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Future Value (future + value)
Selected AbstractsFlorida's Pension Election: From DB to DC and BackJOURNAL OF RISK AND INSURANCE, Issue 3 2004Moshe A. Milevsky During the year 2002, the State of Florida's 600,000 public employees were given the choice of converting their traditional defined benefit (DB) pension plan into an individual-account defined contribution (DC) plan with full control over asset allocation and investment decisions. To mitigate some of the risk and uncertainty in the decision, the State granted each employee electing the DC plan an additional option to switch back (i.e., change their mind once) at any point prior to retirement. This option has been labeled the 2nd election by the State and the cost of reentry is fixed at the accumulated benefit obligation of their pension entitlement, which is the present value of the life annuity. Our article presents some original analytic insights relating to the optimal time and financial value of this unique 2nd election. Although our model is deterministic in nature, we believe that it provides a number of intuitive insights that are quite robust. Our results can be contrasted with Lachance, Mitchell, and Smetters (2003). We estimate that the increase in retirement wealth that arises from having the 2nd election is equivalent to at most 30 percent in future value, and only when utilized optimally. Furthermore, for most State employees above the age of 45, the 2nd election has little economic value because the DB plan dominates the DC plan from day one. Of course, it remains to be seen what percent of Florida's 600,000 employees will elect to behave rationally with their newfound pension autonomy. [source] A multi-ontology framework to guide agriculture and food towards diet and healthJOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, Issue 8 2007Matthew C Lange Abstract Global increases in metabolic diseases that can be influenced by diet have re-emphasized the importance of considering how different foods can improve human health. The entire agricultural enterprise has an unprecedented opportunity to increase its value by producing foods that improve the health of consumers. Research efforts in agriculture/food science/nutrition are endeavoring to do so, although little tangible success has been achieved. At the core of the problem is a failure to define the goal itself: health. Health, as a scientifically measurable concept, is poorly defined relative to disease, and yet consensus-based, curated vocabularies that describe the multiple variations in human health in useful terms are critical to unifying the scientific fields related to agriculture and nutrition. Each of the life-science disciplines relating to health has developed databases, thesauri, and/or ontologies to capture such knowledge. High-throughput and -omic technologies are expanding both the amount and heterogeneity of available information. Unfortunately, the language used to describe substantially similar (even logically equivalent) concepts is often different between information systems. Increasing the future value of agriculture, therefore, will depend on creating a process for generating common ontologies of the concept of health, and guiding the development of a common language. This paper illustrates a framework for integrating heterogeneous ontologies into interdisciplinary, foods-for-health knowledge systems. A common system of language that describes health and is shared by all the life-science disciplines will provide immediate benefits in terms of increased health-claim regulatory efficiencies and predictive functions for individualized diets. Ultimately, these vocabularies will guide agriculture to its next goal of producing health-enhancing foods. Copyright © 2007 Society of Chemical Industry [source] mTOR inhibitors: An overviewLIVER TRANSPLANTATION, Issue 6 2001Peter Neuhaus MD Inhibitors of the mammalian target of rapamycin are a new class of immunosuppressants. In contrast to other macrolides, such as tacrolimus and cyclosporine A, they do not inhibit calcineurin and thus signal I of T-cell activation. By inhibiting signal III, the mechanism of action and side effects of sirolimus (rapamycin) and its derivative RAD are distinct from other immunosuppressants. Reports of synergism with cyclosporine A and tacrolimus in preclinical and clinical studies, avoidance of nephrotoxicity, and possible treatment or prevention of chronic allograft rejection are leading to high expectations for this new class of immunosuppressants. Furthermore, studies evaluating tolerance induction are being conducted. This review summarizes preclinical and clinical results published to date and exploits the future value of sirolimus and RAD for clinical transplantation. [source] Portfolio theory and how parent birds manage investment riskOIKOS, Issue 10 2009Scott Forbes Investment theory is founded on the premise that higher returns are generally associated with greater risk, and that portfolio diversification reduces risk. Here I examine parental investment decisions in birds from this perspective, using data from a model system, a 16-year study of breeding red-winged blackbirds Agelaius phoeniceus. Like many altricial birds, blackbirds structure their brood into core (first-hatched) and marginal (later-hatched) elements that differ in risk profile. I measured risk in two ways: as the coefficient of variation in growth and survival of core and marginal offspring from a given brood structure; and using financial beta derived from the capital asset pricing model of modern portfolio theory. Financial beta correlates changes in asset value with changes in the value of a broader market, defined here as individual reproductive success vs. population reproductive success. Both measures of risk increased with larger core (but not marginal) brood size; and variation in growth and survival was significantly greater during ecologically adverse conditions. Core offspring showed low beta values relative to marginal progeny. The most common brood structures in the population exhibited the highest beta values for both core and marginal offspring: many parent blackbirds embraced rather than avoided risk. But they did so prudently with an investment strategy that resembled a financial instrument, the call option. A call option is a contingent claim on the future value of the asset, and is exercised only if asset value increases beyond a point fixed in advance. Otherwise the option lapses and the investor loses only the initial option price. Parents created high risk marginal progeny that were forfeited during ecological adversity (the option lapses) but raised otherwise (the option called); at the same time parents maintained a constant investment and return in low risk core progeny that varied little with changes in brood size or ecological conditions. [source] Exponential Growth Bias and Household FinanceTHE JOURNAL OF