Module Contact: Dr P Moffatt, ECO Copyright of the University of East Anglia Version 2
|
|
- Geraldine Goodwin
- 5 years ago
- Views:
Transcription
1 UNIVERSITY OF EAST ANGLIA School of Economcs Man Seres PG Examnaton FINANCIAL ECONOMETRICS ECO-M017 Tme allowed: 2 hours Answer ALL FOUR questons. Queston 1 carres a weght of 25%; Queston 2 carres 30%; Queston 3 carres 25%; Queston 4 carres 20%. Marks awarded for ndvdual parts are shown n square brackets. A formula sheet and t-tables are attached to the examnaton paper. Notes are not permtted n ths examnaton. Do not turn over untl you are told to do so by the Invglator. ECO-M017 Module Contact: Dr P Moffatt, ECO Copyrght of the Unversty of East Angla Verson 2
2 Ths page s delberately left blank. Page 2
3 Queston 1. Page 3 ALL WORKING SHOULD BE SHOWN IN YOUR ANSWER TO THIS QUESTION. The share prce of Standard Lfe (Insurance) was followed for a perod of sx months. The percentage monthly change n the stock market ndex (X), and the percentage monthly return on Standard Lfe stock (Y) are presented n the followng table: Month Market(X) Standard Lfe(Y) January -3-4 February -1 0 March 2 1 Aprl 6 5 May 3 2 June 5 8 (a) Obtan estmates of and n the smple regresson model: Y X u t 1,,6 t t t Var u 2 t Report the beta coeffcent for Standard Lfe stock. [10] (b) (c) Fnd the resduals from the smple regresson performed n (a). Hence fnd an estmate of the parameter. Call the estmate ˆ. What s the nterpretaton of ˆ n ths context? [7] Fnd a 95% confdence nterval for. Does the confdence nterval ndcate that Standard Lfe s an aggressve stock, a defensve stock, or nether? Is ths what you would expect for Standard Lfe? [8] TURN OVER
4 rbar Queston 2. Page 4 For each of the 100 companes n the AIM100 ndex, the followng nformaton s found (usng daly data from an unspecfed perod): beta: rbar: beta coeffcent mean (daly) return A scatter plot of the mean return aganst the beta, wth a lowess smoother supermposed, s shown below. Lowess smoother beta bandwdth =.8 (a) (b) Whch features of the plot (f any) are consstent wth the Captal Asset Prcng Model (CAPM)? Whch (f any) are nconsstent wth CAPM? Do you antcpate the problem of heteroscedastcty? Explan your answer. [5] A smple regresson of mean return on beta yelds the followng results:. regress rbar beta Source SS df MS Number of obs = F( 1, 98) = Model e e-07 Prob > F = Resdual e e-08 R-squared = Adj R-squared = Total e e-08 Root MSE =.0001 rbar Coef. Std. Err. t P> t [95% Conf. Interval] beta _cons Interpret the estmate of the ntercept parameter and use t to deduce the annual rsk-free rate (assume there are 260 tradng days n a year). Use a smlar approach to nterpret the slope estmate. [5] (c) The regresson s extended to ntroduce as a second explanatory varable the square of beta (named beta2). The results are:
