Actuarial Mathematics and Life-Table Statistics
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Actuarial Science and Financial Mathematics Faqs
Department of Mathematics 250 Mathematics Building 231 W. 18th Avenue Columbus, OH 43210-1174 Actuarial Science and Financial Mathematics FAQs 1. What is the difference between a major in Actuarial Science and a major in Financial Math? Actuarial Science is the mathematical and statistical underpinning of the design, financing, and operation of all types of insurance, pension plans and benefit plans. Actuarial science prepares students for a career in the actuarial side of the insurance business and in actuarial consulting. Please see http://www.beanactuary.org/about/ for more information. Financial math track provides students with a foundation of the mathematics used in financial markets. It focuses on the pricing of the financial instruments (futures, options and other derivatives) and portfolio selection. Though there are job opportunities in financial institutions for students with an undergraduate degree, students in this track are encouraged to continue with graduate study in financial math, financial engineering, computational finance, or quantitative finance (they essentially mean the same thing). There are many professional master degree programs in this area. 2. How many students are currently majoring in actuarial science at Ohio State? We have close to 350 students in our program. 3. Are there any requirements to my actuarial major? If so, what are they? We place students into a pre-major when they switch or add this major. The major prerequisites and requirements can be found at https://math.osu.edu/undergrad/current- majors/requirements/actuarial. Students will also need to complete the College of Arts and Sciences General Education requirements. Students should meet with their academic advisor in the Math Advising Office or review the requirements online, https://math.osu.edu/undergrad/current-majors/requirements. -
How to Measure the Burden of Mortality? L Bonneux
J Epidemiol Community Health: first published as 10.1136/jech.56.2.128 on 1 February 2002. Downloaded from 128 THEORY AND METHODS How to measure the burden of mortality? L Bonneux ............................................................................................................................. J Epidemiol Community Health 2002;56:128–131 Objectives: To explore various methods to quantify the burden of mortality, with a special interest for the more recent method at the core of calculations of disability adjusted life years (DALY). Design: Various methods calculating the age schedule at death are applied to two historical life table populations. One method calculates the “years of life lost”, by multiplying the numbers of deaths at age ....................... L Bonneux, Department of x by the residual life expectancy. This residual life expectancy may be discounted and age weighted. Public Health, Erasmus The other method calculates the “potential years of life lost” by multiplying the numbers of deaths at age University Rotterdam, the x by the years missing to reach a defined threshold (65 years or 75 years). Netherlands Methods: The period life tables describing the mortality of Dutch male populations from 1900–10 Correspondence to: (high mortality) and from 1990–1994 (low mortality). Dr L Bonneux, Department Results: A standard life table with idealised long life expectancy increases the burden of death more of Public Health, Erasmus if mortality is lower. People at old age, more prevalent if mortality is low, lose more life years in an ide- University Rotterdam, PO alised life table. The discounted life table decreases the burden of death strongly if mortality is high: Box 1738, 3000 DR Rotterdam, the the life lost by a person dying at a young age is discounted. -
Recent Challenges in Actuarial Science
