matrx on left hand sde of dsplay, should read Fx page: 264 lne -5 mssng transpose, should read G 2 = C Ir E E 2 E 2 E A T (AA T ) = A + B Fx page: 267 lne -8 frstmatrx s not postve defnte, so change the (3, 3) element to Fx page: 268 lne -4 where to evaluate the dervatve... wth respect to t at t = 0 s equal to... Fx page: 268 lne - ncorrect formula for Bnomal Inverse Theorem; should read B B 2 I n r (A + UBV) = A A U(B + VA U) VA 2 Acknowledgements x 2 Great thanks are due Profs. Jerry M. Davs, Mohsen Pourahmad, Josh Tebbs, Jm Robson-Cox, Len Stefansk, Howard Bondell, and Guowe L for ther contrbutons to ths lst. JF Monahan, last update 23 May 205 7

Appendix - Normally Distributed Admissible Choices are Optimal

Appendix - Normally Distributed Admissible Choices are Optimal Appendx - Normally Dstrbuted Admssble Choces are Optmal James N. Bodurtha, Jr. McDonough School of Busness Georgetown Unversty and Q Shen Stafford Partners Aprl 994 latest revson September 00 Abstract

More information

Problem Set 6 Finance 1,

Problem Set 6 Finance 1, Carnege Mellon Unversty Graduate School of Industral Admnstraton Chrs Telmer Wnter 2006 Problem Set 6 Fnance, 47-720. (representatve agent constructon) Consder the followng two-perod, two-agent economy.

More information

MgtOp 215 Chapter 13 Dr. Ahn

MgtOp 215 Chapter 13 Dr. Ahn MgtOp 5 Chapter 3 Dr Ahn Consder two random varables X and Y wth,,, In order to study the relatonshp between the two random varables, we need a numercal measure that descrbes the relatonshp The covarance

More information

Appendix for Solving Asset Pricing Models when the Price-Dividend Function is Analytic

Appendix for Solving Asset Pricing Models when the Price-Dividend Function is Analytic Appendx for Solvng Asset Prcng Models when the Prce-Dvdend Functon s Analytc Ovdu L. Caln Yu Chen Thomas F. Cosmano and Alex A. Hmonas January 3, 5 Ths appendx provdes proofs of some results stated n our

More information

Simple Regression Theory II 2010 Samuel L. Baker

Simple Regression Theory II 2010 Samuel L. Baker SIMPLE REGRESSIO THEORY II Smple Regresson Theory II 00 Samuel L. Baker Assessng how good the regresson equaton s lkely to be Assgnment A gets nto drawng nferences about how close the regresson lne mght

More information

Statistical Tables Compiled by Alan J. Terry

Statistical Tables Compiled by Alan J. Terry Statistical Tables Compiled by Alan J. Terry School of Science and Sport University of the West of Scotland Paisley, Scotland Contents Table 1: Cumulative binomial probabilities Page 1 Table 2: Cumulative

More information

Two hours. To be supplied by the Examinations Office: Mathematical Formula Tables and Statistical Tables THE UNIVERSITY OF MANCHESTER

Two hours. To be supplied by the Examinations Office: Mathematical Formula Tables and Statistical Tables THE UNIVERSITY OF MANCHESTER Two hours MATH20802 To be supplied by the Examinations Office: Mathematical Formula Tables and Statistical Tables THE UNIVERSITY OF MANCHESTER STATISTICAL METHODS Answer any FOUR of the SIX questions.

More information

Multifactor Term Structure Models

Multifactor Term Structure Models 1 Multfactor Term Structure Models A. Lmtatons of One-Factor Models 1. Returns on bonds of all maturtes are perfectly correlated. 2. Term structure (and prces of every other dervatves) are unquely determned

More information

Correlations and Copulas

Correlations and Copulas Correlatons and Copulas Chapter 9 Rsk Management and Fnancal Insttutons, Chapter 6, Copyrght John C. Hull 2006 6. Coeffcent of Correlaton The coeffcent of correlaton between two varables V and V 2 s defned

More information

Chapter 3: Black-Scholes Equation and Its Numerical Evaluation

Chapter 3: Black-Scholes Equation and Its Numerical Evaluation Chapter 3: Black-Scholes Equation and Its Numerical Evaluation 3.1 Itô Integral 3.1.1 Convergence in the Mean and Stieltjes Integral Definition 3.1 (Convergence in the Mean) A sequence {X n } n ln of random

More information

Brownian Motion. Richard Lockhart. Simon Fraser University. STAT 870 Summer 2011

Brownian Motion. Richard Lockhart. Simon Fraser University. STAT 870 Summer 2011 Brownian Motion Richard Lockhart Simon Fraser University STAT 870 Summer 2011 Richard Lockhart (Simon Fraser University) Brownian Motion STAT 870 Summer 2011 1 / 33 Purposes of Today s Lecture Describe

