Midterm Exam. Use the end of month price data for the S&P 500 index in the table below to answer the following questions.

Size: px
Start display at page:

Download "Midterm Exam. Use the end of month price data for the S&P 500 index in the table below to answer the following questions."

Transcription

1 Unversty of Washngton Summer 2001 Department of Economcs Erc Zvot Economcs 483 Mdterm Exam Ths s a closed book and closed note exam. However, you are allowed one page of handwrtten notes. Answer all questons and wrte all answers n a blue book or on separate sheets of paper. Tme lmt s 2 hours and 10 mnutes. Total ponts = 100. I. Return Calculatons (15 pts) Use the end of month prce data for the S&P 500 ndex n the table below to answer the followng questons. Month Prce Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec What s the smple return between December, 1998 and December, 1999? Suppose you can get ths return every year for the next fve years. What s the smple fve year return? 2. What s the contnuously compounded return between December, 1998 and December, 1999? Suppose you can get ths return every year for the next fve years. What s the contnuously compounded fve year return? 3. Why do we use contnuously compounded returns nstead of smple returns n our probablty models of returns? II. Random Varables and Probablty (10 pts) Let X be a contnuous random varable wth pdf p(x). Gven the followng shape characterstcs of p(x), draw a rough sketch of the pdfs for the followng cases.

2 1. E[X] = 0, var(x) = 1, skew(x) = 0, excess kurt(x) = E[X] = 0, var(x) = 1, skew(x) = -0.5, excess kurt(x) = E[X] = 0, var(x) = 1, skew(x) = 0.5, excess kurt(x) = E[X] = 0, var(x) = 1, skew(x) = 0, excess kurt(x) = E[X] = 0, var(x) = 1, skew(x) = 0, excess kurt(x) = -3. III. Descrptve Statstcs (20 pts) Consder the monthly contnuously compounded returns on Boeng, Mcrosoft and the S&P 500 computed over the perod June 1992 October Descrptve statstcs for these returns are gven n the table below and hstograms, boxplots and scatterplots are presented on the followng pages. Based on the descrptve statstcs and graphs, answer the followng questons. 1. Compare the return rsk propertes of the three assets. Whch asset appears to be safest asset and whch asset appears to be the most rsky asset? 2. Do the return dstrbutons of the three assets look lke they could be normal dstrbutons? Justfy your answers. 3. Descrbe the drecton and strength of lnear assocaton between the three assets. Whch assets appear to have the hghest and lowest correlatons? 4. Why s the monthly standard devaton for the S&P 500 lower than the standard devaton values for Boeng and Mcrosoft? Unvarate Statstcs rboeng rmsft rsp500 Count Average Medan Standard Devaton Varance Skewness Excess Kurtoss

3 Box plots for Boeng, Mcrosoft and S&P Returns rboeng rmsft rsp Asset

4

5 IV. The CER Model and Monte Carlo Smulaton (20 pts) Consder the constant expected return (CER) model R t = µ + ε t, = 1,, N; t = 1,...,T ε t ~ d N(0, σ ) 2 where R t denotes the return on asset and ε t s a normally dstrbuted random error term. For specfcty, assume that µ = and σ = What s the nterpretaton of ε t n the CER model? 2. Brefly explan how you could generate one Monte Carlo smulaton of T = 50 observatons from the CER model for an asset usng Excel. 3. Recall, the least squares estmator of µ n the CER model s the sample mean T 1 µ ˆ = Rt. T t= 1 The sample mean s an unbased estmator of µ ; that s, E [ µ ˆ] = µ. Usng the concept of Monte Carlo smulatons from the CER model, brefly descrbe what t means for µ ˆ to be an unbased estmate of µ. 4. The precson of µ ˆ s measured by the standard error, SE ( µ ˆ ). Usng the concept of Monte Carlo smulatons from the CER model, brefly descrbe what SE ( µ ˆ ) represents. V. Portfolo Theory (15 pts) Consder the problem of an nvestor tryng to determne the best portfolo of two rsky assets (stocks) and a rsk-free asset (T-bll). Let the two rsky assets be Boeng and Mcrosoft, the rskfree asset be a one-year T-bll and suppose the nvestment horzon s one year. It s assumed that nvestors lke assets wth hgh expected returns but dslke those wth hgh rsk (as measured by return standard devaton) and choose to hold effcent portfolos. Transfer the dagram below to your bluebook and answer the followng questons.

