# 15. Basic Index Number Theory

Save this PDF as:

Size: px
Start display at page:

## Transcription

12 5. Basc Idex Numer Theory ale 39 prce p o mach he aual quay q s he value of oal reveue for produc year dvded y q. Thus, we have 2, m (5.24) p v q ;,..., m 2 m, v m 2 m, m, v p m 2 m, m, s ( p ), m ; usg equao (5.23) where he share of aual reveue o produc moh m of he ase year s (5.25) s m, v m, 2 k, v k ;,...,. Thus, he aual ase-year prce for produc, p, urs ou o e a mohly reveue-weghed harmoc mea of he mohly prces for produc he ase year, p,, p,2,, p, Usg he aual produc prces for he ase year defed y equao (5.24), a vecor of hese prces ca e defed as p [p,,p ]. Usg hs defo, he Lowe dex ca e expressed as a rao of wo Laspeyres dces where he prce vecor p plays he role of ase-perod prces each of he wo Laspeyres dces: (5.26) P ( p, p, q ) Lo Hece, hese aual produc prces are esseally uvalue prces. Uder codos of hgh flao, he aual prces defed y equao (5.24) may o loger e reasoale or represeave of prces durg he ere ase year ecause he reveues he fal mohs of he hghflao year wll e somewha arfcally low up y geeral flao. Uder hese codos, he aual prces ad aual produc reveue shares should e erpreed wh cauo. For more o dealg wh suaos where here s hgh flao wh a year, see Peer Hll (996). pq s p p 0 0 s p p 0 PL( p, p, q )/ PL( p, p, q ) / ( / ) / ( / ), where he Laspeyres formula P L was defed y equao (5.5) aove. Thus, he aove equao shows ha he Lowe mohly prce dex comparg he prces of moh 0 wh hose of moh usg he quaes of ase year as weghs, P Lo (p 0,p,q ), s equal o he Laspeyres dex ha compares he prces of moh wh hose of year, P L (p,p,q ), dvded y he Laspeyres dex ha compares he prces of moh 0 wh hose of year, P L (p,p 0,q ). Noe ha he Laspeyres dex he umeraor ca e calculaed f he ase-year produc reveue shares, s, are kow alog wh he prce raos ha compare he prces of produc moh, p, wh he correspodg aual average prces he ase year, p. The Laspeyres dex he deomaor ca e calculaed f he ase-year produc reveue shares, s, are kow alog wh he prce raos ha compare he prces of produc moh 0, p 0, wh he correspodg aual average prces he ase year, p Aoher covee formula for evaluag he Lowe dex, P Lo (p 0,p,q ), uses he hyrd weghs formula, equao (5.5). I he prese coex, he formula ecomes (5.27) P ( p, p, q ) Lo 0 0 ( p / p ) 0 0 p pq s p, where he hyrd weghs s 0 usg he prces of moh 0 ad he quaes of year are defed y (5.28) pq p jq j j s ;,..., 38

13 Producer Prce Idex Maual 0 pq ( p / p ) 0 pq j j( pj / pj) j Equao (5.28) shows how he ase-year reveues, p q, ca e mulpled y he produc prce dces, p 0 /p, o calculae he hyrd shares. 5.4 Oe addoal formula for he Lowe dex, P Lo (p 0,p,q ), wll e exhed. Noe ha he Laspeyres decomposo of he Lowe dex defed y he hrd le equao (5.26) volves he very log-erm prce relaves, p /p, ha compare he prces moh, p, wh he possly dsa ase-year prces, p. Furher, he hyrd share decomposo of he Lowe dex defed y he hrd le equao (5.27) volves he logerm mohly prce relaves, p /p 0, whch compare he prces moh, p, wh he ase moh prces, p 0. Boh hese formulas are o sasfacory pracce ecause of he prolem of sample aro: each moh, a susaal fraco of producs dsappears from he markeplace, ad hus s useful o have a formula for updag he prevous moh s prce dex usg jus moh-over-moh prce relaves. I oher words, log-erm prce relaves dsappear a a rae ha s oo large pracce o ase a dex umer formula o her use. The Lowe dex for moh +, P Lo (p 0,p +,q ), ca e wre erms of he Lowe dex for moh, P Lo (p 0,p,q ), ad a updag facor as follows: (5.29) P ( p, p, q ) Lo + p q p q + pq 0 pq. + p q 0 PLo ( p, p, q ) pq P p 0 Lo (,, ) p + p pq + 0 p PLo ( p, p, q ) s, p where he hyrd weghs s are defed y (5.30) pq p jq j j s ;,...,. Thus, he requred updag facor, gog from moh o moh +, s he cha-lked dex + ( ) s p p, whch uses he hyrd share weghs s correspodg o moh ad ase year The Lowe dex P Lo (p 0,p,q ) ca e regarded as a approxmao o he ordary Laspeyres dex, P L (p 0,p,q 0 ), ha compares he prces of he ase moh 0, p 0, wh hose of moh, p, usg he quay vecor of moh 0, q 0, as weghs. There s a relavely smple formula ha relaes hese wo dces. To expla hs formula, s frs ecessary o make a few defos. Defe he h prce relave ewee moh 0 ad moh as 0 (5.3) r p / p ;,...,. The ordary Laspeyres prce dex, gog from moh 0 o, ca e defed erms of hese prce relaves as follows: (5.32) P ( p, p, q ) L 0 0 p pq p p 0 s p pq 382

