BAS Sixth Annual Case Competition
|
|
- Ami Thornton
- 5 years ago
- Views:
Transcription
1 Presented by Joan Dai, Annie Chen, Daniel He, Roy Kim 1
2 Background 2
3 Background Unexpected demographic shifts in pension plan Retirement Increase Early retirement Postponed retirement Creeping Active Termination 3
4 Problem Asset and liability mismatch Assets Liabilities Assets Liabilities 4
5 Agenda Binomial Confidence Intervals Bootstrap Estimates Exponential Moving Average Final Funding Status Report 5
6 Rate Analysis 6
7 Rate Analysis First Year Active Second Year Terminated Terminated Vested Retired 7
8 Rate Analysis Hypothesis tests of previous assumption rates Graphically shown with standard 95% confidence intervals Active Retirement Rate Age Current
9 Rate Analysis Hypothesis testing each age level with assumption rates as null Reject Fail to Reject Probability of at least this extremity is too small Variation can be due to chance 9
10 Active Retirement CAL Team Active Termination Terminated Vested
11 Active Retirement Active Termination Terminated Vested 11
12 Rate Derivation 12
13 Rate Derivation Assume original assumption is not credible Based only on participants behavior 13
14 Rate Derivation Method: Bootstrap simulation Sample Size: 750 from designated population Resampling: 500 times All Three Categories: Termination Rate Active Retirement Rate Terminated Vested Retirement Rate 14
15 Rate Derivation Bootstrap Trials (Active Retirement Rate 2012 to 2013) Age Trial 1 Trial 2 Trial 3 Trial 4 Trial Bootstrap Result Age Mean
16 Rate Derivation Putting it all together Assumption rates, fail-to-reject or reject Bootstrap rates Derived Active Retirement Rates
17 Rate Synthesis 17
18 Rate Synthesis Objectives Reduce noise Predict the trend Techniques Exponential moving average 18
19 Rate Synthesis 0.35 Age Active Active Retirement, Age Age 61, αα 61= Transition Probability Transition Years αα = 0.15 αα = 0.5 αα = 1 19
20 Rate Synthesis Table 1: Active Retirement Rates Age Current Proposed Rate 54 NA NA NA NA NA NA NA 0.98 Table 2: Terminated-Vested Retirement Rates Age Current Proposed Rate 54 NA NA NA NA NA NA NA
21 Rate Synthesis Table 3: Active Termination Rates, Age Current Proposed Rate NA Table 3: Active Termination Rates, Age Current Proposed Rate NA NA NA NA NA
22 Rate Synthesis Increased Early Retirement Increased Postponed Retirement Creeping Active Termination 22
23 Rate Synthesis Funding Status Increased Retirement Increased Liability Need for Increased Assets Normal Retirement Lump-Sum 2018 Old Rates New Rates Net Difference $327K $798K $473K 23
24 Index & Thank You Presenters Joan Dai Annie Chen Daniel He Roy Kim Index 1. Title Card 2. Background: Title Card 3. Background: Introduction 4. Background: The Problem 5. Background: Agenda 6. Rate Analysis: Title Card 7. Rate Analysis: Introduction 8. Rate Analysis: Transition Pools 9. Rate Analysis: Testing 10. Rate Analysis: Experience 11. Rate Analysis Experience 12. Rate Derivation: Title Card 13. Rate Derivation: Introduction 14. Rate Derivation: Method 15. Rate Derivation: Results 16. Rate Derivation: Combination 17. Rate Synthesis: Title Card 18. Rate Synthesis: Method 19. Rate Synthesis: Exponential Smoothing 20. Rate Synthesis: Trends 21. Rate Synthesis: Tables 1 & 2 Proposal 22. Rate Synthesis: Table 3 Proposal 23. Rate Synthesis: Funding Status 24
Mean GMM. Standard error
Table 1 Simple Wavelet Analysis for stocks in the S&P 500 Index as of December 31 st 1998 ^ Shapiro- GMM Normality 6 0.9664 0.00281 11.36 4.14 55 7 0.9790 0.00300 56.58 31.69 45 8 0.9689 0.00319 403.49
More informationNonparametric Statistics Notes
Nonparametric Statistics Notes Chapter 3: Some Tests Based on the Binomial Distribution Jesse Crawford Department of Mathematics Tarleton State University (Tarleton State University) Ch 3: Tests Based
More informationRESAMPLING METHOD 1 for the FALL 2007 data (Calculation of the D and D*)
Report to the UTK Faculty Senate Budget and Planning Committee on Analysis of Faculty Data based upon Gender using Data from Louis J. Gross, Faculty Senate Past-President and Professor of Ecology and Evolutionary
More informationWC-5 Just How Credible Is That Employer? Exploring GLMs and Multilevel Modeling for NCCI s Excess Loss Factor Methodology
Antitrust Notice The Casualty Actuarial Society is committed to adhering strictly to the letter and spirit of the antitrust laws. Seminars conducted under the auspices of the CAS are designed solely to
More informationSYLLABUS OF BASIC EDUCATION SPRING 2018 Construction and Evaluation of Actuarial Models Exam 4
The syllabus for this exam is defined in the form of learning objectives that set forth, usually in broad terms, what the candidate should be able to do in actual practice. Please check the Syllabus Updates
More informationSubject CS1 Actuarial Statistics 1 Core Principles. Syllabus. for the 2019 exams. 1 June 2018
` Subject CS1 Actuarial Statistics 1 Core Principles Syllabus for the 2019 exams 1 June 2018 Copyright in this Core Reading is the property of the Institute and Faculty of Actuaries who are the sole distributors.
