Page 1. Real Options for Engineering Systems. What are we up to? Today s agenda. J1: Real Options for Engineering Systems. Richard de Neufville

Size: px
Start display at page:

Download "Page 1. Real Options for Engineering Systems. What are we up to? Today s agenda. J1: Real Options for Engineering Systems. Richard de Neufville"

Transcription

1 Real Optios for Egieerig Systems J: Real Optios for Egieerig Systems By (MIT) Stefa Scholtes (CU) Course website: Stefa Scholtes Judge Istitute of Maagemet, CU Slide What are we up to? Two sides of the coi Egieerig systems desig Maagemet of risk ad opportuity Take-aways: Kowig how to value flexibility ad kow how to icorporate it ito the desig of techology systems Appreciatig the differeces betwee real optios ad traditioal fiacial optios Uderstadig orgaisatioal barriers to implemetatio Formal objectives Uderstadig the optios paradigm Experiece i valuig risky projects Stefa Scholtes Judge Istitute of Maagemet, CU Slide 2 Today s ageda Theme: go/o go decisios for techology projects Makig a ecoomic case for a project Break eve aalysis Rate of retur Net preset value A critique of traditioal NPV How does NPV cope with ucertaity? How does NPV cope with flexibility? It s ot so simple Stefa Scholtes Judge Istitute of Maagemet, CU Slide 3 Page

2 Project valuatio Project valuatio: Makig a ecoomic case for a techology project Covice the board that the compay ca do othig better with the ivestmet capital tha ivestig it i the project Compare project payoffs with alterative ivestmet opportuities withi the compay ad i the market place Take existig portfolio of projects ad log-term strategic cosideratios ito accout (aligmet of project with existig stregths ad strategic positioig of the compay) Desig optimisatio: addig desig features to techology projects to make them ecoomically more attractive Stefa Scholtes Judge Istitute of Maagemet, CU Slide 4 CFO s poit of view Fiace departmet: A project cosists of a iitial ivestmet followed by a stream of future cash flows Ivest i the project oly if there is o better alterative ivestmet opportuity Two major problems: Ucertaity: Cash flows of the project deped o exteral ucertaities Flexibility: Cash flows deped o our (ad our competitors ) maagemet decisios durig the life time of the project How ca we compare streams of ucertai payoffs which deped o future decisios (which i tur deped o ucertai evets)? Let s first look at how projects are evaluated i practice Stefa Scholtes Judge Istitute of Maagemet, CU Slide 5 Traditioal tools for project valuatio Break-eve aalysis Accoutig rates of retur Net preset value See Traditioal Project Appraisal.xls Stefa Scholtes Judge Istitute of Maagemet, CU Slide 6 Page 2

3 Break-eve aalysis Iput: Iitial ivestmet Projected cash flows over a umber of periods Break-eve poit: Number of periods ecessary for the sum of discouted cash flows to exceed the iitial ivestmet Makig a case for the project: Compare break-eve poit with compay bechmark Stefa Scholtes Judge Istitute of Maagemet, CU Slide 7 Accoutig rates of retur Iput: Projected book value of ivestmet over the life time of the project Projected profits of the project over its lifetime Accoutig rate of retur: average profit / average book value Makig a case for the project: Compare the ratio with compay bechmark Stefa Scholtes Judge Istitute of Maagemet, CU Slide 8 Net preset value Most popular valuatio criterio Iputs: Iitial ivestmet Projected cash flows over the life time of the project Discout rate NPV = Preset value of cash flows mius iitial ivestmet Makig a case for the project: NPV>0 Let s have a closer look at NPV Stefa Scholtes Judge Istitute of Maagemet, CU Slide 9 Page 3

4 NPV: uderlyig alterative ivestmet opportuities The NPV criterio is equivalet to comparig the project with a sigle alterative ivestmet opportuity: Suppose you ca ivest a arbitrary amout i a portfolio of ivestmet opportuities with a guarateed retur of r% p.a. How much do you eed to ivest ow to be able to withdraw the project cash flows whe they occur? If the life time of the project is T periods with cash flows x=(x,,x T ) the T xt y = (discretediscouti g) t (+ r) y = t= T t= rt e x (cotiuous discoutig) t y is called the preset value (PV) of the cash flow stream Net preset value NPV = PV iitial ivestmet Ecoomic case: Ivest i the project if NPV>0 Stefa Scholtes Judge Istitute of Maagemet, CU Slide 0 First problem with NPV: Which discout rate? Discout rate should reflect opportuity cost of capital Opportuities: Portfolios of alterative ivestmets Returs of portfolios Are radom Deped o risk Which portfolio? Need for optimal portfolio But: riskier portfolios are likely to have larger returs Portfolio maagemet: Retur depeds o maagemet ( re-balacig ) of portfolio Theoretical questios addressed by Capital Asset Pricig Model (CAPM) uder certai assumptios see e.g. Brealey ad Myers, Priciples of Corporate Fiace or Lueberger, Ivestmet Sciece Stefa Scholtes Judge Istitute of Maagemet, CU Slide Risk premium Approach: Discout rate = risk free rate + risk premium Should the risk premium be costat over time? Assumes risk to be costat over time Techology projects: Most risks get resolved very quickly (techological risk, demad for ew product, regulatory ucertaity, etc.) Stefa Scholtes Judge Istitute of Maagemet, CU Slide 2 Page 4