6 60.0% 50.0% 40.0% 30.0% Mcrosof t 20.0% Boeng 10.0% T-bll 0.0% -10.0% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% Portfolo SD 1. Identfy the nvestment opportunty set consstng of only the two rsky assets. On ths dagram, dentfy the sets of neffcent and effcent portfolos. 2. Identfy the tangency portfolo and the set of effcent combnatons of T-blls and the two rsky assets. On ths effcent set, ndcate and descrbe the asset allocaton of the portfolos that a very rsk averse nvestor would choose to hold and the portfolos that a very rsk tolerant nvestor would choose to hold. 3. State the maxmzaton problem to be solved to determne the tangency portfolo. VI. Effcent Portfolos and Value-at-Rsk (15 pts) Consder an nvestor who has $100,000 to nvest over the next year. Assume that the nvestor ntally holds a portfolo consstng of 80% Boeng and 20% Mcrosoft. Ths asset s denoted asset A n the dagram below. Use the nformaton n the table below to answer the followng questons.

7 60.0% 50.0% 40.0% 30.0% Mcrosof t 20.0% Tangency 10.0% Asset A Boeng T-bll 0.0% -10.0% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% Portfolo SD Asset E[R] SD(R) Asset A Tangency T-Bll What s the 5% Value-at-rsk over the next year on the $100,000 portfolo consstng of just asset A? Assume that returns are contnuously compounded. (FYI NORMINV(0.05, 0.186, 0.223) = ) 2. Fnd the effcent portfolo (combnaton of T-blls, Boeng and Mcrosoft) that has the same expected return as asset A. In ths effcent portfolo, how much s nvested n the T-bll, Boeng and Mcrosoft? What s the rsk (standard devaton) of ths portfolo? 3. Fnd the effcent portfolo (combnaton of T-blls, Boeng and Mcrosoft) that has the same rsk (standard devaton) as asset A. In ths effcent portfolo, how much s nvested n the T- bll, Boeng and Mcrosoft? What s the expected return of ths portfolo?

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

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

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

Principles of Finance

Principles of Finance Prncples of Fnance Grzegorz Trojanowsk Lecture 6: Captal Asset Prcng Model Prncples of Fnance - Lecture 6 1 Lecture 6 materal Requred readng: Elton et al., Chapters 13, 14, and 15 Supplementary readng:

More information

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 9

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 9 Elton, Gruber, Brown, and Goetzmann Modern Portfolo Theory and Investment Analyss, 7th Edton Solutons to Text Problems: Chapter 9 Chapter 9: Problem In the table below, gven that the rskless rate equals

More information

UNIVERSITY OF VICTORIA Midterm June 6, 2018 Solutions

UNIVERSITY OF VICTORIA Midterm June 6, 2018 Solutions UIVERSITY OF VICTORIA Mdterm June 6, 08 Solutons Econ 45 Summer A0 08 age AME: STUDET UMBER: V00 Course ame & o. Descrptve Statstcs and robablty Economcs 45 Secton(s) A0 CR: 3067 Instructor: Betty Johnson

More information

Midterm Exam. b. What are the continuously compounded returns for the two stocks?

Midterm Exam. b. What are the continuously compounded returns for the two stocks? University of Washington Fall 004 Department of Economics Eric Zivot Economics 483 Midterm Exam This is a closed book and closed note exam. However, you are allowed one page of notes (double-sided). Answer

More information

Chapter 5 Student Lecture Notes 5-1

Chapter 5 Student Lecture Notes 5-1 Chapter 5 Student Lecture Notes 5-1 Basc Busness Statstcs (9 th Edton) Chapter 5 Some Important Dscrete Probablty Dstrbutons 004 Prentce-Hall, Inc. Chap 5-1 Chapter Topcs The Probablty Dstrbuton of a Dscrete

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

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

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

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

Module Contact: Dr P Moffatt, ECO Copyright of the University of East Anglia Version 2

Module Contact: Dr P Moffatt, ECO Copyright of the University of East Anglia Version 2 UNIVERSITY OF EAST ANGLIA School of Economcs Man Seres PG Examnaton 2012-13 FINANCIAL ECONOMETRICS ECO-M017 Tme allowed: 2 hours Answer ALL FOUR questons. Queston 1 carres a weght of 25%; Queston 2 carres

More information

PhysicsAndMathsTutor.com

PhysicsAndMathsTutor.com PhscsAndMathsTutor.com phscsandmathstutor.com June 2005 6. A scentst found that the tme taken, M mnutes, to carr out an eperment can be modelled b a normal random varable wth mean 155 mnutes and standard

More information

Introduction. Chapter 7 - An Introduction to Portfolio Management

Introduction. Chapter 7 - An Introduction to Portfolio Management Introducton In the next three chapters, we wll examne dfferent aspects of captal market theory, ncludng: Brngng rsk and return nto the pcture of nvestment management Markowtz optmzaton Modelng rsk and

More information

Mutual Funds and Management Styles. Active Portfolio Management

Mutual Funds and Management Styles. Active Portfolio Management utual Funds and anagement Styles ctve Portfolo anagement ctve Portfolo anagement What s actve portfolo management? How can we measure the contrbuton of actve portfolo management? We start out wth the CP