14 5. Basc Idex Numer Theory 0 s r r, where he moh 0 reveue shares s 0 are defed as follows: (5.33) s 0 pq p jq j j ;,..., Defe he h quay relave as he rao of he quay of produc used he ase year, q, o he quay used moh 0, q 0, as follows: 0 (5.34) q / q ;,...,. The Laspeyres quay dex, Q L (q 0,q,p 0 ), ha compares quaes year, q, wh he correspodg quaes moh 0, q 0, usg he prces of moh 0, p 0, as weghs ca e defed as a weghed average of he quay raos as follows: 0 p q 0 0 L 0 0 p q q p q q 0 0 pq q 0 s 0 q 0 s * (5.35) Q ( q, q, p ) 5.44 Usg equao (A5.2.4) Appedx 5.2, he relaoshp ewee he Lowe dex P Lo (p 0,p,q ) ha uses he quaes of year as weghs o compare he prces of moh wh moh 0 ad he correspodg ordary Laspeyres dex P L (p 0,p,q 0 ) ha uses he quaes of moh 0 as weghs s defed as (5.36) P ( p, p, q ) Lo 0 P ( p, p, q ) + L ( r r )( ) s 0 Q q q p 0 0 L (,, ) Thus, he Lowe prce dex usg he quaes of year as weghs, P Lo (p 0,p,q ), s equal o he usual Laspeyres dex usg he quaes of moh 0 as weghs, P L (p 0,p,q 0 ), plus a covarace erm ( r r )( ) s 0 ewee he prce relaves r p / p 0 ad he quay relaves q /q 0, dvded y he Laspeyres quay dex Q L (q 0,q,p 0 ) ewee moh 0 ad ase year Equao (5.36) shows ha he Lowe prce dex wll cocde wh he Laspeyres prce dex f he covarace or correlao ewee he moh 0 o prce relaves r p /p 0 ad he moh 0 o year quay relaves q /q 0 s zero. Noe ha hs covarace wll e zero uder hree dffere ses of codos: If he moh prces are proporoal o he moh 0 prces so ha all r r*, If he ase year quaes are proporoal o he moh 0 quaes so ha all *, ad If he dsruo of he relave prces r s depede of he dsruo of he relave quaes. The frs wo codos are ulkely o hold emprcally, u he hrd s possle, a leas approxmaely, f purchasers do o sysemacally chage her purchasg has respose o chages relave prces If hs covarace equao (5.36) s egave, he he Lowe dex wll e less ha he Laspeyres, ad, fally, f he covarace s posve, he he Lowe dex wll e greaer ha he Laspeyres dex. Alhough he sg ad magude of he covarace erm s ulmaely a emprcal maer, s possle o make some reasoale cojecures aou s lkely sg. If he ase year precedes he prce referece moh 0 ad here are log-erm reds prces, he s lkely ha hs covarace s posve, ad hece ha he Lowe

### Chapter 4 Multiple-Degree-of-Freedom (MDOF) Systems. Packing of an instrument

Chaper 4 Mulple-Degree-of-Freedom (MDOF Sysems Eamples: Pacg of a srume Number of degrees of freedom Number of masses he sysem X Number of possble ypes of moo of each mass Mehods: Newo s Law ad Lagrage

### REVISTA INVESTIGACION OPERACIONAL Vol. 25, No. 1, 2004. k n ),

REVISTA INVESTIGACION OPERACIONAL Vol 25, No, 24 RECURRENCE AND DIRECT FORMULAS FOR TE AL & LA NUMBERS Eduardo Pza Volo Cero de Ivesgacó e Maemáca Pura y Aplcada (CIMPA), Uversdad de Cosa Rca ABSTRACT

### 7.2 Analysis of Three Dimensional Stress and Strain

eco 7. 7. Aalyss of Three Dmesoal ress ad ra The cocep of raco ad sress was roduced ad dscussed Par I.-.5. For he mos par he dscusso was cofed o wo-dmesoal saes of sress. Here he fully hree dmesoal sress

### The following model solutions are presented for educational purposes. Alternate methods of solution are, of course, acceptable.

The followg model soluos are preseed for educaoal purposes. Alerae mehods of soluo are, of course, accepable.. Soluo: C Gve he same prcpal vesed for he same perod of me yelds he same accumulaed value,

### Vladimir PAPI], Jovan POPOVI] 1. INTRODUCTION

Yugoslav Joural of Operaos Research 200 umber 77-9 VEHICLE FLEET MAAGEMET: A BAYESIA APPROACH Vladmr PAPI] Jova POPOVI] Faculy of Traspor ad Traffc Egeerg Uversy of Belgrade Belgrade Yugoslava Absrac:

### SOCIETY OF ACTUARIES FINANCIAL MATHEMATICS EXAM FM SAMPLE SOLUTIONS

SOCIETY OF ACTUARIES EXAM FM FINANCIAL MATHEMATICS EXAM FM SAMPLE SOLUTIONS Ths page dcaes chages made o Sudy Noe FM-09-05. Aprl 8, 04: Queso ad soluo 6 added. Jauary 4, 04: Quesos ad soluos 58 60 were