5 Practical approaches to discout rates Maagerial praxis I: Use compay-iteral hurdle rate Techology projects have ofte log time horizos Sesitive depedece o discout rate at 0% over a period of 20 years is worth > 7 at 5% over a period of 20 years is worth < 3 Maagerial praxis II: Fid portfolio with the same risk profile as the ew project ad maximal expected retur ad use this maximal expected retur as bechmark discout rate Techology projects: Which project portfolios have similar risk profile? Flaw of averages Lesso : It is ot clear which discout rate should be chose i practice? Stefa Scholtes Judge Istitute of Maagemet, CU Slide 3 Secod problem with NPV: The forecast is always wrog Let s have a look at a spreadsheet example (ope NPV.xls worksheet Project Pla) Cash flow calculated o the basis of forecasted demad Demad i period t is ucertai ad depeds o edogeous (price) ad exogeous (ecoomy, fashio, competitors) variables Ukow demads i periods,,t- may help us to predict demad i period t more accurately (statistical depedece) Lesso 2: NPV is a fuctio of ucertai quatities ad therefore itself ucertai Stefa Scholtes Judge Istitute of Maagemet, CU Slide 4 Let s formalize this Mathematically: NPV depeds o ucertai quatities X,,X (radom variables): NPV=NPV(X,,X ) A fuctio of a radom variable is itself a radom variable A sigle umber (eve the mea) is very limited iformatio about a radom variable Ca make a better ecoomic case from kowledge of the NPV distributio If we ca t get the distributio the we wat at least some of its characteristics: expected NPV variace of NPV 95% cofidece iterval for NPV Stefa Scholtes Judge Istitute of Maagemet, CU Slide 5 Page 5

6 Distributios ad Value at risk NPV cumulative distributio fuctio 00.0% 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 0.0% 0.0% -,500,000 -,000, , ,000,000,000,500,000 2,000,000 2,500,000 0% VAR is roughly 500,000 5% VAR is roughly 800,000 Stefa Scholtes Judge Istitute of Maagemet, CU Slide 6 The flaw of averages I practice, decisios are ofte made o the basis of expected NPV aloe Assumes that variability (i.e. risk) is captured i discout rate Naïve approach: Let s work with expected demads Let s assume marketig departmet has give us a price of 000 ad expected demads for that price Is the NPV the the expected NPV? Let s look at a example (NPV.xls) Lesso 3: The flaw of the averages : Pluggig expected values ito ucertai cells i a spreadsheet does ot give expected values of the formula cells Mathematically: E( f ( X)) f ( E( X)) Stefa Scholtes Judge Istitute of Maagemet, CU Slide 7 The flaw of averages ad discout rates Recall practical advise: Take as discout rate the average retur of the best portfolio with the same risk level as the ew project However: NPV depeds o-liearly o the discout rate (flaw of the averages) Way out: simulate, usig historic period returs istead of averages Jese s iequality: the NPV calculatio o the basis of average rate of retur is lower tha the expected NPV based o historic returs See Flaw of the averages for discout rates.xls But: Returs of ew project ad bechmark portfolio are correlated What s right? Stefa Scholtes Judge Istitute of Maagemet, CU Slide 8 Page 6

7 Third problem with NPV: No maagerial activity NPV assumes that the cash flows of the project are fixed Eve if cash flows are radom ad simulatio is used to evaluate expected NPV, there is o maagerial flexibility i the model Typically, maagemet acts depedig o ufoldig ucertaities Typical actios Postpoe projects Grow project Icrease marketig efforts Abado project Let s look at a example (see NPV.xls, worksheet expasio optio) Stefa Scholtes Judge Istitute of Maagemet, CU Slide 9 Summary Compay should ivest i a project if there are o better ivestmet alteratives NPV-criterio has severe drawbacks: NPV criterio is based o FIXED cash flow projectios ad does ot take maagerial flexibility ito accout (this udervalues the project) Ucertaity is ofte ot take ito accout properly i practice (flaw of averages) What should the discout rate be? Stefa Scholtes Judge Istitute of Maagemet, CU Slide 20 Back to the basics To make a ecoomic case for a ew project we eed to argue that addig the ew project to the existig project portfolio (ad therefore abadoig or dow-sizig other projects) icreases the desirability of the stream of future cash flows Stefa Scholtes Judge Istitute of Maagemet, CU Slide 2 Page 7

8 Settigs The courtroom paradigm Iocece hypothesis: The project does ot add value to the portfolio Jury: The decisio maker Prosecutor (Egieer): I wat the project i the portfolio Costructs a case that the project adds value to the compay s portfolio I particular: eeds to argue how the portfolio re-balacig should be doe (i.e. where the moey should come from) Defece lawyer (CFO): I do t wat the project i the portfolio Need to reply to the prosecutors case by costructig alterative ivestmet portfolios that do ot iclude the ew project ad arguig that these are more beeficial to the compay tha ivestig i the project Questio: Why do t we use as iocece hypothesis that the project adds value to the portfolio? Stefa Scholtes Judge Istitute of Maagemet, CU Slide 22 Usig a computer Prosecutor: Build a stochastic (sceario-based) computer model of the project, icludig decisio poits ad decisio rules (plas of actio for all possible scearios) Defece lawyer: Build a stochastic model of a sesible alterative ivestmet strategy (usig projects or assets from withi the compay or i the market place), icludig possible decisio poits ad decisio rules Jury: Decide whether there is a case for the project o the basis of the (radom) differece betwee the cash flow streams of the project ad the alterative ivestmet strategy Ca use simulatio to estimate the distributio of the differece betwee the cash flow streams of the two ivestmets for give decisio rules Jury will also take strategic issues ito accout Stefa Scholtes Judge Istitute of Maagemet, CU Slide 23 Simulatio Results % Mea 5% 0 periods +2 periods 3+4 periods Differece betwee project cash flow ad alterative ivestmet cash flow Stefa Scholtes Judge Istitute of Maagemet, CU Slide 24 Page 8