5 Page 5. gen beta2=beta^2. regress rbar beta beta2 Source SS df MS Number of obs = F( 2, 97) = Model e e-07 Prob > F = Resdual e e-08 R-squared = Adj R-squared = Total e e-08 Root MSE =.0001 rbar Coef. Std. Err. t P> t [95% Conf. Interval] beta beta e-06 _cons hettest Breusch-Pagan / Cook-Wesberg test for heteroskedastcty Ho: Constant varance Varables: ftted values of rbar ch2(1) = Prob > ch2 = Usng a 2-taled t-test, test the sgnfcance of the effect of the varable beta2. Does the result amount to a rejecton of CAPM? Explan your answer.[5] (d) (e) Explan why the problem of heteroscedastcty s antcpated on a theoretcal bass n ths model. Does the test followng the estmaton above confrm that heteroscedastcty s ndeed a problem? [5] The same regresson as n (c) s performed wth the robust opton, wth the followng results:. regress rbar beta beta2, robust Lnear regresson Number of obs = 100 F( 2, 97) = Prob > F = R-squared = Root MSE =.0001 Robust rbar Coef. Std. Err. t P> t [95% Conf. Interval] beta beta _cons In what sense does ths set of results overcome the problem of heteroscedastcty. Whch numbers n the table have changed as a result of usng the robust opton? [5] (f) Conduct a test of CAPM usng the results of (e). In what way does the concluson dffer from that of (c)? [5] TURN OVER
6 gold Queston 3. Page 6 Ths queston s concerned wth the prce of the commodty gold. Daly data on the gold prce was obtaned for the 5 year perod 8 Jan 2008 to 8 Jan 2013, and s represented n what follows by the varable gold. A tme-seres plot of the varable s shown below. 01jan jan jan jan jan jan2013 date (a) (b) Wth reference to the tme seres plot, descrbe the evoluton of the gold prce over the 5 year perod. Does t have the characterstcs of a random walk? [5] The followng STATA results were obtaned:. regress gold l.gold l2.gold l3.gold Source SS df MS Number of obs = F( 3, 1299) =. Model Prob > F = Resdual R-squared = Adj R-squared = Total Root MSE = gold Coef. Std. Err. t P> t [95% Conf. Interval] gold L L L _cons test (l1.gold=1) ( 1) L.gold = 1 F( 1, 1299) = 1.03 Prob > F = test l2.gold l3.gold ( 1) L2.gold = 0 ( 2) L3.gold = 0 F( 2, 1299) = 0.59
7 Page 7 Prob > F = durbna Durbn's alternatve test for autocorrelaton lags(p) ch2 df Prob > ch H0: no seral correlaton Followng the regresson results above are three tests of the (weak-form) Effcent Market Hypothess (EMH). Explan brefly why each of these tests amounts to a test of EMH. In each of the three cases, s EMH accepted or rejected? [5] (c) A set of day-of-week dummes were added to the model of (b) (day1=monday; day5=frday), wth the results:. regress gold l.gold l2.gold l3.gold day2-day5 Source SS df MS Number of obs = F( 7, 1295) = Model Prob > F = Resdual R-squared = Adj R-squared = Total Root MSE = gold Coef. Std. Err. t P> t [95% Conf. Interval] gold L L L day day day day _cons test day2 day3 day4 day5 ( 1) day2 = 0 ( 2) day3 = 0 ( 3) day4 = 0 ( 4) day5 = 0 F( 4, 1295) = 0.94 Prob > F = Explan why day1 has been excluded from the model. What s the nterpretaton of the coeffcents of the ncluded day dummes? Is there a weekend effect n the market for gold? [5] TURN OVER
8 (d) Page 8 Three lags of the copper prce were added to the model of (b), wth the results:. regress gold l.gold l2.gold l3.gold l.copper l2.copper l3.copper Source SS df MS Number of obs = F( 6, 1296) = Model Prob > F = Resdual R-squared = Adj R-squared = Total Root MSE = gold Coef. Std. Err. t P> t [95% Conf. Interval] gold L L L copper L L L _cons test l.copper l2.copper l3.copper ( 1) L.copper = 0 ( 2) L2.copper = 0 ( 3) L3.copper = 0 F( 3, 1296) = 4.81 Prob > F = Explan why the F-test conducted followng estmaton amounts to a test of the