### Proving the Computer Science Theory P = NP? With the General Term of the Riemann Zeta Function

Research Joural of Mahemacs ad Sascs 3(2): 72-76, 20 ISSN: 2040-7505 Maxwell Scefc Orgazao, 20 Receved: Jauary 08, 20 Acceped: February 03, 20 Publshed: May 25, 20 Provg he ompuer Scece Theory P NP? Wh

### Christopher Dougherty EC220 - Introduction to econometrics: past examinations and marking schemes 2011 exam

Chrsopher Doughery EC0 - Iroduco o ecoomercs: pas examaos ad markg schemes 011 exam Orgal cao: Doughery, C. (01) EC0 - Iroduco o ecoomercs: pas examaos ad markg schemes. [Teachg Resource] 011 The Auhor

### American Journal of Business Education September 2009 Volume 2, Number 6

Amerca Joural of Bue Educao Sepember 9 Volume, umber 6 Tme Value Of Moe Ad I Applcao I Corporae Face: A Techcal oe O L Relaohp Bewee Formula Je-Ho Che, Alba Sae Uver, USA ABSTRACT Tme Value of Moe (TVM

### A new proposal for computing portfolio valueat-risk for semi-nonparametric distributions

A ew proposal for compug porfolo valuea-rsk for sem-oparamerc dsrbuos Tro-Mauel Ñíguez ad Javer Peroe Absrac Ths paper proposes a sem-oparamerc (SNP) mehodology for compug porfolo value-a-rsk (VaR) ha

### ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data

ANOVA Notes Page Aalss of Varace for a Oe-Wa Classfcato of Data Cosder a sgle factor or treatmet doe at levels (e, there are,, 3, dfferet varatos o the prescrbed treatmet) Wth a gve treatmet level there

### Professional Liability Insurance Contracts: Claims Made Versus Occurrence Policies

ARICLES ACADÉMIQUES ACADEMIC ARICLES Assuraces e geso des rsques, vol. 79(3-4), ocobre 2011- javer 2012, 251-277 Isurace ad Rsk Maageme, vol. 79(3-4), Ocober 2011- Jauary 2012, 251-277 Professoal Lably

### The Design of a Forecasting Support Models on Demand of Durian for Domestic Markets and Export Markets by Time Series and ANNs.

The 2 d RMUTP Ieraoal Coferece 2010 Page 108 The Desg of a Forecasg Suppor Models o Demad of Dura for Domesc Markes ad Expor Markes by Tme Seres ad ANNs. Udomsr Nohacho, 1* kegpol Ahakor, 2 Kazuyosh Ish,

### Lecture 13 Time Series: Stationarity, AR(p) & MA(q)

RS C - ecure 3 ecure 3 Tme Seres: Saoar AR & MAq Tme Seres: Iroduco I he earl 97 s was dscovered ha smle me seres models erformed beer ha he comlcaed mulvarae he oular 96s macro models FRB-MIT-Pe. See

### Determinants of Foreign Direct Investment in Malaysia: What Matters Most?

Deermas of Foreg Drec Ivesme Maaysa: Wha Maers Mos? Nursuha Shahrud, Zarah Yusof ad NuruHuda Mohd. Saar Ths paper exames he deermas of foreg drec vesme Maaysa from 970-008. The causay ad dyamc reaoshp

### HIGH FREQUENCY MARKET MAKING

HIGH FREQUENCY MARKET MAKING RENÉ CARMONA AND KEVIN WEBSTER Absrac. Sce hey were auhorzed by he U.S. Secury ad Exchage Commsso 1998, elecroc exchages have boomed, ad by 21 hgh frequecy radg accoued for

### Business School Discipline of Finance. Discussion Paper 2014-005. Modelling the crash risk of the Australian Dollar carry trade

Dscusso Paper: 2014-005 Busess School Dscple of Face Dscusso Paper 2014-005 Modellg he crash rsk of he Ausrala Dollar carry rade Suk-Joog Km Uversy of Sydey Busess School Modellg he crash rsk of he Ausrala

### EQUITY VALUATION USING DCF: A THEORETICAL ANALYSIS OF THE LONG TERM HYPOTHESES

Ivesme Maaeme ad Facal Iovaos Volume 4 Issue 007 9 EQUIY VALUAION USING DCF: A HEOREICAL ANALYSIS OF HE LONG ERM HYPOHESES Luco Cassa * Adrea Pla ** Slvo Vsmara *** Absrac hs paper maches he sesvy aalyss

### TIME-VARYING RISK PREMIUM IN LARGE CROSS-SECTIONAL EQUITY DATASETS

IME-VARYING RISK PREMIUM IN LARGE CROSS-SECIONAL EQUIY DAASES Parck Gaglard a, Elsa Ossola ad Olver Scalle c * Frs draf: Decemer 2 hs verso: Novemer 2 Asrac We develop a ecoomerc mehodology o fer he pah

Tradoal Smoohg Techques Smple Movg Average: or Ceered Movg Average, assume s odd: 2 ( 2 ( Weghed Movg Average: W W (or, of course, you could se up he W so ha hey smply add o oe. Noe Lear Movg Averages

### Markit iboxx USD Liquid Leveraged Loan Index

Mark Boxx USD Lqud Leveraged Loa Idex Sepember 20 Mark Boxx USD Leveraged Loa Idex Idex Gude Coe Overvew... 4 Seleco Crera... 5 Idex Icepo/Rebalacg... 5 Elgbly Crera... 5 Loa Type... 5 Mmum facly ze...