More information

Risk and Return: The Security Markets Line

Risk and Return: The Security Markets Line FIN 614 Rsk and Return 3: Markets Professor Robert B.H. Hauswald Kogod School of Busness, AU 1/25/2011 Rsk and Return: Markets Robert B.H. Hauswald 1 Rsk and Return: The Securty Markets Lne From securtes

More information

Chapter 3 Student Lecture Notes 3-1

Chapter 3 Student Lecture Notes 3-1 Chapter 3 Student Lecture otes 3-1 Busness Statstcs: A Decson-Makng Approach 6 th Edton Chapter 3 Descrbng Data Usng umercal Measures 005 Prentce-Hall, Inc. Chap 3-1 Chapter Goals After completng ths chapter,

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

Measures of Spread IQR and Deviation. For exam X, calculate the mean, median and mode. For exam Y, calculate the mean, median and mode.

Measures of Spread IQR and Deviation. For exam X, calculate the mean, median and mode. For exam Y, calculate the mean, median and mode. Part 4 Measures of Spread IQR and Devaton In Part we learned how the three measures of center offer dfferent ways of provdng us wth a sngle representatve value for a data set. However, consder the followng

More information

EXAMINATIONS OF THE HONG KONG STATISTICAL SOCIETY

EXAMINATIONS OF THE HONG KONG STATISTICAL SOCIETY EXAMINATIONS OF THE HONG KONG STATISTICAL SOCIETY HIGHER CERTIFICATE IN STATISTICS, 2013 MODULE 7 : Tme seres and ndex numbers Tme allowed: One and a half hours Canddates should answer THREE questons.

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

Sampling Distributions of OLS Estimators of β 0 and β 1. Monte Carlo Simulations

Sampling Distributions of OLS Estimators of β 0 and β 1. Monte Carlo Simulations Addendum to NOTE 4 Samplng Dstrbutons of OLS Estmators of β and β Monte Carlo Smulatons The True Model: s gven by the populaton regresson equaton (PRE) Y = β + β X + u = 7. +.9X + u () where β = 7. and

More information

Evaluating Performance

Evaluating Performance 5 Chapter Evaluatng Performance In Ths Chapter Dollar-Weghted Rate of Return Tme-Weghted Rate of Return Income Rate of Return Prncpal Rate of Return Daly Returns MPT Statstcs 5- Measurng Rates of Return

More information

Physics 4A. Error Analysis or Experimental Uncertainty. Error

Physics 4A. Error Analysis or Experimental Uncertainty. Error Physcs 4A Error Analyss or Expermental Uncertanty Slde Slde 2 Slde 3 Slde 4 Slde 5 Slde 6 Slde 7 Slde 8 Slde 9 Slde 0 Slde Slde 2 Slde 3 Slde 4 Slde 5 Slde 6 Slde 7 Slde 8 Slde 9 Slde 20 Slde 2 Error n

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

Random Variables. 8.1 What is a Random Variable? Announcements: Chapter 8

Random Variables. 8.1 What is a Random Variable? Announcements: Chapter 8 Announcements: Quz starts after class today, ends Monday Last chance to take probablty survey ends Sunday mornng. Next few lectures: Today, Sectons 8.1 to 8. Monday, Secton 7.7 and extra materal Wed, Secton

More information

Chapter 6 Risk, Return, and the Capital Asset Pricing Model

Chapter 6 Risk, Return, and the Capital Asset Pricing Model Whch s better? (1) 6% return wth no rsk, or (2) 20% return wth rsk. Chapter 6 Rsk, Return, and the Captal Asset Prcng Model Cannot say - need to know how much rsk comes wth the 20% return. What do we know

More information

Which of the following provides the most reasonable approximation to the least squares regression line? (a) y=50+10x (b) Y=50+x (d) Y=1+50x

Which of the following provides the most reasonable approximation to the least squares regression line? (a) y=50+10x (b) Y=50+x (d) Y=1+50x Whch of the followng provdes the most reasonable approxmaton to the least squares regresson lne? (a) y=50+10x (b) Y=50+x (c) Y=10+50x (d) Y=1+50x (e) Y=10+x In smple lnear regresson the model that s begn

More information

Calibration Methods: Regression & Correlation. Calibration Methods: Regression & Correlation

Calibration Methods: Regression & Correlation. Calibration Methods: Regression & Correlation Calbraton Methods: Regresson & Correlaton Calbraton A seres of standards run (n replcate fashon) over a gven concentraton range. Standards Comprsed of analte(s) of nterest n a gven matr composton. Matr

More information

General Examination in Microeconomic Theory. Fall You have FOUR hours. 2. Answer all questions

General Examination in Microeconomic Theory. Fall You have FOUR hours. 2. Answer all questions HARVARD UNIVERSITY DEPARTMENT OF ECONOMICS General Examnaton n Mcroeconomc Theory Fall 2010 1. You have FOUR hours. 2. Answer all questons PLEASE USE A SEPARATE BLUE BOOK FOR EACH QUESTION AND WRITE THE