More information

SPDE and portfolio choice (joint work with M. Musiela) Princeton University. Thaleia Zariphopoulou The University of Texas at Austin

SPDE and portfolio choice (joint work with M. Musiela) Princeton University. Thaleia Zariphopoulou The University of Texas at Austin SPDE and portfolio choice (joint work with M. Musiela) Princeton University November 2007 Thaleia Zariphopoulou The University of Texas at Austin 1 Performance measurement of investment strategies 2 Market

More information

EDC Introduction

EDC Introduction .0 Introducton EDC3 In the last set of notes (EDC), we saw how to use penalty factors n solvng the EDC problem wth losses. In ths set of notes, we want to address two closely related ssues. What are, exactly,

More information

Final Exam. 7. (10 points) Please state whether each of the following statements is true or false. No explanation needed.

Final Exam. 7. (10 points) Please state whether each of the following statements is true or false. No explanation needed. Fnal Exam Fall 4 Econ 8-67 Closed Book. Formula Sheet Provded. Calculators OK. Tme Allowed: hours Please wrte your answers on the page below each queston. (5 ponts) Assume that the rsk-free nterest rate

More information

3/3/2014. CDS M Phil Econometrics. Vijayamohanan Pillai N. Truncated standard normal distribution for a = 0.5, 0, and 0.5. CDS Mphil Econometrics

3/3/2014. CDS M Phil Econometrics. Vijayamohanan Pillai N. Truncated standard normal distribution for a = 0.5, 0, and 0.5. CDS Mphil Econometrics Lmted Dependent Varable Models: Tobt an Plla N 1 CDS Mphl Econometrcs Introducton Lmted Dependent Varable Models: Truncaton and Censorng Maddala, G. 1983. Lmted Dependent and Qualtatve Varables n Econometrcs.

More information

Introduction to PGMs: Discrete Variables. Sargur Srihari

Introduction to PGMs: Discrete Variables. Sargur Srihari Introducton to : Dscrete Varables Sargur srhar@cedar.buffalo.edu Topcs. What are graphcal models (or ) 2. Use of Engneerng and AI 3. Drectonalty n graphs 4. Bayesan Networks 5. Generatve Models and Samplng

More information

3: Central Limit Theorem, Systematic Errors

3: Central Limit Theorem, Systematic Errors 3: Central Lmt Theorem, Systematc Errors 1 Errors 1.1 Central Lmt Theorem Ths theorem s of prme mportance when measurng physcal quanttes because usually the mperfectons n the measurements are due to several

More information

Point Estimation. Principle of Unbiased Estimation. When choosing among several different estimators of θ, select one that is unbiased.

Point Estimation. Principle of Unbiased Estimation. When choosing among several different estimators of θ, select one that is unbiased. Point Estimation Point Estimation Definition A point estimate of a parameter θ is a single number that can be regarded as a sensible value for θ. A point estimate is obtained by selecting a suitable statistic

More information

Qualifying Exam Solutions: Theoretical Statistics

Qualifying Exam Solutions: Theoretical Statistics Qualifying Exam Solutions: Theoretical Statistics. (a) For the first sampling plan, the expectation of any statistic W (X, X,..., X n ) is a polynomial of θ of degree less than n +. Hence τ(θ) cannot have

More information

Answers to exercises in Macroeconomics by Nils Gottfries 2013

Answers to exercises in Macroeconomics by Nils Gottfries 2013 . a) C C b C C s the ntercept o the consumpton uncton, how much consumpton wll be at zero ncome. We can thnk that, at zero ncome, the typcal consumer would consume out o hs assets. The slope b s the margnal

More information

Version A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise.

Version A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise. Math 224 Q Exam 3A Fall 217 Tues Dec 12 Version A Problem 1. Let X be the continuous random variable defined by the following pdf: { 1 x/2 when x 2, f(x) otherwise. (a) Compute the mean µ E[X]. E[X] x

More information

AMH4 - ADVANCED OPTION PRICING. Contents

AMH4 - ADVANCED OPTION PRICING. Contents AMH4 - ADVANCED OPTION PRICING ANDREW TULLOCH Contents 1. Theory of Option Pricing 2 2. Black-Scholes PDE Method 4 3. Martingale method 4 4. Monte Carlo methods 5 4.1. Method of antithetic variances 5

More information

Foundations of Machine Learning II TP1: Entropy

Foundations of Machine Learning II TP1: Entropy Foundatons of Machne Learnng II TP1: Entropy Gullaume Charpat (Teacher) & Gaétan Marceau Caron (Scrbe) Problem 1 (Gbbs nequalty). Let p and q two probablty measures over a fnte alphabet X. Prove that KL(p

More information

M5MF6. Advanced Methods in Derivatives Pricing

M5MF6. Advanced Methods in Derivatives Pricing Course: Setter: M5MF6 Dr Antoine Jacquier MSc EXAMINATIONS IN MATHEMATICS AND FINANCE DEPARTMENT OF MATHEMATICS April 2016 M5MF6 Advanced Methods in Derivatives Pricing Setter s signature...........................................