9 What s the problem with this approach? Meaig of optimal decisio rule is ot clear What is optimal for cash flow i period may be bad for cash flow i other periods What is optimal i oe sceario may be bad i aother Approach allows us to compare ivestmets with regard to risk but ot with regard to flexibility Alterative ivestmet is ofte more flexible e.g. ivestmets i stock portfolio vs. ivestig i a ew aircraft project Model is complex But: there may be o simple solutios to a complex problem If we could oly do somethig that was similar to the above but simpler Research: Suggest rules of thumb to practitioers which are coceptually soud but have simple ituitive iterpretatio Academics adds to practitioers cofidece that they are doig the right thig by followig their ituitio Stefa Scholtes Judge Istitute of Maagemet, CU Slide 25 Coclusio NPV criterio has serious pitfalls The courtroom is a sesible model for project appraisal Complexity of cases for or agaist a project is a serious hurdle to acceptace i practice But the agai: there may ot be a simple solutio to this complex problem More questios tha aswers Stefa Scholtes Judge Istitute of Maagemet, CU Slide 26 Appedix: Returs of portfolios A portfolio is a ivestmet of w i i ivestmet opportuity i=,, The retur of the portfolio r w) = r(( w,..., w )) = w r w r ( is approximately ormal if is large (cetral limit theorem) The expected retur is E( r( w)) = E( w r w r ) = w E( r ) w E( r ) Covariace of r i ad r j The variace of returs is i = j = T w w σ = w Σ w i j ij Covariace matrix Stefa Scholtes Judge Istitute of Maagemet, CU Slide 27 Page 9

Output Analysis (2, Chapters 10 &11 Law)

Output Analysis (2, Chapters 10 &11 Law) B. Maddah ENMG 6 Simulatio 05/0/07 Output Aalysis (, Chapters 10 &11 Law) Comparig alterative system cofiguratio Sice the output of a simulatio is radom, the comparig differet systems via simulatio should

More information

Determining the sample size

Determining the sample size Determiig the sample size Oe of the most commo questios ay statisticia gets asked is How large a sample size do I eed? Researchers are ofte surprised to fid out that the aswer depeds o a umber of factors

More information

Institute of Actuaries of India Subject CT1 Financial Mathematics

Institute of Actuaries of India Subject CT1 Financial Mathematics Istitute of Actuaries of Idia Subject CT1 Fiacial Mathematics For 2014 Examiatios Subject CT1 Fiacial Mathematics Core Techical Aim The aim of the Fiacial Mathematics subject is to provide a groudig i

More information

CHAPTER 3 THE TIME VALUE OF MONEY

CHAPTER 3 THE TIME VALUE OF MONEY CHAPTER 3 THE TIME VALUE OF MONEY OVERVIEW A dollar i the had today is worth more tha a dollar to be received i the future because, if you had it ow, you could ivest that dollar ad ear iterest. Of all

More information

Pre-Suit Collection Strategies

Pre-Suit Collection Strategies Pre-Suit Collectio Strategies Writte by Charles PT Phoeix How to Decide Whether to Pursue Collectio Calculatig the Value of Collectio As with ay busiess litigatio, all factors associated with the process

More information

Confidence Intervals for One Mean

Confidence Intervals for One Mean Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a

More information

*The most important feature of MRP as compared with ordinary inventory control analysis is its time phasing feature.

*The most important feature of MRP as compared with ordinary inventory control analysis is its time phasing feature. Itegrated Productio ad Ivetory Cotrol System MRP ad MRP II Framework of Maufacturig System Ivetory cotrol, productio schedulig, capacity plaig ad fiacial ad busiess decisios i a productio system are iterrelated.

More information

Chapter 5 Unit 1. IET 350 Engineering Economics. Learning Objectives Chapter 5. Learning Objectives Unit 1. Annual Amount and Gradient Functions

Chapter 5 Unit 1. IET 350 Engineering Economics. Learning Objectives Chapter 5. Learning Objectives Unit 1. Annual Amount and Gradient Functions Chapter 5 Uit Aual Amout ad Gradiet Fuctios IET 350 Egieerig Ecoomics Learig Objectives Chapter 5 Upo completio of this chapter you should uderstad: Calculatig future values from aual amouts. Calculatig

More information

Hypothesis testing. Null and alternative hypotheses

Hypothesis testing. Null and alternative hypotheses Hypothesis testig Aother importat use of samplig distributios is to test hypotheses about populatio parameters, e.g. mea, proportio, regressio coefficiets, etc. For example, it is possible to stipulate

More information

Learning objectives. Duc K. Nguyen - Corporate Finance 21/10/2014

Learning objectives. Duc K. Nguyen - Corporate Finance 21/10/2014 1 Lecture 3 Time Value of Moey ad Project Valuatio The timelie Three rules of time travels NPV of a stream of cash flows Perpetuities, auities ad other special cases Learig objectives 2 Uderstad the time-value

More information

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return EVALUATING ALTERNATIVE CAPITAL INVESTMENT PROGRAMS By Ke D. Duft, Extesio Ecoomist I the March 98 issue of this publicatio we reviewed the procedure by which a capital ivestmet project was assessed. The

More information

Present Value Factor To bring one dollar in the future back to present, one uses the Present Value Factor (PVF): Concept 9: Present Value

Present Value Factor To bring one dollar in the future back to present, one uses the Present Value Factor (PVF): Concept 9: Present Value Cocept 9: Preset Value Is the value of a dollar received today the same as received a year from today? A dollar today is worth more tha a dollar tomorrow because of iflatio, opportuity cost, ad risk Brigig

More information

Simple Annuities Present Value.