More informationDiploma Part 2. Quantitative Methods. Examiner s Suggested Answers
Diploma Part 2 Quantitative Methods Examiner s Suggested Answers Question 1 (a) The binomial distribution may be used in an experiment in which there are only two defined outcomes in any particular trial
More informationConfidence Intervals for the Median and Other Percentiles
Confidence Intervals for the Median and Other Percentiles Authored by: Sarah Burke, Ph.D. 12 December 2016 Revised 22 October 2018 The goal of the STAT COE is to assist in developing rigorous, defensible
More informationWeb Science & Technologies University of Koblenz Landau, Germany. Lecture Data Science. Statistics and Probabilities JProf. Dr.
Web Science & Technologies University of Koblenz Landau, Germany Lecture Data Science Statistics and Probabilities JProf. Dr. Claudia Wagner Data Science Open Position @GESIS Student Assistant Job in Data
More informationUPDATED IAA EDUCATION SYLLABUS
II. UPDATED IAA EDUCATION SYLLABUS A. Supporting Learning Areas 1. STATISTICS Aim: To enable students to apply core statistical techniques to actuarial applications in insurance, pensions and emerging
More information(11) Case Studies: Adaptive clinical trials. ST440/540: Applied Bayesian Analysis
Use of Bayesian methods in clinical trials Bayesian methods are becoming more common in clinical trials analysis We will study how to compute the sample size for a Bayesian clinical trial We will then
More informationProbability and Statistics
Kristel Van Steen, PhD 2 Montefiore Institute - Systems and Modeling GIGA - Bioinformatics ULg kristel.vansteen@ulg.ac.be CHAPTER 3: PARAMETRIC FAMILIES OF UNIVARIATE DISTRIBUTIONS 1 Why do we need distributions?
More informationCalPERS Experience Study and Review of Actuarial Assumptions
California Public Employees Retirement System Experience Study and Review of Actuarial Assumptions CalPERS Experience Study and Review of Actuarial Assumptions CalPERS Actuarial Office December 2013 Table
More informationMonte Carlo Introduction
Monte Carlo Introduction Probability Based Modeling Concepts moneytree.com Toll free 1.877.421.9815 1 What is Monte Carlo? Monte Carlo Simulation is the currently accepted term for a technique used by
More informationFor more information about how to cite these materials visit
Author(s): Kerby Shedden, Ph.D., 2010 License: Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike 3.0 License: http://creativecommons.org/licenses/by-sa/3.0/
More information**BEGINNING OF EXAMINATION** A random sample of five observations from a population is:
**BEGINNING OF EXAMINATION** 1. You are given: (i) A random sample of five observations from a population is: 0.2 0.7 0.9 1.1 1.3 (ii) You use the Kolmogorov-Smirnov test for testing the null hypothesis,
More informationPARAMETRIC AND NON-PARAMETRIC BOOTSTRAP: A SIMULATION STUDY FOR A LINEAR REGRESSION WITH RESIDUALS FROM A MIXTURE OF LAPLACE DISTRIBUTIONS
PARAMETRIC AND NON-PARAMETRIC BOOTSTRAP: A SIMULATION STUDY FOR A LINEAR REGRESSION WITH RESIDUALS FROM A MIXTURE OF LAPLACE DISTRIBUTIONS Melfi Alrasheedi School of Business, King Faisal University, Saudi
More informationFV N = PV (1+ r) N. FV N = PVe rs * N 2011 ELAN GUIDES 3. The Future Value of a Single Cash Flow. The Present Value of a Single Cash Flow
QUANTITATIVE METHODS The Future Value of a Single Cash Flow FV N = PV (1+ r) N The Present Value of a Single Cash Flow PV = FV (1+ r) N PV Annuity Due = PVOrdinary Annuity (1 + r) FV Annuity Due = FVOrdinary
More informationBin(20,.5) and N(10,5) distributions
STAT 600 Design of Experiments for Research Workers Lab 5 { Due Thursday, November 18 Example Weight Loss In a dietary study, 14 of 0 subjects lost weight. If weight is assumed to uctuate up or down by
More informationTable 1. Summary of Faculty Salary Data for Fall Mean Salary Males. Mean Salary Females. Median Salary Males