More information

Optimizing Portfolios

Optimizing Portfolios Optimizing Portfolios An Undergraduate Introduction to Financial Mathematics J. Robert Buchanan 2010 Introduction Investors may wish to adjust the allocation of financial resources including a mixture

More information

2.1 Rademacher Calculus... 3

2.1 Rademacher Calculus... 3 COS 598E: Unsupervsed Learnng Week 2 Lecturer: Elad Hazan Scrbe: Kran Vodrahall Contents 1 Introducton 1 2 Non-generatve pproach 1 2.1 Rademacher Calculus............................... 3 3 Spectral utoencoders

More information

Applied Statistics I

Applied Statistics I Applied Statistics I Liang Zhang Department of Mathematics, University of Utah July 14, 2008 Liang Zhang (UofU) Applied Statistics I July 14, 2008 1 / 18 Point Estimation Liang Zhang (UofU) Applied Statistics

More information

The Use of Importance Sampling to Speed Up Stochastic Volatility Simulations

The Use of Importance Sampling to Speed Up Stochastic Volatility Simulations The Use of Importance Sampling to Speed Up Stochastic Volatility Simulations Stan Stilger June 6, 1 Fouque and Tullie use importance sampling for variance reduction in stochastic volatility simulations.

More information

4.4 Doob s inequalities

4.4 Doob s inequalities 34 CHAPTER 4. MARTINGALES 4.4 Doob s nequaltes The frst nterestng consequences of the optonal stoppng theorems are Doob s nequaltes. If M n s a martngale, denote M n =max applen M. Theorem 4.8 If M n s

More information

Chapter 2. Random variables. 2.3 Expectation

Chapter 2. Random variables. 2.3 Expectation Random processes - Chapter 2. Random variables 1 Random processes Chapter 2. Random variables 2.3 Expectation 2.3 Expectation Random processes - Chapter 2. Random variables 2 Among the parameters representing

More information

ECONOMETRICS - FINAL EXAM, 3rd YEAR (GECO & GADE)

ECONOMETRICS - FINAL EXAM, 3rd YEAR (GECO & GADE) ECONOMETRICS - FINAL EXAM, 3rd YEAR (GECO & GADE) May 17, 2016 15:30 Frst famly name: Name: DNI/ID: Moble: Second famly Name: GECO/GADE: Instructor: E-mal: Queston 1 A B C Blank Queston 2 A B C Blank Queston

More information

RESEARCH ARTICLE. The Penalized Biclustering Model And Related Algorithms Supplemental Online Material

RESEARCH ARTICLE. The Penalized Biclustering Model And Related Algorithms Supplemental Online Material Journal of Applied Statistics Vol. 00, No. 00, Month 00x, 8 RESEARCH ARTICLE The Penalized Biclustering Model And Related Algorithms Supplemental Online Material Thierry Cheouo and Alejandro Murua Département

More information

IMPA Commodities Course : Forward Price Models

IMPA Commodities Course : Forward Price Models IMPA Commodities Course : Forward Price Models Sebastian Jaimungal sebastian.jaimungal@utoronto.ca Department of Statistics and Mathematical Finance Program, University of Toronto, Toronto, Canada http://www.utstat.utoronto.ca/sjaimung

More information

MAS6012. MAS Turn Over SCHOOL OF MATHEMATICS AND STATISTICS. Sampling, Design, Medical Statistics

MAS6012. MAS Turn Over SCHOOL OF MATHEMATICS AND STATISTICS. Sampling, Design, Medical Statistics t r r r t s t SCHOOL OF MATHEMATICS AND STATISTICS Sampling, Design, Medical Statistics Spring Semester 206 207 3 hours t s 2 r t t t t r t t r s t rs t2 r t s s rs r t r t 2 r t st s rs q st s r rt r

More information

Probability Distributions. Statistics and Quantitative Analysis U4320. Probability Distributions(cont.) Probability

Probability Distributions. Statistics and Quantitative Analysis U4320. Probability Distributions(cont.) Probability Statstcs and Quanttatve Analss U430 Dstrbutons A. Dstrbutons: How do smple probablt tables relate to dstrbutons?. What s the of gettng a head? ( con toss) Prob. Segment 4: Dstrbutons, Unvarate & Bvarate

More information

Risk Neutral Measures

Risk Neutral Measures CHPTER 4 Risk Neutral Measures Our aim in this section is to show how risk neutral measures can be used to price derivative securities. The key advantage is that under a risk neutral measure the discounted