### Longitudinal and Panel Data: Analysis and Applications for the Social Sciences. Edward W. Frees

Logudal ad Pael Daa: Aalss ad Applcaos for he Socal Sceces b Edward W. Frees Logudal ad Pael Daa: Aalss ad Applcaos for he Socal Sceces Bref Table of Coes Chaper. Iroduco PART I - LINEAR MODELS Chaper.

### No Regret Learning in Oligopolies: Cournot vs Bertrand

No Regre Learg Olgopoles: Couro vs Berrad Ur Nadav Georgos Plouras Absrac Couro ad Berrad olgopoles cosue he wo mos prevale models of frm compeo. The aalyss of Nash equlbra each model reveals a uque predco

### Standardized Formula Sheet: Formulas Standard Normal Distribution Table Summary of Financial Ratios

Sadardzed Formula See: Formulas Sadard ormal Dsrbuo Table Summary o Facal Raos Formulas. Prese Value o a Sgle Cas Flow CF PV (. Fuure Value o a Sgle Cas Flow FV CF( 3. Prese Value o a Ordary Auy ( PV PT[

### CONVERGENCE AND SPATIAL PATTERNS IN LABOR PRODUCTIVITY: NONPARAMETRIC ESTIMATIONS FOR TURKEY 1

CONVERGENCE AND SPAIAL PAERNS IN LABOR PRODUCIVIY: NONPARAMERIC ESIMAIONS FOR URKEY ugrul emel, Ays asel & Peer J. Alberse Workg Paper 993 Forhcomg he Joural of Regoal Aalyss ad Polcy, 999. We would lke

### APPENDIX III THE ENVELOPE PROPERTY

Apped III APPENDIX III THE ENVELOPE PROPERTY Optmzato mposes a very strog structure o the problem cosdered Ths s the reaso why eoclasscal ecoomcs whch assumes optmzg behavour has bee the most successful

### METHODOLOGY ELECTRICITY, GAS AND WATER DISTRIBUTION INDEX (IDEGA, by its Spanish acronym) (Preliminary version)

MEHODOLOGY ELEY, GAS AND WAE DSBUON NDEX (DEGA, by s Sash acroym) (Prelmary verso) EHNAL SUBDEOAE OPEAONS SUBDEOAE Saago, December 26h, 2007 HDA/GGM/GMA/VM ABLE OF ONENS Pages. roduco 3 2. oceual frameork

### Quantifying Environmental Green Index For Fleet Management Model

Proceedgs of he Easer Asa Socey for Trasporao Sudes, Vol.9, 20 Quafyg Evromeal ree Idex For Flee Maageme Model Lay Eg TEOH a, Hoo Lg KHOO b a Deparme of Mahemacal ad Acuaral Sceces, Faculy of Egeerg ad

### 1. The Time Value of Money

Corporate Face [00-0345]. The Tme Value of Moey. Compoudg ad Dscoutg Captalzato (compoudg, fdg future values) s a process of movg a value forward tme. It yelds the future value gve the relevat compoudg

### Performance Comparisons of Load Balancing Algorithms for I/O- Intensive Workloads on Clusters

Joural of ewor ad Compuer Applcaos, vol. 3, o., pp. 32-46, Jauary 2008. Performace Comparsos of oad Balacg Algorhms for I/O- Iesve Worloads o Clusers Xao Q Deparme of Compuer Scece ad Sofware Egeerg Aubur

### Financial Time Series Forecasting with Grouped Predictors using Hierarchical Clustering and Support Vector Regression

Ieraoal Joural of Grd Dsrbuo Compug, pp.53-64 hp://dx.do.org/10.1457/jgdc.014.7.5.05 Facal Tme Seres Forecasg wh Grouped Predcors usg Herarchcal Cluserg ad Suppor Vecor Regresso ZheGao a,b,* ad JajuYag

### 10.5 Future Value and Present Value of a General Annuity Due

Chapter 10 Autes 371 5. Thomas leases a car worth \$4,000 at.99% compouded mothly. He agrees to make 36 lease paymets of \$330 each at the begg of every moth. What s the buyout prce (resdual value of the

### The Unintended Consequences of Tort Reform: Rent Seeking in New York State s Structured Settlements Statutes

The Ueded Cosequeces of Tor Reform: Re Seeg ew Yor Sae s Srucured Selemes Saues Publshed Joural of Foresc Ecoomcs, Volume 3 o, Wer 2 By Lawrece M. Spzma* Professor of Ecoomcs Mahar Hall Sae Uversy of ew

### European Exotic Options

Hado # for B9.38 rg lecre dae: 4/3/ * Rsk-Neral Valao Eroea Exoc Oos e. Prce rocess of he derlyg secry. e. Payoff of he dervave. e 3. Execao of dscoed ayoff der RNPM.. Chooser Oo oo o oo A me : rchase

### Harmony search algorithms for inventory management problems

Afrca Joural of Busess Maageme Vol.6 (36), pp. 9864-9873, 2 Sepember, 202 Avalable ole a hp://www.academcourals.org/ajbm DOI: 0.5897/AJBM2.54 ISSN 993-8233 202 Academc Jourals Revew Harmoy search algorhms