sem-strong form EMH. Is sem-strong form EMH accepted or rejected? [5] (e) A VAR(3) model of the gold prce and the copper prce s estmated, and a Granger test s performed. The results are as follows:. var gold copper, lags(1 2 3) Vector autoregresson : :. vargranger Granger causalty Wald tests Equaton Excluded ch2 df Prob > ch gold copper gold ALL copper gold copper ALL Explan how the results of the Granger test relate to your answer to (d). What addtonal nformaton does the Granger test convey about the relatonshp between the gold prce and the copper prce? [5]
9 r Queston 4. Page 9 Ths queston s concerned wth a European call opton wrtten on the FTSE250 share ndex, on 8 January The opton has a strke of 8300 and has 60 days to expry. The FTSE250 ndex on 8 January 2013 s (a) Draw a pay-off dagram for ths opton, showng the pay-off at expry aganst the underlyng ndex at expry. Is ths opton n the money, or out of the money? Explan your answer. [5] In order to derve the value of the opton of (a), we requre a measure of the volatlty of the underlyng ndex. We therefore consder the return (r) on the FTSE250 ndex. Fve years of daly returns are plotted below. 01jan jan jan jan jan jan2013 date Summary statstcs for the daly return are:. summ r Varable Obs Mean Std. Dev. Mn Max r (b) Assumng that there are 260 tradng days n the year, deduce an estmate of the annual volatlty of the FTSE250 ndex. [5] TURN OVER
10 (c) Page 10 The Black-Scholes formula s used to compute the value of the opton at varous dfferent values for volatlty. The results are: volatlty call value From the nformaton n the table, deduce as accurately as possble the value of ths call opton. Explan your answer. [5] (d) It s often suggested that the ARCH/GARCH framework s superor to the method used n (b) as a means of computng the volatlty of the prce or ndex underlyng an opton. The followng results are obtaned from a GARCH(1,1) model:. arch r, arch(1) garch(1) : : Sample: Number of obs = 1305 Dstrbuton: Gaussan Wald ch2(.) =. Log lkelhood = Prob > ch2 =. OPG r Coef. Std. Err. z P> z [95% Conf. Interval] r _cons ARCH arch L garch L _cons 1.72e e e e-06 Interpret the results. Do the results provde confrmaton that GARCH s ndeed a superor approach? Explan your answer. [5] END OF PAPER
11 Page 11 The smple regresson model Fnancal Econometrcs Formula Sheet Consder the model: Y X u 1,..., n. The ordnary least squares estmators of and are: ˆ ( X X ) Y ( X X) 2 ˆ Y ˆ X The ftted values of Y are gven by: Yˆ ˆ ˆ X The resduals are: u Y Yˆ ˆ The standard error of the regresson s gven by: ˆ uˆ2 n 2 The estmated standard errors of ˆ and ˆ are gven by: se( ˆ ) ˆ 1 ( X X) 2 se( ˆ ) ˆ 2 1 X n ( X X) 2 Testng jont restrctons n the multple regresson model F 2 2 RU RR / r 2 1 RU / n k ~ F r, nk
12 Table 1: Crtcal values of the t-dstrbuton Page 12 df = 0.10 = 0.05 = = 0.01 =
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 informationMgtOp 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 informationECONOMETRICS - 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 informationWhich 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 informationMidterm Exam. Use the end of month price data for the S&P 500 index in the table below to answer the following questions.