Simple Annuities Present Value. Simple Auities Preset Value. OBJECTIVES (i) To uderstad the uderlyig priciple of a preset value auity. (ii) To use a CASIO CFX-9850GB PLUS to efficietly compute values associated with preset value auities.

More information

I. Chi-squared Distributions

I. Chi-squared Distributions 1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.

More information

PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM

PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY Physical ad Mathematical Scieces 2015, 1, p. 15 19 M a t h e m a t i c s AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM A. G. GULYAN Chair of Actuarial Mathematics

More information

Bond Valuation I. What is a bond? Cash Flows of A Typical Bond. Bond Valuation. Coupon Rate and Current Yield. Cash Flows of A Typical Bond

Bond Valuation I. What is a bond? Cash Flows of A Typical Bond. Bond Valuation. Coupon Rate and Current Yield. Cash Flows of A Typical Bond What is a bod? Bod Valuatio I Bod is a I.O.U. Bod is a borrowig agreemet Bod issuers borrow moey from bod holders Bod is a fixed-icome security that typically pays periodic coupo paymets, ad a pricipal

More information

1. C. The formula for the confidence interval for a population mean is: x t, which was

1. C. The formula for the confidence interval for a population mean is: x t, which was s 1. C. The formula for the cofidece iterval for a populatio mea is: x t, which was based o the sample Mea. So, x is guarateed to be i the iterval you form.. D. Use the rule : p-value

More information

I. Why is there a time value to money (TVM)?

I. Why is there a time value to money (TVM)? Itroductio to the Time Value of Moey Lecture Outlie I. Why is there the cocept of time value? II. Sigle cash flows over multiple periods III. Groups of cash flows IV. Warigs o doig time value calculatios

More information

Center, Spread, and Shape in Inference: Claims, Caveats, and Insights

Center, Spread, and Shape in Inference: Claims, Caveats, and Insights Ceter, Spread, ad Shape i Iferece: Claims, Caveats, ad Isights Dr. Nacy Pfeig (Uiversity of Pittsburgh) AMATYC November 2008 Prelimiary Activities 1. I would like to produce a iterval estimate for the

More information

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value

More information

Math C067 Sampling Distributions

Math C067 Sampling Distributions Math C067 Samplig Distributios Sample Mea ad Sample Proportio Richard Beigel Some time betwee April 16, 2007 ad April 16, 2007 Examples of Samplig A pollster may try to estimate the proportio of voters

More information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties of MLE: consistency, asymptotic normality. Fisher information. Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout

More information

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5 Sectio 13 Kolmogorov-Smirov test. Suppose that we have a i.i.d. sample X 1,..., X with some ukow distributio P ad we would like to test the hypothesis that P is equal to a particular distributio P 0, i.e.

More information

CHAPTER 7: Central Limit Theorem: CLT for Averages (Means)

CHAPTER 7: Central Limit Theorem: CLT for Averages (Means) CHAPTER 7: Cetral Limit Theorem: CLT for Averages (Meas) X = the umber obtaied whe rollig oe six sided die oce. If we roll a six sided die oce, the mea of the probability distributio is X P(X = x) Simulatio:

More information

Measures of Spread and Boxplots Discrete Math, Section 9.4

Measures of Spread and Boxplots Discrete Math, Section 9.4 Measures of Spread ad Boxplots Discrete Math, Sectio 9.4 We start with a example: Example 1: Comparig Mea ad Media Compute the mea ad media of each data set: S 1 = {4, 6, 8, 10, 1, 14, 16} S = {4, 7, 9,

More information

15.075 Exam 3. Instructor: Cynthia Rudin TA: Dimitrios Bisias. November 22, 2011

15.075 Exam 3. Instructor: Cynthia Rudin TA: Dimitrios Bisias. November 22, 2011 15.075 Exam 3 Istructor: Cythia Rudi TA: Dimitrios Bisias November 22, 2011 Gradig is based o demostratio of coceptual uderstadig, so you eed to show all of your work. Problem 1 A compay makes high-defiitio

More information

Non-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring

Non-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring No-life isurace mathematics Nils F. Haavardsso, Uiversity of Oslo ad DNB Skadeforsikrig Mai issues so far Why does isurace work? How is risk premium defied ad why is it importat? How ca claim frequecy

More information

Chapter 14 Nonparametric Statistics

Chapter 14 Nonparametric Statistics Chapter 14 Noparametric Statistics A.K.A. distributio-free statistics! Does ot deped o the populatio fittig ay particular type of distributio (e.g, ormal). Sice these methods make fewer assumptios, they

More information

Case Study. Normal and t Distributions. Density Plot. Normal Distributions

Case Study. Normal and t Distributions. Density Plot. Normal Distributions Case Study Normal ad t Distributios Bret Halo ad Bret Larget Departmet of Statistics Uiversity of Wiscosi Madiso October 11 13, 2011 Case Study Body temperature varies withi idividuals over time (it ca

More information

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets BENEIT-CST ANALYSIS iacial ad Ecoomic Appraisal usig Spreadsheets Ch. 2: Ivestmet Appraisal - Priciples Harry Campbell & Richard Brow School of Ecoomics The Uiversity of Queeslad Review of basic cocepts

More information

1 Computing the Standard Deviation of Sample Means

1 Computing the Standard Deviation of Sample Means Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.

More information

CCH Accountants Starter Pack

CCH Accountants Starter Pack CCH Accoutats Starter Pack We may be a bit smaller, but fudametally we re o differet to ay other accoutig practice. Util ow, smaller firms have faced a stark choice: Buy cheaply, kowig that the practice