ST09CH01_Embrechts ARjats.cls July 30, 2021 15:51 Annual Review of Statistics and Its Application Recent Challenges in Actuarial Science Paul Embrechts and Mario V. Wüthrich RiskLab, Department of Mathematics, ETH Zurich, Zurich, Switzerland, CH-8092; email: [email protected], [email protected] Annu. Rev. Stat. Appl. 2022. 9:1.1–1.22 Keywords The Annual Review of Statistics and Its Application is actuarial science, generalized linear models, life and non-life insurance, online at statistics.annualreviews.org neural networks, risk management, telematics data https://doi.org/10.1146/annurev-statistics-040120- 030244 Abstract Copyright © 2022 by Annual Reviews. For centuries, mathematicians and, later, statisticians, have found natural All rights reserved research and employment opportunities in the realm of insurance. By defini- tion, insurance offers financial cover against unforeseen events that involve an important component of randomness, and consequently, probability the- ory and mathematical statistics enter insurance modeling in a fundamental way. In recent years, a data deluge, coupled with ever-advancing information technology and the birth of data science, has revolutionized or is about to revolutionize most areas of actuarial science as well as insurance practice. We discuss parts of this evolution and, in the case of non-life insurance, show how a combination of classical tools from statistics, such as generalized lin- ear models and, e.g., neural networks contribute to better understanding and analysis of actuarial data. We further review areas of actuarial science where the cross fertilization between stochastics and insurance holds promise for both sides. Of course, the vastness of the field of insurance limits our choice of topics; we mainly focus on topics closer to our main areas of research. -
Department of Statistics and Actuarial Science Three Year Academic Plan 2004-2006 Approved by Department on 27 June 2006
Department of Statistics and Actuarial Science Three Year Academic Plan 2004-2006 Approved by Department on 27 June 2006 Chronology of Department Planning A. Achievement of Previous and Evaluation: Objectives and Recommendations: • May 2001: Formation of Department • Nov 2003: First Three-Year Plan (2003-6) The Department has achieved most of the • Mar 2005: External Review of Department objectives set out in its previous three-year • Mar 2006: Senate Approval of Recommen- plan, and has made a solid start at dations implementing the recommendations stemming • Jun 2006: Second Three-Year Plan (2007- from the 2005 external review. 9) Full, point-by-point assessments are Since its inception, the Department has provided in Appendices A and B. Of particular grown from 9 faculty positions to 20. Of these note, are the following: positions, 7 come with external or endowed New positions: The three-year plan called for funding. All faculty members are active in several academic appointments. As planned, research, and their collective record of we were able to hire two additional actuaries, external awards and funding is very strong. but the other target areas (a theoretician and a Over the last few years, the Department has lecturer) were not addressed. Instead, we took emerged as a leading Canadian center for advantage of our recognizable strengths to statistical teaching and research. A recruit very strong applicants for externally comparative analysis of NSERC grant funded and endowed positions. funding conducted for the recent external The plan also called for further half-positions in review ranked the Department very close to the general office and in the Statistical the best in the country. -
Actuarial Studies Within the Biometry and Statistics and Statistical Science Majors
Actuarial Studies within the Biometry and Statistics and Statistical Science majors An actuary is a business professional who analyzes the financial consequences of risk. Actuaries use mathematics, statistics and financial theory to study uncertain future events, especially those of concern to insurance and pension programs. They evaluate the likelihood of those events, design creative ways to reduce the likelihood and decrease the impact of adverse events that actually do occur. To be more specific, actuaries improve financial decision making by developing models to evaluate the current financial implications of uncertain future events. Actuaries are an important part of the management team of the companies that employ them. Their work requires a combination of strong analytical skills, business knowledge and understanding of human behavior to design and manage programs that control risk. No matter the source, actuary is consistently rated as one of the best jobs in America. US News and World Report, the Jobs Rated Almanac, CNN Money, and others all agree: few other occupations offer the combination of benefits that an actuarial career can offer. In almost every job satisfaction category, such as work environment, employment outlook, job security, growth opportunity, and salary (especially starting salary), a career as an actuary is hard to beat. Required courses for the Biometry and Statistics and Statistical Science majors Calculus I and II: MATH 1110 & 1120 Multivariable Calculus and Linear Algebra: MATH 2210 & 2220 or 2230 & 2240 -