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
More informationTests 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 informationσ 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 informationTesting 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 informationCHAPTER 9 FUNCTIONAL FORMS OF REGRESSION MODELS
CHAPTER 9 FUNCTIONAL FORMS OF REGRESSION MODELS QUESTIONS 9.1. (a) In a log-log model the dependent and all explanatory varables are n the logarthmc form. (b) In the log-ln model the dependent varable
More informationEvaluating 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 informationEXAMINATIONS 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 informationII. 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 informationSampling 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/ 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 informationSolutions to Odd-Numbered End-of-Chapter Exercises: Chapter 12
Introducton to Econometrcs (3 rd Updated Edton) by James H. Stock and Mark W. Watson Solutons to Odd-Numbered End-of-Chapter Exercses: Chapter 1 (Ths verson July 0, 014) Stock/Watson - Introducton to Econometrcs
More informationBasket options and implied correlations: a closed form approach
Basket optons and mpled correlatons: a closed form approach Svetlana Borovkova Free Unversty of Amsterdam CFC conference, London, January 7-8, 007 Basket opton: opton whose underlyng s a basket (.e. a
More informationMultifactor 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 informationiii) 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 informationCalibration 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 informationTeaching 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 informationA Note on Robust Estimation of Repeat Sales Indexes with Serial Correlation in Asset Returns
A Note on Robust Estmaton of Repeat Sales Indexes wth Seral Correlaton n Asset Returns Kathryn Graddy Department of Economcs and Internatonal Busness School Brandes Unversty (kgraddy@brandes.edu) Jonathan
More informationAsian basket options. in oil markets
Asan basket optons and mpled correlatons n ol markets Svetlana Borovkova Vre Unverstet Amsterdam, he etherlands Jont work wth Ferry Permana (Bandung) Basket opton: opton whose underlyng s a basket (e a
More information4. 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 informationIntroduction. 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 informationLecture 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 informationCorrelations 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 information3/3/2014. 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 informationTime series data: Part 2
Plot of Epsilon over Time -- Case 1 1 Time series data: Part Epsilon - 1 - - - -1 1 51 7 11 1 151 17 Time period Plot of Epsilon over Time -- Case Plot of Epsilon over Time -- Case 3 1 3 1 Epsilon - Epsilon
More informationUNIVERSITY 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 informationChapter 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 informationAnalysis 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 informationTHE 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 informationSpurious Seasonal Patterns and Excess Smoothness in the BLS Local Area Unemployment Statistics
Spurous Seasonal Patterns and Excess Smoothness n the BLS Local Area Unemployment Statstcs Keth R. Phllps and Janguo Wang Federal Reserve Bank of Dallas Research Department Workng Paper 1305 September
More informationPrice Formation on Agricultural Land Markets A Microstructure Analysis
Prce Formaton on Agrcultural Land Markets A Mcrostructure Analyss Martn Odenng & Slke Hüttel Department of Agrcultural Economcs, Humboldt-Unverstät zu Berln Department of Agrcultural Economcs, Unversty
More informationFinal 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 informationPrinciples 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 information1 Omitted Variable Bias: Part I. 2 Omitted Variable Bias: Part II. The Baseline: SLR.1-4 hold, and our estimates are unbiased
Introductory Appled Econometrcs EEP/IAS 118 Sprng 2014 Andrew Crane-Droesch Secton #5 Feb 26 2014 1 Omtted Varable Bas: Part I Remember that a key assumpton needed to get an unbased estmate of β 1 n the
More informationSYSTEMATIC LIQUIDITY, CHARACTERISTIC LIQUIDITY AND ASSET PRICING. Duong Nguyen* Tribhuvan N. Puri*
SYSTEMATIC LIQUIDITY, CHARACTERISTIC LIQUIDITY AND ASSET PRICING Duong Nguyen* Trbhuvan N. Pur* Address for correspondence: Trbhuvan N. Pur, Professor of Fnance Char, Department of Accountng and Fnance