Report to the UTK Faculty Senate from the Senate Budget and Planning Committee Analysis of Faculty Salary Data based upon Gender using Data from Fall 2015 Draft August 31, 2016 Louis J. Gross, Chair, Faculty
More informationHomework Problems Stat 479
Chapter 10 91. * A random sample, X1, X2,, Xn, is drawn from a distribution with a mean of 2/3 and a variance of 1/18. ˆ = (X1 + X2 + + Xn)/(n-1) is the estimator of the distribution mean θ. Find MSE(
More informationKARACHI UNIVERSITY BUSINESS SCHOOL UNIVERSITY OF KARACHI BS (BBA) VI
88 P a g e B S ( B B A ) S y l l a b u s KARACHI UNIVERSITY BUSINESS SCHOOL UNIVERSITY OF KARACHI BS (BBA) VI Course Title : STATISTICS Course Number : BA(BS) 532 Credit Hours : 03 Course 1. Statistical
More informationAsymmetric Price Transmission: A Copula Approach
Asymmetric Price Transmission: A Copula Approach Feng Qiu University of Alberta Barry Goodwin North Carolina State University August, 212 Prepared for the AAEA meeting in Seattle Outline Asymmetric price
More informationFinancial Risk Management and Governance Other VaR methods. Prof. Hugues Pirotte
Financial Risk Management and Governance Other VaR methods Prof. ugues Pirotte Idea of historical simulations Why rely on statistics and hypothetical distribution?» Use the effective past distribution
More informationIntroduction to Monte Carlo
Introduction to Monte Carlo Probability Based Modeling Concepts Mark Snodgrass Money Tree Software What is Monte Carlo? Monte Carlo Simulation is the currently accepted term for a technique used by mathematicians
More informationThis is very simple, just enter the sample into a list in the calculator and go to STAT CALC 1-Var Stats. You will get
MATH 111: REVIEW FOR FINAL EXAM SUMMARY STATISTICS Spring 2005 exam: 1(A), 2(E), 3(C), 4(D) Comments: This is very simple, just enter the sample into a list in the calculator and go to STAT CALC 1-Var
More informationConfidence Interval and Hypothesis Testing: Exercises and Solutions
Confidence Interval and Hypothesis Testing: Exercises and Solutions You can use the graphical representation of the normal distribution to solve the problems. Exercise 1: Confidence Interval A sample of
More informationOne Proportion Superiority by a Margin Tests
Chapter 512 One Proportion Superiority by a Margin Tests Introduction This procedure computes confidence limits and superiority by a margin hypothesis tests for a single proportion. For example, you might
More informationLecture 18. Ingo Ruczinski. October 31, Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University
Lecture 18 Department of Bios Johns Hopkins Bloomberg School of Public Health Johns Hopkins University October 31, 2015 1 2 3 4 5 6 1 Tests for a binomial proportion 2 Score test versus Wald 3 Exact binomial
More informationAPPROACHES TO VALIDATING METHODOLOGIES AND MODELS WITH INSURANCE APPLICATIONS
APPROACHES TO VALIDATING METHODOLOGIES AND MODELS WITH INSURANCE APPLICATIONS LIN A XU, VICTOR DE LA PAN A, SHAUN WANG 2017 Advances in Predictive Analytics December 1 2, 2017 AGENDA QCRM to Certify VaR
More informationPROBABILITY. Wiley. With Applications and R ROBERT P. DOBROW. Department of Mathematics. Carleton College Northfield, MN
PROBABILITY With Applications and R ROBERT P. DOBROW Department of Mathematics Carleton College Northfield, MN Wiley CONTENTS Preface Acknowledgments Introduction xi xiv xv 1 First Principles 1 1.1 Random
More informationTABLE OF CONTENTS - VOLUME 2
TABLE OF CONTENTS - VOLUME 2 CREDIBILITY SECTION 1 - LIMITED FLUCTUATION CREDIBILITY PROBLEM SET 1 SECTION 2 - BAYESIAN ESTIMATION, DISCRETE PRIOR PROBLEM SET 2 SECTION 3 - BAYESIAN CREDIBILITY, DISCRETE
More informationStock Price Behavior. Stock Price Behavior
Major Topics Statistical Properties Volatility Cross-Country Relationships Business Cycle Behavior Page 1 Statistical Behavior Previously examined from theoretical point the issue: To what extent can the
More informationLikelihood Approaches to Low Default Portfolios. Alan Forrest Dunfermline Building Society. Version /6/05 Version /9/05. 1.