Using Dynmaic Reliability in Estimating Mortality at Advanced
Using Dynamic Reliability in Estimating Mortality at Advanced Ages Fanny L.F. Lin, Professor Ph.D. Graduate Institute of Statistics and Actuarial Science Feng Chia University P.O. Box 25-181 Taichung 407, Taiwan Tel: +886-4-24517250 ext. 4013, 4012 Fax: +886-4-24507092 E-mail: [email protected] Abstract Traditionally, the Gompertz’s mortality law has been used in studies that show mortality rates continue to increase exponentially with age. The ultimate mortality rate has no maximum limit. In the field of engineering, the reliability theory has been used to measure the hazard-rate function that is dependent on system reliability. Usually, the hazard rate ( H ) and the reliability (R ) have a strong negative coefficient of correlation. If reliability decreases with increasing hazard rate, this type of hazard rate can be expressed as a power function of failure probability, 1- R . Many satisfactory results were found in quality control research in industrial engineering. In this research, this concept is applied to human mortality rates. The reliability R(x) is the probability a newborn will attain age x . Assuming the model between H(x) and R(x) is H(x) = B + C(1- R(x) A ) D , where A represents the survival decaying memory characteristics, B/C is the initial mortality strength, C is the strength of mortality, and D is the survival memory characteristics. Eight Taiwan Complete Life Tables from 1926 to 1991 were used as the data source. Mortality rates level off at a constant B+C for very high ages in the proposed model but do not follow Gompertz’s mortality law to the infinite. -
Statistics and Actuarial Science 1
Statistics and Actuarial Science 1 STAT:3510/IGPI:3510 Biostatistics and STAT:4143/PSQF:4143 Statistics and Introduction to Statistical Methods. Students may not take STAT:3101/IGPI:3101 Introduction Actuarial Science to Mathematical Statistics II and STAT:4101/IGPI:4101 Mathematical Statistics II at the same time and get credit for both (nor go back to STAT:3101/IGPI:3101 after taking Chair STAT:4101/IGPI:4101). • Kung-Sik Chan Statistics Courses Director of Graduate Studies STAT:1000 First-Year Seminar 1 s.h. • Aixin Tan Small discussion class taught by a faculty member; topics Director of Undergraduate Studies, chosen by instructor; may include outside activities (e.g., films, lectures, performances, readings, visits to research Actuarial Science facilities). Requirements: first- or second-semester standing. • Elias S. Shiu STAT:1010 Statistics and Society 3 s.h. Director of Undergraduate Studies, Data Statistical ideas and their relevance to public policy, business, humanities, and the social, health, and physical Science sciences; focus on critical approach to statistical evidence. • Rhonda R. DeCook Requirements: one year of high school algebra or MATH:0100. GE: Quantitative or Formal Reasoning. Director of Undergraduate Studies, STAT:1015 Introduction to Data Science 3 s.h. Statistics Data collection, visualization, and wrangling; basics of • Rhonda R. DeCook probability and statistical inference; fundamentals of data learning including regression, classification, prediction, and Undergraduate majors: actuarial science (B.S.); statistics cross-validation; computing, learning, and reporting in the R (B.S.) environment; literate programming; reproducible research. Undergraduate minor: statistics Requirements: one year of high school algebra or MATH:0100. Graduate degrees: M.S. -
Incidence, Mortality, Disability-Adjusted Life Years And