More information

The analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection

The analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection The aalysis of the Courot oligopoly model cosiderig the subjective motive i the strategy selectio Shigehito Furuyama Teruhisa Nakai Departmet of Systems Maagemet Egieerig Faculty of Egieerig Kasai Uiversity

More information

Terminology for Bonds and Loans

Terminology for Bonds and Loans ³ ² ± Termiology for Bods ad Loas Pricipal give to borrower whe loa is made Simple loa: pricipal plus iterest repaid at oe date Fixed-paymet loa: series of (ofte equal) repaymets Bod is issued at some

More information

CHAPTER 11 Financial mathematics

CHAPTER 11 Financial mathematics CHAPTER 11 Fiacial mathematics I this chapter you will: Calculate iterest usig the simple iterest formula ( ) Use the simple iterest formula to calculate the pricipal (P) Use the simple iterest formula

More information

2 Time Value of Money

2 Time Value of Money 2 Time Value of Moey BASIC CONCEPTS AND FORMULAE 1. Time Value of Moey It meas moey has time value. A rupee today is more valuable tha a rupee a year hece. We use rate of iterest to express the time value

More information

FM4 CREDIT AND BORROWING

FM4 CREDIT AND BORROWING FM4 CREDIT AND BORROWING Whe you purchase big ticket items such as cars, boats, televisios ad the like, retailers ad fiacial istitutios have various terms ad coditios that are implemeted for the cosumer

More information

Lesson 17 Pearson s Correlation Coefficient

Lesson 17 Pearson s Correlation Coefficient Outlie Measures of Relatioships Pearso s Correlatio Coefficiet (r) -types of data -scatter plots -measure of directio -measure of stregth Computatio -covariatio of X ad Y -uique variatio i X ad Y -measurig

More information

University of California, Los Angeles Department of Statistics. Distributions related to the normal distribution

University of California, Los Angeles Department of Statistics. Distributions related to the normal distribution Uiversity of Califoria, Los Ageles Departmet of Statistics Statistics 100B Istructor: Nicolas Christou Three importat distributios: Distributios related to the ormal distributio Chi-square (χ ) distributio.

More information

Solving Logarithms and Exponential Equations

Solving Logarithms and Exponential Equations Solvig Logarithms ad Epoetial Equatios Logarithmic Equatios There are two major ideas required whe solvig Logarithmic Equatios. The first is the Defiitio of a Logarithm. You may recall from a earlier topic:

More information

Overview. Learning Objectives. Point Estimate. Estimation. Estimating the Value of a Parameter Using Confidence Intervals

Overview. Learning Objectives. Point Estimate. Estimation. Estimating the Value of a Parameter Using Confidence Intervals Overview Estimatig the Value of a Parameter Usig Cofidece Itervals We apply the results about the sample mea the problem of estimatio Estimatio is the process of usig sample data estimate the value of

More information

How to use what you OWN to reduce what you OWE

How to use what you OWN to reduce what you OWE How to use what you OWN to reduce what you OWE Maulife Oe A Overview Most Caadias maage their fiaces by doig two thigs: 1. Depositig their icome ad other short-term assets ito chequig ad savigs accouts.

More information

Z-TEST / Z-STATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown

Z-TEST / Z-STATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown Z-TEST / Z-STATISTIC: used to test hypotheses about µ whe the populatio stadard deviatio is kow ad populatio distributio is ormal or sample size is large T-TEST / T-STATISTIC: used to test hypotheses about

More information

France caters to innovative companies and offers the best research tax credit in Europe

France caters to innovative companies and offers the best research tax credit in Europe 1/5 The Frech Govermet has three objectives : > improve Frace s fiscal competitiveess > cosolidate R&D activities > make Frace a attractive coutry for iovatio Tax icetives have become a key elemet of public

More information

Section 11.3: The Integral Test

Section 11.3: The Integral Test Sectio.3: The Itegral Test Most of the series we have looked at have either diverged or have coverged ad we have bee able to fid what they coverge to. I geeral however, the problem is much more difficult

More information

1 Correlation and Regression Analysis

1 Correlation and Regression Analysis 1 Correlatio ad Regressio Aalysis I this sectio we will be ivestigatig the relatioship betwee two cotiuous variable, such as height ad weight, the cocetratio of a ijected drug ad heart rate, or the cosumptio

More information

Maximum Likelihood Estimators.

Maximum Likelihood Estimators. Lecture 2 Maximum Likelihood Estimators. Matlab example. As a motivatio, let us look at oe Matlab example. Let us geerate a radom sample of size 00 from beta distributio Beta(5, 2). We will lear the defiitio

More information

A Guide to the Pricing Conventions of SFE Interest Rate Products

A Guide to the Pricing Conventions of SFE Interest Rate Products A Guide to the Pricig Covetios of SFE Iterest Rate Products SFE 30 Day Iterbak Cash Rate Futures Physical 90 Day Bak Bills SFE 90 Day Bak Bill Futures SFE 90 Day Bak Bill Futures Tick Value Calculatios

More information

Chapter 7 Methods of Finding Estimators

Chapter 7 Methods of Finding Estimators Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of

More information

5: Introduction to Estimation

5: Introduction to Estimation 5: Itroductio to Estimatio Cotets Acroyms ad symbols... 1 Statistical iferece... Estimatig µ with cofidece... 3 Samplig distributio of the mea... 3 Cofidece Iterval for μ whe σ is kow before had... 4 Sample

More information

Erik Ottosson & Fredrik Weissenrieder, 1996-03-01 CVA. Cash Value Added - a new method for measuring financial performance.