More information

Survey of Math Test #3 Practice Questions Page 1 of 5

Survey of Math Test #3 Practice Questions Page 1 of 5 Test #3 Practce Questons Page 1 of 5 You wll be able to use a calculator, and wll have to use one to answer some questons. Informaton Provded on Test: Smple Interest: Compound Interest: Deprecaton: A =

More information

Lecture Note 2 Time Value of Money

Lecture Note 2 Time Value of Money Seg250 Management Prncples for Engneerng Managers Lecture ote 2 Tme Value of Money Department of Systems Engneerng and Engneerng Management The Chnese Unversty of Hong Kong Interest: The Cost of Money

More information

Likelihood Fits. Craig Blocker Brandeis August 23, 2004

Likelihood Fits. Craig Blocker Brandeis August 23, 2004 Lkelhood Fts Crag Blocker Brandes August 23, 2004 Outlne I. What s the queston? II. Lkelhood Bascs III. Mathematcal Propertes IV. Uncertantes on Parameters V. Mscellaneous VI. Goodness of Ft VII. Comparson

More information

σ may be counterbalanced by a larger

σ may be counterbalanced by a larger Questons CHAPTER 5: TWO-VARIABLE REGRESSION: INTERVAL ESTIMATION AND HYPOTHESIS TESTING 5.1 (a) True. The t test s based on varables wth a normal dstrbuton. Snce the estmators of β 1 and β are lnear combnatons

More information

Chapter 5 Bonds, Bond Prices and the Determination of Interest Rates

Chapter 5 Bonds, Bond Prices and the Determination of Interest Rates Chapter 5 Bonds, Bond Prces and the Determnaton of Interest Rates Problems and Solutons 1. Consder a U.S. Treasury Bll wth 270 days to maturty. If the annual yeld s 3.8 percent, what s the prce? $100 P

More information

AS MATHEMATICS HOMEWORK S1

AS MATHEMATICS HOMEWORK S1 Name Teacher AS MATHEMATICS HOMEWORK S1 Mathematcs Department September 015 Verson 1.0 Contents Contents... AS Maths Homework S1 014... 3 HW1 Data1 dscrete data, bo plots, stem and leaf dagrams... 4 HW

More information

Alternatives to Shewhart Charts

Alternatives to Shewhart Charts Alternatves to Shewhart Charts CUSUM & EWMA S Wongsa Overvew Revstng Shewhart Control Charts Cumulatve Sum (CUSUM) Control Chart Eponentally Weghted Movng Average (EWMA) Control Chart 2 Revstng Shewhart

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

FM303. CHAPTERS COVERED : CHAPTERS 5, 8 and 9. LEARNER GUIDE : UNITS 1, 2 and 3.1 to 3.3. DUE DATE : 3:00 p.m. 19 MARCH 2013

FM303. CHAPTERS COVERED : CHAPTERS 5, 8 and 9. LEARNER GUIDE : UNITS 1, 2 and 3.1 to 3.3. DUE DATE : 3:00 p.m. 19 MARCH 2013 Page 1 of 11 ASSIGNMENT 1 ST SEMESTER : FINANCIAL MANAGEMENT 3 () CHAPTERS COVERED : CHAPTERS 5, 8 and 9 LEARNER GUIDE : UNITS 1, 2 and 3.1 to 3.3 DUE DATE : 3:00 p.m. 19 MARCH 2013 TOTAL MARKS : 100 INSTRUCTIONS

More information

Notes are not permitted in this examination. Do not turn over until you are told to do so by the Invigilator.

Notes are not permitted in this examination. Do not turn over until you are told to do so by the Invigilator. UNIVERSITY OF EAST ANGLIA School of Economcs Man Seres PG Examnaton 2016-17 BANKING ECONOMETRICS ECO-7014A Tme allowed: 2 HOURS Answer ALL FOUR questons. Queston 1 carres a weght of 30%; queston 2 carres

More information

Random Variables. b 2.

Random Variables. b 2. Random Varables Generally the object of an nvestgators nterest s not necessarly the acton n the sample space but rather some functon of t. Techncally a real valued functon or mappng whose doman s the sample

More information

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

arxiv: v1 [q-fin.pm] 13 Feb 2018

arxiv: v1 [q-fin.pm] 13 Feb 2018 WHAT IS THE SHARPE RATIO, AND HOW CAN EVERYONE GET IT WRONG? arxv:1802.04413v1 [q-fn.pm] 13 Feb 2018 IGOR RIVIN Abstract. The Sharpe rato s the most wdely used rsk metrc n the quanttatve fnance communty

More information

Economics 330 Money and Banking Problem Set No. 3 Due Tuesday April 3, 2018 at the beginning of class

