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An actuary is a professional with advanced mathematical skills who deals with the measurement and management of risk and uncertainty. [1] These risks can affect both sides of the balance sheet and require asset management, liability management, and valuation skills. [2]
The Society of Actuaries' requirements for Associateship (ASA) include passing 6 preliminary examinations (probability, financial mathematics, fundamentals of actuarial mathematics, statistics for risk modeling, predictive analytics, and one from either advanced long-term actuarial mathematics or advanced short-term actuarial mathematics ...
Actuarial science is the discipline that applies mathematical and statistical methods to assess risk in insurance, pension, finance, investment and other industries ...
Actuarial science – discipline that applies mathematical and statistical methods to assess risk in the insurance and finance industries. What type of thing is ...
The CAS requires all candidates to qualify through a series of actuarial exams covering various aspects of actuarial practice. Passing Exams 1–6 as well as Exam S, the Course on Professionalism, the Validation by Educational Experience (VEE), and two online courses qualifies an actuary for the Associateship designation; passing three additional exams is required to become a Fellow. [10]
That is, if portfolio always has better values than portfolio under almost all scenarios then the risk of should be less than the risk of . [2] E.g. If is an in the money call option (or otherwise) on a stock, and is also an in the money call option with a lower strike price.
The Society of Actuaries (SOA) is a global professional organization for actuaries. It was founded in 1949 as the merger of two major actuarial organizations in the United States: the Actuarial Society of America and the American Institute of Actuaries. [1] It is a full member organization of the International Actuarial Association. [2]
Actuarial credibility describes an approach used by actuaries to improve statistical estimates. Although the approach can be formulated in either a frequentist or Bayesian statistical setting, the latter is often preferred because of the ease of recognizing more than one source of randomness through both "sampling" and "prior" information.