More informationData 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 informationA Comparison of Statistical Methods in Interrupted Time Series Analysis to Estimate an Intervention Effect
Transport and Road Safety (TARS) Research Joanna Wang A Comparson of Statstcal Methods n Interrupted Tme Seres Analyss to Estmate an Interventon Effect Research Fellow at Transport & Road Safety (TARS)
More informationTests for Two Ordered Categorical Variables
Chapter 253 Tests for Two Ordered Categorcal Varables Introducton Ths module computes power and sample sze for tests of ordered categorcal data such as Lkert scale data. Assumng proportonal odds, such
More informationA Comparative Study of Mean-Variance and Mean Gini Portfolio Selection Using VaR and CVaR
Journal of Fnancal Rsk Management, 5, 4, 7-8 Publshed Onlne 5 n ScRes. http://www.scrp.org/journal/jfrm http://dx.do.org/.436/jfrm.5.47 A Comparatve Study of Mean-Varance and Mean Gn Portfolo Selecton
More informationExample 2.3: CEO Salary and Return on Equity. Salary for ROE = 0. Salary for ROE = 30. Example 2.4: Wage and Education
1 Stata Textbook Examples Introductory Econometrics: A Modern Approach by Jeffrey M. Wooldridge (1st & 2d eds.) Chapter 2 - The Simple Regression Model Example 2.3: CEO Salary and Return on Equity summ
More informationInterest rate and stock return volatility indices for the Eurozone. Investors gauges of fear during the recent financial crisis *
Interest rate and stock return volatlty ndces for the Eurozone. Investors gauges of fear durng the recent fnancal crss * Raquel López a, Elseo Navarro b Abstract We suggest a methodology for the constructon
More informationSimple Regression Theory II 2010 Samuel L. Baker
SIMPLE REGRESSIO THEORY II Smple Regresson Theory II 00 Samuel L. Baker Assessng how good the regresson equaton s lkely to be Assgnment A gets nto drawng nferences about how close the regresson lne mght
More informationConditional Beta Capital Asset Pricing Model (CAPM) and Duration Dependence Tests
Condtonal Beta Captal Asset Prcng Model (CAPM) and Duraton Dependence Tests By Davd E. Allen 1 and Imbarne Bujang 1 1 School of Accountng, Fnance and Economcs, Edth Cowan Unversty School of Accountng,
More informationIt is important for a financial institution to monitor the volatilities of the market
CHAPTER 10 Volatlty It s mportant for a fnancal nsttuton to montor the volatltes of the market varables (nterest rates, exchange rates, equty prces, commodty prces, etc.) on whch the value of ts portfolo
More informationGraphical Methods for Survival Distribution Fitting
Graphcal Methods for Survval Dstrbuton Fttng In ths Chapter we dscuss the followng two graphcal methods for survval dstrbuton fttng: 1. Probablty Plot, 2. Cox-Snell Resdual Method. Probablty Plot: The
More informationAMS Financial Derivatives I
AMS 691-03 Fnancal Dervatves I Fnal Examnaton (Take Home) Due not later than 5:00 PM, Tuesday, 14 December 2004 Robert J. Frey Research Professor Stony Brook Unversty, Appled Mathematcs and Statstcs frey@ams.sunysb.edu
More informationİnsan TUNALI 8 November 2018 Econ 511: Econometrics I. ASSIGNMENT 7 STATA Supplement
İnsan TUNALI 8 November 2018 Econ 511: Econometrics I ASSIGNMENT 7 STATA Supplement. use "F:\COURSES\GRADS\ECON511\SHARE\wages1.dta", clear. generate =ln(wage). scatter sch Q. Do you see a relationship
More informationConditional beta capital asset pricing model (CAPM) and duration dependence tests
Edth Cowan Unversty Research Onlne ECU Publcatons Pre. 2011 2009 Condtonal beta captal asset prcng model (CAPM) and duraton dependence tests Davd E. Allen Edth Cowan Unversty Imbarne Bujang Edth Cowan
More informationInstitute of Actuaries of India
Insttute of ctuares of Inda Subject CT8-Fnancal Economcs ay 008 Examnaton INDICTIVE SOLUTION II CT8 0508 Q.1 a F0,5,6 1/6-5*ln0,5/0,6 Where, F0,5,6 s forard rate at tme 0 for delvery beteen tme 5 and 6
More informationA Meta Analysis of Real Estate Fund Performance
A Meta Analyss of Real Estate Fund Performance A Paper Presented at the ARES Annual Meetng Aprl 00 Naples, Florda Abstract Stephen Lee, Unversty of Readng * and Smon Stevenson, Unversty College Dubln Ths
More informationLinear 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 informationFinal Exam - section 1. Thursday, December hours, 30 minutes
Econometrics, ECON312 San Francisco State University Michael Bar Fall 2013 Final Exam - section 1 Thursday, December 19 1 hours, 30 minutes Name: Instructions 1. This is closed book, closed notes exam.