Economics 330 Money and Banking Problem Set No. 3 Due Tuesday April 3, 2018 at the beginning of class Economcs 0 Money and Bankng Problem Set No. Due Tuesday Aprl, 08 at the begnnng of class Fall 08 Dr. Ner I. A. The followng table shows the prce of $000 face value -year, -year, -year, 9-year and 0- year

More information

Applications of Myerson s Lemma

Applications of Myerson s Lemma Applcatons of Myerson s Lemma Professor Greenwald 28-2-7 We apply Myerson s lemma to solve the sngle-good aucton, and the generalzaton n whch there are k dentcal copes of the good. Our objectve s welfare

More information

iii) pay F P 0,T = S 0 e δt when stock has dividend yield δ.

iii) pay F P 0,T = S 0 e δt when stock has dividend yield δ. Fnal s Wed May 7, 12:50-2:50 You are allowed 15 sheets of notes and a calculator The fnal s cumulatve, so you should know everythng on the frst 4 revews Ths materal not on those revews 184) Suppose S t

More information

Economics 483. Midterm Exam. 1. Consider the following monthly data for Microsoft stock over the period December 1995 through December 1996:

Economics 483. Midterm Exam. 1. Consider the following monthly data for Microsoft stock over the period December 1995 through December 1996: University of Washington Summer Department of Economics Eric Zivot Economics 3 Midterm Exam This is a closed book and closed note exam. However, you are allowed one page of handwritten notes. Answer all

More information

THE VOLATILITY OF EQUITY MUTUAL FUND RETURNS

THE VOLATILITY OF EQUITY MUTUAL FUND RETURNS North Amercan Journal of Fnance and Bankng Research Vol. 4. No. 4. 010. THE VOLATILITY OF EQUITY MUTUAL FUND RETURNS Central Connectcut State Unversty, USA. E-mal: BelloZ@mal.ccsu.edu ABSTRACT I nvestgated

More information

Investment Management Active Portfolio Management

Investment Management Active Portfolio Management Investment Management Actve Portfolo Management Road Map The Effcent Markets Hypothess (EMH) and beatng the market Actve portfolo management Market tmng Securty selecton Securty selecton: Treynor&Black

More information

Teaching Note on Factor Model with a View --- A tutorial. This version: May 15, Prepared by Zhi Da *

Teaching Note on Factor Model with a View --- A tutorial. This version: May 15, Prepared by Zhi Da * Copyrght by Zh Da and Rav Jagannathan Teachng Note on For Model th a Ve --- A tutoral Ths verson: May 5, 2005 Prepared by Zh Da * Ths tutoral demonstrates ho to ncorporate economc ves n optmal asset allocaton

More information

Chapter 11: Optimal Portfolio Choice and the Capital Asset Pricing Model

Chapter 11: Optimal Portfolio Choice and the Capital Asset Pricing Model Chapter 11: Optmal Portolo Choce and the CAPM-1 Chapter 11: Optmal Portolo Choce and the Captal Asset Prcng Model Goal: determne the relatonshp between rsk and return key to ths process: examne how nvestors

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

YORK UNIVERSITY Faculty of Science Department of Mathematics and Statistics MATH A Test #2 November 03, 2014

YORK UNIVERSITY Faculty of Science Department of Mathematics and Statistics MATH A Test #2 November 03, 2014 Famly Name prnt): YORK UNIVERSITY Faculty of Scence Department of Mathematcs and Statstcs MATH 2280.00 A Test #2 November 0, 2014 Solutons Gven Name: Student No: Sgnature: INSTRUCTIONS: 1. Please wrte

More information

A Bootstrap Confidence Limit for Process Capability Indices

A Bootstrap Confidence Limit for Process Capability Indices A ootstrap Confdence Lmt for Process Capablty Indces YANG Janfeng School of usness, Zhengzhou Unversty, P.R.Chna, 450001 Abstract The process capablty ndces are wdely used by qualty professonals as an

More information

Midterm Version 2 Solutions

Midterm Version 2 Solutions Econ 45 Fall 07 age UIVERSITY OF VICTORIA Mdterm Verson Solutons October 07 AME: STUDET UMBER: V00 Course ame & o. Descrve Statstcs and robably Secton(s) Economcs 45 A0 CR: 098 Instructor: Betty Johnson

More information

Maturity Effect on Risk Measure in a Ratings-Based Default-Mode Model

Maturity Effect on Risk Measure in a Ratings-Based Default-Mode Model TU Braunschweg - Insttut für Wrtschaftswssenschaften Lehrstuhl Fnanzwrtschaft Maturty Effect on Rsk Measure n a Ratngs-Based Default-Mode Model Marc Gürtler and Drk Hethecker Fnancal Modellng Workshop

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

Elton, Gruber, Brown and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 4

Elton, Gruber, Brown and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 4 Elton, Gruber, Brown and Goetzmann Modern ortfolo Theory and Investment Analyss, 7th Edton Solutons to Text roblems: Chapter 4 Chapter 4: roblem 1 A. Expected return s the sum of each outcome tmes ts assocated