Erik Ottosson & Fredrik Weissenrieder, 1996-03-01 CVA. Cash Value Added - a new method for measuring financial performance. CVA Cash Value Added - a ew method for measurig fiacial performace Erik Ottosso Strategic Cotroller Sveska Cellulosa Aktiebolaget SCA Box 7827 S-103 97 Stockholm Swede Fredrik Weisserieder Departmet of

More information

Annuities Under Random Rates of Interest II By Abraham Zaks. Technion I.I.T. Haifa ISRAEL and Haifa University Haifa ISRAEL.

Annuities Under Random Rates of Interest II By Abraham Zaks. Technion I.I.T. Haifa ISRAEL and Haifa University Haifa ISRAEL. Auities Uder Radom Rates of Iterest II By Abraham Zas Techio I.I.T. Haifa ISRAEL ad Haifa Uiversity Haifa ISRAEL Departmet of Mathematics, Techio - Israel Istitute of Techology, 3000, Haifa, Israel I memory

More information

Time Value of Money. First some technical stuff. HP10B II users

Time Value of Money. First some technical stuff. HP10B II users Time Value of Moey Basis for the course Power of compoud iterest $3,600 each year ito a 401(k) pla yields $2,390,000 i 40 years First some techical stuff You will use your fiacial calculator i every sigle

More information

Analyzing Longitudinal Data from Complex Surveys Using SUDAAN

Analyzing Longitudinal Data from Complex Surveys Using SUDAAN Aalyzig Logitudial Data from Complex Surveys Usig SUDAAN Darryl Creel Statistics ad Epidemiology, RTI Iteratioal, 312 Trotter Farm Drive, Rockville, MD, 20850 Abstract SUDAAN: Software for the Statistical

More information

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,

More information

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n We will cosider the liear regressio model i matrix form. For simple liear regressio, meaig oe predictor, the model is i = + x i + ε i for i =,,,, This model icludes the assumptio that the ε i s are a sample

More information

FI A CIAL MATHEMATICS

FI A CIAL MATHEMATICS CHAPTER 7 FI A CIAL MATHEMATICS Page Cotets 7.1 Compoud Value 117 7.2 Compoud Value of a Auity 118 7.3 Sikig Fuds 119 7.4 Preset Value 122 7.5 Preset Value of a Auity 122 7.6 Term Loas ad Amortizatio 123

More information

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the. Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).

More information

Statistical inference: example 1. Inferential Statistics

Statistical inference: example 1. Inferential Statistics Statistical iferece: example 1 Iferetial Statistics POPULATION SAMPLE A clothig store chai regularly buys from a supplier large quatities of a certai piece of clothig. Each item ca be classified either

More information

Enhancing Oracle Business Intelligence with cubus EV How users of Oracle BI on Essbase cubes can benefit from cubus outperform EV Analytics (cubus EV)

Enhancing Oracle Business Intelligence with cubus EV How users of Oracle BI on Essbase cubes can benefit from cubus outperform EV Analytics (cubus EV) Ehacig Oracle Busiess Itelligece with cubus EV How users of Oracle BI o Essbase cubes ca beefit from cubus outperform EV Aalytics (cubus EV) CONTENT 01 cubus EV as a ehacemet to Oracle BI o Essbase 02

More information

Supply Chain Management

Supply Chain Management Supply Chai Maagemet LOA Uiversity October 9, 205 Distributio D Distributio Authorized to Departmet of Defese ad U.S. DoD Cotractors Oly Aim High Fly - Fight - Wi Who am I? Dr. William A Cuigham PhD Ecoomics

More information

COMPARISON OF THE EFFICIENCY OF S-CONTROL CHART AND EWMA-S 2 CONTROL CHART FOR THE CHANGES IN A PROCESS

COMPARISON OF THE EFFICIENCY OF S-CONTROL CHART AND EWMA-S 2 CONTROL CHART FOR THE CHANGES IN A PROCESS COMPARISON OF THE EFFICIENCY OF S-CONTROL CHART AND EWMA-S CONTROL CHART FOR THE CHANGES IN A PROCESS Supraee Lisawadi Departmet of Mathematics ad Statistics, Faculty of Sciece ad Techoology, Thammasat

More information

Normal Distribution.

Normal Distribution. Normal Distributio www.icrf.l Normal distributio I probability theory, the ormal or Gaussia distributio, is a cotiuous probability distributio that is ofte used as a first approimatio to describe realvalued

More information

Soving Recurrence Relations

Soving Recurrence Relations Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree

More information

CDs Bought at a Bank verses CD s Bought from a Brokerage. Floyd Vest

CDs Bought at a Bank verses CD s Bought from a Brokerage. Floyd Vest CDs Bought at a Bak verses CD s Bought from a Brokerage Floyd Vest CDs bought at a bak. CD stads for Certificate of Deposit with the CD origiatig i a FDIC isured bak so that the CD is isured by the Uited

More information

13 Management Practices That Waste Time & Money (and what to do instead)

13 Management Practices That Waste Time & Money (and what to do instead) 13 Maagemet Practices That Waste Time & Moey (ad what to do istead) By Dr. Aubrey C. Daiels Just Because Every Orgaizatio Uses Them, Does t Make Them Effective To achieve maagemet excellece, you eed to

More information

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,

More information

Present Values, Investment Returns and Discount Rates

Present Values, Investment Returns and Discount Rates Preset Values, Ivestmet Returs ad Discout Rates Dimitry Midli, ASA, MAAA, PhD Presidet CDI Advisors LLC dmidli@cdiadvisors.com May 2, 203 Copyright 20, CDI Advisors LLC The cocept of preset value lies

More information

GCSE STATISTICS. 4) How to calculate the range: The difference between the biggest number and the smallest number.

