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12
Mortality Density Forecasts: An Analysis of Six Stochastic Mortality Models
, 2011
"... This paper develops a framework for developing forecasts of future mortality rates. We discuss the suitability of six stochastic mortality models for forecasting future mortality and estimating the density of mortality rates at different ages. In particular, the models are assessed individually with ..."
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Cited by 26 (16 self)
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This paper develops a framework for developing forecasts of future mortality rates. We discuss the suitability of six stochastic mortality models for forecasting future mortality and estimating the density of mortality rates at different ages. In particular, the models are assessed individually with reference to the following qualitative criteria that focus on the plausibility of their forecasts: biological reasonableness; the plausibility of predicted levels of uncertainty in forecasts at different ages; and the robustness of the forecasts relative to the sample period used to fit the model. An important, though unsurprising, conclusion is that a good fit to historical data does not guarantee sensible forecasts. We also discuss the issue of model risk, common to many modelling situations in demography and elsewhere. We find that even for those models satisfying our qualitative criteria, there are significant differences between both central forecasts of mortality rates at different ages and the distributions surrounding those central forecasts.
Forecast Evaluation of Explanatory Models of Financial Variability. Economics – The OpenAccess, OpenAssessment EJournal 3. Available via: http://www.economicsejournal.org/economics
, 2009
"... A practice that has become widespread and widely endorsed is that of evaluating forecasts of financial variability obtained from discrete time models by comparing them with highfrequency ex post estimates (e.g. realised volatility) based on continuous time theory. In explanatory financial variabili ..."
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Cited by 11 (8 self)
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A practice that has become widespread and widely endorsed is that of evaluating forecasts of financial variability obtained from discrete time models by comparing them with highfrequency ex post estimates (e.g. realised volatility) based on continuous time theory. In explanatory financial variability modelling this raises several methodological and practical issues, which suggests an alternative approach is needed. The contribution of this study is twofold. First, the finite sample properties of operational and practical procedures for the forecast evaluation of explanatory discrete time models of financial variability are studied. Second, based on the simulation results a simple but general framework is proposed and illustrated. The illustration provides an example of where an explanatory model outperforms realised volatility ex post.
Automated Model Selection in Finance: GeneraltoSpecific Modelling of the Mean, Variance and Density ∗
, 2010
"... GeneraltoSpecific (GETS) modelling has witnessed major advances over the last decade thanks to the automation of multipath GETS specification search. However, several scholars have argued that the estimation complexity associated with financial models constitutes an obstacle to multipath GETS mo ..."
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Cited by 8 (4 self)
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GeneraltoSpecific (GETS) modelling has witnessed major advances over the last decade thanks to the automation of multipath GETS specification search. However, several scholars have argued that the estimation complexity associated with financial models constitutes an obstacle to multipath GETS modelling in finance. Making use of a recent result on logGARCH Models, we provide methods and develop a simple but general and flexible algorithm that automates financial multipath GETS modelling. Starting from a general model where the mean specification can contain autoregressive (AR) terms and explanatory variables, and where the exponential variance specification can include logARCH and logGARCH terms, an asymmetry term, Bernoulli jumps and other explanatory variables, the algorithm we propose returns parsimonious mean and variance specifications, and a fattailed distribution of the standardised error if normality is rejected. The finite sample properties of the methods and of the algorithm are studied by means of extensive Monte Carlo simulations, and three empirical applications suggest the methods and algorithm are very useful in practice.
Econometric Reduction Theory and Philosophy ∗
, 2009
"... Econometric reduction theory provides a comprehensive probabilistic framework for the analysis and classification of the reductions (simplifications) associated with empirical econometric models. However, the available approaches to econometric reduction theory are unable to satisfactory accommodate ..."
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Cited by 4 (4 self)
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Econometric reduction theory provides a comprehensive probabilistic framework for the analysis and classification of the reductions (simplifications) associated with empirical econometric models. However, the available approaches to econometric reduction theory are unable to satisfactory accommodate a commonplace theory of social reality, namely that the course of history is indeterministic, that history does not repeat itself and that the future depends on the past. Using concepts from philosophy this paper proposes a solution to these shortcomings, which in addition permits new reductions, interpretations and definitions. JEL Classification: B40, C50
Estimation and Inference in Univariate and Multivariate LogGARCHX Models When the Conditional Density is Unknown∗
, 2013
"... Exponential models of Autoregressive Conditional Heteroscedasticity (ARCH) enable richer dynamics (e.g. contrarian or cyclical), provide greater robustness to jumps and outliers, and guarantee the positivity of volatility. The latter is not guaranteed in ordinary ARCH models, in particular when addi ..."