More informationForeign Exchange Exposures, Financial and Operational Hedge Strategies of Taiwan Firms
Foregn Exchange Exposures, Fnancal and Operatonal Hedge Strateges of Tawan Frms AUTHORS ARTICLE INFO JOURNAL Y-Chen Chang, Hu-Ju Ln Y-Chen Chang and Hu-Ju Ln (7). Foregn Exchange Exposures, Fnancal and
More informationSpatial Variations in Covariates on Marriage and Marital Fertility: Geographically Weighted Regression Analyses in Japan
Spatal Varatons n Covarates on Marrage and Martal Fertlty: Geographcally Weghted Regresson Analyses n Japan Kenj Kamata (Natonal Insttute of Populaton and Socal Securty Research) Abstract (134) To understand
More informationRecovering Risk Aversion from Options
Recoverng Rsk Averson from Optons by Robert R. Blss Research Department Federal Reserve Bank of Chcago 230 South La Salle Street Chcago, IL 60604-1413 U.S.A. (312) 322-2313 (312) 322-2357 Fax Robert.Blss@ch.frb.org
More informationMarket Opening and Stock Market Behavior: Taiwan s Experience
Internatonal Journal of Busness and Economcs, 00, Vol., No., 9-5 Maret Openng and Stoc Maret Behavor: Tawan s Experence Q L * Department of Economcs, Texas A&M Unversty, U.S.A. and Department of Economcs,
More informationAsset Pricing When Returns Are Nonnormal: Fama-French Factors vs. Higher-order Systematic Co-Moments*
Asset Prcng When Returns Are Nonnormal: Fama-French Factors vs. Hgher-order Systematc Co-Moments* Y. Peter Chung Unversty of Calforna, Rversde Herb Johnson Unversty of Calforna, Rversde Mchael J. Schll
More informationIncreasing the Accuracy of Option Pricing by Using Implied Parameters Related to Higher Moments. Dasheng Ji. and. B. Wade Brorsen*
Increasng the Accuracy of Opton Prcng by Usng Impled Parameters Related to Hgher Moments Dasheng J and B. Wade Brorsen* Paper presented at the CR-34 Conference on Appled Commodty Prce Analyss, orecastng,
More informationFM303. 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 informationu panel_lecture . sum
u panel_lecture sum Variable Obs Mean Std Dev Min Max datastre 639 9039644 6369418 900228 926665 year 639 1980 2584012 1976 1984 total_sa 639 9377839 3212313 682 441e+07 tot_fixe 639 5214385 1988422 642
More informationTHE MARKET PORTFOLIO MAY BE MEAN-VARIANCE EFFICIENT AFTER ALL
THE ARKET PORTFOIO AY BE EA-VARIACE EFFICIET AFTER A OSHE EVY and RICHARD RO ABSTRACT Testng the CAP bols down to testng the mean-varance effcency of the market portfolo. any studes have examned the meanvarance
More informationISyE 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 informationChapter 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 informationTransformation and Weighted Least Squares
APM 63 Regresson Analyss Project Transformaton and Weghted Least Squares. INTRODUCTION Yanjun Yan yayan@syr.edu Due on 4/4/5 (Thu.) Turned n on 4/4 (Thu.) Ths project ams at modelng the peak rate of flow
More informationChapter 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 informationMutual 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 informationJean-Paul Murara, Västeras, 26-April Mälardalen University, Sweden. Pricing EO under 2-dim. B S PDE by. using the Crank-Nicolson Method
Prcng EO under Mälardalen Unversty, Sweden Västeras, 26-Aprl-2017 1 / 15 Outlne 1 2 3 2 / 15 Optons - contracts that gve to the holder the rght but not the oblgaton to buy/sell an asset sometmes n the
More informationKent Academic Repository
Kent Academc Repostory Full text document (pdf) Ctaton for publshed verson Economou, Fotn and Katskas, Epamenondas and Vckers, Gregory (2016) Testng for herdng n the Athens Stock Exchange durng the crss
More informationtm / / / / / / / / / / / / Statistics/Data Analysis User: Klick Project: Limited Dependent Variables{space -6}
PS 4 Monday August 16 01:00:42 2010 Page 1 tm / / / / / / / / / / / / Statistics/Data Analysis User: Klick Project: Limited Dependent Variables{space -6} log: C:\web\PS4log.smcl log type: smcl opened on:
More informationInternational ejournals
Avalable onlne at www.nternatonalejournals.com ISSN 0976 1411 Internatonal ejournals Internatonal ejournal of Mathematcs and Engneerng 7 (010) 86-95 MODELING AND PREDICTING URBAN MALE POPULATION OF BANGLADESH:
More informationOn Stable Factor Structures in the Pricing of Risk: Do Time-Varying Betas Help or Hurt?