Likelihood Approaches to Low Default Portfolios Alan Forrest Dunfermline Building Society Version 1.1 22/6/05 Version 1.2 14/9/05 1. Abstract This paper proposes a framework for computing conservative
More information7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4
7. For the table that follows, answer the following questions: x y 1-1/4 2-1/2 3-3/4 4 - Would the correlation between x and y in the table above be positive or negative? The correlation is negative. -
More information(# of die rolls that satisfy the criteria) (# of possible die rolls)
BMI 713: Computational Statistics for Biomedical Sciences Assignment 2 1 Random variables and distributions 1. Assume that a die is fair, i.e. if the die is rolled once, the probability of getting each
More informationFin285a:Computer Simulations and Risk Assessment Section 9 Backtesting and Stress Testing Daníelson, , 8.5, 8.6
Fin285a:Computer Simulations and Risk Assessment Section 9 Backtesting and Stress Testing Daníelson, 8.1-8.3.1, 8.5, 8.6 Overview What is backtesting? Regulatory issues Backtesting details Backtest examples
More informationThe Importance (or Non-Importance) of Distributional Assumptions in Monte Carlo Models of Saving. James P. Dow, Jr.
The Importance (or Non-Importance) of Distributional Assumptions in Monte Carlo Models of Saving James P. Dow, Jr. Department of Finance, Real Estate and Insurance California State University, Northridge
More informationCambridge University Press Risk Modelling in General Insurance: From Principles to Practice Roger J. Gray and Susan M.
adjustment coefficient, 272 and Cramér Lundberg approximation, 302 existence, 279 and Lundberg s inequality, 272 numerical methods for, 303 properties, 272 and reinsurance (case study), 348 statistical
More informationEXAMINATION OF THE THRESHOLD FOR THE TO COMPLETE INDEXES By Walt Lipke, PMI Oklahoma City Chapter
THE MEASURABLE NEWS 2016.01 EXAMINATION OF THE THRESHOLD FOR THE TO COMPLETE INDEXES By Walt Lipke, PMI Oklahoma City Chapter ABSTRACT From time to time in the Earned Value Management literature a claim
More informationMemorandum. Human Resources Division
Memorandum Human Resources Division TO: FROM: RE: Vacellia Clark, Chief Examiner Civil Service Commission Human Resources Staff Establish a Passing Score for Animal Control Officer DATE: October 30, 2013
More informationECE 340 Probabilistic Methods in Engineering M/W 3-4:15. Lecture 10: Continuous RV Families. Prof. Vince Calhoun
ECE 340 Probabilistic Methods in Engineering M/W 3-4:15 Lecture 10: Continuous RV Families Prof. Vince Calhoun 1 Reading This class: Section 4.4-4.5 Next class: Section 4.6-4.7 2 Homework 3.9, 3.49, 4.5,
More informationExam 2 Spring 2015 Statistics for Applications 4/9/2015
18.443 Exam 2 Spring 2015 Statistics for Applications 4/9/2015 1. True or False (and state why). (a). The significance level of a statistical test is not equal to the probability that the null hypothesis
More informationStatistics for Managers Using Microsoft Excel 7 th Edition
Statistics for Managers Using Microsoft Excel 7 th Edition Chapter 5 Discrete Probability Distributions Statistics for Managers Using Microsoft Excel 7e Copyright 014 Pearson Education, Inc. Chap 5-1 Learning
More informationEmpirical Study on Short-Term Prediction of Shanghai Composite Index Based on ARMA Model
Empirical Study on Short-Term Prediction of Shanghai Composite Index Based on ARMA Model Cai-xia Xiang 1, Ping Xiao 2* 1 (School of Hunan University of Humanities, Science and Technology, Hunan417000,
