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Assessing Policies for Retirement Income
The retirement income security of older Americans and the cost of providing that security are increasingly the subject of major debate. This volume assesses what we know and recommends what we need to know to estimate the short- and long-term effects of policy alternatives. It details gaps in data and research and evaluates possible models to estimate the impact of policy changes that could affect retirement income from Social Security, pensions, personal savings, and other sources.
eBook
Predictive modeling applications in actuarial science
\"Predictive modeling involves the use of data to forecast future events. It relies on capturing relationships between explanatory variables and the predicted variables from past occurrences and exploiting this to predict future outcomes. Forecasting future financial events is a core actuarial skill - actuaries routinely apply predictive-modeling techniques in insurance and other risk-management applications. This book is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples of predictive modeling. The book also addresses the needs of more seasoned practicing analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice. Predictive Modeling Applications in Actuarial Science emphasizes life-long learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used by analysts to gain a competitive advantage in situations with complex data\"-- Provided by publisher.
Book
Emerging Perspectives on the Marketing of Financial Services
A fundamental requirement for the social and economic stability of any modern society is the financial security of its citizens. In that sense, financial services marketers have a unique opportunity to engage in activities that help build consumer wealth and establish a mutual sense of trust with their customers. To provide a mechanism for such an engagement, in November of 2006, a conference on the Marketing of Financial Services was hosted at Fordham University in New York. Of the nearly fifty papers that were submitted to the conference, a total of four were selected for publication in this e-book. These papers have been selected based on their innovative focus and methodical approach to improving our understanding of consumer response to the marketing actions of financial services providers. As such, they contribute to our understanding of how financial services marketers could work hand-in-hand with consumer literacy advocates, regulators, academicians, and consumers to improve the impact of their marketing activities.
eBook
Essential health benefits : balancing coverage and cost
In 2010, an estimated 50 million people were uninsured in the United States.A portion of the uninsured reflects unemployment rates; however, this rate is primarily a reflection of the fact that when most health plans meet an individual's needs, most times, those health plans are not affordable.
eBook
Assessing Policies for Retirement Income
Front Matter -- Contents -- Acknowledgments -- Executive Summary -- 1 Introduction -- 2 Considerations in Retirement Income Projections -- 3 Key Research Issues -- 4 Data Needs -- 5 Development of Projection Models -- 6 Furthering Coordination for Data Collection, Research, and Modeling -- APPENDIX A Contents, Assessing Knowledge of Retirement Behavior -- APPENDIX B Retirement-Income-Related Data Sets -- APPENDIX C Examples of Retirement-Income-Related Projection Models -- APPENDIX D Major Aspects of DYNASIM2 and PRISM -- References -- Biographical Sketches of Panel Members and Staff -- Index.
Publication
Forecasting unemployment insurance trust funds in Tennessee
Due to deteriorating solvency in many state unemployment insurance (UI) trust funds, renewed efforts have been made to forecast UI variables. The most comprehensive forecasting model was developed by Mercer Associates for the US Department of Labor. However, there are basic problems connected with nearly all models. Since most are designed to be used by all states, they often do not fit the needs or the data of an individual state very closely. Based on this fact and other inadequacies, the Tennessee Employment Security Insurance Forecasting Model (TESIM) was developed using only Tennessee data. The taxable wage ceiling, tax rates, experience rating rule, and maximum benefit amount all are explicitly contained in the model. Changes in these variables can be simulated by adjusting the parameters of the model. The TESIM model is constructed to fulfill 2 main goals: 1. to provide the most accurate UI systems forecasts possible, and 2. to incorporate important policy parameters into the model so as to simulate policy changes.
Journal Article
Making and Evaluating Point Forecasts
Typically, point forecasting methods are compared and assessed by means of an error measure or scoring function, with the absolute error and the squared error being key examples. The individual scores are averaged over forecast cases, to result in a summary measure of the predictive performance, such as the mean absolute error or the mean squared error. I demonstrate that this common practice can lead to grossly misguided inferences, unless the scoring function and the forecasting task are carefully matched. Effective point forecasting requires that the scoring function be specified ex ante, or that the forecaster receives a directive in the form of a statistical functional, such as the mean or a quantile of the predictive distribution. If the scoring function is specified ex ante, the forecaster can issue the optimal point forecast, namely, the Bayes rule. If the forecaster receives a directive in the form of a functional, it is critical that the scoring function be consistent for it, in the sense that the expected score is minimized when following the directive. A functional is elicitable if there exists a scoring function that is strictly consistent for it. Expectations, ratios of expectations and quantiles are elicitable. For example, a scoring function is consistent for the mean functional if and only if it is a Bregman function. It is consistent for a quantile if and only if it is generalized piecewise linear. Similar characterizations apply to ratios of expectations and to expectiles. Weighted scoring functions are consistent for functionals that adapt to the weighting in peculiar ways. Not all functionals are elicitable; for instance, conditional value-at-risk is not, despite its popularity in quantitative finance.
Journal Article
Tests of Conditional Predictive Ability
We propose a framework for out-of-sample predictive ability testing and forecast selection designed for use in the realistic situation in which the forecasting model is possibly misspecified, due to unmodeled dynamics, unmodeled heterogeneity, incorrect functional form, or any combination of these. Relative to the existing literature (Diebold and Mariano (1995) and West (1996)), we introduce two main innovations: (i) We derive our tests in an environment where the finite sample properties of the estimators on which the forecasts may depend are preserved asymptotically. (ii) We accommodate conditional evaluation objectives (can we predict which forecast will be more accurate at a future date?), which nest unconditional objectives (which forecast was more accurate on average?), that have been the sole focus of previous literature. As a result of (i), our tests have several advantages: they capture the effect of estimation uncertainty on relative forecast performance, they can handle forecasts based on both nested and nonnested models, they allow the forecasts to be produced by general estimation methods, and they are easy to compute. Although both unconditional and conditional approaches are informative, conditioning can help fine-tune the forecast selection to current economic conditions. To this end, we propose a two-step decision rule that uses current information to select the best forecast for the future date of interest. We illustrate the usefulness of our approach by comparing forecasts from leading parameter-reduction methods for macroeconomic forecasting using a large number of predictors.
Journal Article
Modeling and Forecasting Realized Volatility
We provide a framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency return volatilities and return distributions. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic variation, we develop formal links between realized volatility and the conditional covariance matrix. Next, using continuously recorded observations for the Deutschemark/Dollar and Yen/Dollar spot exchange rates, we find that forecasts from a simple long-memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform admirably. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal-normal mixture distribution produces well-calibrated density forecasts of future returns, and correspondingly accurate quantile predictions. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation, and financial risk management applications.
Journal Article