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Cited by 3 (3 self)
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Exponential models of Autoregressive Conditional Heteroscedasticity (ARCH) enable richer dynamics (e.g. contrarian or cyclical), provide greater robustness to jumps and outliers, and guarantee the positivity of volatility. The latter is not guaranteed in ordinary ARCH models, in particular when additional exogenous or predetermined variables (“X”) are included in the volatility specification. Here, we propose estimation and inference methods for univariate and multivariate Generalised logARCHX (i.e. logGARCHX) models when the conditional density is not known via (V)ARMAX representations. The multivariate specification allows for volatility feedback across equations, and timevarying correlations can be fitted in a subsequent step. Finally, our empirical applications on electricity prices show that the modelclass is par
AutoSEARCH: An R Package for Automated Financial Modelling. http://www.sucarrat.net
, 2010
"... This paper provides a brief documentation of the three main functions of AutoSEARCH, an R (R Development Core Team 2010) package for automated financial modelling. “SEARCH ” is short for Stochastic Exponential Autoregressive Condi ..."
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Cited by 2 (1 self)
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This paper provides a brief documentation of the three main functions of AutoSEARCH, an R (R Development Core Team 2010) package for automated financial modelling. “SEARCH ” is short for Stochastic Exponential Autoregressive Condi
Automated Financial MultiPath GETS Modelling∗
, 2009
"... GeneraltoSpecific (GETS) modelling has witnessed major advances over the last decade thanks to the automation of multipath GETS specification search. However, several scholars have argued that the estimation complexity associated with financial models constitutes an obstacle to multipath GETS mo ..."
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GeneraltoSpecific (GETS) modelling has witnessed major advances over the last decade thanks to the automation of multipath GETS specification search. However, several scholars have argued that the estimation complexity associated with financial models constitutes an obstacle to multipath GETS modelling in finance. We provide a result with associated methods that overcome many of the problems, and develop a simple but general and flexible algorithm that automates financial multipath GETS modelling. Starting from a general model where the mean specification can contain autoregressive (AR) terms and explanatory variables, and where the exponential variance specification can include logARCH terms, logGARCH terms, asymmetry terms, Bernoulli jumps and other explanatory variables, the algorithm we propose returns parsimonious mean and variance specifications, and a fattailed distribution of the standardised error if normality is rejected. The finite sample properties of the methods and of the algorithm are studied by means of extensive Monte Carlo simulations, and two empirical applications suggest the methods and algorithm are very useful in practice.
Application to Six Stochastic Mortality Models
, 2010
"... This paper develops a framework for developing forecasts of future mortality rates. We discuss the suitability of six stochastic mortality models for forecasting future mortality and estimating the density of mortality rates at different ages. In particular, the models are assessed individually with ..."
Abstract
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This paper develops a framework for developing forecasts of future mortality rates. We discuss the suitability of six stochastic mortality models for forecasting future mortality and estimating the density of mortality rates at different ages. In particular, the models are assessed individually with reference to the following qualitative criteria that focus on the plausibility of their forecasts: biological reasonableness; the plausibility of predicted levels of uncertainty in forecasts at different ages; and the robustness of the forecasts relative to the sample period used to fit the model. An important, though unsurprising, conclusion is that a good fit to historical data does not guarantee sensible forecasts. We also discuss the issue of model risk, common to many modelling situations in demography and elsewhere. We find that even for those models satisfying our qualitative criteria, there are significant differences between both central forecasts of mortality rates at different ages and the distributions surrounding those central forecasts.
www.imdea.org Automated Model Selection in Finance: GeneraltoSpecific Modelling of the Mean and Volatility Specifications ∗
, 2011
"... social sciences working papers series in Economics and Social Sciences 201109 Automated model selection in finance: Generaltospecific modelling of the mean and volatility specifications ..."
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social sciences working papers series in Economics and Social Sciences 201109 Automated model selection in finance: Generaltospecific modelling of the mean and volatility specifications
Automated Model Selection in Finance: GeneraltoSpeci c Modelling of the Mean and Volatility Specications
, 2011
"... GeneraltoSpecific (GETS) modelling has witnessed major advances over the last decade thanks to the automation of multipath GETS specification search. However, several scholars have argued that the estimation complexity associated with financial models constitutes an obstacle to multipath GETS m ..."
Abstract
 Add to MetaCart
GeneraltoSpecific (GETS) modelling has witnessed major advances over the last decade thanks to the automation of multipath GETS specification search. However, several scholars have argued that the estimation complexity associated with financial models constitutes an obstacle to multipath GETS modelling in finance. Making use of a recent result on logGARCH Models, we provide and study simple but general and flexible methods that automate financial multipath GETS modelling. Starting from a general model where the mean specification can contain autoregressive (AR) terms and explanatory variables, and where the exponential volatility specification can include logARCH terms, asymmetry terms, volatility proxies and other explanatory variables, the algorithm we propose returns parsimonious mean and volatility specifications. The finite sample properties of the methods are studied by means of extensive Monte Carlo simulations, and two empirical applications suggest the methods are very useful in practice.