More informationSOCIETY OF ACTUARIES EXAM STAM SHORT-TERM ACTUARIAL MATHEMATICS EXAM STAM SAMPLE QUESTIONS
SOCIETY OF ACTUARIES EXAM STAM SHORT-TERM ACTUARIAL MATHEMATICS EXAM STAM SAMPLE QUESTIONS Questions 1-307 have been taken from the previous set of Exam C sample questions. Questions no longer relevant
More informationBayesian Hierarchical Modeling for Meta- Analysis
Bayesian Hierarchical Modeling for Meta- Analysis Overview Meta-analysis is an important technique that combines information from different studies. When you have no prior information for thinking any
More informationSession 178 TS, Stats for Health Actuaries. Moderator: Ian G. Duncan, FSA, FCA, FCIA, FIA, MAAA. Presenter: Joan C. Barrett, FSA, MAAA
Session 178 TS, Stats for Health Actuaries Moderator: Ian G. Duncan, FSA, FCA, FCIA, FIA, MAAA Presenter: Joan C. Barrett, FSA, MAAA Session 178 Statistics for Health Actuaries October 14, 2015 Presented
More informationGENERATION OF STANDARD NORMAL RANDOM NUMBERS. Naveen Kumar Boiroju and M. Krishna Reddy
GENERATION OF STANDARD NORMAL RANDOM NUMBERS Naveen Kumar Boiroju and M. Krishna Reddy Department of Statistics, Osmania University, Hyderabad- 500 007, INDIA Email: nanibyrozu@gmail.com, reddymk54@gmail.com
More informationCopyright 2005 Pearson Education, Inc. Slide 6-1
Copyright 2005 Pearson Education, Inc. Slide 6-1 Chapter 6 Copyright 2005 Pearson Education, Inc. Measures of Center in a Distribution 6-A The mean is what we most commonly call the average value. It is
More informationGroup-Sequential Tests for Two Proportions
Chapter 220 Group-Sequential Tests for Two Proportions Introduction Clinical trials are longitudinal. They accumulate data sequentially through time. The participants cannot be enrolled and randomized
More informationStudy Ch. 11.2, #51, 63 69, 73
May 05, 014 11. Inferences for σ's, Populations Study Ch. 11., #51, 63 69, 73 Statistics Home Page Gertrude Battaly, 014 11. Inferences for σ's, Populations Procedures that assume = σ's 1. Pooled t test.
More informationMonte Carlo, Resampling, And Other Estimation Tricks. Mauricio Aguiar ti MÉTRICAS, President IFPUG Immediate Past President
Monte Carlo, Resampling, And Other Estimation Tricks Mauricio Aguiar ti MÉTRICAS, President IFPUG Immediate Past President Agenda Introduction A Simple Example Another Example An Alternative Do It Yourself
More informationOnline Appendix to Grouped Coefficients to Reduce Bias in Heterogeneous Dynamic Panel Models with Small T
Online Appendix to Grouped Coefficients to Reduce Bias in Heterogeneous Dynamic Panel Models with Small T Nathan P. Hendricks and Aaron Smith October 2014 A1 Bias Formulas for Large T The heterogeneous
More information1. You are given the following information about a stationary AR(2) model:
Fall 2003 Society of Actuaries **BEGINNING OF EXAMINATION** 1. You are given the following information about a stationary AR(2) model: (i) ρ 1 = 05. (ii) ρ 2 = 01. Determine φ 2. (A) 0.2 (B) 0.1 (C) 0.4
More informationIntroduction Models for claim numbers and claim sizes
Table of Preface page xiii 1 Introduction 1 1.1 The aim of this book 1 1.2 Notation and prerequisites 2 1.2.1 Probability 2 1.2.2 Statistics 9 1.2.3 Simulation 9 1.2.4 The statistical software package
More informationPart I: Interpreting matlab code: In the following problems you will be asked to interpret some example matlab programs.