FINANCE, Issue 6 2009VICTOR STANGO ABSTRACT Exponential growth bias is the pervasive tendency to linearize exponential functions when assessing them intuitively. We show that exponential growth bias can explain two stylized facts in household finance: the tendency to underestimate an interest rate given other loan terms, and the tendency to underestimate a future value given other investment terms. Bias matters empirically: More-biased households borrow more, save less, favor shorter maturities, and use and benefit more from financial advice, conditional on a rich set of household characteristics. There is little evidence that our measure of exponential growth bias merely proxies for broader financial sophistication. [source] Visual Tracking For References Generated By A Stochastic ModelASIAN JOURNAL OF CONTROL, Issue 3 2003T. Kamiya ABSTRACT This paper describes a visual tracking system for an unknown reference signal. A time-varying reference signal is realized as a random process generated by an auto-regressive (AR) model, which is identifed by a recursive algorithm. Based on the obtained AR model, the future value of reference signal is predicted. We propose a new visual tracking system using generalized minimum variance control (GMVC) and illustrate its properties through experiments. [source] A new numerical algorithm for sub-optimal control of earthquake excited linear structuresINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 12 2001Mehmet Bakioglu Abstract Exact optimal classical closed,open-loop control is not achievable for the buildings under seismic excitations since it requires the whole knowledge of earthquake in the control interval. In this study, a new numerical algorithm for the sub-optimal solution of the optimal closed,open-loop control is proposed based on the prediction of near-future earthquake excitation using the Taylor series method and the Kalman filtering technique. It is shown numerically that how the solution is related to the predicted earthquake acceleration values. Simulation results show that the proposed numerical algorithm are better than the closed-loop control and the instantaneous optimal control and proposed numerical solution will approach the exact optimal solution if the more distant future values of the earthquake excitation can be predicted more precisely. Effectiveness of the Kalman filtering technique is also confirmed by comparing the predicted and the observed time history of NS component of the 1940 El Centro earthquake. Copyright © 2001 John Wiley & Sons, Ltd. [source] Unemployment variation over the business cycles: a comparison of forecasting modelsJOURNAL OF FORECASTING, Issue 7 2004Saeed Moshiri Abstract Asymmetry has been well documented in the business cycle literature. The asymmetric business cycle suggests that major macroeconomic series, such as a country's unemployment rate, are non-linear and, therefore, the use of linear models to explain their behaviour and forecast their future values may not be appropriate. Many researchers have focused on providing evidence for the non-linearity in the unemployment series. Only recently have there been some developments in applying non-linear models to estimate and forecast unemployment rates. A major concern of non-linear modelling is the model specification problem; it is very hard to test all possible non-linear specifications, and to select the most appropriate specification for a particular model. Artificial neural network (ANN) models provide a solution to the difficulty of forecasting unemployment over the asymmetric business cycle. ANN models are non-linear, do not rely upon the classical regression assumptions, are capable of learning the structure of all kinds of patterns in a data set with a specified degree of accuracy, and can then use this structure to forecast future values of the data. In this paper, we apply two ANN models, a back-propagation model and a generalized regression neural network model to estimate and forecast post-war aggregate unemployment rates in the USA, Canada, UK, France and Japan. We compare the out-of-sample forecast results obtained by the ANN models with those obtained by several linear and non-linear times series models currently used in the literature. It is shown that the artificial neural network models are able to forecast the unemployment series as well as, and in some cases better than, the other univariate econometrics time series models in our test. Copyright © 2004 John Wiley & Sons, Ltd. [source] Prediction Variance and Information Worth of Observations in Time SeriesJOURNAL OF TIME SERIES ANALYSIS, Issue 4 2000Mohsen Pourahmadi The problem of developing measures of worth of observations in time series has not received much attention in the literature. Any meaningful measure of worth should naturally depend on the position of the observation as well as the objectives of the analysis, namely parameter estimation or prediction of future values. We introduce a measure that quantifies worth of a set of observations for the purpose of prediction of outcomes of stationary processes. The worth is measured as the change in the information content of the entire past due to exclusion or inclusion of a set of observations. The information content is quantified by the mutual information, which is the information theoretic measure of dependency. For Gaussian processes, the measure of worth turns out to be the relative change in the prediction error variance due to exclusion or inclusion of a set of observations. We provide formulae for computing predictive worth of a set of observations for Gaussian autoregressive moving-average processs. For non-Gaussian processes, however, a simple function of its entropy provides a lower bound for the variance of prediction error in the same manner that Fisher information provides a lower bound for the variance of an unbiased estimator via the Cramer-Rao inequality. Statistical estimation of this lower bound requires estimation of the entropy of a stationary time series. [source] PREDICTION-FOCUSED MODEL SELECTION FOR AUTOREGRESSIVE MODELSAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 4 2007Gerda Claeskens Summary In order to make predictions of future values of a time series, one needs to specify a forecasting model. A popular choice is an autoregressive time-series model, for which the order of the model is chosen by an information criterion. We propose an extension of the focused information criterion (FIC) for model-order selection, with emphasis on a high predictive accuracy (i.e. the mean squared forecast error is low). We obtain theoretical results and illustrate by means of a simulation study and some real data examples that the FIC is a valid alternative to the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of a prediction model. We also illustrate the possibility of using the FIC for purposes other than forecasting, and explore its use in an extended model. [source] | ||||||||||||||||