More information

15-451/651: Design & Analysis of Algorithms January 22, 2019 Lecture #3: Amortized Analysis last changed: January 18, 2019

15-451/651: Design & Analysis of Algorithms January 22, 2019 Lecture #3: Amortized Analysis last changed: January 18, 2019 5-45/65: Desgn & Analyss of Algorthms January, 09 Lecture #3: Amortzed Analyss last changed: January 8, 09 Introducton In ths lecture we dscuss a useful form of analyss, called amortzed analyss, for problems

More information

Linear Combinations of Random Variables and Sampling (100 points)

Linear Combinations of Random Variables and Sampling (100 points) Economcs 30330: Statstcs for Economcs Problem Set 6 Unversty of Notre Dame Instructor: Julo Garín Sprng 2012 Lnear Combnatons of Random Varables and Samplng 100 ponts 1. Four-part problem. Go get some

More information

Lecture Note of Bus 41202, Spring 2008: More Volatility Models. Mr. Ruey Tsay

Lecture Note of Bus 41202, Spring 2008: More Volatility Models. Mr. Ruey Tsay Lecture Note of Bus 41202, Spring 2008: More Volatility Models. Mr. Ruey Tsay The EGARCH model Asymmetry in responses to + & returns: g(ɛ t ) = θɛ t + γ[ ɛ t E( ɛ t )], with E[g(ɛ t )] = 0. To see asymmetry

More information

Structural Models of Credit Risk and Some Applications

Structural Models of Credit Risk and Some Applications Structural Models of Credit Risk and Some Applications Albert Cohen Actuarial Science Program Department of Mathematics Department of Statistics and Probability albert@math.msu.edu August 29, 2018 Outline

More information

Basket options and implied correlations: a closed form approach

Basket options and implied correlations: a closed form approach Basket optons and mpled correlatons: a closed form approach Svetlana Borovkova Free Unversty of Amsterdam CFC conference, London, January 7-8, 007 Basket opton: opton whose underlyng s a basket (.e. a

More information

ECE 586GT: Problem Set 2: Problems and Solutions Uniqueness of Nash equilibria, zero sum games, evolutionary dynamics

ECE 586GT: Problem Set 2: Problems and Solutions Uniqueness of Nash equilibria, zero sum games, evolutionary dynamics Unversty of Illnos Fall 08 ECE 586GT: Problem Set : Problems and Solutons Unqueness of Nash equlbra, zero sum games, evolutonary dynamcs Due: Tuesday, Sept. 5, at begnnng of class Readng: Course notes,

More information

TCOM501 Networking: Theory & Fundamentals Final Examination Professor Yannis A. Korilis April 26, 2002

TCOM501 Networking: Theory & Fundamentals Final Examination Professor Yannis A. Korilis April 26, 2002 TO5 Networng: Theory & undamentals nal xamnaton Professor Yanns. orls prl, Problem [ ponts]: onsder a rng networ wth nodes,,,. In ths networ, a customer that completes servce at node exts the networ wth

More information

STAT 830 Convergence in Distribution

STAT 830 Convergence in Distribution STAT 830 Convergence in Distribution Richard Lockhart Simon Fraser University STAT 830 Fall 2013 Richard Lockhart (Simon Fraser University) STAT 830 Convergence in Distribution STAT 830 Fall 2013 1 / 31

More information

4. Greek Letters, Value-at-Risk

4. Greek Letters, Value-at-Risk 4 Greek Letters, Value-at-Rsk 4 Value-at-Rsk (Hull s, Chapter 8) Math443 W08, HM Zhu Outlne (Hull, Chap 8) What s Value at Rsk (VaR)? Hstorcal smulatons Monte Carlo smulatons Model based approach Varance-covarance

More information

Stochastic Differential equations as applied to pricing of options

Stochastic Differential equations as applied to pricing of options Stochastic Differential equations as applied to pricing of options By Yasin LUT Supevisor:Prof. Tuomo Kauranne December 2010 Introduction Pricing an European call option Conclusion INTRODUCTION A stochastic

More information

STA258 Analysis of Variance

STA258 Analysis of Variance STA258 Analysis of Variance Al Nosedal. University of Toronto. Winter 2017 The Data Matrix The following table shows last year s sales data for a small business. The sample is put into a matrix format

More information

Option pricing and numéraires

Option pricing and numéraires Opton prcng and numérares Daro Trevsan Unverstà degl Stud d Psa San Mnato - 15 September 2016 Overvew 1 What s a numerare? 2 Arrow-Debreu model Change of numerare change of measure 3 Contnuous tme Self-fnancng

More information

Riemannian Geometry, Key to Homework #1

Riemannian Geometry, Key to Homework #1 Riemannian Geometry Key to Homework # Let σu v sin u cos v sin u sin v cos u < u < π < v < π be a parametrization of the unit sphere S {x y z R 3 x + y + z } Fix an angle < θ < π and consider the parallel