### Average Price Ratios

Average Prce Ratos Morgstar Methodology Paper August 3, 2005 2005 Morgstar, Ic. All rghts reserved. The formato ths documet s the property of Morgstar, Ic. Reproducto or trascrpto by ay meas, whole or

### Online Appendix: Measured Aggregate Gains from International Trade

Ole Appedx: Measured Aggregate Gas from Iteratoal Trade Arel Burste UCLA ad NBER Javer Cravo Uversty of Mchga March 3, 2014 I ths ole appedx we derve addtoal results dscussed the paper. I the frst secto,

### Approximate hedging for non linear transaction costs on the volume of traded assets

Noame mauscrp No. wll be sered by he edor Approxmae hedgg for o lear rasaco coss o he volume of raded asses Romuald Ele, Emmauel Lépee Absrac Ths paper s dedcaed o he replcao of a covex coge clam hs a

as Revsed: May 7 Verso 5 Mehods Proocol for he Huma Moraly aabase J.R. Wlmoh K. Adreev. Jdaov ad.a. Gle wh he asssace of C. Boe M. Bubehem. Phlpov V. Sholov P. Vacho Table of Coes Iroduco... Geeral prcples...

### STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ

### Price Volatility, Trading Activity and Market Depth: Evidence from Taiwan and Singapore Taiwan Stock Index Futures Markets

We-Hsu Kuo Asa e Pacfc al./asa Maageme Pacfc Maageme evew (005) evew 0(), (005) 3-3 0(), 3-3 Prce Volaly, Tradg Acvy ad Marke Deph: Evdece from Tawa ad Sgapore Tawa Sock Idex Fuures Markes We-Hsu Kuo a,*,

### Mobile Data Mining for Intelligent Healthcare Support

Moble Daa Mg for Iellge Healhcare uppor Absrac The growh umbers ad capacy of moble devces such as moble phoes coupled wh wdespread avalably of expesve rage of bosesors preses a uprecedeed opporuy for moble

### Object Tracking Based on Online Classification Boosted by Discriminative Features

Ieraoal Joural of Eergy, Iformao ad Commucaos, pp.9-20 hp://dx.do.org/10.14257/jec.2013.4.6.02 Objec Trackg Based o Ole Classfcao Boosed by Dscrmave Feaures Yehog Che 1 ad Pl Seog Park 2 1 Qlu Uversy of

### Evaluation and Modeling of the Digestion and Absorption of Novel Manufacturing Technology in Food Enterprises

Advace Joural of Food Scece ad Techology 9(6): 482-486, 205 ISSN: 2042-4868; e-issn: 2042-4876 Mawell Scefc Orgazao, 205 Submed: Aprl 9, 205 Acceped: Aprl 28, 205 Publshed: Augus 25, 205 Evaluao ad Modelg

### Natural Gas Storage Valuation. A Thesis Presented to The Academic Faculty. Yun Li

Naural Gas Sorage Valuao A Thess Preseed o The Academc Faculy by Yu L I Paral Fulfllme Of he Requremes for he Degree Maser of Scece he School of Idusral ad Sysem Egeerg Georga Isue of Techology December

### CHAPTER 22 ASSET BASED FINANCING: LEASE, HIRE PURCHASE AND PROJECT FINANCING

CHAPTER 22 ASSET BASED FINANCING: LEASE, HIRE PURCHASE AND PROJECT FINANCING Q.1 Defie a lease. How does i differ from a hire purchase ad isalme sale? Wha are he cash flow cosequeces of a lease? Illusrae.

### Jorge Ortega Arjona Departamento de Matemáticas, Facultad de Ciencias, UNAM jloa@fciencias.unam.mx

Usg UML Sae Dagrams for Moellg he Performace of Parallel Programs Uso e Dagramas e Esao UML para la Moelacó el Desempeño e Programas Paralelos Jorge Orega Aroa Deparameo e Maemácas, Facula e Cecas, UNAM

### MEASURES OF CENTRAL TENDENCY

MODULE - 6 Statstcs Measures of Cetral Tedecy 25 MEASURES OF CENTRAL TENDENCY I the prevous lesso, we have leart that the data could be summarsed to some extet by presetg t the form of a frequecy table.

### Analysis of Coalition Formation and Cooperation Strategies in Mobile Ad hoc Networks

Aalss of oalo Formao ad ooperao Sraeges Moble Ad hoc ewors Pero Mchard ad Ref Molva Isu Eurecom 9 Roue des rêes 06904 Sopha-Apols, Frace Absrac. Ths paper focuses o he formal assessme of he properes of

### GARCH Modelling. Theoretical Survey, Model Implementation and

Maser Thess GARCH Modellg Theorecal Survey, Model Imlemeao ad Robusess Aalyss Lars Karlsso Absrac I hs hess we survey GARCH modellg wh secal focus o he fg of GARCH models o facal reur seres The robusess

### Mobile Data Mining for Intelligent Healthcare Support

Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces - 2009 Moble Daa Mg for Iellge Healhcare uppor Par Delr Haghgh, Arkady Zaslavsky, hoal Krshaswamy, Mohamed Medha Gaber Ceer for Dsrbued ysems ad ofware