FIN285a: Computer Simulations and Risk Management Midterm Exam: Wednesday, October 30th. Fall 2013 Professor B. LeBaron Directions: Answer all questions. You have 1 hour and 30 minutes. Point weightings
More informationApplied Quantitative Finance
W. Härdle T. Kleinow G. Stahl Applied Quantitative Finance Theory and Computational Tools m Springer Preface xv Contributors xix Frequently Used Notation xxi I Value at Risk 1 1 Approximating Value at
More informationThe Binomial Distribution
The Binomial Distribution Patrick Breheny February 16 Patrick Breheny STA 580: Biostatistics I 1/38 Random variables The Binomial Distribution Random variables The binomial coefficients The binomial distribution
More informationIntroduction Dickey-Fuller Test Option Pricing Bootstrapping. Simulation Methods. Chapter 13 of Chris Brook s Book.
Simulation Methods Chapter 13 of Chris Brook s Book Christopher Ting http://www.mysmu.edu/faculty/christophert/ Christopher Ting : christopherting@smu.edu.sg : 6828 0364 : LKCSB 5036 April 26, 2017 Christopher
More informationMathematical Flaws in Suzuki and Gojobori s test for selection. Rick Durrett, Cornell University
Mathematical Flaws in Suzuki and Gojobori s test for selection Rick Durrett, Cornell University Abstract. Suzuki and Gojobori introduced a method for detecting positive selection at single amino acid sites.
More informationPRMIA Exam 8002 PRM Certification - Exam II: Mathematical Foundations of Risk Measurement Version: 6.0 [ Total Questions: 132 ]
s@lm@n PRMIA Exam 8002 PRM Certification - Exam II: Mathematical Foundations of Risk Measurement Version: 6.0 [ Total Questions: 132 ] Question No : 1 A 2-step binomial tree is used to value an American
More informationMath 361. Day 8 Binomial Random Variables pages 27 and 28 Inv Do you have ESP? Inv. 1.3 Tim or Bob?
Math 361 Day 8 Binomial Random Variables pages 27 and 28 Inv. 1.2 - Do you have ESP? Inv. 1.3 Tim or Bob? Inv. 1.1: Friend or Foe Review Is a particular study result consistent with the null model? Learning
More informationLecture Slides. Elementary Statistics Tenth Edition. by Mario F. Triola. and the Triola Statistics Series
Lecture Slides Elementary Statistics Tenth Edition and the Triola Statistics Series by Mario F. Triola Slide 1 Chapter 5 Probability Distributions 5-1 Overview 5-2 Random Variables 5-3 Binomial Probability
More informationWeek 7 Quantitative Analysis of Financial Markets Simulation Methods
Week 7 Quantitative Analysis of Financial Markets Simulation Methods Christopher Ting http://www.mysmu.edu/faculty/christophert/ Christopher Ting : christopherting@smu.edu.sg : 6828 0364 : LKCSB 5036 November
More informationASC 718 Valuation Consulting Services
provides a comprehensive range of valuation consulting services for compliance with ASC 718 (FAS 123R), SEC Staff Accounting Bulletin 107/110 and PCAOB ESO Guidance. 1) Fair Value of Share-Based Payment
More informationFinancial Economics. Runs Test
Test A simple statistical test of the random-walk theory is a runs test. For daily data, a run is defined as a sequence of days in which the stock price changes in the same direction. For example, consider
More informationo Hours per week: lecture (4 hours) and exercise (1 hour)
Mathematical study programmes: courses taught in English 1. Master 1.1.Winter term An Introduction to Measure-Theoretic Probability o ECTS: 4 o Hours per week: lecture (2 hours) and exercise (1 hour) o
More informationDoes Commodity Price Index predict Canadian Inflation?