More information

Optimal Dividend Policy of A Large Insurance Company with Solvency Constraints. Zongxia Liang

Optimal Dividend Policy of A Large Insurance Company with Solvency Constraints. Zongxia Liang Optimal Dividend Policy of A Large Insurance Company with Solvency Constraints Zongxia Liang Department of Mathematical Sciences Tsinghua University, Beijing 100084, China zliang@math.tsinghua.edu.cn Joint

More information

Optimal Investment with Deferred Capital Gains Taxes

Optimal Investment with Deferred Capital Gains Taxes Optimal Investment with Deferred Capital Gains Taxes A Simple Martingale Method Approach Frank Thomas Seifried University of Kaiserslautern March 20, 2009 F. Seifried (Kaiserslautern) Deferred Capital

More information

Constructing Markov models for barrier options

Constructing Markov models for barrier options Constructing Markov models for barrier options Gerard Brunick joint work with Steven Shreve Department of Mathematics University of Texas at Austin Nov. 14 th, 2009 3 rd Western Conference on Mathematical

More information

Introduction to Affine Processes. Applications to Mathematical Finance

Introduction to Affine Processes. Applications to Mathematical Finance and Its Applications to Mathematical Finance Department of Mathematical Science, KAIST Workshop for Young Mathematicians in Korea, 2010 Outline Motivation 1 Motivation 2 Preliminary : Stochastic Calculus

More information

BIO5312 Biostatistics Lecture 5: Estimations

BIO5312 Biostatistics Lecture 5: Estimations BIO5312 Biostatistics Lecture 5: Estimations Yujin Chung September 27th, 2016 Fall 2016 Yujin Chung Lec5: Estimations Fall 2016 1/34 Recap Yujin Chung Lec5: Estimations Fall 2016 2/34 Today s lecture and

More information

Stock Loan Valuation Under Brownian-Motion Based and Markov Chain Stock Models

Stock Loan Valuation Under Brownian-Motion Based and Markov Chain Stock Models Stock Loan Valuation Under Brownian-Motion Based and Markov Chain Stock Models David Prager 1 1 Associate Professor of Mathematics Anderson University (SC) Based on joint work with Professor Qing Zhang,

More information

Central Limit Theorem, Joint Distributions Spring 2018

Central Limit Theorem, Joint Distributions Spring 2018 Central Limit Theorem, Joint Distributions 18.5 Spring 218.5.4.3.2.1-4 -3-2 -1 1 2 3 4 Exam next Wednesday Exam 1 on Wednesday March 7, regular room and time. Designed for 1 hour. You will have the full

More information

Mode is the value which occurs most frequency. The mode may not exist, and even if it does, it may not be unique.

Mode is the value which occurs most frequency. The mode may not exist, and even if it does, it may not be unique. 1.7.4 Mode Mode s the value whch occurs most frequency. The mode may not exst, and even f t does, t may not be unque. For ungrouped data, we smply count the largest frequency of the gven value. If all

More information

Chapter 14. The Multi-Underlying Black-Scholes Model and Correlation

Chapter 14. The Multi-Underlying Black-Scholes Model and Correlation Chapter 4 The Multi-Underlying Black-Scholes Model and Correlation So far we have discussed single asset options, the payoff function depended only on one underlying. Now we want to allow multiple underlyings.

More information

332 Mathematical Induction Solutions for Chapter 14. for every positive integer n. Proof. We will prove this with mathematical induction.

332 Mathematical Induction Solutions for Chapter 14. for every positive integer n. Proof. We will prove this with mathematical induction. 33 Mathematcal Inducton. Solutons for Chapter. Prove that 3 n n n for every postve nteger n. Proof. We wll prove ths wth mathematcal nducton. Observe that f n, ths statement s, whch s obvously true. Consder

More information

Course information FN3142 Quantitative finance

Course information FN3142 Quantitative finance Course information 015 16 FN314 Quantitative finance This course is aimed at students interested in obtaining a thorough grounding in market finance and related empirical methods. Prerequisite If taken

More information

Multilevel quasi-monte Carlo path simulation

Multilevel quasi-monte Carlo path simulation Multilevel quasi-monte Carlo path simulation Michael B. Giles and Ben J. Waterhouse Lluís Antoni Jiménez Rugama January 22, 2014 Index 1 Introduction to MLMC Stochastic model Multilevel Monte Carlo Milstein

More information

Large Deviations and Stochastic Volatility with Jumps: Asymptotic Implied Volatility for Affine Models

Large Deviations and Stochastic Volatility with Jumps: Asymptotic Implied Volatility for Affine Models Large Deviations and Stochastic Volatility with Jumps: TU Berlin with A. Jaquier and A. Mijatović (Imperial College London) SIAM conference on Financial Mathematics, Minneapolis, MN July 10, 2012 Implied