GCSE STATISTICS. 4) How to calculate the range: The difference between the biggest number and the smallest number. GCSE STATISTICS You should kow: 1) How to draw a frequecy diagram: e.g. NUMBER TALLY FREQUENCY 1 3 5 ) How to draw a bar chart, a pictogram, ad a pie chart. 3) How to use averages: a) Mea - add up all

More information

Lesson 15 ANOVA (analysis of variance)

Lesson 15 ANOVA (analysis of variance) Outlie Variability -betwee group variability -withi group variability -total variability -F-ratio Computatio -sums of squares (betwee/withi/total -degrees of freedom (betwee/withi/total -mea square (betwee/withi

More information

CCH CRM Books Online Software Fee Protection Consultancy Advice Lines CPD Books Online Software Fee Protection Consultancy Advice Lines CPD

CCH CRM Books Online Software Fee Protection Consultancy Advice Lines CPD Books Online Software Fee Protection Consultancy Advice Lines CPD Books Olie Software Fee Fee Protectio Cosultacy Advice Advice Lies Lies CPD CPD facig today s challeges As a accoutacy practice, maagig relatioships with our cliets has to be at the heart of everythig

More information

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008 I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces

More information

The Big Picture: An Introduction to Data Warehousing

The Big Picture: An Introduction to Data Warehousing Chapter 1 The Big Picture: A Itroductio to Data Warehousig Itroductio I 1977, Jimmy Carter was Presidet of the Uited States, Star Wars hit the big scree, ad Apple Computer, Ic. itroduced the world to the

More information

Mathematical goals. Starting points. Materials required. Time needed

Mathematical goals. Starting points. Materials required. Time needed Level A1 of challege: C A1 Mathematical goals Startig poits Materials required Time eeded Iterpretig algebraic expressios To help learers to: traslate betwee words, symbols, tables, ad area represetatios

More information

Overview of some probability distributions.

Overview of some probability distributions. Lecture Overview of some probability distributios. I this lecture we will review several commo distributios that will be used ofte throughtout the class. Each distributio is usually described by its probability

More information

GROSS MARGIN MANAGEMENT FRAMEWORK FOR MERCHANDISING DECISIONS IN COMPANIES WITH LARGE ASSORTMENT OF PRODUCTS

GROSS MARGIN MANAGEMENT FRAMEWORK FOR MERCHANDISING DECISIONS IN COMPANIES WITH LARGE ASSORTMENT OF PRODUCTS ECONOMICS AND MANAGEMENT: 2013. 18 (1) ISSN 2029-9338 (ONLINE) FOR MERCHANDISING DECISIONS IN COMPANIES WITH LARGE ASSORTMENT OF PRODUCTS Gedimias Jagelavicius Kauas Uiversity of Techology, Lithuaia http://dx.doi.org/10.5755/j01.em.18.1.4116

More information

How To Find FINANCING For Your Business

How To Find FINANCING For Your Business How To Fid FINANCING For Your Busiess Oe of the most difficult tasks faced by the maagemet team of small busiesses today is fidig adequate fiacig for curret operatios i order to support ew ad ogoig cotracts.

More information

Tradigms of Astundithi and Toyota

Tradigms of Astundithi and Toyota Tradig the radomess - Desigig a optimal tradig strategy uder a drifted radom walk price model Yuao Wu Math 20 Project Paper Professor Zachary Hamaker Abstract: I this paper the author iteds to explore

More information

A probabilistic proof of a binomial identity

A probabilistic proof of a binomial identity A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two

More information

INVESTMENT PERFORMANCE COUNCIL (IPC)

INVESTMENT PERFORMANCE COUNCIL (IPC) INVESTMENT PEFOMANCE COUNCIL (IPC) INVITATION TO COMMENT: Global Ivestmet Performace Stadards (GIPS ) Guidace Statemet o Calculatio Methodology The Associatio for Ivestmet Maagemet ad esearch (AIM) seeks

More information

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized?

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized? 5.4 Amortizatio Questio 1: How do you fid the preset value of a auity? Questio 2: How is a loa amortized? Questio 3: How do you make a amortizatio table? Oe of the most commo fiacial istrumets a perso

More information

To c o m p e t e in t o d a y s r e t a i l e n v i r o n m e n t, y o u n e e d a s i n g l e,

To c o m p e t e in t o d a y s r e t a i l e n v i r o n m e n t, y o u n e e d a s i n g l e, Busiess Itelligece Software for Retail To c o m p e t e i t o d a y s r e t a i l e v i r o m e t, y o u e e d a s i g l e, comprehesive view of your busiess. You have to tur the decisio-makig of your

More information

Practice Problems for Test 3

Practice Problems for Test 3 Practice Problems for Test 3 Note: these problems oly cover CIs ad hypothesis testig You are also resposible for kowig the samplig distributio of the sample meas, ad the Cetral Limit Theorem Review all

More information

Rainbow options. A rainbow is an option on a basket that pays in its most common form, a nonequally

Rainbow options. A rainbow is an option on a basket that pays in its most common form, a nonequally Raibow optios INRODUCION A raibow is a optio o a basket that pays i its most commo form, a oequally weighted average of the assets of the basket accordig to their performace. he umber of assets is called

More information

Now here is the important step

Now here is the important step LINEST i Excel The Excel spreadsheet fuctio "liest" is a complete liear least squares curve fittig routie that produces ucertaity estimates for the fit values. There are two ways to access the "liest"

More information

MMQ Problems Solutions with Calculators. Managerial Finance

MMQ Problems Solutions with Calculators. Managerial Finance MMQ Problems Solutios with Calculators Maagerial Fiace 2008 Adrew Hall. MMQ Solutios With Calculators. Page 1 MMQ 1: Suppose Newma s spi lads o the prize of $100 to be collected i exactly 2 years, but