THE JOURNAL OF FINANCE VOL LIII, NO. 2 APRIL 1998 On Stable Factor Structures n the Prcng of Rsk: Do Tme-Varyng Betas Help or Hurt? ERIC GHYSELS* ABSTRACT There s now consderable evdence suggestng that
More informationTHIS PAPER SHOULD NOT BE OPENED UNTIL PERMISSION HAS BEEN GIVEN BY THE INVIGILATOR.
UNVERSTY OF SWAZLAND FACULTY OF SOCAL SCENCES DEPARTMENT OF STATSTCS AND DEMOGRAPHY MAN EXAMNATON 2016 TTTLE OF PAPER: DEMOGRAPHC METHODS 1 COURSE NUMBER: DEM 201 TME ALLOWED: 2 Hours NSTRUCTONS: ANSWER
More informationCreating a zero coupon curve by bootstrapping with cubic splines.
MMA 708 Analytcal Fnance II Creatng a zero coupon curve by bootstrappng wth cubc splnes. erg Gryshkevych Professor: Jan R. M. Röman 0.2.200 Dvson of Appled Mathematcs chool of Educaton, Culture and Communcaton
More informationAdvisory. Category: Capital
Advsory Category: Captal NOTICE* Subject: Alternatve Method for Insurance Companes that Determne the Segregated Fund Guarantee Captal Requrement Usng Prescrbed Factors Date: Ths Advsory descrbes an alternatve
More informationInterest rate and stock return volatility indices for the Eurozone. Investors gauges of fear during the recent financial crisis *
Interest rate and stock return volatlty ndces for the Eurozone. Investors gauges of fear durng the recent fnancal crss * Raquel López a, Elseo Navarro b Abstract We suggest a methodology for the constructon
More informationDomestic Savings and International Capital Flows
Domestc Savngs and Internatonal Captal Flows Martn Feldsten and Charles Horoka The Economc Journal, June 1980 Presented by Mchael Mbate and Chrstoph Schnke Introducton The 2 Vews of Internatonal Captal
More informationPhysicsAndMathsTutor.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 informationRisk 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 informationSolutions for Session 5: Linear Models
Solutions for Session 5: Linear Models 30/10/2018. do solution.do. global basedir http://personalpages.manchester.ac.uk/staff/mark.lunt. global datadir $basedir/stats/5_linearmodels1/data. use $datadir/anscombe.