More information

1 No-arbitrage pricing

1 No-arbitrage pricing BURNABY SIMON FRASER UNIVERSITY BRITISH COLUMBIA Paul Klein Office: WMC 3635 Phone: TBA Email: paul klein 2@sfu.ca URL: http://paulklein.ca/newsite/teaching/809.php Economics 809 Advanced macroeconomic

More information

Option Pricing Models for European Options

Option Pricing Models for European Options Chapter 2 Option Pricing Models for European Options 2.1 Continuous-time Model: Black-Scholes Model 2.1.1 Black-Scholes Assumptions We list the assumptions that we make for most of this notes. 1. The underlying

More information

A new approach to LIBOR modeling

A new approach to LIBOR modeling A new approach to LIBOR modeling Antonis Papapantoleon FAM TU Vienna Based on joint work with Martin Keller-Ressel and Josef Teichmann Istanbul Workshop on Mathematical Finance Istanbul, Turkey, 18 May

More information

Module 2: Monte Carlo Methods

Module 2: Monte Carlo Methods Module 2: Monte Carlo Methods Prof. Mike Giles mike.giles@maths.ox.ac.uk Oxford University Mathematical Institute MC Lecture 2 p. 1 Greeks In Monte Carlo applications we don t just want to know the expected

More information

Ph.D. Preliminary Examination MICROECONOMIC THEORY Applied Economics Graduate Program June 2017

Ph.D. Preliminary Examination MICROECONOMIC THEORY Applied Economics Graduate Program June 2017 Ph.D. Preliminary Examination MICROECONOMIC THEORY Applied Economics Graduate Program June 2017 The time limit for this exam is four hours. The exam has four sections. Each section includes two questions.

More information

Definition 4.1. In a stochastic process T is called a stopping time if you can tell when it happens.

Definition 4.1. In a stochastic process T is called a stopping time if you can tell when it happens. 102 OPTIMAL STOPPING TIME 4. Optimal Stopping Time 4.1. Definitions. On the first day I explained the basic problem using one example in the book. On the second day I explained how the solution to the

More information

Jean-Paul Murara, Västeras, 26-April Mälardalen University, Sweden. Pricing EO under 2-dim. B S PDE by. using the Crank-Nicolson Method

Jean-Paul Murara, Västeras, 26-April Mälardalen University, Sweden. Pricing EO under 2-dim. B S PDE by. using the Crank-Nicolson Method Prcng EO under Mälardalen Unversty, Sweden Västeras, 26-Aprl-2017 1 / 15 Outlne 1 2 3 2 / 15 Optons - contracts that gve to the holder the rght but not the oblgaton to buy/sell an asset sometmes n the

More information

Likelihood Methods of Inference. Toss coin 6 times and get Heads twice.

Likelihood Methods of Inference. Toss coin 6 times and get Heads twice. Methods of Inference Toss coin 6 times and get Heads twice. p is probability of getting H. Probability of getting exactly 2 heads is 15p 2 (1 p) 4 This function of p, is likelihood function. Definition:

More information

Two Hours. Mathematical formula books and statistical tables are to be provided THE UNIVERSITY OF MANCHESTER. 22 January :00 16:00

Two Hours. Mathematical formula books and statistical tables are to be provided THE UNIVERSITY OF MANCHESTER. 22 January :00 16:00 Two Hours MATH38191 Mathematical formula books and statistical tables are to be provided THE UNIVERSITY OF MANCHESTER STATISTICAL MODELLING IN FINANCE 22 January 2015 14:00 16:00 Answer ALL TWO questions

More information

Optimal Asset Allocation with Stochastic Interest Rates in Regime-switching Models

Optimal Asset Allocation with Stochastic Interest Rates in Regime-switching Models Optimal Asset Allocation with Stochastic Interest Rates in Regime-switching Models Ruihua Liu Department of Mathematics University of Dayton, Ohio Joint Work With Cheng Ye and Dan Ren To appear in International

More information

Graphical Methods for Survival Distribution Fitting

Graphical Methods for Survival Distribution Fitting Graphcal Methods for Survval Dstrbuton Fttng In ths Chapter we dscuss the followng two graphcal methods for survval dstrbuton fttng: 1. Probablty Plot, 2. Cox-Snell Resdual Method. Probablty Plot: The

More information

Chapter 8: CAPM. 1. Single Index Model. 2. Adding a Riskless Asset. 3. The Capital Market Line 4. CAPM. 5. The One-Fund Theorem

Chapter 8: CAPM. 1. Single Index Model. 2. Adding a Riskless Asset. 3. The Capital Market Line 4. CAPM. 5. The One-Fund Theorem Chapter 8: CAPM 1. Single Index Model 2. Adding a Riskless Asset 3. The Capital Market Line 4. CAPM 5. The One-Fund Theorem 6. The Characteristic Line 7. The Pricing Model Single Index Model 1 1. Covariance