Original article The global burden of injury: incidence, mortality, disability-adjusted life years and time trends from the Global Burden of Disease study 2013 Juanita A Haagsma,1,60 Nicholas Graetz,1 Ian Bolliger,1 Mohsen Naghavi,1 Hideki Higashi,1 Erin C Mullany,1 Semaw Ferede Abera,2,3 Jerry Puthenpurakal Abraham,4,5 Koranteng Adofo,6 Ubai Alsharif,7 Emmanuel A Ameh,8 9 10 11 12 Open Access Walid Ammar, Carl Abelardo T Antonio, Lope H Barrero, Tolesa Bekele, Scan to access more free content Dipan Bose,13 Alexandra Brazinova,14 Ferrán Catalá-López,15 Lalit Dandona,1,16 Rakhi Dandona,16 Paul I Dargan,17 Diego De Leo,18 Louisa Degenhardt,19 Sarah Derrett,20,21 Samath D Dharmaratne,22 Tim R Driscoll,23 Leilei Duan,24 Sergey Petrovich Ermakov,25,26 Farshad Farzadfar,27 Valery L Feigin,28 Richard C Franklin,29 Belinda Gabbe,30 Richard A Gosselin,31 Nima Hafezi-Nejad,32 Randah Ribhi Hamadeh,33 Martha Hijar,34 Guoqing Hu,35 Sudha P Jayaraman,36 Guohong Jiang,37 Yousef Saleh Khader,38 Ejaz Ahmad Khan,39,40 Sanjay Krishnaswami,41 Chanda Kulkarni,42 Fiona E Lecky,43 Ricky Leung,44 Raimundas Lunevicius,45,46 Ronan Anthony Lyons,47 Marek Majdan,48 Amanda J Mason-Jones,49 Richard Matzopoulos,50,51 Peter A Meaney,52,53 Wubegzier Mekonnen,54 Ted R Miller,55,56 Charles N Mock,57 Rosana E Norman,58 Ricardo Orozco,59 Suzanne Polinder,60 Farshad Pourmalek,61 Vafa Rahimi-Movaghar,62 Amany Refaat,63 David Rojas-Rueda,64 Nobhojit Roy,65,66 David C Schwebel,67 Amira Shaheen,68 Saeid Shahraz,69 Vegard Skirbekk,70 Kjetil Søreide,71 Sergey Soshnikov,72 Dan J Stein,73,74 Bryan L Sykes,75 Karen M Tabb,76 Awoke Misganaw Temesgen,77 Eric Yeboah Tenkorang,78 Alice M Theadom,79 Bach Xuan Tran,80,81 Tommi J Vasankari,82 Monica S Vavilala,57 83 84 85 ▸ Vasiliy Victorovich Vlassov, Solomon Meseret Woldeyohannes, Paul Yip, Additional material is 86 87 88,89 1 published online only. -
Supplementary Notes for Actuarial
EDUCATION COMMITTEE OF THE SOCIETY OF ACTUARIES MLC STUDY NOTE SUPPLEMENTARY NOTES FOR ACTUARIAL MATHEMATICS FOR LIFE CONTINGENT RISKS VERSION 2.0 by Mary R. Hardy, PhD, FIA, FSA, CERA David C. M. Dickson, PhD, FFA, FIAA Howard R. Waters, DPhil, FIA, FFA Copyright 2011. Posted with permission of the authors. The Education and Examination Committee provides study notes to persons preparing for the examinations of the Society of Actuaries. They are intended to acquaint candidates with some of the theoretical and practical considerations involved in the various subjects. While varying opinions are presented where appropriate, limits on the length of the material and other considerations sometimes prevent the inclusion of all possible opinions. These study notes do not, however, represent any official opinion, interpretations or endorsement of the Society of Actuaries or its Education Committee. The Society is grateful to the authors for their contributions in preparing the study notes. Introduction This note is provided as an accompaniment to `Actuarial Mathematics for Life Contingent Risks' by Dickson, Hardy and Waters (2009, Cambridge University Press). Actuarial Mathematics for Life Contingent Risks (AMLCR) includes almost all of the material required to meet the learning objectives developed by the SOA for exam MLC for implementation in 2012. In this note we aim to provide the additional material required to meet the learning objectives in full. This note is designed to be read in conjunction with AMLCR, and we reference section and equation numbers from that text. We expect that this material will be integrated with the text formally in a second edition. -
Lecture Notes 3
4 Testing Hypotheses The next lectures will look at tests, some in an actuarial setting, and in the last subsection we will also consider tests applied to graduation. 4.1 Tests in the regression setting 1) A package will produce a test of whether or not a regression coefficient is 0. It uses properties of mle’s. Let the coefficient of interest be b say. Then the null hypothesis is HO : b =0and the alternative is HA : b =0. At the 5% significance level, HO will be accepted if the p-value p>0.05, and rejected otherwise. 2) In an AL parametric model if α is the shape parameter then we can test HO :logα =0against the alternative HA :logα =0. Again mle properties are used and a p-value is produced as above. In the case of the Weibull if we accept log α =0then we have the simpler exponential distribution (with α =1). 3) We have already mentioned that, to test Weibull v. exponential with null hypothesis HO : exponential is an acceptable fit, we can use 2 2logLweib − 2logLexp ∼ χ (1), asymptotically. 