2011 年 2 月第十四卷一期 Vol. 14, No. 1, February 2011 Does Commodity Price Index predict Canadian Inflation? Tao Chen http://cmr.ba.ouhk.edu.hk Web Journal of Chinese Management Review Vol. 14 No 1 1 Does Commodity
More informationSOCIETY OF ACTUARIES/CASUALTY ACTUARIAL SOCIETY EXAM C CONSTRUCTION AND EVALUATION OF ACTUARIAL MODELS EXAM C SAMPLE QUESTIONS
SOCIETY OF ACTUARIES/CASUALTY ACTUARIAL SOCIETY EXAM C CONSTRUCTION AND EVALUATION OF ACTUARIAL MODELS EXAM C SAMPLE QUESTIONS Copyright 2008 by the Society of Actuaries and the Casualty Actuarial Society
More informationCFA Level I - LOS Changes
CFA Level I - LOS Changes 2018-2019 Topic LOS Level I - 2018 (529 LOS) LOS Level I - 2019 (525 LOS) Compared Ethics 1.1.a explain ethics 1.1.a explain ethics Ethics Ethics 1.1.b 1.1.c describe the role
More informationReview: Population, sample, and sampling distributions
Review: Population, sample, and sampling distributions A population with mean µ and standard deviation σ For instance, µ = 0, σ = 1 0 1 Sample 1, N=30 Sample 2, N=30 Sample 100000000000 InterquartileRange
More informationMonitoring Accrual and Events in a Time-to-Event Endpoint Trial. BASS November 2, 2015 Jeff Palmer
Monitoring Accrual and Events in a Time-to-Event Endpoint Trial BASS November 2, 2015 Jeff Palmer Introduction A number of things can go wrong in a survival study, especially if you have a fixed end of
More informationFinancial Essentials for Nonprofit. Managers
Financial Essentials for Nonprofit Chapter 1: Managers What Every Nonprofit Manager Should Know About Accounting and Finance 1. Recognize financing options available to nonprofit organizations. 2. Identify
More informationBerlin, 10 th February 2017
Forecasting the Distribution of Hourly Electricity Spot Prices - Accounting for Cross Correlation Patterns and Non-Normality of Price Distributions Arne Vogler Co-Authors: Christoph Weber, Christian Pape
More informationAdvanced Extremal Models for Operational Risk
Advanced Extremal Models for Operational Risk V. Chavez-Demoulin and P. Embrechts Department of Mathematics ETH-Zentrum CH-8092 Zürich Switzerland http://statwww.epfl.ch/people/chavez/ and Department of
More informationSection 3 describes the data for portfolio construction and alternative PD and correlation inputs.
Evaluating economic capital models for credit risk is important for both financial institutions and regulators. However, a major impediment to model validation remains limited data in the time series due
More informationAP Stats Review. Mrs. Daniel Alonzo & Tracy Mourning Sr. High
AP Stats Review Mrs. Daniel Alonzo & Tracy Mourning Sr. High sdaniel@dadeschools.net Agenda 1. AP Stats Exam Overview 2. AP FRQ Scoring & FRQ: 2016 #1 3. Distributions Review 4. FRQ: 2015 #6 5. Distribution
More informationA Markov Chain Monte Carlo Approach to Estimate the Risks of Extremely Large Insurance Claims
International Journal of Business and Economics, 007, Vol. 6, No. 3, 5-36 A Markov Chain Monte Carlo Approach to Estimate the Risks of Extremely Large Insurance Claims Wan-Kai Pang * Department of Applied
More informationSTATISTICAL FLOOD STANDARDS
STATISTICAL FLOOD STANDARDS SF-1 Flood Modeled Results and Goodness-of-Fit A. The use of historical data in developing the flood model shall be supported by rigorous methods published in currently accepted
More informationSection 2: Estimation, Confidence Intervals and Testing Hypothesis
Section 2: Estimation, Confidence Intervals and Testing Hypothesis Carlos M. Carvalho The University of Texas at Austin McCombs School of Business http://faculty.mccombs.utexas.edu/carlos.carvalho/teaching/
More informationBehavioral Finance 1-1. Chapter 4 Challenges to Market Efficiency
Behavioral Finance 1-1 Chapter 4 Challenges to Market Efficiency 1 Introduction 1-2 Early tests of market efficiency were largely positive However, more recent empirical evidence has uncovered a series
More informationPlanning Sample Size for Randomized Evaluations Esther Duflo J-PAL
Planning Sample Size for Randomized Evaluations Esther Duflo J-PAL povertyactionlab.org Planning Sample Size for Randomized Evaluations General question: How large does the sample need to be to credibly
More informationMAS187/AEF258. University of Newcastle upon Tyne
MAS187/AEF258 University of Newcastle upon Tyne 2005-6 Contents 1 Collecting and Presenting Data 5 1.1 Introduction...................................... 5 1.1.1 Examples...................................