More information

A Set of new Stochastic Trend Models

A Set of new Stochastic Trend Models A Set of new Stochastc Trend Models Johannes Schupp Longevty 13, Tape, 21 th -22 th September 2017 www.fa-ulm.de Introducton Uncertanty about the evoluton of mortalty Measure longevty rsk n penson or annuty

More information

Mathematical Thinking Exam 1 09 October 2017

Mathematical Thinking Exam 1 09 October 2017 Mathematcal Thnkng Exam 1 09 October 2017 Name: Instructons: Be sure to read each problem s drectons. Wrte clearly durng the exam and fully erase or mark out anythng you do not want graded. You may use

More information

Capability Analysis. Chapter 255. Introduction. Capability Analysis

Capability Analysis. Chapter 255. Introduction. Capability Analysis Chapter 55 Introducton Ths procedure summarzes the performance of a process based on user-specfed specfcaton lmts. The observed performance as well as the performance relatve to the Normal dstrbuton are

More information

Forecasts in Times of Crises

Forecasts in Times of Crises Forecasts n Tmes of Crses Aprl 2017 Chars Chrstofdes IMF Davd J. Kuenzel Wesleyan Unversty Theo S. Echer Unversty of Washngton Chrs Papageorgou IMF 1 Macroeconomc forecasts suffer from three sources of

More information

Data Mining Linear and Logistic Regression

Data Mining Linear and Logistic Regression 07/02/207 Data Mnng Lnear and Logstc Regresson Mchael L of 26 Regresson In statstcal modellng, regresson analyss s a statstcal process for estmatng the relatonshps among varables. Regresson models are

More information

Economic Design of Short-Run CSP-1 Plan Under Linear Inspection Cost

Economic Design of Short-Run CSP-1 Plan Under Linear Inspection Cost Tamkang Journal of Scence and Engneerng, Vol. 9, No 1, pp. 19 23 (2006) 19 Economc Desgn of Short-Run CSP-1 Plan Under Lnear Inspecton Cost Chung-Ho Chen 1 * and Chao-Yu Chou 2 1 Department of Industral

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

Understanding Annuities. Some Algebraic Terminology.

Understanding Annuities. Some Algebraic Terminology. Understandng Annutes Ma 162 Sprng 2010 Ma 162 Sprng 2010 March 22, 2010 Some Algebrac Termnology We recall some terms and calculatons from elementary algebra A fnte sequence of numbers s a functon of natural

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

Understanding price volatility in electricity markets

Understanding price volatility in electricity markets Proceedngs of the 33rd Hawa Internatonal Conference on System Scences - 2 Understandng prce volatlty n electrcty markets Fernando L. Alvarado, The Unversty of Wsconsn Rajesh Rajaraman, Chrstensen Assocates

More information

ISyE 512 Chapter 9. CUSUM and EWMA Control Charts. Instructor: Prof. Kaibo Liu. Department of Industrial and Systems Engineering UW-Madison

ISyE 512 Chapter 9. CUSUM and EWMA Control Charts. Instructor: Prof. Kaibo Liu. Department of Industrial and Systems Engineering UW-Madison ISyE 512 hapter 9 USUM and EWMA ontrol harts Instructor: Prof. Kabo Lu Department of Industral and Systems Engneerng UW-Madson Emal: klu8@wsc.edu Offce: Room 317 (Mechancal Engneerng Buldng) ISyE 512 Instructor:

More information

Spheria Australian Smaller Companies Fund

Spheria Australian Smaller Companies Fund 29-Jun-18 $ 2.7686 $ 2.7603 $ 2.7520 28-Jun-18 $ 2.7764 $ 2.7681 $ 2.7598 27-Jun-18 $ 2.7804 $ 2.7721 $ 2.7638 26-Jun-18 $ 2.7857 $ 2.7774 $ 2.7690 25-Jun-18 $ 2.7931 $ 2.7848 $ 2.7764 22-Jun-18 $ 2.7771

More information

Chapter 11: Optimal Portfolio Choice and the Capital Asset Pricing Model

Chapter 11: Optimal Portfolio Choice and the Capital Asset Pricing Model Chapter 11: Optmal Portolo Choce and the CAPM-1 Chapter 11: Optmal Portolo Choce and the Captal Asset Prcng Model Goal: determne the relatonshp between rsk and return => key to ths process: examne how

More information

2) In the medium-run/long-run, a decrease in the budget deficit will produce:

2) In the medium-run/long-run, a decrease in the budget deficit will produce: 4.02 Quz 2 Solutons Fall 2004 Multple-Choce Questons ) Consder the wage-settng and prce-settng equatons we studed n class. Suppose the markup, µ, equals 0.25, and F(u,z) = -u. What s the natural rate of

More information

Money, Banking, and Financial Markets (Econ 353) Midterm Examination I June 27, Name Univ. Id #

Money, Banking, and Financial Markets (Econ 353) Midterm Examination I June 27, Name Univ. Id # Money, Bankng, and Fnancal Markets (Econ 353) Mdterm Examnaton I June 27, 2005 Name Unv. Id # Note: Each multple-choce queston s worth 4 ponts. Problems 20, 21, and 22 carry 10, 8, and 10 ponts, respectvely.