More information

ENERGY STORAGE ROADMAP NL 2030. System integration and the role of energy storage

ENERGY STORAGE ROADMAP NL 2030. System integration and the role of energy storage ENERGY STORAGE ROADMAP NL 2030 System itegratio ad the role of eergy storage CONTENTS Itroductio Project method Services that ca be provided with eergy storage systems Sceario aalysis ad ecoomic aalysis

More information

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem Lecture 4: Cauchy sequeces, Bolzao-Weierstrass, ad the Squeeze theorem The purpose of this lecture is more modest tha the previous oes. It is to state certai coditios uder which we are guarateed that limits

More information

Amendments to employer debt Regulations

Amendments to employer debt Regulations March 2008 Pesios Legal Alert Amedmets to employer debt Regulatios The Govermet has at last issued Regulatios which will amed the law as to employer debts uder s75 Pesios Act 1995. The amedig Regulatios

More information

INVESTMENT PERFORMANCE COUNCIL (IPC) Guidance Statement on Calculation Methodology

INVESTMENT PERFORMANCE COUNCIL (IPC) Guidance Statement on Calculation Methodology Adoptio Date: 4 March 2004 Effective Date: 1 Jue 2004 Retroactive Applicatio: No Public Commet Period: Aug Nov 2002 INVESTMENT PERFORMANCE COUNCIL (IPC) Preface Guidace Statemet o Calculatio Methodology

More information

The Arithmetic of Investment Expenses

The Arithmetic of Investment Expenses Fiacial Aalysts Joural Volume 69 Number 2 2013 CFA Istitute The Arithmetic of Ivestmet Expeses William F. Sharpe Recet regulatory chages have brought a reewed focus o the impact of ivestmet expeses o ivestors

More information

PENSION ANNUITY. Policy Conditions Document reference: PPAS1(7) This is an important document. Please keep it in a safe place.

PENSION ANNUITY. Policy Conditions Document reference: PPAS1(7) This is an important document. Please keep it in a safe place. PENSION ANNUITY Policy Coditios Documet referece: PPAS1(7) This is a importat documet. Please keep it i a safe place. Pesio Auity Policy Coditios Welcome to LV=, ad thak you for choosig our Pesio Auity.

More information

A Test of Normality. 1 n S 2 3. n 1. Now introduce two new statistics. The sample skewness is defined as:

A Test of Normality. 1 n S 2 3. n 1. Now introduce two new statistics. The sample skewness is defined as: A Test of Normality Textbook Referece: Chapter. (eighth editio, pages 59 ; seveth editio, pages 6 6). The calculatio of p values for hypothesis testig typically is based o the assumptio that the populatio

More information

RRR 8-2013, AUGUST 21, 2013. email: jinwook.lee@rutgers.edu b RUTCOR, Rutgers University, 640 Bartholomew Road, Piscataway, NJ 08854;

RRR 8-2013, AUGUST 21, 2013. email: jinwook.lee@rutgers.edu b RUTCOR, Rutgers University, 640 Bartholomew Road, Piscataway, NJ 08854; R U T C O R RE S E A R C H R E P O R T PRICE-BANDS: A TECHNICAL TOOL FOR STOCK TRADING Jiwook Lee a Joohee Lee b Adrás Prékopa c RRR 8-2013, AUGUST 21, 2013 RUTCOR Rutgers Ceter for Operatios Research

More information

Estimating Probability Distributions by Observing Betting Practices

Estimating Probability Distributions by Observing Betting Practices 5th Iteratioal Symposium o Imprecise Probability: Theories ad Applicatios, Prague, Czech Republic, 007 Estimatig Probability Distributios by Observig Bettig Practices Dr C Lych Natioal Uiversity of Irelad,

More information

Sole trader financial statements

Sole trader financial statements 3 Sole trader fiacial statemets this chapter covers... I this chapter we look at preparig the year ed fiacial statemets of sole traders (that is, oe perso ruig their ow busiess). We preset the fiacial

More information

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles The followig eample will help us uderstad The Samplig Distributio of the Mea Review: The populatio is the etire collectio of all idividuals or objects of iterest The sample is the portio of the populatio

More information

Systems Design Project: Indoor Location of Wireless Devices

Systems Design Project: Indoor Location of Wireless Devices Systems Desig Project: Idoor Locatio of Wireless Devices Prepared By: Bria Murphy Seior Systems Sciece ad Egieerig Washigto Uiversity i St. Louis Phoe: (805) 698-5295 Email: bcm1@cec.wustl.edu Supervised

More information

TO: Users of the ACTEX Review Seminar on DVD for SOA Exam MLC

TO: Users of the ACTEX Review Seminar on DVD for SOA Exam MLC TO: Users of the ACTEX Review Semiar o DVD for SOA Eam MLC FROM: Richard L. (Dick) Lodo, FSA Dear Studets, Thak you for purchasig the DVD recordig of the ACTEX Review Semiar for SOA Eam M, Life Cotigecies

More information

Inference on Proportion. Chapter 8 Tests of Statistical Hypotheses. Sampling Distribution of Sample Proportion. Confidence Interval

Inference on Proportion. Chapter 8 Tests of Statistical Hypotheses. Sampling Distribution of Sample Proportion. Confidence Interval Chapter 8 Tests of Statistical Hypotheses 8. Tests about Proportios HT - Iferece o Proportio Parameter: Populatio Proportio p (or π) (Percetage of people has o health isurace) x Statistic: Sample Proportio

More information