More informationIntroduction to Probability and Inference HSSP Summer 2017, Instructor: Alexandra Ding July 19, 2017
Introduction to Probability and Inference HSSP Summer 2017, Instructor: Alexandra Ding July 19, 2017 Please fill out the attendance sheet! Suggestions Box: Feedback and suggestions are important to the
More informationAPPENDIX V. The probability that a single underlying binomial distribution yields two given success proportions: A hypothesis test
APPENDIX V The probability that a single underlying binomial distribution yields two given success proportions: A hypothesis test 328 Binomial hypothesis test, 329 The problem Binomial distributions are
More informationAP Stats. Review. Mrs. Daniel Alonzo & Tracy Mourning Sr. High
AP Stats Review Mrs. Daniel Alonzo & Tracy Mourning Sr. High sdaniel@dadeschools.net Agenda 1. AP Stats Exam Overview 2. AP FRQ Scoring & FRQ: 2016 #1 3. Distributions Review 4. FRQ: 2015 #6 5. Distribution
More informationA First Course in Probability
A First Course in Probability Seventh Edition Sheldon Ross University of Southern California PEARSON Prentice Hall Upper Saddle River, New Jersey 07458 Preface 1 Combinatorial Analysis 1 1.1 Introduction
More informationTesting Static Tradeoff Against Pecking Order Models. Of Capital Structure: A Critical Comment. Robert S. Chirinko. and. Anuja R.
Testing Static Tradeoff Against Pecking Order Models Of Capital Structure: A Critical Comment Robert S. Chirinko and Anuja R. Singha * October 1999 * The authors thank Hashem Dezhbakhsh, Som Somanathan,
More informationYou will also see that the same calculations can enable you to calculate mortgage payments.
Financial maths 31 Financial maths 1. Introduction 1.1. Chapter overview What would you rather have, 1 today or 1 next week? Intuitively the answer is 1 today. Even without knowing it you are applying
More informationIntroduction to Statistical Data Analysis II
Introduction to Statistical Data Analysis II JULY 2011 Afsaneh Yazdani Preface Major branches of Statistics: - Descriptive Statistics - Inferential Statistics Preface What is Inferential Statistics? Preface
More informationJackknife Empirical Likelihood Inferences for the Skewness and Kurtosis
Georgia State University ScholarWorks @ Georgia State University Mathematics Theses Department of Mathematics and Statistics 5-10-2014 Jackknife Empirical Likelihood Inferences for the Skewness and Kurtosis
More informationHomework Problems Stat 479
Chapter 2 1. Model 1 is a uniform distribution from 0 to 100. Determine the table entries for a generalized uniform distribution covering the range from a to b where a < b. 2. Let X be a discrete random
More informationData Analysis. BCF106 Fundamentals of Cost Analysis
Data Analysis BCF106 Fundamentals of Cost Analysis June 009 Chapter 5 Data Analysis 5.0 Introduction... 3 5.1 Terminology... 3 5. Measures of Central Tendency... 5 5.3 Measures of Dispersion... 7 5.4 Frequency
More informationIt is common in the field of mathematics, for example, geometry, to have theorems or postulates
CHAPTER 5 POPULATION DISTRIBUTIONS It is common in the field of mathematics, for example, geometry, to have theorems or postulates that establish guiding principles for understanding analysis of data.
More informationMachine Learning for Quantitative Finance
Machine Learning for Quantitative Finance Fast derivative pricing Sofie Reyners Joint work with Jan De Spiegeleer, Dilip Madan and Wim Schoutens Derivative pricing is time-consuming... Vanilla option pricing
More informationTesting for the martingale hypothesis in Asian stock prices: a wild bootstrap approach
Testing for the martingale hypothesis in Asian stock prices: a wild bootstrap approach Jae H. Kim Department of Econometrics and Business Statistics Monash University, Caulfield East, VIC 3145, Australia
More informationBacktesting Trading Book Models
Backtesting Trading Book Models Using Estimates of VaR Expected Shortfall and Realized p-values Alexander J. McNeil 1 1 Heriot-Watt University Edinburgh ETH Risk Day 11 September 2015 AJM (HWU) Backtesting
More informationSTAT 479 Test 3 Spring 2016 May 3, 2016
The final will be set as a case study. This means that you will be using the same set up for all the problems. It also means that you are using the same data for several problems. This should actually
More informationTests for Two Means in a Multicenter Randomized Design
Chapter 481 Tests for Two Means in a Multicenter Randomized Design Introduction In a multicenter design with a continuous outcome, a number of centers (e.g. hospitals or clinics) are selected at random
More information