More information

Information Flow and Recovering the. Estimating the Moments of. Normality of Asset Returns

Information Flow and Recovering the. Estimating the Moments of. Normality of Asset Returns Estmatng the Moments of Informaton Flow and Recoverng the Normalty of Asset Returns Ané and Geman (Journal of Fnance, 2000) Revsted Anthony Murphy, Nuffeld College, Oxford Marwan Izzeldn, Unversty of Lecester

More information

Merton-model Approach to Valuing Correlation Products

Merton-model Approach to Valuing Correlation Products Merton-model Approach to Valung Correlaton Products Vral Acharya & Stephen M Schaefer NYU-Stern and London Busness School, London Busness School Credt Rsk Electve Sprng 2009 Acharya & Schaefer: Merton

More information

Optimal Portfolio Construction (A Case Study of LQ45 Index in Indonesia Stock Exchange)

Optimal Portfolio Construction (A Case Study of LQ45 Index in Indonesia Stock Exchange) Internatonal Journal of Scence and Research (IJSR) ISS (Onlne): 319-7064 Index Coperncus Value (013): 6.14 Impact Factor (013): 4.438 Optmal Portfolo Constructon (A Case Study of LQ45 Index n Indonesa

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

Economics 1410 Fall Section 7 Notes 1. Define the tax in a flexible way using T (z), where z is the income reported by the agent.

Economics 1410 Fall Section 7 Notes 1. Define the tax in a flexible way using T (z), where z is the income reported by the agent. Economcs 1410 Fall 2017 Harvard Unversty Yaan Al-Karableh Secton 7 Notes 1 I. The ncome taxaton problem Defne the tax n a flexble way usng T (), where s the ncome reported by the agent. Retenton functon:

More information

R Square Measure of Stock Synchronicity

R Square Measure of Stock Synchronicity Internatonal Revew of Busness Research Papers Vol. 7. No. 1. January 2011. Pp. 165 175 R Square Measure of Stock Synchroncty Sarod Khandaker* Stock market synchroncty s a new area of research for fnance

More information

Harry M. Markowitz. Investors Do Not Get Paid for Bearing Risk 1

Harry M. Markowitz. Investors Do Not Get Paid for Bearing Risk 1 Investors Do Not Get Pad for Bearng Rsk Harry M. Markowtz The relatonshp between the excess return of each securty and ts beta, where beta s defned as ts regresson aganst the return on the market portfolo,

More information

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 16

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 16 lton, Gruer, rown, and Goetzmann Modern Portfolo Theory and Investment nalyss, 7th dton Solutons to Text Prolems: hapter 6 hapter 6: Prolem From the text we know that three ponts determne a plane. The

More information

occurrence of a larger storm than our culvert or bridge is barely capable of handling? (what is The main question is: What is the possibility of

occurrence of a larger storm than our culvert or bridge is barely capable of handling? (what is The main question is: What is the possibility of Module 8: Probablty and Statstcal Methods n Water Resources Engneerng Bob Ptt Unversty of Alabama Tuscaloosa, AL Flow data are avalable from numerous USGS operated flow recordng statons. Data s usually

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

XML Publisher Balance Sheet Vision Operations (USA) Feb-02

XML Publisher Balance Sheet Vision Operations (USA) Feb-02 Page:1 Apr-01 May-01 Jun-01 Jul-01 ASSETS Current Assets Cash and Short Term Investments 15,862,304 51,998,607 9,198,226 Accounts Receivable - Net of Allowance 2,560,786

More information

02_EBA2eSolutionsChapter2.pdf 02_EBA2e Case Soln Chapter2.pdf

02_EBA2eSolutionsChapter2.pdf 02_EBA2e Case Soln Chapter2.pdf 0_EBAeSolutonsChapter.pdf 0_EBAe Case Soln Chapter.pdf Chapter Solutons: 1. a. Quanttatve b. Categorcal c. Categorcal d. Quanttatve e. Categorcal. a. The top 10 countres accordng to GDP are lsted below.