### T = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are :

Bullets bods Let s descrbe frst a fxed rate bod wthout amortzg a more geeral way : Let s ote : C the aual fxed rate t s a percetage N the otoal freq ( 2 4 ) the umber of coupo per year R the redempto of

### Anomaly Detection of Network Traffic Based on Prediction and Self-Adaptive Threshold

Ieraoal Joural of Fuure Geerao Coucao ad eworkg Vol. 8, o. 6 (15), pp. 5-14 hp://d.do.org/1.1457/fgc.15.8.6. Aoaly Deeco of ework raffc Based o Predco ad Self-Adapve hreshold Haya Wag Depare of Iforao

### Chapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization

Chapter 3 Mathematcs of Face Secto 4 Preset Value of a Auty; Amortzato Preset Value of a Auty I ths secto, we wll address the problem of determg the amout that should be deposted to a accout ow at a gve

### A quantization tree method for pricing and hedging multi-dimensional American options

A quazao ree mehod for prcg ad hedgg mul-dmesoal Amerca opos Vlad BALLY Glles PAGÈS Jacques PRINTEMS Absrac We prese here he quazao mehod whch s well-adaped for he prcg ad hedgg of Amerca opos o a baske

### ECONOMIC CHOICE OF OPTIMUM FEEDER CABLE CONSIDERING RISK ANALYSIS. University of Brasilia (UnB) and The Brazilian Regulatory Agency (ANEEL), Brazil

ECONOMIC CHOICE OF OPTIMUM FEEDER CABE CONSIDERING RISK ANAYSIS I Camargo, F Fgueredo, M De Olvera Uversty of Brasla (UB) ad The Brazla Regulatory Agecy (ANEE), Brazl The choce of the approprate cable

### Banking (Early Repayment of Housing Loans) Order, 5762 2002 1

akg (Early Repaymet of Housg Loas) Order, 5762 2002 y vrtue of the power vested me uder Secto 3 of the akg Ordace 94 (hereafter, the Ordace ), followg cosultato wth the Commttee, ad wth the approval of

### AUTOCALLABLE STRUCTURED PRODUCTS

AUTOCALLABLE STRUCTURED PRODUCTS Auhor : Trsa Gullaume Professoal le : Lecurer ad research fellow Malg address : Uversé de Cergy-Poose Laboraore Thema boulevard du por F-95 Cergy-Poose Cedex Frace E-mal

### Value of information sharing in marine mutual insurance

Value of formao sharg mare muual surace Kev L, Joh Lu, Ja Ya 3 ad Je M Deparme of Logscs & Marme Sudes, The Hog Kog Polechc Uvers, Hog Kog. Emal address:.x.l@polu.edu.h. Deparme of Logscs & Marme Sudes,

### MORE ON TVM, "SIX FUNCTIONS OF A DOLLAR", FINANCIAL MECHANICS. Copyright 2004, S. Malpezzi

MORE ON VM, "SIX FUNCIONS OF A DOLLAR", FINANCIAL MECHANICS Copyrgh 2004, S. Malpezz I wan everyone o be very clear on boh he "rees" (our basc fnancal funcons) and he "fores" (he dea of he cash flow model).

### PORTFOLIO CHOICE WITH HEAVY TAILED DISTRIBUTIONS 1. Svetlozar Rachev 2 Isabella Huber 3 Sergio Ortobelli 4

PORTFOLIO CHOIC WITH HAVY TAILD DISTRIBUTIONS Sveloar Rachev Isabella Huber 3 Sergo Orobell 4 We are graeful o Boryaa Racheva-Joova Soya Soyaov ad Almra Bglova for he comuaoal aalyss ad helful commes.

### CHAPTER 2. Time Value of Money 6-1

CHAPTER 2 Tme Value of Moey 6- Tme Value of Moey (TVM) Tme Les Future value & Preset value Rates of retur Autes & Perpetutes Ueve cash Flow Streams Amortzato 6-2 Tme les 0 2 3 % CF 0 CF CF 2 CF 3 Show

### Chapter 11 Systematic Sampling

Chapter Sstematc Samplg The sstematc samplg techue s operatoall more coveet tha the smple radom samplg. It also esures at the same tme that each ut has eual probablt of cluso the sample. I ths method of

### The real value of stock

he real value of sock Collars ivolve he paye of a variable aou of sock, depedig o a average sock price. I his arcle, Ahoy Pavlovich uses he Black-Scholes fraework o value hese exoc derivaves ad explore

### Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract

Preset Value of Autes Uder Radom Rates of Iterest By Abraham Zas Techo I.I.T. Hafa ISRAEL ad Uversty of Hafa, Hafa ISRAEL Abstract Some attempts were made to evaluate the future value (FV) of the expected

### Valuation Methods of a Life Insurance Company

Valuao Mehods of a Lfe Isurace Comay ISORY...3 2 PRODUC ASSESSMEN : PROFI ESING...4 2. E PROFI ESING IN 3 SEPS...5 2.. Equalece Prcle...5 2..2 radoal Marg...6 2..3 Prof esg...6 2.2 COMMON CRIERIA O EVALUAE