4.2 Non-parametric testing of survival between groups We will just consider the case where the data splits into two groups. There is a relatively easy extension to k(>2) groups. We need to set up some notation. d =#events at t in group 1, Notation: i1 i ni1 =#in risk set at ti from group 1, similarly di2,ni2. Event times are (0 <)t1 <t2 < ···<tm. di =#events at ti ni =#in risk set at ti di = di1 + di2,ni = ni1 + ni2 All the tests are based on the following concepts: Observed # events in group 1 at time ti, = di1 di Expected # events in group 1 at time ti, = ni1 n under the null hypothesis below i H O : there is no difference between the hazard rates of the two groups Usually the alternative hypothesis for all tests is 2-sided and simply states that there is a difference in the hazard rates. -
A Review of Life Table Construction
Biometrics & Biostatistics International Journal Research Article Open Access A review of life table construction Abstract Volume 5 Issue 3 - 2017 Life table is the one of the oldest statistical techniques and is extensively used by Ilker Etikan, Sulaiman Abubakar, Rukayya medical statisticians and actuaries. It is also the scheme for expressing the form of mortality in terms of probabilities. The life table is constructed from census data Alkassim Near East University Faculty of Medicine Department of and death registration data, it can also be constructed from vital registration or Biostatistics, Nicosia-TRNC, Cyprus retrospective surveys. Generally, they are constructed for age, gender, ethnic groups and occupational groups and so on. Some classification of life table such as cohort Correspondence: Ilker Etikan, Near East University Faculty of or current life table which are classified according to the reference year, unabridged Medicine Department of Biostatistics, Nicosia-TRNC, Cyprus, (complete) or abridge life table classified according to length of age interval and single Email or multiple decrement life table classified according to the number of characteristics considered were discussed in the article. Received: November 24, 2016 | Published: March 02, 2017 Keywords: life tables, survival analysis, kaplan meirer Introduction In 1693, the first mortality table is usually credited to Edmund halley who constructed a life table base on the data from Breslau city in A life table is a terse way of showing the probabilities of a member Poland. The table thought to be the first and as well as best of its kind. 1 of a particular population living to or dying at a precise age. -
Life Tables for 191 Countries: Data, Methods and Results
LIFE TABLES FOR 191 COUNTRIES: DATA, METHODS AND RESULTS Alan D Lopez Joshua Salomon Omar Ahmad Christopher JL Murray Doris Mafat GPE Discussion Paper Series: No.9 EIP/GPE/EBD World Health Organization 1 I Introduction The life table is a key summary tool for assessing and comparing mortality conditions prevailing in populations. From the time that the first modern life tables were constructed by Graunt and Halley during the latter part of the 17th century, life tables have served as a valuable analytical tool for demographers, epidemiologists, actuaries and other scientists. The basic summary measure of mortality from the life table, the expectation of life at birth, is widely understood by the general public and trends in life expectancy are closely monitored as the principal measure of changes in a population's health status. The construction of a life table requires reliable data on a population's mortality rates, by age and sex. The most reliable source of such data is a functioning vital registration system where all deaths are registered. Deaths at each age are related to the size of the population in that age group, usually estimated from population censuses, or continuous registration of all births, deaths and migrations. The resulting age-sex-specific death rates are then used to calculate a life table. While the legal requirement for the registration of deaths is virtually universal, the cost of establishing and maintaining a system to record births and deaths implies that reliable data from routine registration is generally only available in the more economically advanced countries. Reasonably complete national data to calculate life tables in the late 1990s was only available for about 65 countries, covering about one-third of the deaths estimated to have occurred in 1999.