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

Chapter 5 Risk and return

Chapter 5 Risk and return Chapter 5 Rsk and return Instructor s resources Overvew Ths chapter focuses on the fundamentals of the rsk and return relatonshp of assets and ther valuaton. For the sngle asset held n solaton, rsk s measured

More information

7.4. Annuities. Investigate

7.4. Annuities. Investigate 7.4 Annutes How would you lke to be a mllonare wthout workng all your lfe to earn t? Perhaps f you were lucky enough to wn a lottery or have an amazng run on a televson game show, t would happen. For most

More information

Finance 402: Problem Set 1 Solutions

Finance 402: Problem Set 1 Solutions Fnance 402: Problem Set 1 Solutons Note: Where approprate, the fnal answer for each problem s gven n bold talcs for those not nterested n the dscusson of the soluton. 1. The annual coupon rate s 6%. A

More information

Efficient Sensitivity-Based Capacitance Modeling for Systematic and Random Geometric Variations

Efficient Sensitivity-Based Capacitance Modeling for Systematic and Random Geometric Variations Effcent Senstvty-Based Capactance Modelng for Systematc and Random Geometrc Varatons 16 th Asa and South Pacfc Desgn Automaton Conference Nck van der Mejs CAS, Delft Unversty of Technology, Netherlands

More information

Financial Risk Management in Portfolio Optimization with Lower Partial Moment

Financial Risk Management in Portfolio Optimization with Lower Partial Moment Amercan Journal of Busness and Socety Vol., o., 26, pp. 2-2 http://www.ascence.org/journal/ajbs Fnancal Rsk Management n Portfolo Optmzaton wth Lower Partal Moment Lam Weng Sew, 2, *, Lam Weng Hoe, 2 Department

More information

Prospect Theory and Asset Prices

Prospect Theory and Asset Prices Fnance 400 A. Penat - G. Pennacch Prospect Theory and Asset Prces These notes consder the asset prcng mplcatons of nvestor behavor that ncorporates Prospect Theory. It summarzes an artcle by N. Barbers,

More information

ARE BENCHMARK ASSET ALLOCATIONS FOR AUSTRALIAN PRIVATE INVESTORS OPTIMAL?

ARE BENCHMARK ASSET ALLOCATIONS FOR AUSTRALIAN PRIVATE INVESTORS OPTIMAL? ARE BENCHMARK ASSET ALLOCATIONS FOR AUSTRALIAN PRIVATE INVESTORS OPTIMAL? Publshed n the Journal of Wealth Management, 2009, vol. 12, no. 3, pp. 60-70. Lujer Santacruz and Dr Peter J. Phllps Lecturer and

More information

REGULATORY REFORM IN THE JAPANESE ELECTRIC POWER INDUSTRY AN EVENT STUDY ANALYSIS IAEE 2017 Conference, Singapore 20 th June 2017 Koichiro Tezuka,

REGULATORY REFORM IN THE JAPANESE ELECTRIC POWER INDUSTRY AN EVENT STUDY ANALYSIS IAEE 2017 Conference, Singapore 20 th June 2017 Koichiro Tezuka, REGULATORY REFORM IN THE JAPANESE ELECTRIC POWER INDUSTRY AN EVENT STUDY ANALYSIS IAEE 2017 Conference, Sngapore 20 th June 2017 Kochro Tezuka, Nhon Unversty, Masahro Ish, Sopha Unversty, Satoru Hashmoto,

More information

Consumption Based Asset Pricing

Consumption Based Asset Pricing Consumpton Based Asset Prcng Mchael Bar Aprl 25, 208 Contents Introducton 2 Model 2. Prcng rsk-free asset............................... 3 2.2 Prcng rsky assets................................ 4 2.3 Bubbles......................................

More information

OCR Statistics 1 Working with data. Section 2: Measures of location

OCR Statistics 1 Working with data. Section 2: Measures of location OCR Statstcs 1 Workng wth data Secton 2: Measures of locaton Notes and Examples These notes have sub-sectons on: The medan Estmatng the medan from grouped data The mean Estmatng the mean from grouped data

More information

Final Examination MATH NOTE TO PRINTER

Final Examination MATH NOTE TO PRINTER Fnal Examnaton MATH 329 2005 01 1 NOTE TO PRINTER (These nstructons are for the prnter. They should not be duplcated.) Ths examnaton should be prnted on 8 1 2 14 paper, and stapled wth 3 sde staples, so

More information

Int. Statistical Inst.: Proc. 58th World Statistical Congress, 2011, Dublin (Session STS041) p The Max-CUSUM Chart

Int. Statistical Inst.: Proc. 58th World Statistical Congress, 2011, Dublin (Session STS041) p The Max-CUSUM Chart Int. Statstcal Inst.: Proc. 58th World Statstcal Congress, 1, Dubln (Sesson STS41) p.2996 The Max-CUSUM Chart Smley W. Cheng Department of Statstcs Unversty of Mantoba Wnnpeg, Mantoba Canada, R3T 2N2 smley_cheng@umantoba.ca

More information

Testing for Omitted Variables

Testing for Omitted Variables Testng for Omtted Varables Jeroen Weese Department of Socology Unversty of Utrecht The Netherlands emal J.weese@fss.uu.nl tel +31 30 2531922 fax+31 30 2534405 Prepared for North Amercan Stata users meetng

More information