### - Models: - Classical: : Mastermodel (clay( Curves. - Example: - Independent variable t

Compue Gaphcs Geomec Moelg Iouco - Geomec Moelg (GM) sce e of 96 - Compue asssace fo - Desg: CAD - Maufacug: : CAM - Moels: - Classcal: : Masemoel (cla( cla, poopes,, Mock-up) - GM: mahemacal escpo fo

### The Consumer Price Index for All Urban Consumers (Inflation Rate)

The Cosumer Prce Idex for All Urba Cosumers (Iflato Rate) Itroducto: The Cosumer Prce Idex (CPI) s the crtero of the average prce chage of goods ad servces cosumed by Iraa households. Ths crtero, as a

### Chap.5 Unit Roots and Cointegration in panels

Chap.5 U Roos ad Coegrao paels 5. Iroduco Wh he growg use of cross-coury daa over me o sudy purchasg power pary, growh covergece ad eraoal R&D spllovers, he focus of pael daa ecoomercs has shfed owards

### The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0

Chapter 2 Autes ad loas A auty s a sequece of paymets wth fxed frequecy. The term auty orgally referred to aual paymets (hece the ame), but t s ow also used for paymets wth ay frequecy. Autes appear may

### Trust Evaluation and Dynamic Routing Decision Based on Fuzzy Theory for MANETs

JOURNAL OF SOFTWARE, VOL. 4, NO. 10, ECEBER 2009 1091 Trus Evaluao ad yamc Roug ecso Based o Fuzzy Theory for ANETs Hogu a, Zhpg Ja ad Zhwe Q School of Compuer Scece ad Techology, Shadog Uversy, Ja, Cha.P.R.

### Confidence Intervals for Paired Means

Chaper 496 Cofidece Iervals for Paired Meas Iroducio This rouie calculaes he sample size ecessary o achieve a specified disace from he paired sample mea erece o he cofidece limi(s) a a saed cofidece level

### Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R =

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS Objectves of the Topc: Beg able to formalse ad solve practcal ad mathematcal problems, whch the subjects of loa amortsato ad maagemet of cumulatve fuds are

### Spline. Computer Graphics. B-splines. B-Splines (for basis splines) Generating a curve. Basis Functions. Lecture 14 Curves and Surfaces II

Lecure 4 Curves and Surfaces II Splne A long flexble srps of meal used by drafspersons o lay ou he surfaces of arplanes, cars and shps Ducks weghs aached o he splnes were used o pull he splne n dfferen

### of the relationship between time and the value of money.

TIME AND THE VALUE OF MONEY Most agrbusess maagers are famlar wth the terms compoudg, dscoutg, auty, ad captalzato. That s, most agrbusess maagers have a tutve uderstadg that each term mples some relatoshp

### Internal model in life insurance : application of least squares monte carlo in risk assessment

Ieral model lfe surace : applcao of leas squares moe carlo rs assessme - Oberla euam Teugua (HSB) - Jae Re (Uversé yo, HSB) - rédérc Plache (Uversé yo, aboraore SA) 04. aboraore SA 50 Aveue Toy Garer -

Deb Isrumes ad Markes Professor Carpeer Duraio Oulie ad Readig Oulie Ieres Rae Sesiiviy Dollar Duraio Duraio Buzzwords Parallel shif Basis pois Modified duraio Macaulay duraio Readig Tuckma, Chapers 5

### Methodology of the CBOE S&P 500 PutWrite Index (PUT SM ) (with supplemental information regarding the CBOE S&P 500 PutWrite T-W Index (PWT SM ))

ehodology of he CBOE S&P 500 PuWre Index (PUT S ) (wh supplemenal nformaon regardng he CBOE S&P 500 PuWre T-W Index (PWT S )) The CBOE S&P 500 PuWre Index (cker symbol PUT ) racks he value of a passve

The Ecoomcs of Admserg Impor uoas wh Lceses-o-Demad Jaa Hraaova, James Falk ad Harry de Gorer Prepared for he World Bak s Agrculural Trade Group Jauary 2003 Absrac Ths paper exames he effecs of raog mpor

### UNDERWRITING AND EXTRA RISKS IN LIFE INSURANCE Katarína Sakálová

The process of uderwriig UNDERWRITING AND EXTRA RISKS IN LIFE INSURANCE Kaaría Sakálová Uderwriig is he process by which a life isurace compay decides which people o accep for isurace ad o wha erms Life

### Critical Approach of the Valuation Methods of a Life Insurance Company under the Traditional European Statutory View

Crcal Aroach of he Valuao Mehods of a Lfe Isurace Comay uder he radoal Euroea Sauory Vew Dr. Paul-Aoe Darbellay ParerRe Belleresrasse 36 C-8034 Zürch Swzerlad Phoe: 4 385 34 63 Fa: 4 385 37 04 E-mal: aulaoe.darbellay@arerre.com

### The Term Structure of Interest Rates

The Term Srucure of Ieres Raes Wha is i? The relaioship amog ieres raes over differe imehorizos, as viewed from oday, = 0. A cocep closely relaed o his: The Yield Curve Plos he effecive aual yield agais

### Claims Reserving When There Are Negative Values in the Runoff Triangle

Clams Reservg Whe There Are Negave Values he Ruo Tragle Erque de Alba ITAM Meco ad Uversy o Waerloo Caada 7 h. Acuaral Research Coerece The Uversy o Waerloo Augus 7-0 00 . INTRODUCTION The may uceraes

### The Increasing Participation of China in the World Soybean Market and Its Impact on Price Linkages in Futures Markets

The Icreasg arcpao of Cha he Word Soybea Marke ad Is Ipac o rce Lkages Fuures Markes by Mara Ace Móz Chrsofoe Rodofo Margao da Sva ad Fabo Maos Suggesed cao fora: Chrsofoe M. A. R. Sva ad F. Maos. 202.

### Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression

Spaal Auocorrelao Approaches o Tesg Resduals from Leas Squares Regresso Yaguag Che Deparme of Geography, College of Urba ad Evromeal Sceces, Pekg Uversy, 0087, Beg, Cha. Emal: cheyg@pku.edu.c Absrac: I

### Solving Fuzzy Linear Programming Problems with Piecewise Linear Membership Function

Avalable a hp://pvamu.edu/aam Appl. Appl. Mah. ISSN: 9-966 Vol., Issue December ), pp. Prevously, Vol., Issue, pp. 6 6) Applcaos ad Appled Mahemacs: A Ieraoal Joural AAM) Solvg Fuzzy Lear Programmg Problems

### FINANCIAL MATHEMATICS 12 MARCH 2014

FINNCIL MTHEMTICS 12 MRCH 2014 I ths lesso we: Lesso Descrpto Make use of logarthms to calculate the value of, the tme perod, the equato P1 or P1. Solve problems volvg preset value ad future value autes.

### Causal relationship between gross domestic product and personal consumption expenditure of Nigeria

Afrca Joural of Mahemacs ad Compuer Scece Research Vol. (8), pp. 179-183, Sepember, 009 Avalable ole a hp://www.academcjourals.org/ajmcsr 009 Academc Jourals Full Legh Research Paper Causal relaoshp bewee

### Duration and Convexity ( ) 20 = Bond B has a maturity of 5 years and also has a required rate of return of 10%. Its price is \$613.

Graduae School of Business Adminisraion Universiy of Virginia UVA-F-38 Duraion and Convexiy he price of a bond is a funcion of he promised paymens and he marke required rae of reurn. Since he promised

### Classic Problems at a Glance using the TVM Solver

C H A P T E R 2 Classc Problems at a Glace usg the TVM Solver The table below llustrates the most commo types of classc face problems. The formulas are gve for each calculato. A bref troducto to usg the

### APPLICATIONS OF GEOMETRIC

APPLICATIONS OF GEOMETRIC SEQUENCES AND SERIES TO FINANCIAL MATHS The mos powerful force i he world is compoud ieres (Alber Eisei) Page of 52 Fiacial Mahs Coes Loas ad ivesmes - erms ad examples... 3 Derivaio

### FORECASTING MODEL FOR AUTOMOBILE SALES IN THAILAND

FORECASTING MODEL FOR AUTOMOBILE SALES IN THAILAND by Wachareepor Chaimogkol Naioal Isiue of Developme Admiisraio, Bagkok, Thailad Email: wachare@as.ida.ac.h ad Chuaip Tasahi Kig Mogku's Isiue of Techology

### Curve Fitting and Solution of Equation

UNIT V Curve Fttg ad Soluto of Equato 5. CURVE FITTING I ma braches of appled mathematcs ad egeerg sceces we come across epermets ad problems, whch volve two varables. For eample, t s kow that the speed

### IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki

IDENIFICAION OF HE DYNAMICS OF HE GOOGLE S RANKING ALGORIHM A. Khak Sedgh, Mehd Roudak Cotrol Dvso, Departmet of Electrcal Egeerg, K.N.oos Uversty of echology P. O. Box: 16315-1355, ehra, Ira sedgh@eetd.ktu.ac.r,

### The Time Value of Money

The Tme Value of Moey 1 Iversemet Optos Year: 1624 Property Traded: Mahatta Islad Prce : \$24.00, FV of \$24 @ 6%: FV = \$24 (1+0.06) 388 = \$158.08 bllo Opto 1 0 1 2 3 4 5 t (\$519.37) 0 0 0 0 \$1,000 Opto

### Generating Intelligent Teaching Learning Systems using Concept Maps and Case Based Reasoning

17 Geerag Iellge Teachg Learg Sysems usg Cocep Maps ad Case Based Reasog Makel L. Esposa, MSc. Naala Maríez S. y Zeada García V. Deparme of Compuer Scece, Ceral Uversy of Las Vllas, Hghway o Camajuaí,

### CHAPTER 5 MOS FIELD EFFECT TRANSISTORS (MOSFETs)

CHAPTER 5 MOS FIE EFFECT TRANSISTORS (MOSFETs Chaper Oule 5. ece Srucure ad Physcal Operao 5. Curre olage Characerscs 5.3 MOSFET Crcus a C 5.4 Applyg he MOSFET Amplfer esg 5.5 Small Sgal Operao ad Models

### s :risk parameter for company size

UNDESTANDING ONLINE TADES: TADING AND EFOMANCE IN COMMON STOCK INVESTMENT Y. C. George L, Y. C. Elea Kag 2 ad Chug-L Chu 3 Deparme of Accoug ad Iformao Techology, Naoal Chug Cheg Uversy, Tawa,.O.C acycl@ccu.edu.w;