More informationLikelihood 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 informationStochastic ALM models - General Methodology
Stochastc ALM models - General Methodology Stochastc ALM models are generally mplemented wthn separate modules: A stochastc scenaros generator (ESG) A cash-flow projecton tool (or ALM projecton) For projectng
More informationJoe Hirschberg and Jenny Lye Economics, University of Melbourne. September 2017
A graphc comparson of the Feller and Delta ntervals for ratos of parameter estmates. Joe Hrschberg and Jenny Lye Economcs, Unversty of Melbourne. September 017 1 Introducton Feller s 1954 proposal for
More informationISE High Income Index Methodology
ISE Hgh Income Index Methodology Index Descrpton The ISE Hgh Income Index s desgned to track the returns and ncome of the top 30 U.S lsted Closed-End Funds. Index Calculaton The ISE Hgh Income Index s
More informationStockholder Wealth Implications of the Firm s Choice Between Dividends. and Stock Repurchases
Stockholder Wealth Implcatons of the Frm s Choce Between Dvdends and Stock Repurchases by Noel R.Reynolds for The Unversty of the West Indes, St. Augustne Campus Inaugural Internatonal Conference on Busness,
More informationA copy can be downloaded for personal non-commercial research or study, without prior permission or charge
Sganos, A. (2013) Google attenton and target prce run ups. Internatonal Revew of Fnancal Analyss. ISSN 1057-5219 Copyrght 2012 Elsever A copy can be downloaded for personal non-commercal research or study,
More informationThe Mack-Method and Analysis of Variability. Erasmus Gerigk
The Mac-Method and Analyss of Varablty Erasmus Gerg ontents/outlne Introducton Revew of two reservng recpes: Incremental Loss-Rato Method han-ladder Method Mac s model assumptons and estmatng varablty
More informationTopic 6 Introduction to Portfolio Theory
Topc 6 Introducton to ortfolo Theory 1. racttoners fundamental ssues. ortfolo optmzaton usng Markowtz effcent fronter 3. ortfolo dversfcaton & beta coeffcent 4. Captal asset prcng model 04/03/015 r. Dder
More informationEmpirical Evidence on Spatial Contagion Between Financial Markets
Fnance etters, 5, 3 (1, 77-86 Emprcal Evdence on Spatal Contagon Between Fnancal arkets Brendan O. Bradley a and urad S. Taqqu b, a Acadan Asset anagement Inc., USA b Boston Unversty, USA Abstract We say
More informationoccurrence 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 informationJ. Basic. Appl. Sci. Res., 2(10) , , TextRoad Publication
202, TextRoad Publcaton ISSN 2090-4304 Journal of Basc and Appled Scentfc Research www.textroad.com Comparng the Effect of Proft Increase Crtera wth the Cash Recovery Rate of Companes Lsted on Tehran Stock
More informationHeteroskedasticity. . reg wage black exper educ married tenure
Heteroskedasticity. reg Source SS df MS Number of obs = 2,380 -------------+---------------------------------- F(2, 2377) = 72.38 Model 14.4018246 2 7.20091231 Prob > F = 0.0000 Residual 236.470024 2,377.099482551
More informationA 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 informationTRADING RULES IN HOUSING MARKETS WHAT CAN WE LEARN? GREG COSTELLO Curtin University of Technology
ABSTRACT TRADING RULES IN HOUSING MARKETS WHAT CAN WE LEARN? GREG COSTELLO Curtn Unversty of Technology Ths paper examnes the applcaton of tradng rules n testng nformatonal effcency n housng markets. The
More informationUsing Conditional Heteroskedastic
ITRON S FORECASTING BROWN BAG SEMINAR Usng Condtonal Heteroskedastc Varance Models n Load Research Sample Desgn Dr. J. Stuart McMenamn March 6, 2012 Please Remember» Phones are Muted: In order to help
More informationMeasurement of Dynamic Portfolio VaR Based on Mixed Vine Copula Model
Journal of Fnance and Accountng 207; 5(2): 80-86 http://www.scencepublshnggroup.com/j/jfa do: 0.648/j.jfa.2070502.2 ISSN: 2330-733 (Prnt); ISSN: 2330-7323 (Onlne) Measurement of Dynamc Portfolo VaR Based
More informationFinal 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 informationInformation 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