More information

Convergence Analysis of Monte Carlo Calibration of Financial Market Models

Convergence Analysis of Monte Carlo Calibration of Financial Market Models Analysis of Monte Carlo Calibration of Financial Market Models Christoph Käbe Universität Trier Workshop on PDE Constrained Optimization of Certain and Uncertain Processes June 03, 2009 Monte Carlo Calibration

More information

Supplementary online material to Information tradeoffs in dynamic financial markets

Supplementary online material to Information tradeoffs in dynamic financial markets Supplementary online material to Information tradeoffs in dynamic financial markets Efstathios Avdis University of Alberta, Canada 1. The value of information in continuous time In this document I address

More information

STAT 425: Introduction to Bayesian Analysis

STAT 425: Introduction to Bayesian Analysis STAT 45: Introduction to Bayesian Analysis Marina Vannucci Rice University, USA Fall 018 Marina Vannucci (Rice University, USA) Bayesian Analysis (Part 1) Fall 018 1 / 37 Lectures 9-11: Multi-parameter

More information

Chapter 3 Descriptive Statistics: Numerical Measures Part B

Chapter 3 Descriptive Statistics: Numerical Measures Part B Sldes Prepared by JOHN S. LOUCKS St. Edward s Unversty Slde 1 Chapter 3 Descrptve Statstcs: Numercal Measures Part B Measures of Dstrbuton Shape, Relatve Locaton, and Detectng Outlers Eploratory Data Analyss

More information

Random variables. Contents

Random variables. Contents Random variables Contents 1 Random Variable 2 1.1 Discrete Random Variable............................ 3 1.2 Continuous Random Variable........................... 5 1.3 Measures of Location...............................

More information

Exam M Fall 2005 PRELIMINARY ANSWER KEY

Exam M Fall 2005 PRELIMINARY ANSWER KEY Exam M Fall 005 PRELIMINARY ANSWER KEY Question # Answer Question # Answer 1 C 1 E C B 3 C 3 E 4 D 4 E 5 C 5 C 6 B 6 E 7 A 7 E 8 D 8 D 9 B 9 A 10 A 30 D 11 A 31 A 1 A 3 A 13 D 33 B 14 C 34 C 15 A 35 A

More information

RMSC 4005 Stochastic Calculus for Finance and Risk. 1 Exercises. (c) Let X = {X n } n=0 be a {F n }-supermartingale. Show that.

RMSC 4005 Stochastic Calculus for Finance and Risk. 1 Exercises. (c) Let X = {X n } n=0 be a {F n }-supermartingale. Show that. 1. EXERCISES RMSC 45 Stochastic Calculus for Finance and Risk Exercises 1 Exercises 1. (a) Let X = {X n } n= be a {F n }-martingale. Show that E(X n ) = E(X ) n N (b) Let X = {X n } n= be a {F n }-submartingale.

More information

Statistics for Business and Economics

Statistics for Business and Economics Statistics for Business and Economics Chapter 5 Continuous Random Variables and Probability Distributions Ch. 5-1 Probability Distributions Probability Distributions Ch. 4 Discrete Continuous Ch. 5 Probability

More information

Parameters Estimation in Stochastic Process Model

Parameters Estimation in Stochastic Process Model Parameters Estimation in Stochastic Process Model A Quasi-Likelihood Approach Ziliang Li University of Maryland, College Park GEE RIT, Spring 28 Outline 1 Model Review The Big Model in Mind: Signal + Noise

More information

. Social Security Actuarial Balance in General Equilibrium. S. İmrohoroğlu (USC) and S. Nishiyama (CBO)

. Social Security Actuarial Balance in General Equilibrium. S. İmrohoroğlu (USC) and S. Nishiyama (CBO) ....... Social Security Actuarial Balance in General Equilibrium S. İmrohoroğlu (USC) and S. Nishiyama (CBO) Rapid Aging and Chinese Pension Reform, June 3, 2014 SHUFE, Shanghai ..... The results in this

More information

Non-informative Priors Multiparameter Models

Non-informative Priors Multiparameter Models Non-informative Priors Multiparameter Models Statistics 220 Spring 2005 Copyright c 2005 by Mark E. Irwin Prior Types Informative vs Non-informative There has been a desire for a prior distributions that

More information

Exact Sampling of Jump-Diffusion Processes

Exact Sampling of Jump-Diffusion Processes 1 Exact Sampling of Jump-Diffusion Processes and Dmitry Smelov Management Science & Engineering Stanford University Exact Sampling of Jump-Diffusion Processes 2 Jump-Diffusion Processes Ubiquitous in finance

More information

continuous rv Note for a legitimate pdf, we have f (x) 0 and f (x)dx = 1. For a continuous rv, P(X = c) = c f (x)dx = 0, hence

continuous rv Note for a legitimate pdf, we have f (x) 0 and f (x)dx = 1. For a continuous rv, P(X = c) = c f (x)dx = 0, hence continuous rv Let X be a continuous rv. Then a probability distribution or probability density function (pdf) of X is a function f(x) such that for any two numbers a and b with a b, P(a X b) = b a f (x)dx.

