Description: The cloud inspires awe and fear in equal parts these days. There is a need to learn more, but there’s much information to learn and it may be difficult to know what to learn next. In this session, we’ll carve off a small corner of the cloud and make it your own. Setting up a Data Science Virtual Machine on Digital Ocean will be demonstrated. It’s the fastest way to get a data science development environment setup for your own use. Many of the topics and ideas covered will apply to almost all other cloud offerings. What use cases Digital Ocean is good for, and what use cases you should look elsewhere for, will also be discussed.

Description: This panel discussion session focused on the practical applications of risk theory.

Description: The minimum distance estimator proposed allows for the selection of the best fitting family member without prior distributional assumptions. It is shown that this estimator is consistent and can be made either robust or asymptotically efficient. Its asymptotic distribution is derived and and illustrated with automobile insurance data. From the Actuarial Research Clearing House 1990 Vol. 1

Description: This paper presents a model is which can be used for certain annuities when interest rates and future lifetimes are random. These can be used in determining contingency reserves for possible adverse interest and mortality experience for collections of life annuity contracts. Certain boundary crossing probabilities for the stochastic process component of the model are obtained. From Actuarial Research Clearing House 1991, VOL. 1.

Description: The authors offer a method for estimating parameters for well-known three-parameter models of force of mortality. The method involves fitting a series of linear regressions, and finding the least-squares estimates of all the parameters by using the Golden Section Search method. From Actuarial Research Clearing House 1993, VOL. 1.

Description: This paper discusses the numerical stability of Panjer's recursion in computing aggregate claims distributions. From Actuarial Research Clearing House 1993, VOL. 1.

Description: The purpose of this paper is to present a parsimonious parametric model that explains the pattern of mortality for select and ultimate mortality tables. Specifically, the author modeled the combined rates found in the 1965-70 ALB Basic Tables. From Actuarial Research Clearing House 1994, VOL. 1.

Description: This paper focuses on the fact that life and annuity reserves that are determined from discounted case flows mask the dynamic nature of interest rates. To study this effect in a stochastic interest environment, the authors look at a change in discounted reserves. By looking at the appropriate measure of change, the paper explores potential short-term consequences of changes in the interest environment.

Description: The main aim of this paper is to apply some of the techniques of modern time series and econometric analysis to analyse investment data in order to better understand the nature of long run relationships in investment series typically used in stochastic investment models. The paper attempts to identify and address fundamental issues that need to be considered before developing a particular stochastic investment model.

Description: This 1997 presentation from the 32nd Actuarial Research Conference develops a stochastic generator for the generation of scenarios of the S&P 500 index, dividend yield, consumer price index, and U.S. Treasury yields. The authors first create a set of 'stylized facts' for these series. They estimate statistical models for these series. These in-sample statistical models are themselves not suitable for generation of scenarios for decision making, but instead are additional 'stylized facts' that assist in model development. The 'best' statistical model according to standard statistical model selection criteria can easily lead to a model that is highly unsuitable for generation of scenarios for decision making. The authors develop a stochastic generator that is suitable for decision making under uncertainty.