More information

Tests for Two Correlations

Tests for Two Correlations PASS Sample Sze Software Chapter 805 Tests for Two Correlatons Introducton The correlaton coeffcent (or correlaton), ρ, s a popular parameter for descrbng the strength of the assocaton between two varables.

More information

M.I.T Fall Practice Problems

M.I.T Fall Practice Problems M.I.T. 15.450-Fall 2010 Sloan School of Management Professor Leonid Kogan Practice Problems 1. Consider a 3-period model with t = 0, 1, 2, 3. There are a stock and a risk-free asset. The initial stock

More information

The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay. Solutions to Final Exam

The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay. Solutions to Final Exam The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay Solutions to Final Exam Problem A: (40 points) Answer briefly the following questions. 1. Consider

More information

Analysis of Variance and Design of Experiments-II

Analysis of Variance and Design of Experiments-II Analyss of Varance and Desgn of Experments-II MODULE VI LECTURE - 4 SPLIT-PLOT AND STRIP-PLOT DESIGNS Dr. Shalabh Department of Mathematcs & Statstcs Indan Insttute of Technology Kanpur An example to motvate

More information

Random Variables Handout. Xavier Vilà

Random Variables Handout. Xavier Vilà Random Variables Handout Xavier Vilà Course 2004-2005 1 Discrete Random Variables. 1.1 Introduction 1.1.1 Definition of Random Variable A random variable X is a function that maps each possible outcome

More information

Actuarial Mathematics and Statistics Statistics 5 Part 2: Statistical Inference Tutorial Problems

Actuarial Mathematics and Statistics Statistics 5 Part 2: Statistical Inference Tutorial Problems Actuarial Mathematics and Statistics Statistics 5 Part 2: Statistical Inference Tutorial Problems Spring 2005 1. Which of the following statements relate to probabilities that can be interpreted as frequencies?

More information

/ Computational Genomics. Normalization

/ Computational Genomics. Normalization 0-80 /02-70 Computatonal Genomcs Normalzaton Gene Expresson Analyss Model Computatonal nformaton fuson Bologcal regulatory networks Pattern Recognton Data Analyss clusterng, classfcaton normalzaton, mss.

More information

VaR Estimation under Stochastic Volatility Models

VaR Estimation under Stochastic Volatility Models VaR Estimation under Stochastic Volatility Models Chuan-Hsiang Han Dept. of Quantitative Finance Natl. Tsing-Hua University TMS Meeting, Chia-Yi (Joint work with Wei-Han Liu) December 5, 2009 Outline Risk

More information

Economathematics. Problem Sheet 1. Zbigniew Palmowski. Ws 2 dw s = 1 t

Economathematics. Problem Sheet 1. Zbigniew Palmowski. Ws 2 dw s = 1 t Economathematics Problem Sheet 1 Zbigniew Palmowski 1. Calculate Ee X where X is a gaussian random variable with mean µ and volatility σ >.. Verify that where W is a Wiener process. Ws dw s = 1 3 W t 3

More information

Approximating a life table by linear combinations of exponential distributions and valuing life-contingent options

Approximating a life table by linear combinations of exponential distributions and valuing life-contingent options Approximating a life table by linear combinations of exponential distributions and valuing life-contingent options Zhenhao Zhou Department of Statistics and Actuarial Science The University of Iowa Iowa

More information

II. Random Variables. Variable Types. Variables Map Outcomes to Numbers

II. Random Variables. Variable Types. Variables Map Outcomes to Numbers II. Random Varables Random varables operate n much the same way as the outcomes or events n some arbtrary sample space the dstncton s that random varables are smply outcomes that are represented numercally.

More information

Corrections to the Second Edition of Modeling and Analysis of Stochastic Systems

Corrections to the Second Edition of Modeling and Analysis of Stochastic Systems Corrections to the Second Edition of Modeling and Analysis of Stochastic Systems Vidyadhar Kulkarni November, 200 Send additional corrections to the author at his email address vkulkarn@email.unc.edu.

More information

χ 2 distributions and confidence intervals for population variance

χ 2 distributions and confidence intervals for population variance χ 2 distributions and confidence intervals for population variance Let Z be a standard Normal random variable, i.e., Z N(0, 1). Define Y = Z 2. Y is a non-negative random variable. Its distribution is

More information